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Learning networks AND social network analysis

Lindsay E Young, Kayo Fujimoto, John A Schneider
Online social networking sites (SNS)-the Internet-based platforms that enable connection and communication between users-are increasingly salient social environments for young adults and, consequently, offer tremendous opportunity for HIV behavioral research and intervention among vulnerable populations like young men who have sex with men (YMSM). Drawing from a cohort of 525 young Black MSM (YBMSM) living in Chicago, IL, USA April 2014-May 2015, we conducted social network analysis, estimating an exponential random graph model (ERGM) to model YBMSM's group affiliations on Facebook in relation to their sex behaviors and HIV prevention traits...
March 13, 2018: AIDS and Behavior
Behnaz Moradabadi, Mohammad Reza Meybodi
This paper proposes a new cellular learning automaton, called a wavefront cellular learning automaton (WCLA). The proposed WCLA has a set of learning automata mapped to a connected structure and uses this structure to propagate the state changes of the learning automata over the structure using waves. In the WCLA, after one learning automaton chooses its action, if this chosen action is different from the previous action, it can send a wave to its neighbors and activate them. Each neighbor receiving the wave is activated and must choose a new action...
February 2018: Chaos
Bruna de Paula Fonseca E Fonseca, Priscila Costa Albuquerque, Ed Noyons, Fabio Zicker
BACKGROUND: South-south collaboration on health and development research is a critical mechanism for social and economic progress. It allows sharing and replicating experiences to find a "southern solution" to meet shared health challenges, such as access to adequate HIV/AIDS prevention and treatment. This study aimed to generate evidence on the dynamics of south-south collaboration in HIV/AIDS research, which could ultimately inform stakeholders on the progress and nature of collaboration towards increased research capacities in low- and middle-income countries (LMIC)...
March 1, 2018: Globalization and Health
Darunee Rujkorakarn, Supatra Buatee, Surada Jundeekrayom, Andrew C Mills
In the rural villages of Thailand, rich social support networks exist that bond the community members to help each other. This study explored the barriers and facilitators of living with schizophrenia in Thai villages. A descriptive qualitative study was conducted using semi-structured interviews with individuals with schizophrenia, family members, and significant others. Content analysis of transcripts involved examining the data, recording observations, data reduction, and coding themes. Four main themes emerged from the narratives: (i) keep doing day-to-day activities as a way of life; (ii) support sustains day-to-day living; (iii) controlling medication side effects maintains daily living; and (iv) managing self maintains daily living...
February 27, 2018: International Journal of Mental Health Nursing
Mohammed Saqr, Uno Fors, Matti Tedre
BACKGROUND: Collaborative learning facilitates reflection, diversifies understanding and stimulates skills of critical and higher-order thinking. Although the benefits of collaborative learning have long been recognized, it is still rarely studied by social network analysis (SNA) in medical education, and the relationship of parameters that can be obtained via SNA with students' performance remains largely unknown. The aim of this work was to assess the potential of SNA for studying online collaborative clinical case discussions in a medical course and to find out which activities correlate with better performance and help predict final grade or explain variance in performance...
February 6, 2018: BMC Medical Education
QiQi Chen, Camilla K M Lo, Yuhong Zhu, Anne Cheung, Ko Ling Chan, Patrick Ip
The sustained increase in their use of social networking facilitates the development of adolescents but comes with the risk of cyberbullying, which creates new challenges in regard to adolescent protection. Past evidence shows that family victimization may play an essential role in the way adolescents learn cyberbullying behaviors. Yet, research on the co-occurrence of family victimization and cyberbullying is limited. This study aims to investigate the associations between cyberbullying and family victimization among adolescents, and to examine the health correlates of cyberbullying and family poly-victimization...
February 1, 2018: Child Abuse & Neglect
Elaine Green, Dan Ritman, Graeme Chisholm
The plurality of healthcare providers and funders in low- and middle-income countries (LMICs) has given rise to an era in which health partnerships are becoming the norm in international development. Whether mandated or emergent, three common drivers are essential for ensuring successful health partnerships: trust; a diverse and inclusive network; and a clear governance structure. Mandated and emergent health partnerships operate as very different models and at different scales. However, there is potential for sharing and learning between these types of partnerships...
June 11, 2017: International Journal of Health Policy and Management
Sue Kirby, Fabian P Held, Debra Jones, David Lyle
Aim This study explored the partnership between universities and local primary schools to deliver a classroom-based paediatric communication impairment service provided by undergraduate speech pathology students. It aimed to understand how partnerships work to facilitate programme replication. BACKGROUND: The partners included universities sending students on rural clinical placement, local host academic units and primary schools who worked together to provide paediatric speech and language services in primary schools in three sites in Australia...
January 10, 2018: Primary Health Care Research & Development
A Bolderston, J Watson, N Woznitza, A Westerink, L Di Prospero, G Currie, C Beardmore, J Hewis
INTRODUCTION: Online Twitter journal clubs are a recent and popular innovation with the potential to increase research awareness and inform practice. The medical radiation sciences' MedRadJournalClub (MJRC) is a Twitter-based event that attracts a global group of participants at the monthly chats. An analysis of a recent MedRadJournalClub discussion evaluated the perceived benefits and limitations of medical radiation practitioners participating in an online journal club. METHODS: The February 2017 chat used for analysis was based on the Journal of Medical Imaging and Radiation Sciences article by Currie et al...
February 2018: Radiography
Leo Nicolai, Moritz Schmidbauer, Maximilian Gradel, Sabine Ferch, Sofía Antón, Boj Hoppe, Tanja Pander, Philip von der Borch, Severin Pinilla, Martin Fischer, Konstantinos Dimitriadis
BACKGROUND: Social networking sites, in particular Facebook, are not only predominant in students' social life but are to varying degrees interwoven with the medical curriculum. Particularly, Facebook groups have been identified for their potential in higher education. However, there is a paucity of data on user types, content, and dynamics of study-related Facebook groups. OBJECTIVE: The aim of this study was to identify the role of study-related Facebook group use, characterize medical students that use or avoid using Facebook groups (demographics, participation pattern, and motivation), and analyze student posting behavior, covered topics, dynamics, and limitations in Facebook groups with regards to educational usage...
December 22, 2017: Journal of Medical Internet Research
Svitlana Volkova, Ellyn Ayton, Katherine Porterfield, Courtney D Corley
This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs) units capable of nowcasting (predicting in "real-time") and forecasting (predicting the future) ILI dynamics in the 2011 - 2014 influenza seasons...
2017: PloS One
Ahmad P Tafti, Jonathan Badger, Eric LaRose, Ehsan Shirzadi, Andrea Mahnke, John Mayer, Zhan Ye, David Page, Peggy Peissig
BACKGROUND: The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs. OBJECTIVE: The aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs...
December 8, 2017: JMIR Medical Informatics
Kevin Louis Bardosh, Melanie Murray, Antony M Khaemba, Kirsten Smillie, Richard Lester
BACKGROUND: Mobile health (mHealth) applications have proliferated across the globe with much enthusiasm, although few have reached scale and shown public health impact. In this study, we explored how different contextual factors influenced the implementation, effectiveness and potential for scale-up of WelTel, an easy-to-use and evidence-based mHealth intervention. WelTel uses two-way SMS communication to improve patient adherence to medication and engagement in care, and has been developed and tested in Canada and Kenya...
December 6, 2017: Globalization and Health
Katherine A Grisanzio, Andrea N Goldstein-Piekarski, Michelle Yuyun Wang, Abdullah P Rashed Ahmed, Zoe Samara, Leanne M Williams
Importance: The symptoms that define mood, anxiety, and trauma disorders are highly overlapping across disorders and heterogeneous within disorders. It is unknown whether coherent subtypes exist that span multiple diagnoses and are expressed functionally (in underlying cognition and brain function) and clinically (in daily function). The identification of cohesive subtypes would help disentangle the symptom overlap in our current diagnoses and serve as a tool for tailoring treatment choices...
February 1, 2018: JAMA Psychiatry
M G Gottschalk, P Mortas, M Haman, S Ozcan, B Biemans, S Bahn
While anhedonia is considered a core symptom of major depressive disorder (MDD), less attention has been paid to cognitive dysfunctions. We evaluated the behavioural and molecular effects of a selective serotonin re-uptake inhibitor (SSRI, fluoxetine) and an acetylcholinesterase inhibitor (AChEI, donepezil) on emotional-cognitive endophenotypes of depression and the hippocampal proteome. A chronic social defeat (SD) procedure was followed up by "reminder" sessions of direct and indirect SD. Anhedonia-related behaviour was assessed longitudinally by intracranial self-stimulation (ICSS)...
January 2018: European Neuropsychopharmacology: the Journal of the European College of Neuropsychopharmacology
Karmele Lopez-de-Ipina, Unai Martinez-de-Lizarduy, Pilar M Calvo, Jiri Mekyska, Blanca Beitia, Nora Barroso, Ainara Estanga, Milkel Tainta, Mirian Ecay-Torres
OBJECTIVE: Nowadays proper detection of cognitive impairment has become a challenge for the scientific community. Alzheimer's Disease (AD), the most common cause of dementia, has a high prevalence that is increasing at a fast pace towards epidemic level. In the not-so-distant future this fact could have a dramatic social and economic impact. In this scenario, an early and accurate diagnosis of AD could help to decrease its effects on patients, relatives and society. Over the last decades there have been useful advances not only in classic assessment techniques, but also in novel non-invasive screening methodologies...
2018: Current Alzheimer Research
Erin Rose Ellison, Regina Day Langhout
We describe our ethics-driven process of addressing missing data within a social network study about accountability for racism, classism, sexism, heterosexism, cis-sexism, ableism, and other forms of oppression among social justice union organizers. During data collection, some would-be participants did not return emails and others explicitly refused to engage in the research. All refusals came from women of color. We faced an ethical dilemma: Should we continue to seek participation from those who had not yet responded, with the hopes of recruiting more women of color from within the network so their perspectives would not be tokenized? Or, should we stop asking those who had been contacted multiple times, which would compromise the social network data and analysis? We delineate ways in which current discussions of the ethics of social network studies fell short, given our framework and our community psychology (CP) values...
November 20, 2017: American Journal of Community Psychology
Alessandro Muscoloni, Josephine Maria Thomas, Sara Ciucci, Ginestra Bianconi, Carlo Vittorio Cannistraci
Physicists recently observed that realistic complex networks emerge as discrete samples from a continuous hyperbolic geometry enclosed in a circle: the radius represents the node centrality and the angular displacement between two nodes resembles their topological proximity. The hyperbolic circle aims to become a universal space of representation and analysis of many real networks. Yet, inferring the angular coordinates to map a real network back to its latent geometry remains a challenging inverse problem...
November 20, 2017: Nature Communications
Jessica E Koski, Jessica A Collins, Ingrid R Olson
Social status is a salient cue that shapes our perceptions of other people and ultimately guides our social interactions. Despite the pervasive influence of status on social behavior, how information about the status of others is represented in the brain remains unclear. Here, we tested the hypothesis that social status information is embedded in our neural representations of other individuals. Participants learned to associate faces with names, job titles that varied in associated status, and explicit markers of reputational status (star ratings)...
December 2017: European Journal of Neuroscience
Alex Finnegan, Jun S Song
New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data...
October 2017: PLoS Computational Biology
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