An emotional student model for game-based learning

Students’ performance and motivation are influenced by their emotions. Game-based learning (GBL) environments comprise elements that facilitate learning and the creation of an emotional connection with students. GBL environments include Intelligent Tutoring Systems (ITSs) to ensure personalized learning. ITSs reason about students’ needs and characteristics (student modeling) to provide suitable instruction (tutor modeling). The authors’ research is focused on the design and implementation of an emotional student model for GBL environments based on the Control-Value Theory of achievement emotions by Pekrun et al. (2007). The model reasons about answers to questions in game dialogues and contextual variables related to student behavior acquired through students’ interaction with PlayPhysics. The authors’ model is implemented using Dynamic Bayesian Networks (DBNs), which are derived using Probabilistic Relational Models (PRMs), machine learning techniques, and statistical methods. This work compares an earlier approach that uses Multinomial Logistic Regression (MLR) and cross-tabulation for learning the structure and conditional probability tables with an approach that employs Necessary Path Condition and Expectation Maximization algorithms. Results showed that the latter approach is more effective at classifying the control of outcome-prospective emotions. Future work will focus on applying this approach to classification of activity and outcome-retrospective emotions.</jats:p

Predicting invasive breast cancer versus DCIS in different age groups.

BackgroundIncreasing focus on potentially unnecessary diagnosis and treatment of certain breast cancers prompted our investigation of whether clinical and mammographic features predictive of invasive breast cancer versus ductal carcinoma in situ (DCIS) differ by age.MethodsWe analyzed 1,475 malignant breast biopsies, 1,063 invasive and 412 DCIS, from 35,871 prospectively collected consecutive diagnostic mammograms interpreted at University of California, San Francisco between 1/6/1997 and 6/29/2007. We constructed three logistic regression models to predict the probability of invasive cancer versus DCIS for the following groups: women ≥ 65 (older group), women 50-64 (middle age group), and women &lt; 50 (younger group). We identified significant predictors and measured the performance in all models using area under the receiver operating characteristic curve (AUC).ResultsThe models for older and the middle age groups performed significantly better than the model for younger group (AUC = 0.848 vs, 0.778; p = 0.049 and AUC = 0.851 vs, 0.778; p = 0.022, respectively). Palpability and principal mammographic finding were significant predictors in distinguishing invasive from DCIS in all age groups. Family history of breast cancer, mass shape and mass margins were significant positive predictors of invasive cancer in the older group whereas calcification distribution was a negative predictor of invasive cancer (i.e. predicted DCIS). In the middle age group--mass margins, and in the younger group--mass size were positive predictors of invasive cancer.ConclusionsClinical and mammographic finding features predict invasive breast cancer versus DCIS better in older women than younger women. Specific predictive variables differ based on age

Profile of Class I Histone Deacetylases (HDAC) by Human Dendritic Cells after Alcohol Consumption and In Vitro Alcohol Treatment and Their Implication in Oxidative Stress: Role of HDAC Inhibitors Trichostatin A and Mocetinostat

Epigenetic mechanisms have been shown to play a role in alcohol use disorders (AUDs) and may prove to be valuable therapeutic targets. However, the involvement of histone deacetylases (HDACs) on alcohol-induced oxidative stress of human primary monocyte-derived dendritic cells (MDDCs) has not been elucidated. In the current study, we took a novel approach combining ex vivo, in vitro and in silico analyses to elucidate the mechanisms of alcohol-induced oxidative stress and role of HDACs in the periphery. ex vivo and in vitro analyses of alcohol-modulation of class I HDACs and activity by MDDCs from self-reported alcohol users and non-alcohol users was performed. Additionally, MDDCs treated with alcohol were assessed using qRT-PCR, western blot, and fluorometric assay. The functional effects of alcohol-induce oxidative stress were measured in vitro using PCR array and in silico using gene expression network analysis. Our findings show, for the first time, that MDDCs from self-reported alcohol users have higher levels of class I HDACs compare to controls and alcohol treatment in vitro differentially modulates HDACs expression. Further, HDAC inhibitors (HDACi) blocked alcohol-induction of class I HDACs and modulated alcohol-induced oxidative stress related genes expressed by MDDCs. In silico analysis revealed new target genes and pathways on the mode of action of alcohol and HDACi. Findings elucidating the ability of alcohol to modulate class I HDACs may be useful for the treatment of alcohol-induced oxidative damage and may delineate new potential immune-modulatory mechanisms

Measuring Spinal Mobility Using an Inertial Measurement Unit System: a Reliability study in Axial Spondyloarthritis

The objectives of this study were to evaluate the reliability of wearable inertial motion unit (IMU) sensors in measuring spinal range of motion under supervised and unsupervised conditions in both laboratory and ambulatory settings. A secondary aim of the study was to evaluate the reliability of composite IMU metrology scores (IMU-ASMI (Amb)). Forty people with axSpA participated in this clinical measurement study. Participant spinal mobility was assessed by conventional metrology (Bath Ankylosing Spondylitis Metrology Index, linear version—BASMILin) and by a wireless IMU sensor-based system which measured lumbar flexion-extension, lateral flexion and rotation. Each sensor-based movement test was converted to a normalized index and used to calculate IMU-ASMI (Amb) scores. Test-retest reliability was evaluated using intra-class correlation coefficients (ICC). There was good to excellent agreement for all spinal range of movements (ICC &gt; 0.85) and IMU-ASMI (Amb) scores (ICC &gt; 0.87) across all conditions. Correlations between IMU-ASMI (Amb) scores and conventional metrology were strong (Pearson correlation ≥ 0.85). An IMU sensor-based system is a reliable way of measuring spinal lumbar mobility in axSpA under supervised and unsupervised conditions. While not a replacement for established clinical measures, composite IMU-ASMI (Amb) scores may be reliably used as a proxy measure of spinal mobility

Wearable Technology Supported Home Rehabilitation Services in Rural Areas:– Emphasis on Monitoring Structures and Activities of Functional Capacity Handbook

The sustainability of modern healthcare systems is under threat. – the ageing of the population, the prevalence of chronic disease and a need to focus on wellness and preventative health management, in parallel with the treatment of disease, pose significant social and economic challenges. The current economic situation has made these issues more acute. Across Europe, healthcare expenditure is expected to rice to almost 16% of GDP by 2020. (OECD Health Statistics 2018). Coupled with a shortage of qualified personnel, European nations are facing increasing challenges in their ability to provide better-integrated and sustainable health and social services. The focus is currently shifting from treatment in a care center to prevention and health promotion outside the care institute. Improvements in technology offers one solution to innovate health care and meet demand at a low cost. New technology has the potential to decrease the need for hospitals and health stations (Lankila et al., 2016. In the future the use of new technologies – including health technologies, sensor technologies, digital media, mobile technology etc. - and digital services will dramatically increase interaction between healthcare personnel and customers (Deloitte Center for Health Solutions, 2015a; Deloitte Center for Health Solutions 2015b). Introduction of technology is expected to drive a change in healthcare delivery models and the relationship between patients and healthcare providers. Applications of wearable sensors are the most promising technology to aid health and social care providers deliver safe, more efficient and cost-effective care as well as improving people’s ability to self-manage their health and wellbeing, alert healthcare professionals to changes in their condition and support adherence to prescribed interventions. (Tedesco et al., 2017; Majumder et al., 2017). While it is true that wearable technology can change how healthcare is monitored and delivered, it is necessary to consider a few things when working towards the successful implementation of this new shift in health care. It raises challenges for the healthcare systems in how to implement these new technologies, and how the growing amount of information in clinical practice, integrates into the clinical workflows of healthcare providers. Future challenges for healthcare include how to use the developing technology in a way that will bring added value to healthcare professionals, healthcare organizations and patients without increasing the workload and cost of the healthcare services. For wearable technology developers, the challenge will be to develop solutions that can be easily integrated and used by healthcare professionals considering the existing constraints. This handbook summarizes key findings from clinical and laboratory-controlled demonstrator trials regarding wearables to assist rehabilitation professionals, who are planning the use of wearable sensors in rehabilitation processes. The handbook can also be used by those developing wearable sensor systems for clinical work and especially for use in hometype environments with specific emphasis on elderly patients, who are our major health care consumers

Unveiling a Rich System of Faint Dwarf Galaxies in the Next Generation Fornax Survey

We report the discovery of 158 previously undetected dwarf galaxies in the Fornax cluster central regions using a deep coadded $u, g$ and $i$-band image obtained with the DECam wide-field camera mounted on the 4-meter Blanco telescope at the Cerro Tololo Interamerican Observatory as part of the {\it Next Generation Fornax Survey} (NGFS). The new dwarf galaxies have quasi-exponential light profiles, effective radii $0.1\!<\!r_e\!<\!2.8$ kpc and average effective surface brightness values $22.0\!<\!\mu_i\!<\!28.0$ mag arcsec$^{-2}$. We confirm the existence of ultra-diffuse galaxies (UDGs) in the Fornax core regions that resemble counterparts recently discovered in the Virgo and Coma galaxy clusters.~We also find extremely low surface brightness NGFS dwarfs, which are several magnitudes fainter than the classical UDGs. The faintest dwarf candidate in our NGFS sample has an absolute magnitude of $M_i\!=\!-8.0$\,mag. The nucleation fraction of the NGFS dwarf galaxy sample appears to decrease as a function of their total luminosity, reaching from a nucleation fraction of $>\!75\%$ at luminosities brighter than $M_i\!\simeq\!-15.0$ mag to $0\%$ at luminosities fainter than $M_i\!\simeq\!-10.0$ mag. The two-point correlation function analysis of the NGFS dwarf sample shows an excess on length scales below $\sim\!100$ kpc, pointing to the clustering of dwarf galaxies in the Fornax cluster core.Comment: 6 pages, 3 figures. Accepted for publication in The Astrophysical Journal Letters. Download the high-resolution version of the paper from the following link: https://www.dropbox.com/s/xb9vz8s29wlzjgf/ms.pdf?dl=

Feasibility of Sensor Technology for Balance Assessment in Home Rehabilitation Settings

The increased use of sensor technology has been crucial in releasing the potential for remote rehabilitation. However, it is vital that human factors, that have potential to affect real-world use, are fully considered before sensors are adopted into remote rehabilitation practice. The smart sensor devices for rehabilitation and connected health (SENDoc) project assesses the human factors associated with sensors for remote rehabilitation of elders in the Northern Periphery of Europe. This article conducts a literature review of human factors and puts forward an objective scoring system to evaluate the feasibility of balance assessment technology for adaption into remote rehabilitation settings. The main factors that must be considered are: Deployment constraints, usability, comfort and accuracy. This article shows that improving accuracy, reliability and validity is the main goal of research focusing on developing novel balance assessment technology. However, other aspects of usability related to human factors such as practicality, comfort and ease of use need further consideration by researchers to help advance the technology to a state where it can be applied in remote rehabilitation settings