Virtual Mathematics Laboratory Based on Cognitive Load Theory

Teachers need high levels of creativity and innovation to deliver subject matter during this period of digital learning. Literacy and numeracy skills are top priorities in schools, and mathematics is very relevant for the acceleration of literacy and numeracy development. The goal of this study was to develop a virtual math lab based on the cognitive load theory to improve literacy skills. The ADDIE development model was used, consisting of the stages of analysis, design, development, implementation and evaluation. Data were obtained on the needs for a complete mathematical learning medium that would be accessible and fun. The virtual design was carried out using the Drag and Drop app builder software. In the next stage, a virtual lab design framework was implemented with this app builder. The validation results showed that 80% of the math teaching materials were in accordance with the school’s math curriculum; 80% of the virtual math lab design was categorized as dynamic, and the remaining 20% was identified as needing synchronization between the videos displayed and the level of attractiveness and interactivity of the lab. According to the results, the modules and virtual laboratory that were developed are valid and suitable for use. Keywords: virtual mathematics lab, cognitive load theory, literac

Biological network analysis with CentiScaPe: centralities and experimental dataset integration

The growing dimension and complexity of the available experimental data generating biological networks have increased the need for tools that help in categorizing nodes by their topological relevance. Here we present CentiScaPe, a Cytoscape app specifically designed to calculate centrality indexes used for the identification of the most important nodes in a network. CentiScaPe is a comprehensive suite of algorithms dedicated to network nodes centrality analysis, computing several centralities for undirected, directed and weighted networks. The results of the topological analysis can be integrated with data set from lab experiments, like expression or phosphorylation levels for each protein represented in the network. Our app opens new perspectives in the analysis of biological networks, since the integration of topological analysis with lab experimental data enhance the predictive power of the bioinformatics analysis

Finding the shortest path with PesCa: A tool for network reconstruction

The growing dimension and complexity of the available experimental data generating biological networks have increased the need for tools that help in categorizing nodes by their topological relevance. Here we present CentiScaPe, a Cytoscape app specifically designed to calculate centrality indexes used for the identification of the most important nodes in a network. CentiScaPe is a comprehensive suite of algorithms dedicated to network nodes centrality analysis, computing several centralities for undirected, directed and weighted networks. The results of the topological analysis can be integrated with data set from lab experiments, like expression or phosphorylation levels for each protein represented in the network. Our app opens new perspectives in the analysis of biological networks, since the integration of topological analysis with lab experimental data enhance the predictive power of the bioinformatics analysis

Directory App

The Directory App project was proposed by the Scottish Ambulance Service (SAS), and accepted by the Digital Health Institute (DHI) as an Experience Lab, which took place in December 2014. The Experience Lab aimed to explore and develop a basic prototype of a Directory App to support initial small-scale testing. The design-led approach aimed to deliver a set of requirements that were firmly user driven. The Lab provided a safe and realistic environment through which the Lab Team helped users explore the concept and share their ideas to design and prototype the Directory App. A series of activities including mapping, role-play, prototyping, and evaluating current proposed solutions were designed to iteratively develop ideas for the Directory App. The outputs from the Lab included audio, photos, videos and field notes which were analysed for emerging themes. The findings of the Lab provide validation and extension of the concept to include contact information, local referral criteria and national clinical guidelines. Service and pathway information should be collated locally and held nationally, and effective implementation will rely on consistent and accurate information that is maintained and updated regularly. Four functional prototypes were created and tested, which generated a set of requirements and design ideas for the development of the Directory App

StressAware: App for Continuously Measuring and Monitoring Stress Levels in Real Time on the Amulet Wearable Device

Stress is the root cause of many diseases. Being able to monitor when and why a person is stressed could inform personal stress management as well as interventions when necessary. In this thesis, I present StressAware, an application on the Amulet wearable platform to measure the stress levels of individuals continuously and in real time. The app implements a stress detection model, continuously streams heart rate data from a commercial heart-rate monitor such as a Zephyr and Polar H7, classifies the stress level of an individual, logs the stress level and then displays it as a graph on the screen. I developed a stress detection model using a Linear Support Vector Machine. I trained my classifiers using data from 3 sources: PhysioNet, a public database with various physiological data, a field study, where subjects went about their normal daily activities and a lab study in a controlled environment, where subjects were exposed to various stressors. I used 73 data segments of stress data obtained from PhysioNet, 120 data segments from the field study, and 14 data segments from the lab study. I extracted 14 heart rate and heart rate variability features. With 10-fold cross validation for Radial Basis Function (RBF) SVM, I obtained an accuracy of 94.5% for the PhysioNet dataset and 100% for the field study dataset. And for the lab study, I obtained an accuracy of 64.29% with leave-one-out cross-validation. Testing the StressAware app revealed a projected battery life of up to 12 days before needing to recharge. Also, the usability feedback from subjects showed that the Amulet and Zephyr have a potential to be used by people for monitoring their stress levels. The results are promising, indicating that the app may be used for stress detection, and eventually for the development of stress-related intervention that could improve the health of individuals

Development of a Living Lab for a Mobile-Based Health Program for Korean-Chinese Working Women in South Korea: Mixed Methods Study

BACKGROUND: Korean-Chinese (KC) women make up the largest group of female migrants in South Korea. To prevent and manage chronic diseases in middle-aged KC women working full time, it is necessary to develop health promotion programs that utilize an online platform because such a platform would allow individuals to participate in health promotion interventions at their convenience. OBJECTIVE: This study aimed to develop a living lab for a mobile-based health (LLm Health) program focused on improving the physical activity and cultural adaptation of KC women workers. METHODS: We used a mixed methods design. Living lab principles were factored into the LLm Health program, including the use of multiple methods, user engagement, multistakeholder participants, real-life settings, and cocreation. The program was developed using the 4 steps of the intervention mapping method: needs assessment, setting of objectives, identification of intervention strategies, and intervention design. Needs assessment was conducted through a literature review, focus group interviews with a total of 16 middle-aged KC women, and an online survey related to health promotion of migrant workers given to 38 stakeholders. KC middle-aged women participated in the early stages of program development and provided the idea of developing programs and mobile apps to enhance physical activity and acculturation. The mobile app developed in the program was validated with the help of 12 KC women and 4 experts, including 3 nursing professors and a professor of physical education. They were asked to rate each item based on content, interface design, and technology on a 4-point scale using a 23-item Smartphone App Evaluation Tool for Health Care. RESULTS: The LLm Health program comprised a 24-week walking program using Fitbit devices, the mobile app, and social cognitive interventions. The mobile app contained 6 components: a step counter, an exercise timer, an online chat function, health information, level of cardiovascular risk, and health status. The cultural aspects and lifestyles of KC women were accommodated in the entire process of program development. The content validity of the mobile app was found to be 0.90 and 0.96 according to the 12 KC women and 4 experts, respectively. CONCLUSIONS: The mobile app was found to be valid and acceptable for KC women. The living lab approach was a useful strategy for developing a culturally adaptive LLm Health program for KC women workers, leading to their active participation in the overall research process, including needs assessment, program composition, and pre-evaluation.ope

Implementation and Evaluation of a Neonatal Chest Tube Simulation Lab for App Competency

Pneumothorax in the neonatal period is a potentially life threatening condition that requires Neonatal Advanced Practice Providers to respond rapidly and competently in order to prevent complications of morbidity and mortality. It has become increasingly difficult to establish initial competency as well as maintain competency in low volume high acuity emergency procedures such as chest tube placement. The purpose of this practice based quality improvement project was to implement a simulation based training program in chest tube placement for the Neonatal Advanced Practice Providers within Norton Children’s Neonatology. The goal was to have all Advanced Practice Providers (APP) be able to demonstrate competency in chest tube placement for initial and ongoing clinical practice privileges within the Norton Healthcare System. Participants in the project included five APP within Norton Children’s Neonatology. Data included collecting responses asking the number of chest tubes the APP had placed in the previous two years and collecting APP demographic data through a questionnaire. This was followed by subject participation in a chest tube simulation lab exercise involving chest tube placement for pneumothorax using a high-fidelity mannequin. Post simulation descriptive statistical means and standard deviations were used to assess satisfaction and self-confidence in learning. Mean scores and standard deviations for each participating provider were calculated for each performance point on the Evaluation of the Neonatal Nurse Practitioner Procedural Competency Checklist published by the National Association of Neonatal Nurse Practitioners. It is important for healthcare organizations to employ professionals who can provide timely, effective and safe care. Establishing a continued performance competency stimulation lab in chest tube placement was a evidence based strategy to support Neonatal APP to maintain competency and privileges within the Norton Healthcare system and ultimately improved outcomes for its neonatal population

Role Of The Alzheimer\u27s Amyloid Precursor Protein In High Fat Diet Induced Obesity And Regulating Macrophage Phenotype

Amyloid precursor protein (APP) derived amyloid beta peptides have been extensively investigated in Alzheimer\u27s disease pathology of the brain. However, the function of full length APP in the central nervous system remains unclear. Even less is known about the behavior of this ubiquitously expressed protein and it metabolites outside of the central nervous system. Therefore, we sought to broaden our understanding of the expression and function of APP and its proteolytic fragments in specific non-neuronal tissues. Although the majority of research effort is currently focused on neuronal amyloid beta production and its effects on cells, prior work in our lab demonstrated a novel role for APP in regulating the phenotype of monocytic lineage cells. Therefore, we hypothesized that APP can behave as a proinflammatory receptor on these cells involved in modulating their tissue infiltration and differentiation. Based upon the fact that midlife obesity is a risk factor for Alzheimer\u27s disease and both obese adipose tissue and Alzheimer\u27s disease brains share a common presence of increased, reactive macrophage and microglia, respectively, we hypothesized that APP may have a common role in both diseases regulating the infiltration or proinflammatory activation of microglia and macrophage characterizing both diseases. Indeed, recent data has demonstrated that APP levels are increased in adipose tissue from obese versus control individuals. To test this idea we utilized a high fat diet feeding paradigm on both C57BL6 wild type and APP-/- mice to examine any role for APP and high fat diet dependent changes in adipose tissue, brain, and intestine. In vivo changes were compared to those obtained using primary cells isolated from the murine models. Collectively, these data suggest that APP does regulate microglia and macrophage phenotype in a manner responsible for altering their behavior in tissue specific fashion. This suggests that immune-related functions of APP may be a common type of pathophysiology linking the complex diseases of obesity and Alzheimer\u27s disease