TMNT: Dynamic Models of Cancer and HIV

Differential equations are used to build dynamic mathematical models for systems and nonlinear phenomena, which dynamically change with time. Ordinary differential equations describe a relation that contains functions of only one independent variable, and one or more of their derivatives with respect to that variable. Applications of those models are found in biological systems. In one study, homogeneous mathematical models are used to describe the interactions between cancerous cells and the immune system. Modeling using differential equations will allow better understanding of the behavior and spreading of those malignant cells. The models will investigate the dynamics of populations of cancer cells, the mechanism of immune surveillance, whereby the immune system identifies and kills foreign cells, the interactions between cancer cells, immune cells, and other type of cells or signaling proteins and the interacting components of the tumor microenvironment. These mathematical models of differential equations will provide a simpler framework within which to explore the interactions among tumor cells and the different types of immune and healthy tissue cells. Another application of models is in HIV dynamics, which have aided significantly in AIDS research. Deterministic dynamic models are used to study the viral dynamic process for understanding the pathogenesis of HIV Type 1 infections as well as antiviral treatment strategies. This study estimates the parameters of a long-term HIV dynamic model containing constant and time varying parameters by using HIV viral load and CD4 + T cell counts

Preference Prediction Based on Eye Movement Using Multi-layer Combinatorial Fusion

Face image preference is influenced by many factors and can be detected by analyzing eye movement data. When comparing two face images, our gaze shifts within and between the faces. Eye tracking data can give us insights into the cognitive processes involved in forming a preference. In this paper, a gaze tracking dataset is analyzed using three machine learning algorithms (MLA): AdaBoost, Random Forest, and Mixed Group Ranks (MGR) as well as a newly developed machine learning framework called Multi-Layer Combinatorial Fusion (MCF) to predict a subject’s face image preference. Attributes constructed from the dataset are treated as input scoring systems. MCF involves a series of layers that consist of expansion and reduction processes. The expansion process involves performing exhaustive score and rank combinations, while the reduction process uses performance and diversity to select a subset of systems that will be passed onto the next layer of analysis. Performance and cognitive diversity are used in weighted scoring system combinations and system selection. The results outperform the Mixed Group Ranks algorithm, as well as our previous work using pairwise scoring system combinations

Preference Prediction Based on Eye Movement Using Multi-layer Combinatorial Fusion

Face image preference is influenced by many factors and can be detected by analyzing eye movement data. When comparing two face images, our gaze shifts within and between the faces. Eye tracking data can give us insights into the cognitive processes involved in forming a preference. In this paper, a gaze tracking dataset is analyzed using three machine learning algorithms (MLA): AdaBoost, Random Forest, and Mixed Group Ranks (MGR) as well as a newly developed machine learning framework called Multi-Layer Combinatorial Fusion (MCF) to predict a subject’s face image preference. Attributes constructed from the dataset are treated as input scoring systems. MCF involves a series of layers that consist of expansion and reduction processes. The expansion process involves performing exhaustive score and rank combinations, while the reduction process uses performance and diversity to select a subset of systems that will be passed onto the next layer of analysis. Performance and cognitive diversity are used in weighted scoring system combinations and system selection. The results outperform the Mixed Group Ranks algorithm, as well as our previous work using pairwise scoring system combinations

Food-seeking behavior is mediated by Fos-expressing neuronal ensembles formed at first learning in rats

Neuronal ensembles in the infralimbic cortex (IL) develop after prolonged food self- administration training. However, rats demonstrate evidence of learning the food self- administration response as early as day 1, with responding quickly increasing to asymptotic levels. Since the contribution of individual brain regions to task performance shifts over the course of training, it remains unclear whether IL ensembles are gradually formed and refined over the course of extensive operant training, or if functionally- relevant ensembles might be recruited and formed as early as the initial acquisition of food self-administration behavior. Here, we aimed to determine the role of IL ensembles at the earliest possible point after demonstrable learning of a response-outcome association. We first allowed rats to lever press for palatable food pellets and stopped training rats once their behavior evidenced the response-outcome association (learners). We compared their food-seeking behavior and neuronal activation (Fos protein expression) to similarly trained rats that did not form this association (non- learners). Learners had greater food-seeking behavior and neuronal activation within the medial prefrontal cortex (mPFC), suggesting that mPFC subregions might encode initial food self-administration memories. To test the functional relevance of mPFC Fos- expressing ensembles to subsequent food seeking, we tested region-wide inactivation of the IL using muscimol+baclofen and neuronal ensemble-specific ablation using the Daun02 inactivation procedure. Both region-wide inactivation and ensemble-specific inactivation of the IL significantly decreased food seeking. These data suggest that IL neuronal ensembles form during initial learning of food self-administration behavior, and furthermore, that these ensembles play a functional role in food-seeking.Significance statement Neuronal ensembles within the infralimbic cortex (IL) play a causal role in mediating established food self-administration and food seeking. Here, we conducted region-wide and neuronal ensemble specific inactivation within the IL to determine whether IL neuronal ensembles are involved initial acquisition of food self-administration behavior. We demonstrate that neuronal ensembles within the IL control initial learning of food self-administration behavior

Life-Threatening Allergies: Using a Patient-Engaged Approach

BACKGROUND: Adolescents at risk for anaphylaxis are a growing concern. Novel training methods are needed to better prepare individuals to manage anaphylaxis in the community. INTRODUCTION: Didactic training as the sole method of anaphylaxis education has been shown to be ineffective. We developed a smartphone-based interactive teaching tool with decision support and epinephrine auto-injector (EAI) training to provide education accessible beyond the clinic. METHODS: This study consisted of two parts: (1) Use of food allergy scenarios to assess the decision support\u27s ability to improve allergic reaction management knowledge. (2) An assessment of our EAI training module on participant\u27s ability to correctly demonstrate the use of an EAI by comparing it to label instructions. RESULTS: Twenty-two adolescents were recruited. The median (range) baseline number of correct answers on the scenarios before the intervention was 9 (3-11). All subjects improved with decision support, increasing to 11 (9-12) (p \u3c .001). The median (range) demonstration score was 6 (5-6) for the video training module group and 4.5 (3-6) for the label group (p \u3c 0.001). DISCUSSION: Results suggest that the use of this novel m-health application can improve anaphylaxis symptom recognition and increase the likelihood of choosing the appropriate treatment. In addition, performing EAI steps in conjunction with the video training resulted in more accurate medication delivery with fewer missed steps compared to the use of written instructions alone. CONCLUSION: The results suggest that mobile health decision support technology for anaphylaxis emergency preparedness may support traditional methods of training by providing improved access to anaphylaxis training in the community setting