Outcomes of Directly Observed Therapy in People Living with HIV Who Experience Homelessness and Substance Use Disorder

Background: Antiretroviral directly observed therapy, in which nurses or other allied health professionals provide patients with daily medication, is an evidence-based solution for viral load suppression in people living with HIV who experience homelessness. Purpose: This quality improvement project aimed to assess the outcomes of antiretroviral directly observed therapy at one urban clinic caring for people living with human immunodeficiency virus who experience homelessness and substance use disorder. Methods: Data was collected from the electronic health record for the antiretroviral directly observed therapy patient cohort (n = 33); 10 of them were surveyed. Fisher exact tests determined nonrandom associations between viral suppression and all other categorical variables. Survey answers were mapped to the modified Andersen’s Behavioral Model domains and subcategories. Results: Seventy-three percent of program patients were virally suppressed after participating in the program; 42% were virally undetectable. The relationship between viral suppression and date of most recent primary care provider visit was significant (p = 0.01). Eighty percent of surveyed patient participants reported that they liked the program, and 70% said that taking ART makes them feel better. Eighty percent of patients described their health as “Fair” or “Poor” before initiating treatment with the program, while 90% of patients reported their own health as being “good”, “very good”, or “excellent” after participating. Conclusion: Results suggest that when nurse-led teams actively engage PLWH, viral suppression, and consequent undetectability, is more likely. Clear implications for practice include continuing nurse navigation to promote viral suppression

Games on Cellular Spaces: How Mobility Affects Equilibrium

In this work we propose a new model for spatial games. We present a definition of mobility in terms of the satisfaction an agent has with its spatial location. Agents compete for space through a non-cooperative game by using mixed strategies. We are particularly interested in studyig the relation between Nash equilibrium and the winner strategy of a given model with mobility, and how the mobility can affect the results. The experiments show that mobility is an important variable concerning spatial games. When we change parameters that affect mobility, it may lead to the success of strategies away from Nash equilibrium.Spatial Games, Agent-Based Modelling, Mobility, Satisfaction, Chicken Game, Nash Equilibrium

Comparison of markets for organic food in six EU states.

This report was presented at the UK Organic Research 2002 Conference. Recent research confirms that the decision to convert is now highly influenced by financial incentives arising from EU regulations but the exact mix of incentives depends on prevailing government policies and access to premium markets so that the organic sector in most countries is now referred to as either government-led or market-driven. The objective of the paper is to compare development of the sector along these two polarities but set within the context of "common elements of interest" within new agrifood methodologies: time, space, power, and meaning (Cooke, Uranga and Etxebarria 1998; Morgan and Murdoch 2000). The paper presents preliminary findings relating to six EU States: UK, Ireland, Austria, Denmark, Portugal and Italy, and through the application of "worlds of production" to market outlets and suggests discourses that define these outlets. The analysis aims to inform the further study of farmer marketing decisions and practices

An investigation into the factors contributing to rail freight losing market share : a South African perspective

Abstract: Transnet Freight Rail (TFR) has positioned itself in the market as a low cost provider of transporting freight, with the intention of achieving a sustainable competitive edge and a bigger market share. However, despite this strategy, which involves Market Demand Strategic planning, TFR has been losing market share to road freight and that has influenced the organisation’s revenue and sustainability. Transportation is a vital link in the value chain and contributes millions to an economy, yet produces many negative externalities, such as CO2 emissions. In South Africa, rail and road are the preferred modes of transport, and while railway transport produces less than one third of the emissions produced by road transport, the latter has become the preferred mode. Freight rail is losing market share to road freight and this study investigated the reasons. An interpretivist, qualitative content analysis was performed. Eight senior managers at Transnet Rail Freight were interviewed and it was found that the organisation lacks management skills, is unable to adapt to market demands; operates under unfavourable economic conditions; has to get by with aging infrastructure; under-utilises other assets; and is not customer centric. It is recommended that a skills audit be performed, and an audit of underutilised assets. Change should be managed more proactively to regain market share. This paper contributes theoretically to the body of knowledge on transportation in South Africa. Practically, senior managers at TFR will become aware of the challenges pointed out, which may aid decision making in the future

Human Activity Recognition Using CNN and Lstm Deep Learning Algorithms

Human Activity Recognition recognizes and classifies the activities performed by the users or people based on the data collected from the sensors of special devices such as smart-watches, smartphones etc. It has become easy to collect a huge amount of data from inertial sensors that are embedded in wearable devices. An accelerometer and gyroscope sensors are most commonly used inertial sensors. There are various already available datasets, in our paper, we are using the Wireless Sensor Data Mining dataset which contains 1,098,207 data of 6 physical activities performed. In this paper, the activities we aim to classify are walking, jogging, going up and downstairs, standing, and sitting. There are various algorithms applied on the various datasets. In our paper, we use Convolutional Neural Network and Long Short-Term Memory deep learning algorithm on the data set, we split the data into training data [80%] and testing data [20%]. By using a confusion matrix, we recognize and classify the activities performed using maximum accuracy

Clathrin binding by the adaptor Ent5 promotes late stages of clathrin coat maturation

Clathrin is a ubiquitous protein that mediates membrane traffic at many locations. To function, clathrin requires clathrin adaptors that link it to transmembrane protein cargo. In addition to this cargo selection function, many adaptors also play mechanistic roles in the formation of the transport carrier. However, the full spectrum of these mechanistic roles is poorly understood. Here we report that Ent5, an endosomal clathrin adaptor in Saccharomyces cerevisiae, regulates the behavior of clathrin coats after the recruitment of clathrin. We show that loss of Ent5 disrupts clathrin-dependent traffic and prolongs the lifespan of endosomal structures that contain clathrin and other adaptors, suggesting a defect in coat maturation at a late stage. We find that the direct binding of Ent5 with clathrin is required for its role in coat behavior and cargo traffic. Surprisingly, the interaction of Ent5 with other adaptors is dispensable for coat behavior but not cargo traffic. These findings support a model in which Ent5 clathrin binding performs a mechanistic role in coat maturation, whereas Ent5 adaptor binding promotes cargo incorporation