Control of Visceral Leishmaniasis in Latin America—A Systematic Review

Visceral leishmaniasis is a vector-borne disease characterized by fever, spleen and liver enlargement, and low blood cell counts. In the Americas VL is zoonotic, with domestic dogs as main animal reservoirs, and is caused by the intracellular parasite Leishmania infantum (syn. Leishmania chagasi). Humans acquire the infection through the bite of an infected sand fly. The disease is potentially lethal if untreated. VL is reported from Mexico to Argentina, with recent trends showing a rapid spread in Brazil. Control measures directed against the canine reservoir and insect vectors have been unsuccessful, and early detection and treatment of human cases remains as the most important strategy to reduce case fatality. Well-designed studies evaluating diagnosis, treatment, and prevention/control interventions are scarce. The available scientific evidence reasonably supports the use of rapid diagnostic tests for the diagnosis of human disease. Properly designed randomized controlled trials following good clinical practices are needed to inform drug policy. Routine control strategies against the canine reservoirs and insect vectors are based on weak and conflicting evidence, and vector control strategies and vaccine development should constitute research priorities

Scoping review of indicators and methods of measurement used to evaluate the impact of dog population management interventions

Background: Dogs are ubiquitous in human society and attempts to manage their populations are common to most countries. Managing dog populations is achieved through a range of interventions to suit the dog population dynamics and dog ownership characteristics of the location, with a number of potential impacts or goals in mind. Impact assessment provides the opportunity for interventions to identify areas of inefficiencies for improvement and build evidence of positive change. Methods: This scoping review collates 26 studies that have assessed the impacts of dog population management interventions. Results: It reports the use of 29 indicators of change under 8 categories of impact and describes variation in the methods used to measure these indicators. Conclusion: The relatively few published examples of impact assessment in dog population management suggest this field is in its infancy; however this review highlights those notable exceptions. By describing those indicators and methods of measurement that have been reported thus far, and apparent barriers to efficient assessment, this review aims to support and direct future impact assessment

Optimal Gearshift Control for a Novel Hybrid Electric Drivetrain

Torque-fill capability during gearshifts is an important customer requirement in automated transmission systems. This functionality can be achieved through transmission system layouts (e.g., based on dual-clutch technology) characterized by significant mechanical complexity, and hence with relatively high cost and mass. This paper describes a parallel hybrid electric drivetrain concept, based on the integration of an electric motor drive into a relatively simple six-speed automated manual transmission. The resulting hybrid electric drivetrain actuates the torque-fill function through control of the electric motor torque during the gearshifts on the engine side of the drivetrain. An optimal controller, based on the off-line computation of the control gain profiles, is presented for the clutch re-engagement phase. The novel controller allows computationally efficient consideration of clutch energy dissipation during the clutch re-engagement phase of the gearshift. The performance with the optimal controller is contrasted with that of two conventional clutch engagement controllers, along a set of gearshifts simulated with an experimentally validated vehicle model

Unravelling the Physiological Correlates of Mental Workload Variations in Tracking and Collision Prediction Tasks.

Modern work environments have extensive interactions with technology and greater cognitive complexity of the tasks, which results in human operators experiencing increased mental workload. Air traffic control operators routinely work in such complex environments, and we designed tracking and collision prediction tasks to emulate their elementary tasks. The physiological response to the workload variations in these tasks was elucidated to untangle the impact of workload variations experienced by operators. Electroencephalogram (EEG), eye activity, and heart rate variability (HRV) data were recorded from 24 participants performing tracking and collision prediction tasks with three levels of difficulty. Our findings indicate that variations in task load in both these tasks are sensitively reflected in EEG, eye activity and HRV data. Multiple regression results also show that operators' performance in both tasks can be predicted using the corresponding EEG, eye activity and HRV data. The results also demonstrate that the brain dynamics during each of these tasks can be estimated from the corresponding eye activity, HRV and performance data. Furthermore, the markedly distinct neurometrics of workload variations in the tracking and collision prediction tasks indicate that neurometrics can provide insights on the type of mental workload. These findings have applicability to the design of future mental workload adaptive systems that integrate neurometrics in deciding not just "when" but also "what" to adapt. Our study provides compelling evidence in the viability of developing intelligent closed-loop mental workload adaptive systems that ensure efficiency and safety in complex work environments