Mating behaviour, life history and adaptation to insecticides determine species exclusion between whiteflies

Summary 1. Negative interspecific interactions, such as resource competition or reproductive interference, can lead to the displacement of species (species exclusion). 2. Here, we investigated the effect of life history, mating behaviour and adaptation to insecticides on species exclusion between cryptic whitefly species that make up the Bemisia tabaci species complex. We conducted population cage experiments independently in China, Australia, the United States and Israel to observe patterns of species exclusion between an invasive species commonly referred to as the B biotype and three other species commonly known as biotypes ZHJ1, AN and Q. 3. Although experimental conditions and species varied between regions, we were able to predict the observed patterns of exclusion in each region using a stochastic model that incorporated data on development time, mating behaviour and resistance to insecticides. 4. Between-species variation in mating behaviour was a more significant factor affecting species exclusion than variation in development time. Specifically, the ability of B to copulate more effectively than other species resulted in a faster rate of population increase for B, as well as a reduced rate of population growth for other species, leading to species exclusion. The greater ability of B to evolve resistance to insecticides also contributed to exclusion of other species in some cases. 5. Results indicate that an integrative analysis of the consequences of variation in life-history traits, mating behaviours and adaption to insecticides could provide a robust framework for predicting species exclusion following whitefly invasions

Examination Of The Effectiveness Of Predictors For Musculoskeletal Injuries In Female Soldiers

The amount of training days lost to injury during military train-ing has highlighted the need to identify a screening tool to pre-dict injury. One hundred and fifty-eight female soldiers from the Combat Fitness Instructor Course (CFIC) of the Israel Defense Forces volunteered to participate in this study. All soldiers were free of orthopedic and neurologic conditions for at least one month before the study. All participants performed a battery of measurements during the first week of the course. Measures included anthropometric, functional movement screen (FMS), power performances (counter movement jump [CMJ], drop jump, single leg triple hop jump [SLTH], 10-m sprint) and a 2K run. Injury data was collected throughout the 3 month course. Median tests were used to compare between injured/non-injured soldiers. Chi-square and/or logistic regression analysis was used to examine the association between various predictors and injury. Percent body fat [%BF] was higher (p = 0.04), distance for SLTH was less for both left and right legs (p = 0.029, p = 0.047 respectively) and 2K run was slower (p =0.044) in injured com-pared to non-injured soldiers. No differences between groups were noted in total FMS score, however more zero scores in one or more movement pattern were found in the injured group (51.35 % vs. 30.5% p=0.0293). Only %BF, 2K run and SLTH distance were significant predictors of injury (p = 0.05, p = 0.02, p =0.016 respectively). The results of this study indicated that the FMS total score is not a predictor of injury in female soldiers in a CFIC. We found that %BF, SLTH, 2K run time, 10 meter sprint time and zero scores differentiated between injured and non-injured soldiers. In addition, %BF, 2K run and SLTH were each found to be separate predictors of injury. Further research is needed to determine threshold scores that predict injury

SMART METERS ADOPTION: RECENT ADVANCES AND FUTURE TRENDS

El alto crecimiento en la demanda de electricidad y los picos en la curva de carga, causados principalmente por hogares, hacen necesarias grandes inversiones en infraestructura que solo es usada para periodos cortos. Esto ocasiona la búsqueda de desarrollos que permitan suplir las necesidades de los usuarios y usar los recursos del sistema eficientemente. Esto es posible por medio de las Redes Eléctricas Inteligentes, las cuales adicionalmente brindan a los usuarios autonomía en la cadena de suministro eléctrico. El foco de esta investigación son los hogares, ya que estos pueden monitorear su consumo y ayudar a reducir los picos en la curva de carga, Para esto los usuarios deben usar Medidores Inteligentes, los cuales le permiten obtener información necesaria para controlar su demanda. Este artículo presenta un análisis sistemático de la literatura publicada relacionada con el estudio de las redes eléctricas inteligentes desde el lado de la demanda, analiza la situación actual sobre este tema y el impacto de los medidores inteligentes en los hogares

Prediction of the Wingate anaerobic mechanical power outputs from a maximal incremental cardiopulmonary exercise stress test using machine-learning approach.

The Wingate Anaerobic Test (WAnT) is a short-term maximal intensity cycle ergometer test, which provides anaerobic mechanical power output variables. Despite the physiological significance of the variables extracted from the WAnT, the test is very intense, and generally applies for athletes. Our goal, in this paper, was to develop a new approach to predict the anaerobic mechanical power outputs using maximal incremental cardiopulmonary exercise stress test (CPET). We hypothesized that maximal incremental exercise stress test hold hidden information about the anaerobic components, which can be directly translated into mechanical power outputs. We therefore designed a computational model that included aerobic variables (features), and used a new computational \ predictive algorithm, which enabled the prediction of the anaerobic mechanical power outputs. We analyzed the chosen predicted features using clustering on a network. For peak power (PP) and mean power (MP) outputs, the equations included six features and four features, respectively. The combination of these features produced a prediction model of r = 0.94 and r = 0.9, respectively, on the validation set between the real and predicted PP/MP values (P< 0.001). The newly predictive model allows the accurate prediction of the anaerobic mechanical power outputs at high accuracy. The assessment of additional tests is desired for the development of a robust application for athletes, older individuals, and/or non-healthy populations

Examination of the Effectiveness of Predictors for Musculoskeletal Injuries in Female Soldiers

The amount of training days lost to injury during military training has highlighted the need to identify a screening tool to predict injury. One hundred and fifty-eight female soldiers from the Combat Fitness Instructor Course (CFIC) of the Israel Defense Forces volunteered to participate in this study. All soldiers were free of orthopedic and neurologic conditions for at least one month before the study. All participants performed a battery of measurements during the first week of the course. Measures included anthropometric, functional movement screen (FMS), power performances (counter movement jump [CMJ], drop jump, single leg triple hop jump [SLTH], 10-m sprint) and a 2K run. Injury data was collected throughout the 3 month course. Median tests were used to compare between injured/non-injured soldiers. Chi-square and/or logistic regression analysis was used to examine the association between various predictors and injury. Percent body fat [%BF] was higher (p = 0.04), distance for SLTH was less for both left and right legs (p = 0.029, p = 0.047 respectively) and 2K run was slower (p =0.044) in injured compared to non-injured soldiers. No differences between groups were noted in total FMS score, however more zero scores in one or more movement pattern were found in the injured group (51.35 % vs. 30.5% p=0.0293). Only %BF, 2K run and SLTH distance were significant predictors of injury (p = 0.05, p = 0.02, p =0.016 respectively). The results of this study indicated that the FMS total score is not a predictor of injury in female soldiers in a CFIC. We found that %BF, SLTH, 2K run time, 10 meter sprint time and zero scores differentiated between injured and non-injured soldiers. In addition, %BF, 2K run and SLTH were each found to be separate predictors of injury. Further research is needed to determine threshold scores that predict injury