Validation of the TracmorD Triaxial Accelerometer to Assess Physical Activity in Preschool Children

Objectives: To assess validity evidence of Tracmor_D to determine energy used for physical activity in 3‐4‐year‐old children. Design and Methods: Participants were randomly selected from GECKO Drenthe cohort (n = 30, age 3.4 ± 0.3 years). Total energy expenditure (TEE) was measured using the doubly labeled water method. Sleeping metabolic rate (SMR) was measured by indirect calorimetry (Deltatrac). TEE and SMR were used to calculate physical activity level (PAL) and activity energy expenditure (AEE). Physical activity was monitored using a DirectLife triaxial accelerometer, Tracmor_D with activity counts per minute (ACM) and activity counts per day (ACD) as outcome measures. Results: The best predictor for PAL was ACM with gender and weight, the best predictor for AEE was ACM alone (backward regression, R² = 0.50, P = 0.010 and R² = 0.31, P = 0.011, respectively). With ACD, the prediction model for PAL included ACD, height, gender, and sleep duration (R² = 0.48, P = 0.033), the prediction model for AEE included ACD, gender and sleep duration (R² = 0.39, P = 0.042). The accelerometer was worn for 5 days, but 3 days did not give a different estimated PAL. Conclusion: Tracmor_D provides moderate‐to‐strong validity evidence that supports its use to evaluate energy used for physical activity in 3‐4‐year‐old children.     

Barriers and Facilitators of Participation in Sports: A Qualitative Study on Dutch Individuals with Lower Limb Amputation

Introduction

Although individuals with lower limb amputation may benefit from participation in sports, less than 40% do so.

Aim

To identify the barriers and facilitators that influence participation in sports for individuals with lower limb amputation.

Design

Qualitative study.

Participants

Twenty six individuals with lower limb amputation, all originating from the Dutch provinces of Groningen and Drenthe, of which 13 athletes.

Methods

Semi-structured interviews were used to gather information. Following thematic analysis, emerging themes were organized in three categories Technical, Social and Personal.

Results

Sport was perceived as enjoyable activity that would help participants to become and stay healthy, improve the number of social contacts, reduce phantom pain and decrease daily tension. Inadequate facilities, problematic transportation, trivialization from others, poor health and lack of motivation or the lack of a sports partner were barriers commonly mentioned by non-athletes. Remarkably, while all athletes were successful prosthetic users, the majority chose to participate in sports for which prosthesis was neither required nor needed.

Conclusions

Each individual with lower limb amputation needs to be counselled according to the barriers and facilitators he/she personally experiences. Athletes appeared to be more proactive in searching for a solution and also appeared less discouraged by failing.     

Multiple Locus Variable number of tandem repeat Analysis: A molecular genotyping tool for Paenibacillus larvae

American Foulbrood, caused by Paenibacillus larvae, is the most severe bacterial disease of honey bees (Apis mellifera). To perform genotyping of P. larvae in an epidemiological context, there is a need of a fast and cheap method with a high resolution. Here, we propose Multiple Locus Variable number of tandem repeat Analysis (MLVA). MLVA has been used for typing a collection of 209 P. larvae strains from which 23 different MLVA types could be identified. Moreover, the developed methodology not only permits the identification of the four Enterobacterial Repetitive Intergenic Consensus (ERIC) genotypes, but allows also a discriminatory subdivision of the most dominant ERIC type I and ERIC type II genotypes. A biogeographical study has been conducted showing a significant correlation between MLVA genotype and the geographical region where it was isolated.     

Impact of land‐use change to Jatropha bioenergy plantations on biomass and soil carbon stocks: a field study in Mali

Small‐scale Jatropha cultivation and biodiesel production have the potential of contributing to local development, energy security, and greenhouse gas (GHG) mitigation. In recent years however, the GHG mitigation potential of biofuel crops is heavily disputed due to the occurrence of a carbon debt, caused by CO₂ emissions from biomass and soil after land‐use change (LUC). Most published carbon footprint studies of Jatropha report modeled results based on a very limited database. In particular, little empirical data exist on the effects of Jatropha on biomass and soil C stocks. In this study, we used field data to quantify these C pools in three land uses in Mali, that is, Jatropha plantations, annual cropland, and fallow land, to estimate both the Jatropha C debt and its C sequestration potential. Four‐year‐old Jatropha plantations hold on average 2.3 Mg C ha^?1 in their above‐ and belowground woody biomass, which is considerably lower compared to results from other regions. This can be explained by the adverse growing conditions and poor local management. No significant soil organic carbon (SOC) sequestration could be demonstrated after 4 years of cultivation. While the conversion of cropland to Jatropha does not entail significant C losses, the replacement of fallow land results in an average C debt of 34.7 Mg C ha^?1, mainly caused by biomass removal (73%). Retaining native savannah woodland trees on the field during LUC and improved crop management focusing on SOC conservation can play an important role in reducing Jatropha's C debt. Although planting Jatropha on degraded, carbon‐poor cropland results in a limited C debt, the low biomass production, and seed yield attained on these lands reduce Jatropha's potential to sequester C and replace fossil fuels. Therefore, future research should mainly focus on increasing Jatropha's crop productivity in these degraded lands.     

Causal Inference for Spatial Constancy across Saccades

Our ability to interact with the environment hinges on creating a stable visual world despite the continuous changes in retinal input. To achieve visual stability, the brain must distinguish the retinal image shifts caused by eye movements and shifts due to movements of the visual scene. This process appears not to be flawless: during saccades, we often fail to detect whether visual objects remain stable or move, which is called saccadic suppression of displacement (SSD). How does the brain evaluate the memorized information of the presaccadic scene and the actual visual feedback of the postsaccadic visual scene in the computations for visual stability? Using a SSD task, we test how participants localize the presaccadic position of the fixation target, the saccade target or a peripheral non-foveated target that was displaced parallel or orthogonal during a horizontal saccade, and subsequently viewed for three different durations. Results showed different localization errors of the three targets, depending on the viewing time of the postsaccadic stimulus and its spatial separation from the presaccadic location. We modeled the data through a Bayesian causal inference mechanism, in which at the trial level an optimal mixing of two possible strategies, integration vs. separation of the presaccadic memory and the postsaccadic sensory signals, is applied. Fits of this model generally outperformed other plausible decision strategies for producing SSD. Our findings suggest that humans exploit a Bayesian inference process with two causal structures to mediate visual stability.     

The Possible Role of Staphylococcus epidermidis LPxTG Surface Protein SesC in Biofilm Formation

Staphylococcus epidermidis is the most common cause of device-associated infections. It has been shown that active and passive immunization in an animal model against protein SesC significantly reduces S. epidermidis biofilm-associated infections. In order to elucidate its role, knock-out of sesC or isolation of S. epidermidis sesC-negative mutants were attempted, however, without success. As an alternative strategy, sesC was introduced into Staphylococcus aureus 8325–4 and its isogenic icaADBC and srtA mutants, into the clinical methicillin-sensitive S. aureus isolate MSSA4 and the MRSA S. aureus isolate BH1CC, which all lack sesC. Transformation of these strains with sesC i) changed the biofilm phenotype of strains 8325–4 and MSSA4 from PIA-dependent to proteinaceous even though PIA synthesis was not affected, ii) converted the non-biofilm-forming strain 8325–4 ica::tet to a proteinaceous biofilm-forming strain, iii) impaired PIA-dependent biofilm formation by 8325–4 srtA::tet, iv) had no impact on protein-mediated biofilm formation of BH1CC and v) increased in vivo catheter and organ colonization by strain 8325–4. Furthermore, treatment with anti-SesC antibodies significantly reduced in vitro biofilm formation and in vivo colonization by these transformants expressing sesC. These findings strongly suggest that SesC is involved in S. epidermidis attachment to and subsequent biofilm formation on a substrate.     

The macroecology of infectious diseases: a new perspective on global‐scale drivers of pathogen distributions and impacts

Identifying drivers of infectious disease patterns and impacts at the broadest scales of organisation is one of the most crucial challenges for modern science, yet answers to many fundamental questions remain elusive. These include what factors commonly facilitate transmission of pathogens to novel host species, what drives variation in immune investment among host species, and more generally what drives global patterns of parasite diversity and distribution? Here we consider how the perspectives and tools of macroecology, a field that investigates patterns and processes at broad spatial, temporal and taxonomic scales, are expanding scientific understanding of global infectious disease ecology. In particular, emerging approaches are providing new insights about scaling properties across all living taxa, and new strategies for mapping pathogen biodiversity and infection risk. Ultimately, macroecology is establishing a framework to more accurately predict global patterns of infectious disease distribution and emergence.