Influence of IMU position and orientation placement errors on ground reaction force estimation

Wearable inertial measurement units (IMU) have been proposed to estimate GRF outside of specialized laboratories, however the precise influence of sensor placement error on accuracy is unknown. We investigated the influence of IMU position and orientation placement errors on GRF estimation accuracy. Methods: Kinematic data from twelve healthy subjects based on marker trajectories were used to simulate 1848 combinations of sensor position placement errors (range ± 100 mm) and orientation placement errors (range ± 25°) across eight body segments (trunk, pelvis, left/right thighs, left/right shanks, and left/right feet) during normal walking trials for baseline cases when a single sensor was misplaced and for the extreme cases when all sensors were simultaneously misplaced. Three machine learning algorithms were used to estimate GRF for each placement error condition and compared with the no placement error condition to evaluate performance. Results: Position placement errors for a single misplaced IMU reduced vertical GRF (VGRF), medio-lateral GRF (MLGRF), and anterior-posterior GRF (APGRF) estimation accuracy by up to 1.1%, 2.0%, and 0.9%, respectively and for all eight simultaneously misplaced IMUs by up to 4.9%, 6.0%, and 4.3%, respectively. Orientation placement errors for a single misplaced IMU reduced VGRF, MLGRF, and APGRF estimation accuracy by up to 4.8%, 7.3%, and 1.5%, respectively and for all eight simultaneously misplaced IMUs by up to 20.8%, 23.4%, and 12.3%, respectively. Conclusion: IMU sensor misplacement, particularly orientation placement errors, can significantly reduce GRF estimation accuracy and thus measures should be taken to account for placement errors in implementations of GRF estimation via wearable IMUs.     

Effect of laterality discrimination on joint position sense and cervical range of motion in patients with chronic neck pain: a randomized single-blind clinical trial

Purpose: The main objective of the present study was to evaluate the effects of laterality discrimination training on neck joint position sense and cervical range of motion (ROM) in patients with chronic non-specific neck pain (NSCNP).

Materials and methods: Forty-eight patients with NSCNP were randomly assigned to the neck group (NG) that observed neck images or the foot group (FG) that observed foot images. Response time, response accuracy, cervical ROM, and joint position error (JPE) were the main variables. The secondary outcome measures included psychosocial variables.

Results: Differences between groups in the cervical ROM for flexion (p?=?.043) were obtained, being NG group the one which obtained greater values. NG showed an improvement in right rotation (p?=?.018) and a decrease in flexion was found in the FG (p?=?.039). In JPE, differences between groups were obtained in the left rotation (p?=?.021) and significant changes were found in the NG for flexion, extension, and left rotation movements (p?<?.05). Moderate associations were found between left and right accuracy regarding to post-intervention flexion and right rotation (r?=?0.46, r?=?0.41; p?<?.05) in NG.

Conclusion: Improvements in cervical range of motion and joint position sense are obtained after the performance of the laterality discrimination task of images of the neck but not the feet. Visualization of images of the painful region presents moderate correlations with the accuracy and response time in the movements of flexion and right rotation.     

Impact of apolipoprotein A1- or lecithin:cholesterol acyltransferase-deficiency on white adipose tissue metabolic activity and glucose homeostasis in mice

High density lipoprotein (HDL) has attracted the attention of biomedical community due to its well-documented role in atheroprotection. HDL has also been recently implicated in the regulation of islets of Langerhans secretory function and in the etiology of peripheral insulin sensitivity. Indeed, data from numerous studies strongly indicate that the functions of pancreatic β-cells, skeletal muscles and adipose tissue could benefit from improved HDL functionality. To better understand how changes in HDL structure may affect diet-induced obesity and type 2 diabetes we aimed at investigating the impact of Apoa1 or Lcat deficiency, two key proteins of peripheral HDL metabolic pathway, on these pathological conditions in mouse models. We report that universal deletion of apoa1 or lcat expression in mice fed western-type diet results in increased sensitivity to body-weight gain compared to control C57BL/6 group. These changes in mouse genome correlate with discrete effects on white adipose tissue (WAT) metabolic activation and plasma glucose homeostasis. Apoa1-deficiency results in reduced WAT mitochondrial non-shivering thermogenesis. Lcat-deficiency causes a concerted reduction in both WAT oxidative phosphorylation and non-shivering thermogenesis, rendering lcat^?/? mice the most sensitive to weight gain out of the three strains tested, followed by apoa1^?/? mice. Nevertheless, only apoa1^?/? mice show disturbed plasma glucose homeostasis due to dysfunctional glucose-stimulated insulin secretion in pancreatic β-islets and insulin resistant skeletal muscles. Our analyses show that both apoa1^?/? and lcat^?/? mice fed high-fat diet have no measurable Apoa1 levels in their plasma, suggesting no direct involvement of Apoa1 in the observed phenotypic differences among groups.     

Genotyping‐by‐sequencing analysis of Orobanche crenata populations in Algeria reveals genetic differentiation

Crenate broomrape (Orobanche crenata Forsk.) is a serious long‐standing parasitic weed problem in Algeria, mainly affecting legumes but also vegetable crops. Unresolved questions for parasitic weeds revolve around the extent to which these plants undergo local adaptation, especially with respect to host specialization, which would be expected to be a strong selective factor for obligate parasitic plants. In the present study, the genotyping‐by‐sequencing (GBS) approach was used to analyze genetic diversity and population structure of 10 Northern Algerian O. crenata populations with different geographical origins and host species (faba bean, pea, chickpea, carrot, and tomato). In total, 8004 high‐quality single‐nucleotide polymorphisms (5% missingness) were obtained and used across the study. Genetic diversity and relationships of 95 individuals from 10 populations were studied using model‐based ancestry analysis, principal components analysis, discriminant analysis of principal components, and phylogeny approaches. The genetic differentiation (F _ST) between pairs of populations was lower between adjacent populations and higher between geographically separated ones, but no support was found for isolation by distance. Further analyses identified four genetic clusters and revealed evidence of structuring among populations and, although confounded with location, among hosts. In the clearest example, O. crenata growing on pea had a SNP profile that was distinct from other host/location combinations. These results illustrate the importance and potential of GBS to reveal the dynamics of parasitic weed dispersal and population structure.     

The Circadian Clock Gene Circuit Controls Protein and Phosphoprotein Rhythms in Arabidopsis thaliana

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Errors-in-variables in joint population pharmacokinetic/pharmacodynamic modeling

Pharmacokinetic (PK) models describe the relationship between the administered dose and the concentration of drug (and/or metabolite) in the blood as a function of time. Pharmacodynamic (PD) models describe the relationship between the concentration in the blood (or the dose) and the biologic response. Population PK/PD studies aim to determine the sources of variability in the observed concentrations/responses across groups of individuals. In this article, we consider the joint modeling of PK/PD data. The natural approach is to specify a joint model in which the concentration and response data are simultaneously modeled. Unfortunately, this approach may not be optimal if, due to sparsity of concentration data, an overly simple PK model is specified. As an alternative, we propose an errors-in-variables approach in which the observed-concentration data are assumed to be measured with error without reference to a specific PK model. We give an example of an analysis of PK/PD data obtained following administration of an anticoagulant drug. The study was originally carried out in order to make dosage recommendations. The prior for the distribution of the true concentrations, which may incorporate an individual's covariate information, is derived as a predictive distribution from an earlier study. The errors-in-variables approach is compared with the joint modeling approach and more naive methods in which the observed concentrations, or the separately modeled concentrations, are substituted into the response model. Throughout, a Bayesian approach is taken with implementation via Markov chain Monte Carlo methods.