Pallesengeertsen5643

this turning strategy may be associated with the decreased step length when turning. These findings could help in providing instructions which prevent the exaggeration of step-length reduction when turning.

Machine-learning (ML) approaches have been repeatedly coupled with raw accelerometry to classify physical activity classes, but the features required to optimize their predictive performance are still unknown. Our aim was to identify appropriate combination of feature subsets and prediction algorithms for activity class prediction from hip-based raw acceleration data.

The hip-based raw acceleration data collected from 27 participants was split into training (70 %) and validation (30 %) subsets. A total of 206 time- (TD) and frequencydomain (FD) features were extracted from 6-second non-overlapping windows of the signal. Feature selection was done using seven filter-based, two wrapper-based, and one embedded algorithm, and classification was performed with artificial neural network (ANN), support vector machine (SVM), and random forest (RF). For every combination between the feature selection method and the classifiers, the most appropriate feature subsets were found and used for model training within the training set. These models were then validated with the left-out validation set.

The appropriate number of features for the ANN, SVM, and RF ranged from 20 to 45. Overall, the accuracy of all the three classifiers was higher when trained with feature subsets generated using filter-based methods compared with when they were trained with wrapper-based methods (range 78.1 %-88 % vs. 66 %-83.5 %). TD features that reflect how signals vary around the mean, how they differ with one another, and how much and how often they change were more frequently selected via the feature selection methods.

A subset of TD features from raw accelerometry could be sufficient for ML-based activity classification if properly selected from different axes.

A subset of TD features from raw accelerometry could be sufficient for ML-based activity classification if properly selected from different axes.

Within the Italian Association of Medical Physics and Health Physics (AIFM) working group "FutuRuS" we carried out a survey regarding the number of the peer-reviewed articles by AIFM members.

We surveyed papers published in the years 2015-2019. Data extracted from Scopus included information regarding authors, title, journal, impact factor (IF), leading or standard authorship by AIFM members, keywords, type of collaboration (monocentric/multicentric/international), area of interest [radiation oncology (RO), radiology (RAD), nuclear medicine (NM), radioprotection (RP) and professional issue (PI)] and topics.

We found 1210 papers published in peer-reviewed journals 48%, 22%, 16%, 6%, 2 and 6% in RO, RAD, NM, RP, PI and other topics, respectively. Forty-seven percent of the papers involved monocentric teams, 31% multicentric and 22% international collaborations. Leading authorship of AIFM members was in 56% of papers, with a corresponding IF equal to 52% of the total IF (3342, IF

=2.8, IF

=35.4). Bcl-2 cancer The mosn Europe.Optimization of artificial reproduction is essential for minimizing genetic diversity, especially when fish are captured from their natural habitats and spawned in controlled conditions. In the present study, there was evaluation of the effects of gonadotropin-releasing hormone analogue (GnRHa) and human chorionic gonadotropin (hCG) with or without dopamine receptor antagonists such as domperidone (DOM) and metoclopramide (MET) on the spawning efficiency of African catfish (Clarias gariepinus) reared in captivity. The control group was intramuscularly (IM) injected with 1 mL of sterile saline solution. The fish specimens of the other six groups were injected IM with GnRHa or hCG, or in combination with either DOM or MET. None of the specimens had ovulations in the control group. There was the longest latency period in specimens treated with only GnRHa or hCG. There were the largest egg mass weight, fecundity, and hatchability (%) in specimens of the GnRHa + MET group. These findings indicate that GnRHa or hCG combined with dopamine receptor antagonists such as DOM and MET resulted in a marked enhancement of ovulation rate and increased the egg mass, fecundity, and hatchability of the treated C. gariepinus, and the values when there was inclusion of the MET treatment exceeded those when there was treatment with DOM.Perinatal mortality of lambs is the major source of reproductive loss in extensive sheep production systems. Treatment with caffeine has reduced intra-partum mortality and/or improved metabolic indicators in other species following hypoxia. This study was conducted to evaluate the efficacy of caffeine for improving perinatal lamb survival. Experiment 1 comprised group-fed Merino ewes grazing pasture and offered 1.8 g/day (estimated 20 mg/kg live weight) caffeine throughout a 4-week lambing period, and a control without caffeine. The survival of lambs to marking (vaccinated, tail docked, males castrated) age in the caffeine treatment group (0.81) did not differ (P =  0.199) from that of control lambs (0.73; total born n = 877). Experiment 2 comprised Merino ewes lambing from three successive weekly joining groups. Treated ewes were drenched with an aqueous caffeine solution at a dose rate of 10 mg/kg live weight from the day before anticipated lambing, until the individual lambed. Control ewes were drenched with water. The proportion of lambs born dead (0.07) and the survival of lambs to marking age (caffeine 0.61; control 0.62) were similar between treatment groups (total born n = 1158). In both experiments, ewe mortality and the weight of lambs at marking were not altered by caffeine treatments. The results from this large-scale field study indicate caffeine is not an effective therapeutic agent to increase either intra-partum or perinatal survival, or lamb growth rates.This study assessed the spatial dependence of daily tobacco consumption and how it is spatially impacted by individual and neighborhood socioeconomic determinants, and tobacco consumption facilities before and after a smoke-free implementation. Individual data was obtained from the Bus Santé, a cross-sectional survey in Geneva. Spatial clusters of high and low tobacco consumption were assessed using Getis-Ord Gi*. Daily tobacco consumption was not randomly clustered in Geneva and may be impacted by tobacco consumption facilities independently of socioeconomic factors and a smoking ban. Spatial analysis should be considered to highlight the impact of smoke-free policies and guide public health interventions.