THE EFFECT OF SLEEP QUANTITY AND QUALITY ON DIRECT CURRENT POTENTIAL IN COLLEGIATE AMERICAN FOOTBALL PLAYERS

Direct current (DC) potential is an objective measure of the functional state of the human organism. It is a sensitive and accurate indicator of short- and long-term adaptations to stress, adaptive capacities, and it is an important marker of athlete readiness. Sleep is posited to be the most efficacious strategy for improving recovery to enhance sport performance, and adequate sleep is considered vital to normal psycho-physiological function. Thus, optimal sleep may enhance the functional state, in turn enhancing an athlete’s adaptability to training stress. However, little is known about the relationship between sleep and DC potential. Therefore, the purpose of this study was to examine the effect of acute (one-night) and extended (two-night) sleep quantity and quality on DC potentials in collegiate American football players. Twenty-four Division 1 American football players (Age: 20.6 ± 1.30 yr; Height: 183.4 ± 6.40 cm; Body mass: 114.40 ± 24.60 kg) wore a wrist-worn actigraphy band seven days per week over the course of 136 days, which spanned the pre-season training camp and competitive season, to measure sleep quantity and quality. DC potential was assessed six days per week using the Omegawave Ltd (Espoo, Finland) athlete monitoring system either 30 minutes upon waking or 75-120 minutes prior to the onset of the football training session. Sleep quantity was stratified into duration categories and sleep quality was stratified within sleep latency, number of awakenings, and sleep efficiency variables. Sleep quantity and quality were evaluated using acute (one night) and extended (rolling average of two consecutive nights) sleep outcomes. Within subject comparisons of DC potential were made across sleep quantity and quality categories using repeated-measures analysis of variance to examine the influence of acute and extended sleep quantity and quality on DC potential outcomes. The level of significance was set at p ≤ 0.025. Statistically significant main effects were identified for acute sleep (F3,16 = 4.68, p \u3c .02, η2p = 0.47) and extended sleep durations (F2,17 = 7.71, p \u3c 0.005, η2p = 0.48). Specifically, for acute sleep durations, there was a 17.1% increase in DC potentials (3.59, p \u3c 0.01, Cohen’s d = 0.52, SE 1.18) for sleep durations ≥ 7 hours to \u3c 9 hours, compared to sleeping \u3c 6. For extended sleep, there was a 20% increase in DC potentials (4.53, p \u3c 0.002, Cohen’s d = 0.68, SE = 1.13) when recording a two-day sleep average of ≥ 7.5 hours and \u3c 9 hours, compared to an extended sleep duration of \u3c 6 hours. A statistically significant main effect was also identified for extended wake episodes (F2,19 = 4.5, p = 0.025, η2p = 0.32). For extended sleep periods with \u3e 4 wake episodes there was a 12% increase in DC potentials (2.57 ± 2.24mV, p \u3c 0.25, Cohen’s d = 0.34) compared to extended sleep periods with 2-3 wake episodes. There was not a significant effect of acute (p ≥ 0.20) sleep quality or extended latency (p \u3e 0.18) and efficiency (p \u3e 0.08) on DC potentials. These findings suggest that sleep quantity affects DC bio-potentials and thus the functional state of the athlete. Specifically, sleep durations between 7.00/7.50 to 9 hours correspond with higher measures of DC potentials compared to lesser durations. Given the effect of sleep quantity on biological markers for training adaptability, practitioners should prioritize sleep in the training process and educate athletes on proper sleep hygiene and sleep quantity to enhance their readiness to train

Public transport investments as generators of economic and social activity

Background: High-quality public transport systems increase accessibility, which is linked to wider economic and social benefits that improve the health of the populations served. This paper reviews evidence on the existence and magnitude of these wider benefits. // Methods: We searched for academic studies that evaluated the effects of specific public transport investments or disinvestments on levels of economic and social activity. // Results: Public transport improvements increase economic activity, both at an aggregate level (higher gross domestic product) and household level (higher income), although the effect can be geographically imbalanced. Better public transport boosts employment but tends to increase house prices, leading to gentrification, although suitable policies can prevent this effect. Public transport improves social connections, especially for older people in isolated rural areas. In urban areas, it can reduce connections due to barriers to pedestrians. Disinvestment in public transport, such as closure of bus services, has multiple economic and social costs, although the evidence is still scarce. // Conclusions: Public transport has potentially wide but possibly unequal economic and social benefits

Types of online scaffolds provided by a teacher educator in a communal blog for supporting pre-service teachers\u27 reflective practice: A case study

Pre-service teachers\u27 reflection is a common professional practice in the context of teacher education. The integration of blogs as reflective journals in teacher education may contribute to bridging practical field know-how with the academic knowledge base. In particular, the blog may serve as a virtual space for a community of practice where all partners equally develop professionally. The current study focused on the role of the teacher educator in promoting reflective practice by providing online scaffolds in a communal blog. In a qualitative research design that is based on a case study approach, two hundred and four teacher educators\u27 blog comments were collected throughout an academic year and were analyzed. Five major types of online scaffolds within the teacher educator comments were identified: (1) positive feedback; (2) expressing emotions; (3) peer teaching; (4) meta-cognition; and (5) developing a professional language. It can be concluded that shifting from a traditional platform of individual feedback to a more communal online platform is not automatically linked to a model of a community of practice.  As long as the hierarchical positioning of teacher educators compared to pre-service teachers is preserved by codes of academic status and grades the teacher educator\u27s scaffolds would continue to reflect a traditional model rather than the construction of a genuine academic community of practice

Distributional bias compromises leave-one-out cross-validation

Cross-validation is a common method for estimating the predictive performance of machine learning models. In a data-scarce regime, where one typically wishes to maximize the number of instances used for training the model, an approach called "leave-one-out cross-validation" is often used. In this design, a separate model is built for predicting each data instance after training on all other instances. Since this results in a single test data point available per model trained, predictions are aggregated across the entire dataset to calculate common rank-based performance metrics such as the area under the receiver operating characteristic or precision-recall curves. In this work, we demonstrate that this approach creates a negative correlation between the average label of each training fold and the label of its corresponding test instance, a phenomenon that we term distributional bias. As machine learning models tend to regress to the mean of their training data, this distributional bias tends to negatively impact performance evaluation and hyperparameter optimization. We show that this effect generalizes to leave-P-out cross-validation and persists across a wide range of modeling and evaluation approaches, and that it can lead to a bias against stronger regularization. To address this, we propose a generalizable rebalanced cross-validation approach that corrects for distributional bias. We demonstrate that our approach improves cross-validation performance evaluation in synthetic simulations and in several published leave-one-out analyses.Comment: 20 pages, 5 figures, supplementary informatio

Limpact De La Politique Fiscale Sur Les Petites Et Moyennes Entreprises Au Togo

Cette étude a pour objectif principal d'évaluer l'effet de la fiscalité sur la croissance des petites  et moyennes entreprises (PME) togolaises. Il s'agit de voir si les taxes fiscales  encouragent la croissance de PME (effet de complémentarité) ou au contraire découragent  l'émergence des PME (effet d'éviction). Les données exploitées sont des données primaires et  proviennent essentiellement d'une enquête effectuée en 2012 auprès de 301 PME togolaises du secteur formel

Les PME et la Politique Fiscale Nationale: Effects D'éviction ou de Complémentarité dans L'economie Togolaise?

Principalement cette étude a pour objectif d'évaluer l'effet de la fiscalité sur la croissance des PME togolaises en termes d'effet d'éviction ou de complémentarité. Notre méthodologie s'inspire des travaux de Moati et al (2006) et se base sur la méthode des Moindres Carrées Ordinaires et une modélisation logistique. De ces résultats, nous pouvons tirer deux enseignements majeurs : d'abord, on peut conclure que la fiscalité a un effet d'éviction sur la croissance des PME togolaises. Ensuite, il n'existe pas de relation en cloche entre les taxes et la croissance des PME tel que prédit par Laffer

Domain adaptation in small-scale and heterogeneous biological datasets.

Machine-learning models are key to modern biology, yet models trained on one dataset are often not generalizable to other datasets from different cohorts or laboratories due to both technical and biological differences. Domain adaptation, a type of transfer learning, alleviates this problem by aligning different datasets so that models can be applied across them. However, most state-of-the-art domain adaptation methods were designed for large-scale data such as images, whereas biological datasets are smaller and have more features, and these are also complex and heterogeneous. This Review discusses domain adaptation methods in the context of such biological data to inform biologists and guide future domain adaptation research. We describe the benefits and challenges of domain adaptation in biological research and critically explore some of its objectives, strengths, and weaknesses. We argue for the incorporation of domain adaptation techniques to the computational biologists toolkit, with further development of customized approaches

Talking about cross-talk: the immune system and the microbiome

A report on the first EMBO conference entitled “Next Gen Immunology—From Host Genome to the Microbiome: Immunity in the Genomic Era”, held at the Weizmann Institute of Science, Israel, 14–16 February, 2016

Delirium screening in an acute care setting with a machine learning classifier based on routinely collected nursing data: A model development study

Delirium screening in acute care settings is a resource intensive process with frequent deviations from screening protocols. A predictive model relying only on daily collected nursing data for delirium screening could expand the populations covered by such screening programs. Here, we present the results of the development and validation of a series of machine-learning based delirium prediction models. For this purpose, we used data of all patients 18 years or older which were hospitalized for more than a day between January 1, 2014, and December 31, 2018, at a single tertiary teaching hospital in Zurich, Switzerland. A total of 48,840 patients met inclusion criteria. 18,873 (38.6%) were excluded due to missing data. Mean age (SD) of the included 29,967 patients was 71.1 (12.2) years and 12,231 (40.8%) were women. Delirium was assessed with the Delirium Observation Scale (DOS) with a total score of 3 or greater indicating that a patient is at risk for delirium. Additional measures included structured data collected for nursing process planning and demographic characteristics. The performance of the machine learning models was assessed using the area under the receiver operating characteristic curve (AUC). The training set consisted of 21,147 patients (mean age 71.1 (12.1) years; 8,630 (40.8%) women|) including 233,024 observations with 16,167 (6.9%) positive DOS screens. The test set comprised 8,820 patients (median age 71.1 (12.4) years; 3,601 (40.8%) women) with 91,026 observations with 5,445 (6.0%) positive DOS screens. Overall, the gradient boosting machine model performed best with an AUC of 0.933 (95% CI, 0.929 - 0.936). In conclusion, machine learning models based only on structured nursing data can reliably predict patients at risk for delirium in an acute care setting. Prediction models, using existing data collection processes, could reduce the resources required for delirium screening procedures in clinical practice