APPLICATION OF BIOMECHANICS TO THE PREVENTION OF OVERLOAD INJURIES IN ELITE SOCCER PLAYERS.

Structural alterations of the foot and inadequate design of sports footwear, as well as overtraining, have been determined to be risk factors for overload injuries during sports practice. A biomechanical analysis protocol was designed to study both, the foot and sports footwear statically and dynamically. This protocol was applied on 47 soccer players of the Spanish Premier League. Amongst the results we should point out that 53.3% of the players had cavus feet. 14.8% of the players deformed the boots. 44.7% of the players studied registered high pressures over the metatarsal heads. and 19.1 % registered high ones at the first toe. 44.7% of the players showed an excessive supination pattern. The data obtained gave us information about the static and dynamic patterns of the elite soccer player. With the individual information the assessment of suitable footwear was carried out for each player. The correct application of this protocol could be used in the diagnosis or prevention of overtraining and in the detection of foot and gait pathologies

Using MCDA to generate and interpret evidence to inform local government investment in public health

Smoking is the single biggest cause of preventable death in the Uited Kingdom (UK) and is a major cause of coronary heart disease, some cancers, and respiratory disease, including chronic obstructive pulmonary disease. At the time of initiating the project, smoking prevalence had not changed across four local government areas in South Yorkshire for some years. Most spending had been focussed on helping people quit, an intervention where there was clear evidence of effectiveness. A number of changes occurred in public health structures and targets, requiring a reappraisal of the range of interventions offered. This was challenging due to a lack of clear evidence for some of the areas’ alternative interventions. The aim of this paper is to describe the use of a multi-criteria decision analysis (MCDA) approach to support the health priority setting in local authorities to reduce smoking prevalence. There were three phases to this process: (1) problem structuring; (2) the multiple criteria decision analysis; (3) and using the MCDA results to influence decision making at the local government level. The MCDA approach was used to collate information in a consistent and transparent manner, using expert, stakeholder and public opinion to fill known gaps in evidence. Fifteen interventions (such as stop smoking support services, smoke-free spaces, communication and marketing exercises, and increased investment in enforcement) were ranked across eight criteria (relating to reductions in prevalence across relevant groups, as well as aspects relating to equity and feasibility), allowing a range of relevant concerns to be incorporated. Subsequent steps were taken to translate the results of this stage into workable policy options. The results differed significantly from current practice. Sensitivity analysis showed that the findings were robust to changes in preference weights. These results informed subsequent changes to the interventions offered across the four boroughs. The ability of MCDA techniques to incorporate data and both qualitative and quantitative judgements in a formal manner mean that they are well suited to support public health decision making, where evidence is often only partially available and many policies are value driven. MCDA methods, if used, should be chosen carefully based on their resource/time constraints, scientific validity, and the significance and broader context of the decision problem.This is the final version of the article. It first appeared from Springer via http://dx.doi.org/10.1007/s40070-016-0059-

Developmental ethanol exposure causes central nervous system dysfunction and may slow the aging process in a Drosophila model of fetal alcohol spectrum disorder

Alcohol is a known teratogen, and developmental exposure to ethanol results in fetal alcohol spectrum disorder (FASD). Children born with FASD can exhibit a range of symptoms including low birth weight, microcephaly, and neurobehavioral problems. Treatment of patients with FASD is estimated to cost 4 billion dollars per year in the United States alone, and 2 million dollars per affected individual\u27s lifetime. We have established Drosophila melanogaster as a model organism for the study of FASD. Here we report that mutations in Dementin (Dmtn), the Drosophila ortholog of the Alzheimer\u27s disease-associated protein TMCC2, convey sensitivity to developmental ethanol exposure, and provide evidence that Dmtn expression is disrupted by ethanol. In addition, we find that flies reared on ethanol exhibit mild climbing defects suggestive of neurodegeneration. Surprisingly, our data also suggest that flies reared on ethanol age more slowly than control animals, and we find that a number of slow-aging mutants are sensitive to developmental ethanol exposure. Finally, we find that flies reared on ethanol showed a persistent upregulation of genes encoding antioxidant enzymes, which may contribute to a reduced rate of central nervous system aging. Thus, in addition to the well-documented negative effects of developmental alcohol exposure on the nervous system, there may be a previously unsuspected neuroprotective effect in adult animals

Short-term emission line and continuum variations in Mrk110

We present results of a variability campaign of Mrk110 performed with the 9.2-m Hobby-Eberly Telescope (HET) at McDonald Observatory. The high S/N spectra cover most of the optical range. They were taken from 1999 November through 2000 May. The average interval between the observations was 7.3 days and the median interval was only 3.0 days. Mrk110 is a narrow-line Seyfert 1 galaxy. During our campaign the continuum flux was in a historically low stage. Considering the delays of the emission lines with respect to the continuum variations we could verify an ionization stratification of the BLR. We derived virial masses of the central black hole from the radial distances of the different emission lines and from their widths. The calculated central masses agree within 20%. Furthermore, we identified optical HeI singlet emission lines emitted in the broad-line region. The observed line fluxes agree with theoretical predictions. We show that a broad wing on the red side of the [OIII]5007 line is caused by the HeI singlet line at 5016A.Comment: 11 pages, 16 figures, A&A Latex. Accepted for publication in A&A Main Journa

Influence Diffusion in Social Networks under Time Window Constraints

We study a combinatorial model of the spread of influence in networks that generalizes existing schemata recently proposed in the literature. In our model, agents change behaviors/opinions on the basis of information collected from their neighbors in a time interval of bounded size whereas agents are assumed to have unbounded memory in previously studied scenarios. In our mathematical framework, one is given a network $G=(V,E)$, an integer value $t(v)$ for each node $v\in V$, and a time window size $\lambda$. The goal is to determine a small set of nodes (target set) that influences the whole graph. The spread of influence proceeds in rounds as follows: initially all nodes in the target set are influenced; subsequently, in each round, any uninfluenced node $v$ becomes influenced if the number of its neighbors that have been influenced in the previous $\lambda$ rounds is greater than or equal to $t(v)$. We prove that the problem of finding a minimum cardinality target set that influences the whole network $G$ is hard to approximate within a polylogarithmic factor. On the positive side, we design exact polynomial time algorithms for paths, rings, trees, and complete graphs.Comment: An extended abstract of a preliminary version of this paper appeared in: Proceedings of 20th International Colloquium on Structural Information and Communication Complexity (Sirocco 2013), Lectures Notes in Computer Science vol. 8179, T. Moscibroda and A.A. Rescigno (Eds.), pp. 141-152, 201

Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice

Purpose: This concise review aims to explore the potential for the clinical implementation of artificial intelligence (AI) strategies for detecting glaucoma and monitoring glaucoma progression. / Methods: Nonsystematic literature review using the search combinations "Artificial Intelligence," "Deep Learning," "Machine Learning," "Neural Networks," "Bayesian Networks," "Glaucoma Diagnosis," and "Glaucoma Progression." Information on sensitivity and specificity regarding glaucoma diagnosis and progression analysis as well as methodological details were extracted. / Results: Numerous AI strategies provide promising levels of specificity and sensitivity for structural (e.g. optical coherence tomography [OCT] imaging, fundus photography) and functional (visual field [VF] testing) test modalities used for the detection of glaucoma. Area under receiver operating curve (AROC) values of > 0.90 were achieved with every modality. Combining structural and functional inputs has been shown to even more improve the diagnostic ability. Regarding glaucoma progression, AI strategies can detect progression earlier than conventional methods or potentially from one single VF test. / Conclusions: AI algorithms applied to fundus photographs for screening purposes may provide good results using a simple and widely accessible test. However, for patients who are likely to have glaucoma more sophisticated methods should be used including data from OCT and perimetry. Outputs may serve as an adjunct to assist clinical decision making, whereas also enhancing the efficiency, productivity, and quality of the delivery of glaucoma care. Patients with diagnosed glaucoma may benefit from future algorithms to evaluate their risk of progression. Challenges are yet to be overcome, including the external validity of AI strategies, a move from a "black box" toward "explainable AI," and likely regulatory hurdles. However, it is clear that AI can enhance the role of specialist clinicians and will inevitably shape the future of the delivery of glaucoma care to the next generation. / Translational Relevance: The promising levels of diagnostic accuracy reported by AI strategies across the modalities used in clinical practice for glaucoma detection can pave the way for the development of reliable models appropriate for their translation into clinical practice. Future incorporation of AI into healthcare models may help address the current limitations of access and timely management of patients with glaucoma across the world