Position statement--altitude training for improving team-sport players' performance: current knowledge and unresolved issues.

Despite the limited research on the effects of altitude (or hypoxic) training interventions on team-sport performance, players from all around the world engaged in these sports are now using altitude training more than ever before. In March 2013, an Altitude Training and Team Sports conference was held in Doha, Qatar, to establish a forum of research and practical insights into this rapidly growing field. A round-table meeting in which the panellists engaged in focused discussions concluded this conference. This has resulted in the present position statement, designed to highlight some key issues raised during the debates and to integrate the ideas into a shared conceptual framework. The present signposting document has been developed for use by support teams (coaches, performance scientists, physicians, strength and conditioning staff) and other professionals who have an interest in the practical application of altitude training for team sports. After more than four decades of research, there is still no consensus on the optimal strategies to elicit the best results from altitude training in a team-sport population. However, there are some recommended strategies discussed in this position statement to adopt for improving the acclimatisation process when training/competing at altitude and for potentially enhancing sea-level performance. It is our hope that this information will be intriguing, balanced and, more importantly, stimulating to the point that it promotes constructive discussion and serves as a guide for future research aimed at advancing the bourgeoning body of knowledge in the area of altitude training for team sports.

Évaluation de la Pensée Constructive chez les adolescents délinquants: une approche pour comprendre les réponses inadaptées au stress

Objective: This pilot study aims at assessing Constructive Thinking in a sample of adolescent offenders and in a normative sample of adolescents. Method: 66 adolescent offenders (12-18 years) were compared to 540 control adolescents on the different subscales of the "Constructive Thinking Inventory". Results and Conclusion: Adolescent offenders show a less efficient Constructive Thinking: they show cognitive styles that may hamper their ability to take appropriate decisions when facing stressful situations, increasing self-defeating behaviors. Interventions may focus on improving adequate coping with stress.

Improving generalized regression analysis for the spatial prediction of forest communities

Aim This study used data from temperate forest communities to assess: (1) five different stepwise selection methods with generalized additive models, (2) the effect of weighting absences to ensure a prevalence of 0.5, (3) the effect of limiting absences beyond the environmental envelope defined by presences, (4) four different methods for incorporating spatial autocorrelation, and (5) the effect of integrating an interaction factor defined by a regression tree on the residuals of an initial environmental model. Location State of Vaud, western Switzerland. Methods Generalized additive models (GAMs) were fitted using the grasp package (generalized regression analysis and spatial predictions, http://www.cscf.ch/grasp). Results Model selection based on cross-validation appeared to be the best compromise between model stability and performance (parsimony) among the five methods tested. Weighting absences returned models that perform better than models fitted with the original sample prevalence. This appeared to be mainly due to the impact of very low prevalence values on evaluation statistics. Removing zeroes beyond the range of presences on main environmental gradients changed the set of selected predictors, and potentially their response curve shape. Moreover, removing zeroes slightly improved model performance and stability when compared with the baseline model on the same data set. Incorporating a spatial trend predictor improved model performance and stability significantly. Even better models were obtained when including local spatial autocorrelation. A novel approach to include interactions proved to be an efficient way to account for interactions between all predictors at once. Main conclusions Models and spatial predictions of 18 forest communities were significantly improved by using either: (1) cross-validation as a model selection method, (2) weighted absences, (3) limited absences, (4) predictors accounting for spatial autocorrelation, or (5) a factor variable accounting for interactions between all predictors. The final choice of model strategy should depend on the nature of the available data and the specific study aims. Statistical evaluation is useful in searching for the best modelling practice. However, one should not neglect to consider the shapes and interpretability of response curves, as well as the resulting spatial predictions in the final assessment.

Integration of high-resolution geophysical data for the characterization of alluvial aquifers : new approaches and implications for hydrological predictions

Abstract Accurate characterization of the spatial distribution of hydrological properties in heterogeneous aquifers at a range of scales is a key prerequisite for reliable modeling of subsurface contaminant transport, and is essential for designing effective and cost-efficient groundwater management and remediation strategies. To this end, high-resolution geophysical methods have shown significant potential to bridge a critical gap in subsurface resolution and coverage between traditional hydrological measurement techniques such as borehole log/core analyses and tracer or pumping tests. An important and still largely unresolved issue, however, is how to best quantitatively integrate geophysical data into a characterization study in order to estimate the spatial distribution of one or more pertinent hydrological parameters, thus improving hydrological predictions. Recognizing the importance of this issue, the aim of the research presented in this thesis was to first develop a strategy for the assimilation of several types of hydrogeophysical data having varying degrees of resolution, subsurface coverage, and sensitivity to the hydrologic parameter of interest. In this regard a novel simulated annealing (SA)-based conditional simulation approach was developed and then tested in its ability to generate realizations of porosity given crosshole ground-penetrating radar (GPR) and neutron porosity log data. This was done successfully for both synthetic and field data sets. A subsequent issue that needed to be addressed involved assessing the potential benefits and implications of the resulting porosity realizations in terms of groundwater flow and contaminant transport. This was investigated synthetically assuming first that the relationship between porosity and hydraulic conductivity was well-defined. Then, the relationship was itself investigated in the context of a calibration procedure using hypothetical tracer test data. Essentially, the relationship best predicting the observed tracer test measurements was determined given the geophysically derived porosity structure. Both of these investigations showed that the SA-based approach, in general, allows much more reliable hydrological predictions than other more elementary techniques considered. Further, the developed calibration procedure was seen to be very effective, even at the scale of tomographic resolution, for predictions of transport. This also held true at locations within the aquifer where only geophysical data were available. This is significant because the acquisition of hydrological tracer test measurements is clearly more complicated and expensive than the acquisition of geophysical measurements. Although the above methodologies were tested using porosity logs and GPR data, the findings are expected to remain valid for a large number of pertinent combinations of geophysical and borehole log data of comparable resolution and sensitivity to the hydrological target parameter. Moreover, the obtained results allow us to have confidence for future developments in integration methodologies for geophysical and hydrological data to improve the 3-D estimation of hydrological properties.

IRM cardiaque: imagerie de référence dans le diagnostic de la myocardite aiguë [Cardiac magnetic resonance in acute myocarditis: a new non-invasive diagnostic gold standard?].

Acute myocarditis was until recently one of the most difficult diagnoses in cardiology. The spectrum of signs and symptoms is very wide, the usual non-invasive tests lack specificity and the myocardial biopsy is only performed in a minority of cases to confirm the diagnosis. Due to its unique ability to directly image myocardial necrosis, fibrosis and oedema, cardiac magnetic resonance (CMR) is now considered the primary tool for noninvasive assessment of patients with suspected myocarditis. CMR is also useful for monitoring disease activity under treatment. Myocarditis has been associated with the development of dilated cardiomyopathy; CMR could play a role in the follow-up of such cases to detect the progression toward a dilatative phenotype. Precise mapping of myocardial lesions with cardiac MRI is invaluable to guide myocardial biopsy and increase its diagnostic yield by improving sensitivity.

Historical contingency affects signaling strategies and competitive abilities in evolving populations of simulated robots.

One of the key innovations during the evolution of life on earth has been the emergence of efficient communication systems, yet little is known about the causes and consequences of the great diversity within and between species. By conducting experimental evolution in 20 independently evolving populations of cooperatively foraging simulated robots, we found that historical contingency in the occurrence order of novel phenotypic traits resulted in the emergence of two distinct communication strategies. The more complex foraging strategy was less efficient than the simpler strategy. However, when the 20 populations were placed in competition with each other, the populations with the more complex strategy outperformed the populations with the less complex strategy. These results demonstrate a tradeoff between communication efficiency and robustness and suggest that stochastic events have important effects on signal evolution and the outcome of competition between distinct populations.

A hypomorphic mutation in lpin1 induces progressively improving neuropathy and lipodystrophy in the rat.

The Lpin1 gene encodes the phosphatidate phosphatase (PAP1) enzyme Lipin 1, which plays a critical role in lipid metabolism. In this study we describe the identification and characterization of a rat model with a mutated Lpin1 gene (Lpin1(1Hubr)), generated by N-ethyl-N-nitrosourea mutagenesis. Lpin1(1Hubr) rats are characterized by hindlimb paralysis and mild lipodystrophy that are detectable from the second postnatal week. Sequencing of Lpin1 identified a point mutation in the 5'-end splice site of intron 18 resulting in mis-splicing, a reading frameshift, and a premature stop codon. As this mutation does not induce nonsense-mediated decay, it allows the production of a truncated Lipin 1 protein lacking PAP1 activity. Lpin1(1Hubr) rats developed hypomyelination and mild lipodystrophy rather than the pronounced demyelination and adipocyte defects characteristic of Lpin1(fld/fld) mice, which carry a null allele for Lpin1. Furthermore, biochemical, histological, and molecular analyses revealed that these lesions improve in older Lpin1(1Hubr) rats as compared with young Lpin1(1Hubr) rats and Lpin1(fld/fld) mice. We observed activation of compensatory biochemical pathways substituting for missing PAP1 activity that, in combination with a possible non-enzymatic Lipin 1 function residing outside of its PAP1 domain, may contribute to the less severe phenotypes observed in Lpin1(1Hubr) rats as compared with Lpin1(fld/fld) mice. Although we are cautious in making a direct parallel between the presented rodent model and human disease, our data may provide new insight into the pathogenicity of recently identified human LPIN1 mutations.

Coexistence of specialist and generalist species is shaped by dispersal and environmental factors.

Disentangling the mechanisms mediating the coexistence of habitat specialists and generalists has been a long-standing subject of investigation. However, the roles of species traits and environmental and spatial factors have not been assessed in a unifying theoretical framework. Theory suggests that specialist species are more competitive in natural communities. However, empirical work has shown that specialist species are declining worldwide due to habitat loss and fragmentation. We addressed the question of the coexistence of specialist and generalist species with a spatially explicit metacommunity model in continuous and heterogeneous environments. We characterized how species' dispersal abilities, the number of interacting species, environmental spatial autocorrelation, and disturbance impact community composition. Our results demonstrated that species' dispersal ability and the number of interacting species had a drastic influence on the composition of metacommunities. More specialized species coexisted when species had large dispersal abilities and when the number of interacting species was high. Disturbance selected against highly specialized species, whereas environmental spatial autocorrelation had a marginal impact. Interestingly, species richness and niche breadth were mainly positively correlated at the community scale but were negatively correlated at the metacommunity scale. Numerous diversely specialized species can thus coexist, but both species' intrinsic traits and environmental factors interact to shape the specialization signatures of communities at both the local and global scales.

Strategies for improving outcomes in NSCLC: a look to the future.

Advances in the management of non-small cell lung cancer (NSCLC) over the past 30 years have led to small increases in 5-year survival rates across Europe, though further improvements may require new treatment strategies. In order to improve efficiency and reduce the cost of development, future trials for new targeted agents in NSCLC should aim to recruit patients on the basis of tumour biology rather than clinical characteristics. However, identification of predictive biomarkers is required to maximise the benefits of new approaches and expedite the drug development process. Nevertheless, the NSCLC landscape is changing rapidly, and recent improvements in our understanding of the molecular biology of the disease will help in the identification of novel targeted agents as well as assisting in the development of personalised strategies for the numerous small subsets of defined NSCLC. Progress in imaging and treatment delivery is also likely to improve outcomes for patients with the disease. This article outlines recent progress in the treatment of NSCLC, identifies current challenges and describes proposals for improving the future management of the disease. It is hoped that implementation of some of these strategies will go some way to improving the outlook for patients with NSCLC.

Development of an erythropoietin prescription simulator to improve abilities for the prescription of erythropoietin stimulating agents: is it feasible?

BACKGROUND: The increasing use of erythropoietins with long half-lives and the tendency to lengthen the administration interval to monthly injections call for raising awareness on the pharmacokinetics and risks of new erythropoietin stimulating agents (ESA). Their pharmacodynamic complexity and individual variability limit the possibility of attaining comprehensive clinical experience. In order to help physicians acquiring prescription abilities, we have built a prescription computer model to be used both as a simulator and education tool. METHODS: The pharmacokinetic computer model was developed using Visual Basic on Excel and tested with 3 different ESA half-lives (24, 48 and 138 hours) and 2 administration intervals (weekly vs. monthly). Two groups of 25 nephrologists were exposed to the six randomised combinations of half-life and administration interval. They were asked to achieve and maintain, as precisely as possible, the haemoglobin target of 11-12 g/dL in a simulated naïve patient. Each simulation was repeated twice, with or without randomly generated bleeding episodes. RESULTS: The simulation using an ESA with a half-life of 138 hours, administered monthly, compared to the other combinations of half-lives and administration intervals, showed an overshooting tendency (percentages of Hb values > 13 g/dL 15.8 ± 18.3 vs. 6.9 ± 12.2; P < 0.01), which was quickly corrected with experience. The prescription ability appeared to be optimal with a 24 hour half-life and weekly administration (ability score indexing values in the target 1.52 ± 0.70 vs. 1.24 ± 0.37; P < 0.05). The monthly prescription interval, as suggested in the literature, was accompanied by less therapeutic adjustments (4.9 ± 2.2 vs. 8.2 ± 4.9; P < 0.001); a direct correlation between haemoglobin variability and number of therapy modifications was found (P < 0.01). CONCLUSIONS: Computer-based simulations can be a useful tool for improving ESA prescription abilities among nephrologists by raising awareness about the pharmacokinetic characteristics of the various ESAs and recognizing the factors that influence haemoglobin variability.

Improving the prediction of plant species distribution and community composition by adding edaphic to topo-climatic variables

Questions Soil properties have been widely shown to influence plant growth and distribution. However, the degree to which edaphic variables can improve models based on topo-climatic variables is still unclear. In this study, we tested the roles of seven edaphic variables, namely (1) pH; (2) the content of nitrogen and of (3) phosphorus; (4) silt; (5) sand; (6) clay and (7) carbon-to-nitrogen ratio, as predictors of species distribution models in an edaphically heterogeneous landscape. We also tested how the respective influence of these variables in the models is linked to different ecological and functional species characteristics. Location The Western Alps, Switzerland. Methods With four different modelling techniques, we built models for 115 plant species using topo-climatic variables alone and then topo-climatic variables plus each of the seven edaphic variables, one at a time. We evaluated the contribution of each edaphic variable by assessing the change in predictive power of the model. In a second step, we evaluated the importance of the two edaphic variables that yielded the largest increase in predictive power in one final set of models for each species. Third, we explored the change in predictive power and the importance of variables across plant functional groups. Finally, we assessed the influence of the edaphic predictors on the prediction of community composition by stacking the models for all species and comparing the predicted communities with the observed community. Results Among the set of edaphic variables studied, pH and nitrogen content showed the highest contributions to improvement of the predictive power of the models, as well as the predictions of community composition. When considering all topo-climatic and edaphic variables together, pH was the second most important variable after degree-days. The changes in model results caused by edaphic predictors were dependent on species characteristics. The predictions for the species that have a low specific leaf area, and acidophilic preferences, tolerating low soil pH and high humus content, showed the largest improvement by the addition of pH and nitrogen in the model. Conclusions pH was an important predictor variable for explaining species distribution and community composition of the mountain plants considered in our study. pH allowed more precise predictions for acidophilic species. This variable should not be neglected in the construction of species distribution models in areas with contrasting edaphic conditions.