Violence, exclusion and the role of children and adolescents moral features in the sport domain: The save project

The prevention of violence and exclusion in the sport domain is one of the principal priorities of the European Union Programs. Sport Against Violence and Exclusion (SAVE) Project is an initiative co-founded by the Erasmus Plus Programme of the European Commission, whose aim is to train coach to recognize and manage violent and exclusion episodes in the sport situation. According to the psychological literature, one of the predictors of aggressiveness and antisocial tendencies is morality. In particular, for what concerns children, a less mature moral reasoning is associated to a greater tendency to aggress, while for adolescents, the moral atmosphere, in particular, the perception of the coach norms, seems to have the greatest influence on their behavior. For this reason, an in-depth training for coaches has been developed within SAVE Project. Furthermore, a scientific research involving seven countries has been conducted. The current Editorial Letter shows one year progress of SAVE Project . This and other initiatives, if supported by scientific community, could be an inspiration for broader policy reform

Prosocial and aggressive behavior occurrence in young athletes: Field research results in six European countries

Aggression and violence among youth areresearched as social phenomena in sport. This paper was designed to determine the occurrence of these behaviors as well as prosocial behaviorsamong young athletes. The current paper is a research report aiming to detect the frequency of aggressive behavior, social exclusion, prosocial behavior and cohesion in the youth environment, the frequency of personal experience of peer violence or social exclusion, and to evaluate cross-national differences in terms of occurrence of these phenomena. The field research was conducted in six European countries (Austria, Bosnia and Herzegovina, Croatia, Italy, Lithuania, and Serbia) on a sample of 482 children aged 6 to 16. The conducted questionnaire consisted of pre-existing scales and measures for specific behaviors and social aspects that formed the Youth Environment Assessment and Youth Characteristics Questionnaire. Previous personal experience of violence and social exclusion determined groups in the sample. One-way ANOVA and discriminant analysis were conducted to compare various variables and groups within the sample. The results have shown that aggressive and social exclusion behaviors are rare or very rare, predominantly in the form of verbal aggression in the sports club environment. The results of the conducted discriminant analysis indicate that prosocial and cohesion behaviors occur "quite often" to "often" among sports club athletes' samples. The percentage of athletes who have had personal experience of violence or social exclusion in the last two years and whose feeling of hurt by that experience was assessed as "a lot" or "fully" on the measurement scale is estimated to be approximately 25%. Mild cross-national differences emerged in the overmentioned variables, probably due to the sample specificity, or to cultural variety. The results indicate the need for longitudinal research on this topic since the sport is an environment in which cohesion can be developed among young athletes, but it is not free from social exclusion or aggression

Replica theory for learning curves for Gaussian processes on random graphs

Statistical physics approaches can be used to derive accurate predictions for the performance of inference methods learning from potentially noisy data, as quantified by the learning curve defined as the average error versus number of training examples. We analyse a challenging problem in the area of non-parametric inference where an effectively infinite number of parameters has to be learned, specifically Gaussian process regression. When the inputs are vertices on a random graph and the outputs noisy function values, we show that replica techniques can be used to obtain exact performance predictions in the limit of large graphs. The covariance of the Gaussian process prior is defined by a random walk kernel, the discrete analogue of squared exponential kernels on continuous spaces. Conventionally this kernel is normalised only globally, so that the prior variance can differ between vertices; as a more principled alternative we consider local normalisation, where the prior variance is uniform

Modified divergence theorem for analysis and optimization of wall reflecting cylindrical UV reactor

In this paper, the modified divergence theorem (MDT), known in earlier literature as the Gauss-Ostrogradsky theorem, was formulated and proposed as a general approach to electromagnetic (EM) radiation, especially ultraviolet (UV) radiation reactor modeling. A formulated mathematical model, based on MDT, for a multilamp UV reactor was applied to all sources in a reactor in order to obtain intensity profiles at chosen surfaces inside the reactor. Applied modification of MDT means that intensity at a real opaque or transparent surface or through a virtual surface, opened or closed, from different sides of the surface are added and not subtracted as in some other areas of physics. The derived model is applied to an example of the multiple UV sources reactor, where sources are arranged inside a cylindrical reactor at the coaxial virtual cylinder, having the radius smaller than the radius of the reactor. In this work, optimization of a reactor means maximum transfer of EM energy sources into the fluid for given fluid absorbance and fluid flow-dose product. The obtained results, for water quality known in advance, give a unique solution for an optimized model of a multilamp reactor geometry. As everyone can easily verify, MDT is a very good starting point for every reactor modeling and analysis

Disorder Predictors Also Predict Backbone Dynamics for a Family of Disordered Proteins

Several algorithms have been developed that use amino acid sequences to predict whether or not a protein or a region of a protein is disordered. These algorithms make accurate predictions for disordered regions that are 30 amino acids or longer, but it is unclear whether the predictions can be directly related to the backbone dynamics of individual amino acid residues. The nuclear Overhauser effect between the amide nitrogen and hydrogen (NHNOE) provides an unambiguous measure of backbone dynamics at single residue resolution and is an excellent tool for characterizing the dynamic behavior of disordered proteins. In this report, we show that the NHNOE values for several members of a family of disordered proteins are highly correlated with the output from three popular algorithms used to predict disordered regions from amino acid sequence. This is the first test between an experimental measure of residue specific backbone dynamics and disorder predictions. The results suggest that some disorder predictors can accurately estimate the backbone dynamics of individual amino acids in a long disordered region

Rules Governing Selective Protein Carbonylation

BACKGROUND:Carbonyl derivatives are mainly formed by direct metal-catalysed oxidation (MCO) attacks on the amino-acid side chains of proline, arginine, lysine and threonine residues. For reasons unknown, only some proteins are prone to carbonylation. METHODOLOGY/PRINCIPAL FINDINGS:we used mass spectrometry analysis to identify carbonylated sites in: BSA that had undergone in vitro MCO, and 23 carbonylated proteins in Escherichia coli. The presence of a carbonylated site rendered the neighbouring carbonylatable site more prone to carbonylation. Most carbonylated sites were present within hot spots of carbonylation. These observations led us to suggest rules for identifying sites more prone to carbonylation. We used these rules to design an in silico model (available at http://www.lcb.cnrs-mrs.fr/CSPD/), allowing an effective and accurate prediction of sites and of proteins more prone to carbonylation in the E. coli proteome. CONCLUSIONS/SIGNIFICANCE:We observed that proteins evolve to either selectively maintain or lose predicted hot spots of carbonylation depending on their biological function. As our predictive model also allows efficient detection of carbonylated proteins in Bacillus subtilis, we believe that our model may be extended to direct MCO attacks in all organisms

Learning pair-wise gene functional similarity by multiplex gene expression maps

Abstract Background The relationships between the gene functional similarity and gene expression profile, and between gene function annotation and gene sequence have been studied extensively. However, not much work has considered the connection between gene functions and location of a gene's expression in the mammalian tissues. On the other hand, although unsupervised learning methods have been commonly used in functional genomics, supervised learning cannot be directly applied to a set of normal genes without having a target (class) attribute. Results Here, we propose a supervised learning methodology to predict pair-wise gene functional similarity from multiplex gene expression maps that provide information about the location of gene expression. The features are extracted from expression maps and the labels denote the functional similarities of pairs of genes. We make use of wavelet features, original expression values, difference and average values of neighboring voxels and other features to perform boosting analysis. The experimental results show that with increasing similarities of gene expression maps, the functional similarities are increased too. The model predicts the functional similarities between genes to a certain degree. The weights of the features in the model indicate the features that are more significant for this prediction. Conclusions By considering pairs of genes, we propose a supervised learning methodology to predict pair-wise gene functional similarity from multiplex gene expression maps. We also explore the relationship between similarities of gene maps and gene functions. By using AdaBoost coupled with our proposed weak classifier we analyze a large-scale gene expression dataset and predict gene functional similarities. We also detect the most significant single voxels and pairs of neighboring voxels and visualize them in the expression map image of a mouse brain. This work is very important for predicting functions of unknown genes. It also has broader applicability since the methodology can be applied to analyze any large-scale dataset without a target attribute and is not restricted to gene expressions

On the quest for selective constraints shaping the expressivity of the genes casting retropseudogenes in human

<p>Abstract</p> <p>Background</p> <p>Pseudogenes, the nonfunctional homologues of functional genes are now coming to light as important resources regarding the study of human protein evolution. Processed pseudogenes arising by reverse transcription and reinsertion can provide molecular record on the dynamics and evolution of genomes. Researches on the progenitors of human processed pseudogenes delved out their highly expressed and evolutionarily conserved characters. They are reported to be short and GC-poor indicating their high efficiency for retrotransposition. In this article we focused on their high expressivity and explored the factors contributing for that and their relevance in the milieu of protein sequence evolution.</p> <p>Results</p> <p>We here, analyzed the high expressivity of these genes configuring processed or retropseudogenes by their immense connectivity in protein-protein interaction network, an inclination towards alternative splicing mechanism, a lower rate of mRNA disintegration and a slower evolutionary rate. While the unusual trend of the upraised disorder in contrast with the high expressivity of the proteins encoded by processed pseudogene ancestors is accredited by a predominance of hub-protein encoding genes, a high propensity of repeat sequence containing genes, elevated protein stability and the functional constraint to perform the transcription regulatory jobs. Linear regression analysis demonstrates mRNA decay rate and protein intrinsic disorder as the influential factors controlling the expressivity of these retropseudogene ancestors while the latter one is found to have the most significant regulatory power.</p> <p>Conclusions</p> <p>Our findings imply that, the affluence of disordered regions elevating the network attachment to be involved in important cellular assignments and the stability in transcriptional level are acting as the prevailing forces behind the high expressivity of the human genes configuring processed pseudogenes.</p

FLASH Knockdown Sensitizes Cells To Fas-Mediated Apoptosis via Down-Regulation of the Anti-Apoptotic Proteins, MCL-1 and Cflip Short

FLASH (FLICE-associated huge protein or CASP8AP2) is a large multifunctional protein that is involved in many cellular processes associated with cell death and survival. It has been reported to promote apoptosis, but we show here that depletion of FLASH in HT1080 cells by siRNA interference can also accelerate the process. As shown previously, depletion of FLASH halts growth by down-regulating histone biosynthesis and arrests the cell cycle in S-phase. FLASH knockdown followed by stimulating the cells with Fas ligand or anti-Fas antibodies was found to be associated with a more rapid cleavage of PARP, accelerated activation of caspase-8 and the executioner caspase-3 and rapid progression to cellular disintegration. As is the case for most anti-apoptotic proteins, FLASH was degraded soon after the onset of apoptosis. Depletion of FLASH also resulted in the reduced intracellular levels of the anti-apoptotic proteins, MCL-1 and the short isoform of cFLIP. FLASH knockdown in HT1080 mutant cells defective in p53 did not significantly accelerate Fas mediated apoptosis indicating that the effect was dependent on functional p53. Collectively, these results suggest that under some circumstances, FLASH suppresses apoptosis