Bullying among adolescents in a sub-Saharan middle-income setting.

PURPOSE: This study explored factors associated with self-reported bullying among adolescents in a sub-Saharan country. METHODS: A cross-sectional sample of adolescents (n = 1,427) in the Seychelles was drawn from the Global School-based Student Health Survey. Bullied adolescents were compared with non-bullied adolescents with respect to several sociodemographic factors. Bivariate and multivariate analyses were performed. RESULTS: Within a 30 day period, 38.8% of adolescents reported being bullied. Bullied youths were more likely to be depressed (adjusted odds ratio [aOR] = 1.63; confidence intervals [CI] = 1.27-1.07) and socially deprived (aOR = 1.85; CI = 1.30-2.61). Being older (aOR = .83; CI = .77-.90) and having close friends (aOR = .53; CI = .31-.91) were protective factors. CONCLUSIONS: The prevalence of bullying in the Seychelles is high, and social correlates are similar to those in industrialized settings. More research is needed to examine bullying patterns outside the school environment.

Chromosomal rearrangement and genetic structure in the shrews of the Sorex araneus group

ABSTRACT The role of chromosomal rearrangements in the speciation process is much debated and many theoretical models have been developed. The shrews of the Sorex araneus group offer extraordinary opportunities to study the relationship between chromosomal variation and speciation. Indeed, this group of morphologically very similar species received a great deal of attention due to its karyotypic variability, which is mainly attributed to Robertsonian fusions. To explore the impact of karyotypic changes on genetic differentiation, we first studied the relationship between genetic and karyotypic structure among Alpine species and among chromosome races of the S. araneus group using Bayesian admixture analyses. The results of these analyses confirmed the taxonomic status of the studied species even though introgression can still be detected between species. Moreover, the strong spatial sub-structure highlighted the role of historical factors (e.g. geographical isolation) on genetic structure. Next, we studied gene flow at the chromosome level to address the question of the impact of chromosomal rearrangements on genetic differentiation. We used flow sorted chromosomes from three different karyotypic taxa of the S. araneus group to map microsatellite markers at the chromosóme arm level. We have been able to map 24 markers and to show that the karyotypic organisation of these taxa is well conserved, which suggests that these markers can be used for further inter-taxa studies. A general prediction of chromosomal speciation models is that genetic differentiation between two taxa should be larger across rearranged chromosomes than across chromosomes common to both taxa. We combined two approaches using mapped microsatellites to test this prediction. First, we studied the genetic differentiation among five shrew taxa placed at different evolutionary levels (i.e. within and among species). In this large scale study, we detected an overall significant difference in genetic structure between rearranged vs. common chromosomes. Moreover, this effect varied among pairwise comparisons, which allowed us to differentiate the role of the karyotypic complexity of hybrids and of the evolutionary divergence between taxa. Secondly, we compared the levels of gene flow measured across common vs. rearranged chromosomes in two karyotypically different hybrid zones (strong vs. low complexity of hybrids), which show similar levels of genetic structure. We detected a significantly stronger genetic structure across rearranged chromosomes in the hybrid zone showing the highest level of hybrid complexity. The large variance observed among loci suggested that other factors, such as the position of markers within the chromosome, also certainly affects genetic structure. In conclusion, our results strongly support the role of chromosomal rearrangements in the reproductive barrier and suggest their importance in speciation process of the S. araneus group. RESUME Le rôle des réarrangements chromosomiques dans les processus de spéciation est fortement débattu et de nombreux modèles théoriques ont été développés sur le sujet. Les musaraignes du groupe Sorex araneus présentent de nombreuses opportunités pour étudier les relations entre les variations chromosomiques et la spéciation. En effet, ce groupe d'espèces morphologiquement très proches a attiré l'attention des chercheurs en raison de sa variabilité caryotypique principalement attribuée à des fusions Robertsoniennes. Pour explorer l'impact des changements caryotypiques sur la différenciation génétique, nous avons tout d'abord étudié les relations entre la structure génétique et caryotypique de races chromosomiques et d'espèces alpine du groupe S. araneus en utilisant des analyses Bayesiennes d' « admixture ». Les résultats de ces analyses ont confirmé le statut taxonomique des espèces étudiées bien que nous ayons détecté de l'introgression entre espèces. L'observation d'une sous structure spatiale relativement forte souligne l'importance des facteurs historiques (telle que l'isolation géographique) sur la structure génétique de ce groupe. Ensuite, nous avons étudié le flux de gène au niveau des chromosomes pour aborder de manière directe la question de l'impact des réarrangements chromosomiques sur la différenciation génétique. En conséquence, nous avons utilisé des tris de chromosomes de trois taxons du groupe S. araneus pour localiser des marqueurs microsatellites au niveau du bras chromosomique. Au cours de cette étude, nous avons pu localiser 24 marqueurs et montrer une forte conservation dans l'organisation du caryotype de ces taxa. Ce résultat suggère que leur utilisation est appropriée pour des études entre taxa. Une prédiction générale à tous les modèles de spéciation chromosomique correspond à la plus grande différenciation génétique des chromosomes réarrangés que des chromosomes communs. Nous avons combiné deux approches utilisant des microsatellites localisés au niveau du bras chromosomique pour tester cette prédiction. Premièrement, nous avons étudié la différenciation génétique entre cinq taxa du groupe S. araneus se trouvant à des niveaux évolutifs différents (i.e. à l'intérieur et entre espèce). Au cours de cette étude, nous avons détecté une différenciation globale significativement plus élevée sur les chromosomes réarrangés. Cet effet varie entre les comparaisons, ce qui nous a permis de souligner le rôle de la complexité caryotypique des hybrides et du niveau de divergence évolutive entre taxa. Deuxièmement, nous avons comparé le flux de gènes des chromosomes communs et réarrangés dans deux zones d'hybridation caryotypiquement différentes (forte vs. Faible complexité des hybrides) mais présentant un niveau de différenciation génétique similaire. Ceci nous a permis de détecter une structure génétique significativement plus élevée sur les chromosomes réarrangés au centre de la zone d'hybridation présentant la plus grande complexité caryotypic. La forte variance observée entre loci souligne en outre le fait que d'autres facteurs, tel que la position du marqueur sur le chromosome, affectent probablement aussi la structure génétique mesurée. En conclusion, nos résultats supportent fortement le rôle des réarrangements chromosomiques dans la barrière reproductive entre espèces ainsi que leur importance dans les processus de spéciation des musaraignes du groupe S. araneus.

Uncertainty Quantification with Support Vector Regression Prediction Models

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. The paper considers a data driven approach in modelling uncertainty in spatial predictions. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic features and describe stochastic variability and non-uniqueness of spatial properties. It is able to capture and preserve key spatial dependencies such as connectivity, which is often difficult to achieve with two-point geostatistical models. Semi-supervised SVR is designed to integrate various kinds of conditioning data and learn dependences from them. A stochastic semi-supervised SVR model is integrated into a Bayesian framework to quantify uncertainty with multiple models fitted to dynamic observations. The developed approach is illustrated with a reservoir case study. The resulting probabilistic production forecasts are described by uncertainty envelopes.

Diabetische Neuropathien: Klinische Manifestationsformen, Früherkennung und Zuweisung zu einem Neurologen [Diabetic Neuropathies: Clinical sub-Types, Early Detection, and Asking Help from Neurologist].

In diabetes mellitus, it is expected to see a common, mainly sensitive, distal symmetrical polyneuropathy (DPN) involving a large proportion of diabetic patients according to known risk factors. Several other diabetic peripheral neuropathies are recognized, such as dysautonomia and multifocal neuropathies including lumbosacral radiculoplexus and oculomotor palsies. In this review, general aspects of DPN and other diabetic neuropathies are examined, and it is discussed why and how the general practionner has to perform a yearly examination. At the present time, some consensuses emerge to ask help from neurologist when faced to other forms of peripheral neuropathies than distal symmetrical DPN.

The prediction of speed and incline in outdoor running in humans using accelerometry.

PURPOSE: To explore whether triaxial accelerometric measurements can be utilized to accurately assess speed and incline of running in free-living conditions. METHODS: Body accelerations during running were recorded at the lower back and at the heel by a portable data logger in 20 human subjects, 10 men, and 10 women. After parameterizing body accelerations, two neural networks were designed to recognize each running pattern and calculate speed and incline. Each subject ran 18 times on outdoor roads at various speeds and inclines; 12 runs were used to calibrate the neural networks whereas the 6 other runs were used to validate the model. RESULTS: A small difference between the estimated and the actual values was observed: the square root of the mean square error (RMSE) was 0.12 m x s(-1) for speed and 0.014 radiant (rad) (or 1.4% in absolute value) for incline. Multiple regression analysis allowed accurate prediction of speed (RMSE = 0.14 m x s(-1)) but not of incline (RMSE = 0.026 rad or 2.6% slope). CONCLUSION: Triaxial accelerometric measurements allows an accurate estimation of speed of running and incline of terrain (the latter with more uncertainty). This will permit the validation of the energetic results generated on the treadmill as applied to more physiological unconstrained running conditions.

Validated prediction of clinical outcome in sarcomas and multiple types of cancer on the basis of a gene expression signature related to genome complexity.

Sarcomas are heterogeneous and aggressive mesenchymal tumors. Histological grading has so far been the best predictor for metastasis-free survival, but it has several limitations, such as moderate reproducibility and poor prognostic value for some histological types. To improve patient grading, we performed genomic and expression profiling in a training set of 183 sarcomas and established a prognostic gene expression signature, complexity index in sarcomas (CINSARC), composed of 67 genes related to mitosis and chromosome management. In a multivariate analysis, CINSARC predicts metastasis outcome in the training set and in an independent 127 sarcomas validation set. It is superior to the Fédération Francaise des Centres de Lutte Contre le Cancer grading system in determining metastatic outcome for sarcoma patients. Furthermore, it also predicts outcome for gastrointestinal stromal tumors (GISTs), breast carcinomas and lymphomas. Application of the signature will permit more selective use of adjuvant therapies for people with sarcomas, leading to decreased iatrogenic morbidity and improved outcomes for such individuals.

Novel methods improve prediction of species' distributions from occurrence data

Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.

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.

Prediction of difficult intubation with advanced face recognition techniques: a preliminary study

Introduction: Difficult tracheal intubation remains a constant and significant source of morbidity and mortality in anaesthetic practice. Insufficient airway assessment in the preoperative period continues to be a major cause of unanticipated difficult intubation. Although many risk factors have already been identified, preoperative airway evaluation is not always regarded as a standard procedure and the respective weight of each risk factor remains unclear. Moreover the predictive scores available are not sensitive, moderately specific and often operator-dependant. In order to improve the preoperative detection of patients at risk for difficult intubation, we developed a system for automated and objective evaluation of morphologic criteria of the face and neck using video recordings and advanced techniques borrowed from face recognition. Method and results: Frontal video sequences were recorded in 5 healthy volunteers. During the video recording, subjects were requested to perform maximal flexion-extension of the neck and to open wide the mouth with tongue pulled out. A robust and real-time face tracking system was then applied, allowing to automatically identify and map a grid of 55 control points on the face, which were tracked during head motion. These points located important features of the face, such as the eyebrows, the nose, the contours of the eyes and mouth, and the external contours, including the chin. Moreover, based on this face tracking, the orientation of the head could also be estimated at each frame of the video sequence. Thus, we could infer for each frame the pitch angle of the head pose (related to the vertical rotation of the head) and obtain the degree of head extension. Morphological criteria used in the most frequent cited predictive scores were also extracted, such as mouth opening, degree of visibility of the uvula or thyreo-mental distance. Discussion and conclusion: Preliminary results suggest the high feasibility of the technique. The next step will be the application of the same automated and objective evaluation to patients who will undergo tracheal intubation. The difficulties related to intubation will be then correlated to the biometric characteristics of the patients. The objective in mind is to analyze the biometrics data with artificial intelligence algorithms to build a highly sensitive and specific predictive test.

Development and validation of a clinical prediction rule for chest wall syndrome in primary care

Le "Chest wall syndrome" (CWS) est défini comme étant une source bénigne de douleurs thoraciques, localisées sur la paroi thoracique antérieure et provoquées par une affection musculosquelettique. Le CWS représente la cause la plus fréquente de douleurs thoraciques en médecine de premier recours. Le but de cette étude est de développer et valider un score de prédiction clinique pour le CWS. Une revue de la littérature a d'abord été effectuée, d'une part pour savoir si un tel score existait déjà, et d'autre part pour retrouver les variables décrites comme étant prédictives d'un CWS. Le travail d'analyse statistique a été effectué avec les données issues d'une cohorte clinique multicentrique de patients qui avaient consulté en médecine de premier recours en Suisse romande avec une douleur thoracique (59 cabinets, 672 patients). Un diagnostic définitif avait été posé à 12 mois de suivi. Les variables pertinentes ont été sélectionnées par analyses bivariées, et le score de prédiction clinique a été développé par régression logistique multivariée. Une validation externe de ce score a été faite en utilisant les données d'une cohorte allemande (n= 1212). Les analyses bivariées ont permis d'identifier 6 variables caractérisant le CWS : douleur thoracique (ni rétrosternale ni oppressive), douleur en lancées, douleur bien localisée, absence d'antécédent de maladie coronarienne, absence d'inquiétude du médecin et douleur reproductible à la palpation. Cette dernière variable compte pour 2 points dans le score, les autres comptent pour 1 point chacune; le score total s'étend donc de 0 à 7 points. Dans la cohorte de dérivation, l'aire sous la courbe sensibilité/spécificité (courbe ROC) est de 0.80 (95% de l'intervalle de confiance : 0.76-0.83). Avec un seuil diagnostic de > 6 points, le score présente 89% de spécificité et 45% de sensibilité. Parmi tous les patients qui présentaient un CWS (n = 284), 71% (n = 201) avaient une douleur reproductible à la palpation et 45% (n= 127) sont correctement diagnostiqués par le score. Pour une partie (n = 43) de ces patients souffrant de CWS et correctement classifiés, 65 investigations complémentaires (30 électrocardiogrammes, 16 radiographies du thorax, 10 analyses de laboratoire, 8 consultations spécialisées, et une tomodensitométrie thoracique) avaient été réalisées pour parvenir au diagnostic. Parmi les faux positifs (n = 41), on compte trois angors stables (1.8% de tous les positifs). Les résultats de la validation externe sont les suivants : une aire sous la courbe ROC de 0.76 (95% de l'intervalle de confiance : 0.73-0.79) avec une sensibilité de 22% et une spécificité de 93%. Ce score de prédiction clinique pour le CWS constitue un complément utile à son diagnostic, habituellement obtenu par exclusion. En effet, pour les 127 patients présentant un CWS et correctement classifiés par notre score, 65 investigations complémentaires auraient pu être évitées. Par ailleurs, la présence d'une douleur thoracique reproductible à la palpation, bien qu'étant sa plus importante caractéristique, n'est pas pathognomonique du CWS.

Ambient seismic noise monitoring of a clay landslide: Toward failure prediction

Given that clay-rich landslides may become mobilized, leading to rapid mass movements (earthflows and debris flows), they pose critical problems in risk management worldwide. The most widely proposed mechanism leading to such flow-like movements is the increase in water pore pressure in the sliding mass, generating partial or complete liquefaction. This solid-to-liquid transition results in a dramatic reduction of mechanical rigidity in the liquefied zones, which could be detected by monitoring shear wave velocity variations. With this purpose in mind, the ambient seismic noise correlation technique has been applied to measure the variation in the seismic surface wave velocity in the Pont Bourquin landslide (Swiss Alps). This small but active composite earthslide-earthflow was equipped with continuously recording seismic sensors during spring and summer 2010. An earthslide of a few thousand cubic meters was triggered in mid-August 2010, after a rainy period. This article shows that the seismic velocity of the sliding material, measured from daily noise correlograms, decreased continuously and rapidly for several days prior to the catastrophic event. From a spectral analysis of the velocity decrease, it was possible to determine the location of the change at the base of the sliding layer. These results demonstrate that ambient seismic noise can be used to detect rigidity variations before failure and could potentially be used to predict landslides.