Changes from 1986 to 2006 in reasons for liking leisure-time physical activity among adolescents

Reasons for participating in physical activity (PA) may have changed in accordance with the general modernization of society. The aim is to examine changes in self-reported reasons for liking leisure-time physical activity (LTPA) and their association with self-reported LTPA over a 20-year period. Data were collected among nationally representative samples of 13-year-olds in Finland, Norway, and Wales in 1986 and 2006 (N = 9252) as part of the WHO cross-national Health Behaviour in School-aged Children (HBSC) study. Univariate ANOVAs to establish differences according to gender, year, and country were conducted. In all countries, 13-year-olds in 2006 tended to report higher importance in terms of achievement and social reasons than their counterparts in 1986, while changes in health reasons were minor. These reasons were associated with LTPA in a similar way at both time points. Health reasons for liking LTPA were considered most important, and were the strongest predictor of LTPA. The findings seem robust as they were consistent across countries and genders. Health education constitutes the most viable strategy for promoting adolescents' motivation for PA, and interventions and educational efforts could be improved by an increased focus on LTPA and sport as a social activity

Estimation of the occurrence, severity, and volume of heartwood rot using airborne laser scanning and optical satellite data

Rot in commercial timber reduces the value of the wood substantially and estimating the occurrence, severity, and volume of heartwood rot would be a useful tool in decision-making to minimize economic losses. Remotely sensed data has recently been used for mapping rot on a single-tree level, and although the results have been relatively poor, some potential has been shown. This study applied area-based approaches to predict rot occurrence, rot severity, and rot volume , at an area level. Ground reference data were collected from harvester operations in 2019–2021. Predictor variables were calculated from multi-temporal remotely sensed data together with environmental variables. Response variables from the harvester data and predictor variables from remotely sensed data were aggregated to grid cells and to forest stands. Random Forest models were built for the different combinations of response variables and predictor subsets, and validated with both random- and spatial cross-validation. The results showed that it was not possible to estimate rot occurrence and rot severity with the applied modeling procedure (pR2: 0.00–0.16), without spatially close training data. The better performance of rot volume models (pR2: 0.12–0.37) was mainly due to the correlation between timber volume and rot volum

Abstract basins of attraction

Abstract basins appear naturally in different areas of several complex variables. In this survey we want to describe three different topics in which they play an important role, leading to interesting open problems

Measurement of the local Jahn-Teller distortion in LaMnO_3.006

The atomic pair distribution function (PDF) of stoichiometric LaMnO_3 has been measured. This has been fit with a structural model to extract the local Jahn-Teller distortion for an ideal Mn(3+)O_6 octahedron. These results are compared to Rietveld refinements of the same data which give the average structure. Since the local structure is being measured in the PDF there is no assumption of long-range orbital order and the real, local, Jahn-Teller distortion is measured directly. We find good agreement both with published crystallographic results and our own Rietveld refinements suggesting that in an accurately stoichiometric material there is long range orbital order as expected. The local Jahn-Teller distortion has 2 short, 2 medium and 2 long bonds.Comment: 5 pages, 3 postscript figures, minor change

Surgical Parameters for Minimally Invasive Trans- Eustachian Tube CSF Leak Repair: A Cadaveric Study and Literature Review

Background Cerebrospinal fluid rhinorrhea from a lateral skull base defect refractory to spontaneous healing and/or conservative management is most commonly managed via open surgery. Approach for repair is dictated by location of the defect, which may require surgical exploration. The final common pathway is the eustachian tube (ET). Endoscopic ET obliteration via endonasal and lateral approaches is under development. Whereas ET anatomy has been studied, surgical landmarks have not been previously described or quantified. We aimed to define surgical parameters of specific utility to endoscopic ET obliteration. Methods A literature review was performed of known ET anatomic parameters. Next, using a combination of endoscopic and open techniques in cadavers, we cannulated the intact ET and dissected its posterior component to define the major curvature position of the ET, defined as the genu, and quantified the relative distances through the ET lumen. The genu was targeted as a major obstacle encountered when cannulating the ET from the nasopharynx. Results Among 10 ETs, we found an average distance of 23 ± 5 mm from the nasopharynx to the ET genu, distance of 24 ± 3 mm from the genu to the anterior aspect of the tympanic membrane and total ET length of 47 ± 4 mm. Conclusions Although membranous and petrous components of the ET are important to its function, the genu may be a more useful surgical landmark. Basic surgical parameters for endoscopic ET obliteration are defined

The quest to model chronic traumatic encephalopathy: a multiple model and injury paradigm experience

Chronic neurodegeneration following a history of neurotrauma is frequently associated with neuropsychiatric and cognitive symptoms. In order to enhance understanding about the underlying pathophysiology linking neurotrauma to neurodegeneration, a multi-model preclinical approach must be established to account for the different injury paradigms and pathophysiologic mechanisms. We investigated the development of tau pathology and behavioral changes using a multi-model and multi-institutional approach, comparing the preclinical results to tauopathy patterns seen in post-mortem human samples from athletes diagnosed with chronic traumatic encephalopathy (CTE). We utilized a scaled and validated blast-induced traumatic brain injury model in rats and a modified pneumatic closed-head impact model in mice. Tau hyperphosphorylation was evaluated by western blot and immunohistochemistry. Elevated-plus maze and Morris water maze were employed to measure impulsive-like behavior and cognitive deficits respectively. Animals exposed to single blast (~50 PSI reflected peak overpressure) exhibited elevated AT8 immunoreactivity in the contralateral hippocampus at 1 month compared to controls (q = 3.96, p \u3c 0.05). Animals exposed to repeat blast (six blasts over 2 weeks) had increased AT8 (q = 8.12, p \u3c 0.001) and AT270 (q = 4.03, p \u3c 0.05) in the contralateral hippocampus at 1 month post-injury compared to controls. In the modified controlled closed-head impact mouse model, no significant difference in AT8 was seen at 7 days, however a significant elevation was detected at 1 month following injury in the ipsilateral hippocampus compared to control (q = 4.34, p \u3c 0.05). Elevated-plus maze data revealed that rats exposed to single blast (q = 3.53, p \u3c 0.05) and repeat blast (q = 4.21, p \u3c 0.05) spent more time in seconds exploring the open arms compared to controls. Morris water maze testing revealed a significant difference between groups in acquisition times on days 22–27. During the probe trial, single blast (t = 6.44, p \u3c 0.05) and repeat blast (t = 8.00, p \u3c 0.05) rats spent less time in seconds exploring where the platform had been located compared to controls. This study provides a multi-model example of replicating tau and behavioral changes in animals and provides a foundation for future investigation of CTE disease pathophysiology and therapeutic development

Modeling Chronic Traumatic Encephalopathy: The Way Forward for Future Discovery

Despite the extensive media coverage associated with the diagnosis of chronic traumatic encephalopathy (CTE), our fundamental understanding of the disease pathophysiology remains in its infancy. Only recently have scientific laboratories and personnel begun to explore CTE pathophysiology through the use of preclinical models of neurotrauma. Some studies have shown the ability to recapitulate some aspects of CTE in rodent models, through the use of various neuropathologic, biochemical, and/or behavioral assays. Many questions related to CTE development however remain unanswered. These include the role of impact severity, the time interval between impacts, the age at which impacts occur, and the total number of impacts sustained. Other important variables such as the location of impacts, character of impacts, and effect of environment/lifestyle and genetics also warrant further study. In this work we attempt to address some of these questions by exploring work previously completed using single and repetitive injury paradigms. Despite some models producing some deficits similar to CTE symptoms, it is clear that further studies are required to understand the development of neuropathological and neurobehavioral features consistent with CTE-like features in rodents. Specifically, acute and chronic studies are needed that characterize the development of tau-based pathology

A Cross-Lingual Similarity Measure for Detecting Biomedical Term Translations

Bilingual dictionaries for technical terms such as biomedical terms are an important resource for machine translation systems as well as for humans who would like to understand a concept described in a foreign language. Often a biomedical term is first proposed in English and later it is manually translated to other languages. Despite the fact that there are large monolingual lexicons of biomedical terms, only a fraction of those term lexicons are translated to other languages. Manually compiling large-scale bilingual dictionaries for technical domains is a challenging task because it is difficult to find a sufficiently large number of bilingual experts. We propose a cross-lingual similarity measure for detecting most similar translation candidates for a biomedical term specified in one language (source) from another language (target). Specifically, a biomedical term in a language is represented using two types of features: (a) intrinsic features that consist of character n-grams extracted from the term under consideration, and (b) extrinsic features that consist of unigrams and bigrams extracted from the contextual windows surrounding the term under consideration. We propose a cross-lingual similarity measure using each of those feature types. First, to reduce the dimensionality of the feature space in each language, we propose prototype vector projection (PVP)—a non-negative lower-dimensional vector projection method. Second, we propose a method to learn a mapping between the feature spaces in the source and target language using partial least squares regression (PLSR). The proposed method requires only a small number of training instances to learn a cross-lingual similarity measure. The proposed PVP method outperforms popular dimensionality reduction methods such as the singular value decomposition (SVD) and non-negative matrix factorization (NMF) in a nearest neighbor prediction task. Moreover, our experimental results covering several language pairs such as English–French, English–Spanish, English–Greek, and English–Japanese show that the proposed method outperforms several other feature projection methods in biomedical term translation prediction tasks

MetaFIND: A feature analysis tool for metabolomics data

<p>Abstract</p> <p>Background</p> <p>Metabolomics, or metabonomics, refers to the quantitative analysis of all metabolites present within a biological sample and is generally carried out using NMR spectroscopy or Mass Spectrometry. Such analysis produces a set of peaks, or <it>features</it>, indicative of the metabolic composition of the sample and may be used as a basis for sample classification. Feature selection may be employed to improve classification accuracy or aid model explanation by establishing a subset of class discriminating features. Factors such as experimental noise, choice of technique and threshold selection may adversely affect the set of selected features retrieved. Furthermore, the high dimensionality and multi-collinearity inherent within metabolomics data may exacerbate discrepancies between the set of features retrieved and those required to provide a complete explanation of metabolite signatures. Given these issues, the latter in particular, we present the MetaFIND application for 'post-feature selection' correlation analysis of metabolomics data.</p> <p>Results</p> <p>In our evaluation we show how MetaFIND may be used to elucidate metabolite signatures from the set of features selected by diverse techniques over two metabolomics datasets. Importantly, we also show how MetaFIND may augment standard feature selection and aid the discovery of additional significant features, including those which represent novel class discriminating metabolites. MetaFIND also supports the discovery of higher level metabolite correlations.</p> <p>Conclusion</p> <p>Standard feature selection techniques may fail to capture the full set of relevant features in the case of high dimensional, multi-collinear metabolomics data. We show that the MetaFIND 'post-feature selection' analysis tool may aid metabolite signature elucidation, feature discovery and inference of metabolic correlations.</p