Soil thermal buffer and regeneration niche may favour calcareous fen resilience to climate change

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Folia Geobotanica following peer review. The version of record (Fernández-Pascual, E., Jiménez-Alfaro, B., Hájek, M., Díaz, T. E., & Pritchard, H. W. (2015). Soil thermal buffer and regeneration niche may favour calcareous fen resilience to climate change. Folia Geobotanica 50, 293-301) is available online at: http://dx.doi.org/10.1007/s12224-015-9223-y.Calcareous fens are azonal habitats permanently saturated by groundwater. This is expected to have a buffer effect on soil temperature, alleviating climate changes and allowing plant communities to occupy diverse climatic regions. We analysed the extent of such buffering and its relation with a relevant plant trait, the seed germination niche breadth, along altitudinal gradients in fens of the Cantabrian Mountains (Spain) and the Western Carpathians (Slovakia). In each fen we recorded soil temperature for several years and compared it with WorldClim predictions for air temperature. We also collected seeds from five Cyperaceae fen specialists to evaluate the influence of soil temperature on germination. Although soil temperatures and WorldClim were strongly correlated, their absolute values differed substantially, showing a narrower thermal amplitude and warmer minimum winter temperature in the soil. The greatest differences in soil temperature and germination niche breadth were those between mountain regions. Narrower germination niches correlated with the colder Slovakian winter. Our results suggest that the soil thermal buffer allows species to prevent frost temperatures in winter, but also high summer temperatures in warm regions, explaining their wide distribution ranges. The warm regeneration niche does not match the cooler soils, but shows variability and potential for adaptation. While this findings support resilience to climate warming, changes in precipitation rather than temperature seem to be the main threat for fen persistence.The Masaryk University of Brno provided institutional support. E.F.P. was supported by the Government of Asturias (Grant BP09-107, Programa de Ayudas Predoctorales ‘Severo Ochoa’, Plan de Ciencia, Tecnología e Innovación del Principado de Asturias) and the FP7-Marie-Curie-COFUND programme of the European Commission (Grant ‘Clarín’ ACA14-19); B.J.A. by the project ‘Employment of Best Young Scientists for International Cooperation Empowerment’ (CZ.1.07/2.3.00/30.0037) co-financed by the European Social Fund and the state budget of the Czech Republic; M.H. by the Academy of Sciences of the Czech Republic (RVO 67985939)

ViaRODOS: Monitoring and Visualisation of Current Traffic Situation on Highways

Part 5: Industrial Management and Other Applications; International audience; This paper describes methods of traffic monitoring based on on-line retrieval of big data both from cars equipped with GPS devices and stationary sensor systems. Various visualization methods and styles of presentation are discussed with focus on linear structure of gathered traffic data along observed routes. Visualized data is available via interactive web interface which uses modern vector graphic standard and enables presentation of as much information as possible with common, well-known traffic symbolism. Document type: Part of book or chapter of boo

Magnetic Resonance Tracking of Human CD34 + Progenitor Cells Separated by Means of Immunomagnetic Selection and Transplanted Into Injured Rat Brain

Magnetic resonance imaging (MRI) provides a noninvasive method for studying the fate of transplanted cells in vivo. We studied whether superparamagnetic nanoparticles (CD34 microbeads), used clinically for specific magnetic sorting, can be used as a magnetic cell label for in vivo cell visualization. Human cells from peripheral blood were selected by CliniMACS ® CD34 Selection Technology (Miltenyi). Purified CD34 + cells were implanted into rats with a cortical photochemical lesion, contralaterally to the lesion. Twenty-four hours after grafting, the implanted cells were detected in the contralateral hemisphere as a hypointense spot on T 2 weighted images; the hypointensity of the implant decreased during the first week. At the lesion site we observed a hypointensive signal 10 days after grafting that persisted for the next 3 weeks, until the end of the experiment. Prussian blue and anti-human nuclei staining confirmed the presence of magnetically labeled human cells in the corpus callosum and in the lesion 4 weeks after grafting. CD34 + cells were also found in the subventricular zone (SVZ). Human DNA (a human-specific 850 base pair fragment of αsatellite DNA from human chromosome 17) was detected in brain tissue sections from the lesion usin

Using structural MRI to identify bipolar disorders ? 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47–67.00, ROC-AUC = 71.49%, 95% CI = 69.39–73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70–60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen’s Kappa = 0.83, 95% CI = 0.829–0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data