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Among other objectives, forest inventories are aimed to identify ecological communities and to correlate community composition with environmental variables. The identification of different communities would require several forest inventories, each covering small sampling areas with relatively homogeneous environmental conditions. The multiple plot sampling method, traditionally used in local inventories of tropical forests, cannot assure such homogeneity, since even small sampling areas would show environmental heterogeneity influencing vegetation. In this paper we assessed the consequences of this heterogeneity for sampling by quantifying the variability of species abundance ranks for species sampled with 10 or more individuals in a set of plots covering a small sampling area. The species reference abundance ranks were obtained from a sample of 100 plots of 10 m × 10 m each randomly set in a sampling area of 6.5 ha in a tropical forest fragment (Southeastern Brazil). For each species we used resamplings (30 trials) to obtain the species abundance ranks in sub-samples, considering different sampling intensities (n = 25, 50 and 75 plots), and compared these ranks with the species reference rank (n = 100 plots). Then, we compared the species ranks in sub-samples of 50 plots (10.000 trials) with the reference rank and assessed the frequency and extent of rank displacements. Species rank was highly variable across resampling trials for the sampling intensities of n = 25 and n = 50, but decreased considerable with a sampling intensity of n = 75 plots. The mean rank variability and especially the maximum displacement raised significantly from the seventh most abundant species on, and some species occupied quite discrepant abundance ranks in up to 10% of the 10.000 resampling trials. This high internal variability of forest samples may impair the search for floristic patterns as scale lessens, say, to the meso-scale (1–100 km2). We discussed some possible ways to increase internal homogeneity of tropical forest samples with the multiple plot sampling method. Among these, objective entitation, based on an ancient phytosociological procedure, is suggested as the most appropriate for use on the hilly relieves of the Atlantic forest biome.

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Although universities’ world rankings are popular, their design and methods still request considerable elaborations. The paper demonstrates some shortcomings in the Academic World Ranking of Universities (ARWU, Shanghai Jiao Tong University) ranking methods. One deficiency is that universities’ scale differences are neglected due to omitting the whole input side. By resampling and reanalyzing the ARWU data, the paper proposes an input-output analysis for measuring universities’ scientific productivity with special emphasis on those universities which meet the productivity threshold (i.e. share of output exceeds share of input) in a certain group of universities. The productivity analysis on Scandinavian universities evaluates multidisciplinary and specialized universities on their own terms; consequently the ranking based on scientific productivity deviates significantly from the ARWU.

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Spatial distribution modelling can be a useful tool for elaborating conservation strategies for tree species characterized by fragmented and sparse populations. We tested five statistical models—Support Vector Regression (SVR), Multivariate Adaptive Regression Splines (MARS), Gaussian processes with radial basis kernel functions (GP), Regression Tree Analysis (RTA) and Random Forests (RF)—for their predictive performances. To perform the evaluation, we applied these techniques to three tree species for which conservation measures should be elaborated and implemented: one Mediterranean species ( Quercus suber ) and two temperate species ( Ilex aquifolium and Taxus baccata ). Model evaluation was measured by MSE, Goodman-Kruskal and sensitivity statistics and map outputs based on the minimal predicted area criterion. All the models performed well, confirming the validity of this approach when dealing with species characterized by narrow and specialized niches and when adequate data (more than 40–50 samples) and environmental and climatic variables, recognized as important determinants of plant distribution patterns, are available. Based on the evaluation processes, RF resulted the most accurate algorithm thanks to bootstrap-resampling, trees averaging, randomization of predictors and smoother response surface.

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Authors: Bálint Gergely Szabó, Ilona Bobek, Marienn Réti, László Gopcsa, Dóra Mathiász, Botond Lakatos, Gabriella Bekő, Mónika Pető, János Sinkó, Gábor Mikala, Zoltán Kis, János Szlávik, Péter Reményi, and István Vályi-Nagy


A COVID–19 a SARS-CoV-2 vírus által okozott, járványosan terjedő, légúti kiindulású betegség. A kórokozó magas patogenitású, zoonotikus eredetű humán coronavírus, mely hatékonyan terjed emberről emberre cseppfertőzéssel és közeli kontaktussal. A vírusdiagnosztika a légutakból vett minta PCR-vizsgálatán alapul, melynek ismétlésére szükség lehet a fertőzés kizárására. A PCR-eredményt a klinikummal egybe kell vetni, mivel a preszimptomatikus beteg már vírust üríthet, a gyógyultak PCR-pozitivitása pedig hetekig elhúzódik. A terápiás stratégiák két ágát az antivirális gyógyszerek, valamint a hiperinflammációt gátló immunmodulánsok adják. Jelen összefoglalásunk a második azon két társközlemény közül, melyek célja a 2020. május 25-ig elérhető legfőbb nemzetközi és hazai betegséggel kapcsolatos eredmények ismertetése, elsősorban, de nem kizárólag hematológus kollégák számára.

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): Resampling method for unsupervised estimation of cluster validity. — Neural Computation 13 : 2573–2593. Domany E. Resampling method for unsupervised estimation of cluster validity

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Community Ecology
Authors: M. Marignani, C. Ricotta, F. Rossi, A. Pavesi, and G.C. Avena

. 67 80 Efron, B. 1982. The Jackknife, the Bootstrap, and other Resampling Plans. SIAM, Philadelphia. The Jackknife, the Bootstrap

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. MULTIV. Multivariate Exploratory Analysis, Randomization Testing and Bootstrap Resampling. Universidade Federal Rio Grande do Sul, Porto Alegre, Brazil. MULTIV. Multivariate Exploratory Analysis, Randomization

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Pillar, V.D. 2006. MULTIV: Multivariate Exploratory Analysis, Randomization Testing and Bootstrap Resampling, User’s Guide v. 2.4 . Universidade Federal do Rio Grande do Sul, Porto Alegre. Pillar V

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Pillar, V.D. 2006. MULTIV: Multivariate Exploratory Analysis, Randomization Testing and Bootstrap Resampling, User’s Guide version 2.4 , Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil. Pillar

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743 D’Alessandro, L. and L. Fattorini. 2002. Resampling estimators of species richness from presence-absence data: why they don’t work. Metron 61: 5

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