Local adaptation is a key concept in biology: shift of genetic structures of populations due to differential survival of genotypes is expected to lead to phenotypes providing an advantage in the local environment. Variation of sequences of twelve candidate genes was investigated in 13 Norway spruce (Picea abies (L.) Karst.) provenances originating from sites distributed along an altitudinal gradient from 550 to 1300 m a.s.l. Signals of selection were assessed in 103 single nucleotide polymorphisms (SNP). The Bayesian FST-outlier identification methods as implemented in the programs BayeScan and Arlequin did not identify any SNP with a clear evidence of selection. The approaches relying on SNP-climate associations (spatial analysis method based on logistic regression of allele frequencies with environmental variables, Bayesian method applied in BayEnv2) identified several relationships but none of them remained significant after correction for multiple testing. Gene flow, epigenetic inheritance and former management of the studied populations are discussed as potential reasons for this weak evidence of selection signals.
The aim of this paper is to compare the efficiency of various outlier correction methods for ECG signal processing in biometric applications. The main idea is to correct anomalies in various segments of ECG waveform rather than skipping a corrupted ECG heartbeat in order to achieve better statistics. Experiments were performed using a self-collected Lviv Biometric Dataset. This database contains over 1400 records for 95 unique persons. The baseline identification accuracy without any correction is around 86%. After applying the outlier correction the results were improved up to 98% for autoencoder based algorithms and up to 97.1% for sliding Euclidean window. Adding outlier correction stage in the biometric identification process results in increased processing time (up to 20%), however, it is not critical in the most use-cases.
The use of quantitative methods, including stochastic and exploratory techniques in environmental studies does not seem to be sufficient in practical aspects. There is no comprehensive analytical system dedicated to this issue, as well as research regarding this subject. The aim of this study is to present the Eco Data Miner system, its idea, construction and implementation possibility to the existing environmental information systems. The methodological emphasis was placed on the one-dimensional data quality assessment issue in terms of using the proposed QAAH1 method - using harmonic model and robust estimators beside the classical tests of outlier values with their iterative expansions. The results received demonstrate both the complementarity of proposed classical methods solution as well as the fact that they allow for extending the range of applications significantly. The practical usefulness is also highly significant due to the high effectiveness and numerical efficiency as well as simplicity of using this new tool.