RESUMEN
Developing an electroencephalogram (EEG)-based motor imagery and motor execution (MI/ME) decoding system that is both highly accurate and calibration-free for cross-subject applications remains challenging due to domain shift problem inherent in such scenario. Recent research has increasingly embraced transfer learning strategies, especially domain adaptation techniques. However, domain adaptation becomes impractical when the target subject data is either difficult to obtain or unknown. To address this issue, we propose a supervised contrastive learning-based domain generalization network (SCLDGN) for cross-subject MI/ME decoding. Firstly, the feature encoder is purposefully designed to learn the EEG discriminative feature representations. Secondly, the domain alignment based on deep correlation alignment constrains the representations distance across various domains to learn domain-invariant features. In addition, the class regularization block is proposed, where the supervised contrastive learning with domain-agnostic mixup is established to learn the class-relevant features and achieve class-level alignment. Finally, in the latent space, clusters of domain-agnostic representations from the same class are mapped closer together. Consequently, SCLDGN is capable of learning domain-invariant and class-relevant discriminative representations, which are essential for effective cross-subject decoding. Extensive experiments conducted on six MI/ME datasets demonstrate the effectiveness of the proposed method in comparison with other state-of-the-art approaches. Furthermore, ablation study and visualization analyses explain the generalization mechanism of the proposed method and also show neurophysiologically meaningful patterns related to MI/ME.
RESUMEN
Ferns and lycophytes have remarkably large genomes. However, little is known about how their genome size evolved in fern lineages. To explore the origins and evolution of chromosome numbers and genome size in ferns, we used flow cytometry to measure the genomes of 240 species (255 samples) of extant ferns and lycophytes comprising 27 families and 72 genera, of which 228 species (242 samples) represent new reports. We analyzed correlations among genome size, spore size, chromosomal features, phylogeny, and habitat type preference within a phylogenetic framework. We also applied ANOVA and multinomial logistic regression analysis to preference of habitat type and genome size. Using the phylogeny, we conducted ancestral character reconstruction for habitat types and tested whether genome size changes simultaneously with shifts in habitat preference. We found that 2C values had weak phylogenetic signal, whereas the base number of chromosomes (x) had a strong phylogenetic signal. Furthermore, our analyses revealed a positive correlation between genome size and chromosome traits, indicating that the base number of chromosomes (x), chromosome size, and polyploidization may be primary contributors to genome expansion in ferns and lycophytes. Genome sizes in different habitat types varied significantly and were significantly correlated with habitat types; specifically, multinomial logistic regression indicated that species with larger 2C values were more likely to be epiphytes. Terrestrial habitat is inferred to be ancestral for both extant ferns and lycophytes, whereas transitions to other habitat types occurred as the major clades emerged. Shifts in habitat types appear be followed by periods of genomic stability. Based on these results, we inferred that habitat type changes and multiple whole-genome duplications have contributed to the formation of large genomes of ferns and their allies during their evolutionary history.
RESUMEN
Degradation and loss of species' suitable habitats in response to global warming are well documented, which are assumed to be affected by increasing temperature. Conversely, habitat increase of species is little reported and is often considered anomalous and unrelated to climate change. In this study, we first revealed the climate-change-driven habitat shifts of six endangered wetland plants - Bruguiera gymnorrhiza, Carex doniana, Glyptostrobus pensilis, Leersia hexandra, Metasequoia glyptostroboides, and Pedicularis longiflora. The current and future potential habitats of the six species in China were predicted using a maximum entropy model based on thirty-year occurrence records and climate monitoring (from 1960 to 1990). Furthermore, we observed the change of real habitats of the six species based on eight-year field observations (from 2011 to 2019). We found that the six species exhibited three different patterns of habitat shifts including decrease, unstable, and increase. The analysis on the main decisive environmental factors showed that these patterns of habitat shifts are counter to what would be expected global warming but are mostly determined by precipitation-related environmental factors rather than temperature. Collectively, our findings highlight the importance of combining multiple environmental factors including temperature and precipitation for understanding plant responses to climate change.