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Electroencephalographic measurement of cortical activity subserving motor behavior varies among different individuals, restricting the potential of brain computer interfaces (BCIs) based on motor imagery (MI). How to deal with this variability and thereby improve the accuracy of BCI classification remains a key issue. This paper proposes a deep learning-based approach to transfer the data distribution from BCI-friendly — "golden subjects" to the data from more typical BCI-illiterate users. In this work, we use the perceptual loss to align the dimensionality-reduced BCI-illiterate data with the data of golden subjects in low dimensions, by which a subject transfer neural network (STNN) is proposed. The network consists of two parts: 1) a generator, which generates the transferred BCI-illiterate features, and 2) a CNN classifier, which is used for the classification of the transferred features, thus outperforming traditional classification methods both in terms of accuracy and robustness. Electroencephalography (EEG) signals from 25 healthy subjects performing MI of the right hand and foot were classified with an average accuracy of 88. 2 % ± 5. 1 %. The proposed model was further validated on the BCI Competition IV dataset 2b, and was demonstrated to be robust to inter-subject variations. The advantages of STNN allow it to bridge the gap between the golden subjects and the BCI-illiterate ones, paving the way to real-time BCI applications. [ABSTRACT FROM AUTHOR]

Background: The endothelial-to-hematopoietic transition (EHT) process during definitive hematopoiesis is highly conserved in vertebrates. Stage-specific expression of transposable elements (TEs) has been detected during zebrafish EHT and may promote hematopoietic stem cell (HSC) formation by activating inflammatory signaling. However, little is known about how TEs contribute to the EHT process in human and mouse. Results: We reconstructed the single-cell EHT trajectories of human and mouse and resolved the dynamic expression patterns of TEs during EHT. Most TEs presented a transient co-upregulation pattern along the conserved EHT trajectories, coinciding with the temporal relaxation of epigenetic silencing systems. TE products can be sensed by multiple pattern recognition receptors, triggering inflammatory signaling to facilitate HSC emergence. Interestingly, we observed that hypoxia-related signals were enriched in cells with higher TE expression. Furthermore, we constructed the hematopoietic cis-regulatory network of accessible TEs and identified potential TE-derived enhancers that may boost the expression of specific EHT marker genes. Conclusions: Our study provides a systematic vision of how TEs are dynamically controlled to promote the hematopoietic fate decisions through transcriptional and cis-regulatory networks, and pre-train the immunity of nascent HSCs. [ABSTRACT FROM AUTHOR]

Background: Biological age (BA) has been recognized as a more accurate indicator of aging than chronological age (CA). However, the current limitations include: insufficient attention to the incompleteness of medical data for constructing BA; Lack of machine learning-based BA (ML-BA) on the Chinese population; Neglect of the influence of model overfitting degree on the stability of the association results. Methods and results: Based on the medical examination data of the Chinese population (45–90 years), we first evaluated the most suitable missing interpolation method, then constructed 14 ML-BAs based on biomarkers, and finally explored the associations between ML-BAs and health statuses (healthy risk indicators and disease). We found that round-robin linear regression interpolation performed best, while AutoEncoder showed the highest interpolation stability. We further illustrated the potential overfitting problem in ML-BAs, which affected the stability of ML-Bas' associations with health statuses. We then proposed a composite ML-BA based on the Stacking method with a simple meta-model (STK-BA), which overcame the overfitting problem, and associated more strongly with CA (r = 0.66, P < 0.001), healthy risk indicators, disease counts, and six types of disease. Conclusion: We provided an improved aging measurement method for middle-aged and elderly groups in China, which can more stably capture aging characteristics other than CA, supporting the emerging application potential of machine learning in aging research. [ABSTRACT FROM AUTHOR]

SUMMARY: Stigma characteristics are important factors affecting the seed yield of hybrid rice per unit area. Natural variation of stigma characteristics has been reported in rice, but the genetic basis for this variation is largely unknown. We performed a genome‐wide association study on three stigma characteristics in six environments using 1.3 million single‐nucleotide polymorphism (SNPs) characterized in 353 diverse accessions of Oryza sativa. An abundance of phenotypic variation was present in the three stigma characteristics of these collections. We identified four significant SNPs associated with stigma length, 20 SNPs with style length (SYL), and 17 SNPs with the sum of stigma and style length, which were detected repeatedly in more than four environments. Of these SNPs, 28 were novel. We identified two causal gene loci for SYL, OsSYL3 and OsSYL2; OsSYL3 was co‐localized with the grain size gene GS3. The SYL of accessions carrying allele OsSYL3AA was significantly longer than that of those carrying allele OsSYL3CC. We also demonstrated that the outcrossing rate of female parents carrying allele OsSYL2AA increased by 5.71% compared with that of the isogenic line carrying allele OsSYL2CC in an F1 hybrid seed production field. The allele frequencies of OsSYL3AA and OsSYL2AA decreased gradually with an increase in latitude in the Northern Hemisphere. Our results should facilitate the improvement in stigma characteristics of parents of hybrid rice. Significance Statement: The utilization of heterosis in rice is an effective strategy to enhance the grain yield. However, this entails the production of F1 hybrid seeds every year. The yield of F1 hybrid seeds is affected by stigma characteristics. The cloning of OsSYL2AA filled a gap in the genetic study of style length and could be used for improving stigma characteristics to increase the yield of F1 seeds. [ABSTRACT FROM AUTHOR]