Systematic single-cell analysis reveals dynamic control of transposable element activity orchestrating the endothelial-to-hematopoietic transition.

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.

A machine learning-based data mining in medical examination data: a biological features-based biological age prediction model.

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.

OsSYL2AA , an allele identified by gene-based association, increases style length in rice (Oryza sativa L.).

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.

HALD, a human aging and longevity knowledge graph for precision gerontology and geroscience analyses.

Human aging is a natural and inevitable biological process that leads to an increased risk of aging-related diseases. Developing anti-aging therapies for aging-related diseases requires a comprehensive understanding of the mechanisms and effects of aging and longevity from a multi-modal and multi-faceted perspective. However, most of the relevant knowledge is scattered in the biomedical literature, the volume of which reached 36 million in PubMed. Here, we presented HALD, a text mining-based human aging and longevity dataset of the biomedical knowledge graph from all published literature related to human aging and longevity in PubMed. HALD integrated multiple state-of-the-art natural language processing (NLP) techniques to improve the accuracy and coverage of the knowledge graph for precision gerontology and geroscience analyses. Up to September 2023, HALD had contained 12,227 entities in 10 types (gene, RNA, protein, carbohydrate, lipid, peptide, pharmaceutical preparations, toxin, mutation, and disease), 115,522 relations, 1,855 aging biomarkers, and 525 longevity biomarkers from 339,918 biomedical articles in PubMed. HALD is available at https://bis.zju.edu.cn/hald .

Golden subject is everyone: A subject transfer neural network for motor imagery-based brain computer interfaces.

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.

Training-Free Deep Generative Networks for Compressed Sensing of Neural Action Potentials.

Energy consumption is an important issue for resource-constrained wireless neural recording applications with limited data bandwidth. Compressed sensing (CS) is a promising framework for addressing this challenge because it can compress data in an energy-efficient way. Recent work has shown that deep neural networks (DNNs) can serve as valuable models for CS of neural action potentials (APs). However, these models typically require impractically large datasets and computational resources for training, and they do not easily generalize to novel circumstances. Here, we propose a new CS framework, termed APGen, for the reconstruction of APs in a training-free manner. It consists of a deep generative network and an analysis sparse regularizer. We validate our method on two in vivo datasets. Even without any training, APGen outperformed model-based and data-driven methods in terms of reconstruction accuracy, computational efficiency, and robustness to AP overlap and misalignment. The computational efficiency of APGen and its ability to perform without training make it an ideal candidate for long-term, resource-constrained, and large-scale wireless neural recording. It may also promote the development of real-time, naturalistic brain-computer interfaces.

Motor imagery recognition with automatic EEG channel selection and deep learning.

Objective.Modern motor imagery (MI)-based brain computer interface systems often entail a large number of electroencephalogram (EEG) recording channels. However, irrelevant or highly correlated channels would diminish the discriminatory ability, thus reducing the control capability of external devices. How to optimally select channels and extract associated features remains a big challenge. This study aims to propose and validate a deep learning-based approach to automatically recognize two different MI states by selecting the relevant EEG channels.Approach.In this work, we make use of a sparse squeeze-and-excitation module to extract the weights of EEG channels based on their contribution to MI classification, by which an automatic channel selection (ACS) strategy is developed. Further, we propose a convolutional neural network to fully exploit the time-frequency features, thus outperforming traditional classification methods both in terms of accuracy and robustness.Main results.We execute the experiments using EEG signals recorded at MI where 25 healthy subjects performed MI movements of the right hand and feet to generate motor commands. An average accuracy of87.2±5.0% (mean±std)is obtained, providing a 37.3% improvement with respect to a state-of-the-art channel selection approach.Significance.The proposed ACS method has been found to be significantly advantageous compared to the typical approach of using a fixed channel configuration. This work shows that fewer EEG channels not only reduces computational complexity but also improves the MI classification performance. The proposed method selects EEG channels related to hand and feet movement, which paves the way to real-time and more natural interfaces between the patient and the robotic device.

Compressed sensing of large-scale local field potentials using adaptive sparsity analysis and non-convex optimization.

Objective.Energy consumption is a critical issue in resource-constrained wireless neural recording applications with limited data bandwidth. Compressed sensing (CS) has emerged as a powerful framework in addressing this issue owing to its highly efficient data compression procedure. In this paper, a CS-based approach termed simultaneous analysis non-convex optimization (SANCO) is proposed for large-scale, multi-channel local field potentials (LFPs) recording.Approach.The SANCO method consists of three parts: (1) the analysis model is adopted to reinforce sparsity of the multi-channel LFPs, therefore overcoming the drawbacks of conventional synthesis models. (2) An optimal continuous order difference matrix is constructed as the analysis operator, enhancing the recovery performance while saving both computational resources and data storage space. (3) A non-convex optimizer that can by efficiently solved with alternating direction method of multipliers is developed for multi-channel LFPs reconstruction.Main results.Experimental results on real datasets reveal that the proposed approach outperforms state-of-the-art CS methods in terms of both recovery quality and computational efficiency.Significance.Energy efficiency of the SANCO make it an ideal candidate for resource-constrained, large scale wireless neural recording. Particularly, the proposed method ensures that the key features of LFPs had little degradation even when data are compressed by 16x, making it very suitable for long term wireless neural recording applications.