The Chinese pine genome and methylome unveil key features of conifer evolution.

Conifers dominate the world's forest ecosystems and are the most widely planted tree species. Their giant and complex genomes present great challenges for assembling a complete reference genome for evolutionary and genomic studies. We present a 25.4-Gb chromosome-level assembly of Chinese pine (Pinus tabuliformis) and revealed that its genome size is mostly attributable to huge intergenic regions and long introns with high transposable element (TE) content. Large genes with long introns exhibited higher expressions levels. Despite a lack of recent whole-genome duplication, 91.2% of genes were duplicated through dispersed duplication, and expanded gene families are mainly related to stress responses, which may underpin conifers' adaptation, particularly in cold and/or arid conditions. The reproductive regulation network is distinct compared with angiosperms. Slow removal of TEs with high-level methylation may have contributed to genomic expansion. This study provides insights into conifer evolution and resources for advancing research on conifer adaptation and development.

Arranged cohabitation among Chinese Muslims.

While modern family-related ideas and behaviors have become more widely accepted in contemporary China, Chinese Muslim minorities continue to hold on to traditional religious practices. Surprisingly, data from our survey conducted in Gansu province in China's northwestern borderlands reveal that Muslims of the Hui and Dongxiang ethnicities reported much higher rates of cohabitation experience than the secular majority Han. Based on follow-up qualitative interviews, we found the answer to lie in the interplay between the highly interventionist Chinese state and the robust cultural resilience of local Islamic communities. While the state maintains a high minimum legal age of marriage, the early marriage norm remains strong in Chinese Muslim communities, where religion constitutes an alternative and often more powerful source of legitimacy-at least in the private sphere of life. Using the 2000 census data, we further show that women in almost all 10 Muslim ethnic groups have higher percentages of underage births and premarital births than Han women, both nationally and in the northwest where most Chinese Muslims live. As the once-outlawed behavior of cohabitation became more socially acceptable during the reform and opening-up era, young Muslim Chinese often found themselves in "arranged cohabitations" as de facto marriages formed at younger-than-legal ages. In doing so, Chinese Muslim communities have reinvented the meaning of cohabitation. Rather than liberal intimate relationship based on individual autonomy, cohabitation has served as a coping strategy by which Islamic patriarchs circumvent the Chinese state's aggressive regulations aimed at "modernizing" the Muslim family.

Social mobility in the Tang Dynasty as the Imperial Examination rose and aristocratic family pedigree declined, 618-907 CE.

Data from the distant past are fertile ground for testing social science theories of education and social mobility. In this study, we construct a dataset from 3,640 tomb epitaphs of males in China's Tang Dynasty (618-907 CE), which contain granular and extensive information about the ancestral origins, family background, and career histories of the deceased elites. Our statistical analysis of the complete profiles yields evidence of the transition away from an aristocratic society in three key trends: 1) family pedigree (i.e., aristocracy) mattered less for career achievement over time, 2) passing the Imperial Examination (Keju) became an increasingly important predictor of one's career achievement, and 3) father's position always mattered throughout the Tang, especially for men who did not pass the Keju. The twilight of medieval Chinese aristocracy, according to the data, began in as early as the mid-seventh century CE.

Social-ecological vulnerability and risk of China's marine capture fisheries to climate change.

Climate change is a new disrupter to global fisheries systems and their governance frameworks. It poses a pressing management challenge, particularly in China, which is renowned as the world's largest fishing country and seafood producer. As climate change continues to intensify in the region and climate awareness grows within the country's national policy, the need to understand China's fisheries' resilience to the escalating climate crisis becomes paramount. In this study, we conduct an interdisciplinary analysis to assess the vulnerability and risk of China's marine capture fisheries in response to climate change. This study employs a spatially explicit, indicator-based approach with a coupled social-ecological framework, focusing on 67 species and 11 coastal regions. By integrating diverse sets of climatic, ecological, economic, societal, and governance indicators and information, we elucidate the factors that could hinder climate adaptation, including a limited understanding of fish early life stages, uncertainty in seafood production, unequal allocation and accessibility of resources, and inadequate consideration of inclusive governance and adaptive management. Our results show that species, which have managed to survive the stress of overfishing, demonstrate a remarkable ability to adapt to climate change. However, collapsing stocks such as large yellow croaker face a high risk due to the synergistic effects of inherent biological traits and external management interventions. We emphasize the imperative to build institutional, scientific, and social capacity to support fisheries adaptation. The scientific insights provided by this study can inform fisheries management decisions and promote the operationalization of climate-resilient fisheries in China and other regions.

Multiple forces facilitate the aquatic acrobatics of grasshopper and bioinspired robot.

Aquatic locomotion is challenging for land-dwelling creatures because of the high degree of fluidity with which the water yields to loads. We surprisingly found that the Chinese rice grasshopper Oxya chinensis, known for its terrestrial acrobatics, could swiftly launch itself off the water's surface in around 25 ms and seamlessly transition into flight. Biological observations showed that jumping grasshoppers use their front and middle legs to tilt up bodies first and then lift off by propelling the water toward the lower back with hind legs at angular speeds of up to 18°/ms, whereas the swimming grasshoppers swing their front and middle legs in nearly horizontal planes and move hind legs less violently (~8°/ms). Force measurement and model analysis indicated that the weight support could be achieved by hydrostatics which are proportionate to the mass of the grasshoppers, while the propulsions for motion are derived from the controlled limb-water interactions (i.e., the hydrodynamics). After learning the structural and behavioral strategies of the grasshoppers, a robot was created and was capable of swimming and jumping on the water surface like the insects, further demonstrating the effectiveness of decoupling the challenges of aquatic locomotion by the combined use of the static and dynamic hydro forces. This work not only uncovered the combined mechanisms responsible for facilitating aquatic acrobatics in this species but also laid a foundation for developing bioinspired robots that can locomote across multiple media.

Dual-channel hypergraph convolutional network for predicting herb-disease associations.

Herbs applicability in disease treatment has been verified through experiences over thousands of years. The understanding of herb-disease associations (HDAs) is yet far from complete due to the complicated mechanism inherent in multi-target and multi-component (MTMC) botanical therapeutics. Most of the existing prediction models fail to incorporate the MTMC mechanism. To overcome this problem, we propose a novel dual-channel hypergraph convolutional network, namely HGHDA, for HDA prediction. Technically, HGHDA first adopts an autoencoder to project components and target protein onto a low-dimensional latent space so as to obtain their embeddings by preserving similarity characteristics in their original feature spaces. To model the high-order relations between herbs and their components, we design a channel in HGHDA to encode a hypergraph that describes the high-order patterns of herb-component relations via hypergraph convolution. The other channel in HGHDA is also established in the same way to model the high-order relations between diseases and target proteins. The embeddings of drugs and diseases are then aggregated through our dual-channel network to obtain the prediction results with a scoring function. To evaluate the performance of HGHDA, a series of extensive experiments have been conducted on two benchmark datasets, and the results demonstrate the superiority of HGHDA over the state-of-the-art algorithms proposed for HDA prediction. Besides, our case study on Chuan Xiong and Astragalus membranaceus is a strong indicator to verify the effectiveness of HGHDA, as seven and eight out of the top 10 diseases predicted by HGHDA for Chuan-Xiong and Astragalus-membranaceus, respectively, have been reported in literature.

Machine-learning analysis of leadership formation in China to parse the roles of loyalty and institutional norms.

A thriving cottage industry has long tried to predict the selection outcomes of the Chinese leadership using qualitative judgments based on historical trends and elite interviews. This study contributes to the discourse by adopting machine-learning techniques to quantitatively and systematically evaluate the promotion prospects of Chinese high-ranking officials. By incorporating over 250 individual features of approximately 20,000 high-ranking positions from 1982 to 2020, this paper calculated predicted probabilities of promotion for the 19th Politburo members of the Communist Party of China. The rankings of the promotion probabilities can be used not only to identify candidates who would have traditionally advanced within the party's promotion norms but also to gauge Xi Jinping's personal favoritism toward specific individuals. Based on different specifications for positions and periods, we developed measurements to quantify candidates' levels of perceived loyalty and promotion eligibility. The empirical results demonstrated that the newly formed 20th Politburo Standing Committee was predominantly composed of loyalists who would not have risen to such positions under conventional promotion standards. We further found that, even within his circle of known allies, Xi Jinping did not opt for candidates with strong credentials. The findings of this study underscore the increasing emphasis on loyalty and the diminishing role of institutional norms in China's high-ranking selections.

CO2-forced Late Miocene cooling and ecosystem reorganizations in East Asia.

In parallel with pronounced cooling in the oceans, vast areas of the continents experienced enhanced aridification and restructuring of vegetation and animal communities during the Late Miocene. Debate continues over whether pCO2-induced global cooling was the primary driver of this climate and ecosystem upheaval on land. Here we present an 8 to 5 Ma land surface temperatures (LST) record from East Asia derived from paleosol carbonate clumped isotopes and integrated with climate model simulations. The LST cooled by ~7 °C between 7.5 and 5.7 Ma, followed by rapid warming across the Miocene-Pliocene transition (5.5 to 5 Ma). These changes occurred synchronously with variations in alkenone and Mg/Ca-based sea surface temperatures and with hydroclimate and ecosystem shifts in East Asia, highlighting a global climate forcing mechanism. Our modeling experiments additionally demonstrate that pCO2-forced cooling would have altered moisture transfer and pathways and driven extensive aridification in East Asia. We, thus, conclude that the East Asian hydroclimate and ecosystem shift was primarily controlled by pCO2-forced global cooling between 8 and 5 Ma.

Bioactive compounds from Huashi Baidu decoction possess both antiviral and anti-inflammatory effects against COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic is an ongoing global health concern, and effective antiviral reagents are urgently needed. Traditional Chinese medicine theory-driven natural drug research and development (TCMT-NDRD) is a feasible method to address this issue as the traditional Chinese medicine formulae have been shown effective in the treatment of COVID-19. Huashi Baidu decoction (Q-14) is a clinically approved formula for COVID-19 therapy with antiviral and anti-inflammatory effects. Here, an integrative pharmacological strategy was applied to identify the antiviral and anti-inflammatory bioactive compounds from Q-14. Overall, a total of 343 chemical compounds were initially characterized, and 60 prototype compounds in Q-14 were subsequently traced in plasma using ultrahigh-performance liquid chromatography with quadrupole time-of-flight mass spectrometry. Among the 60 compounds, six compounds (magnolol, glycyrrhisoflavone, licoisoflavone A, emodin, echinatin, and quercetin) were identified showing a dose-dependent inhibition effect on the SARS-CoV-2 infection, including two inhibitors (echinatin and quercetin) of the main protease (Mpro), as well as two inhibitors (glycyrrhisoflavone and licoisoflavone A) of the RNA-dependent RNA polymerase (RdRp). Meanwhile, three anti-inflammatory components, including licochalcone B, echinatin, and glycyrrhisoflavone, were identified in a SARS-CoV-2-infected inflammatory cell model. In addition, glycyrrhisoflavone and licoisoflavone A also displayed strong inhibitory activities against cAMP-specific 3',5'-cyclic phosphodiesterase 4 (PDE4). Crystal structures of PDE4 in complex with glycyrrhisoflavone or licoisoflavone A were determined at resolutions of 1.54 Å and 1.65 Å, respectively, and both compounds bind in the active site of PDE4 with similar interactions. These findings will greatly stimulate the study of TCMT-NDRD against COVID-19.

Eccentricity-paced geomagnetic field and monsoon rainfall variations over the last 870 kyr.

Whether there are links between geomagnetic field and Earth's orbital parameters remains unclear. Synchronous reconstructions of parallel long-term quantitative geomagnetic field and climate change records are rare. Here, we present 10Be-derived changes of both geomagnetic field and Asian monsoon (AM) rainfall over the last 870 kyr from the Xifeng loess-paleosol sequence on the central Chinese Loess Plateau. The 10BeGM flux (a proxy for geomagnetic field-induced 10Be production rate) reveals 13 consecutive geomagnetic excursions in the Brunhes chron, which are synchronized with the global records, providing key time markers for Chinese loess-paleosol sequences. The 10Be-derived rainfall exhibits distinct ~100 kyr glacial-interglacial cycles, and superimposed precessional (~23 kyr) cycles that match with those in Chinese speleothem δ18O record. We find that changes in the geomagnetic field and AM rainfall share a common ~100 kyr cyclicity, implying a likely eccentricity modulation of both the geomagnetic field and climate.

A chromosome-scale genome of Rhus chinensis Mill. provides new insights into plant-insect interaction and gallotannins biosynthesis.

Rhus chinensis Mill., an economically valuable Anacardiaceae species, is parasitized by the galling aphid Schlechtendalia chinensis, resulting in the formation of the Chinese gallnut (CG). Here, we report a chromosomal-level genome assembly of R. chinensis, with a total size of 389.40 Mb and scaffold N50 of 23.02 Mb. Comparative genomic and transcriptome analysis revealed that the enhanced structure of CG and nutritional metabolism contribute to improving the adaptability of R. chinensis to S. chinensis by supporting CG and galling aphid growth. CG was observed to be abundant in hydrolysable tannins (HT), particularly gallotannin and its isomers. Tandem repeat clusters of dehydroquinate dehydratase/shikimate dehydrogenase (DQD/SDH) and serine carboxypeptidase-like (SCPL) and their homologs involved in HT production were determined as specific to HT-rich species. The functional differentiation of DQD/SDH tandem duplicate genes and the significant contraction in the phenylalanine ammonia-lyase (PAL) gene family contributed to the accumulation of gallic acid and HT while minimizing the production of shikimic acid, flavonoids, and condensed tannins in CG. Furthermore, we identified one UDP glucosyltransferase (UGT84A), three carboxylesterase (CXE), and six SCPL genes from conserved tandem repeat clusters that are involved in gallotannin biosynthesis and hydrolysis in CG. We then constructed a regulatory network of these genes based on co-expression and transcription factor motif analysis. Our findings provide a genomic resource for the exploration of the underlying mechanisms of plant-galling insect interaction and highlight the importance of the functional divergence of tandem duplicate genes in the accumulation of secondary metabolites.

DKADE: a novel framework based on deep learning and knowledge graph for identifying adverse drug events and related medications.

Adverse drug events (ADEs) are common in clinical practice and can cause significant harm to patients and increase resource use. Natural language processing (NLP) has been applied to automate ADE detection, but NLP systems become less adaptable when drug entities are missing or multiple medications are specified in clinical narratives. Additionally, no Chinese-language NLP system has been developed for ADE detection due to the complexity of Chinese semantics, despite ˃10 million cases of drug-related adverse events occurring annually in China. To address these challenges, we propose DKADE, a deep learning and knowledge graph-based framework for identifying ADEs. DKADE infers missing drug entities and evaluates their correlations with ADEs by combining medication orders and existing drug knowledge. Moreover, DKADE can automatically screen for new adverse drug reactions. Experimental results show that DKADE achieves an overall F1-score value of 91.13%. Furthermore, the adaptability of DKADE is validated using real-world external clinical data. In summary, DKADE is a powerful tool for studying drug safety and automating adverse event monitoring.

TCMFP: a novel herbal formula prediction method based on network target's score integrated with semi-supervised learning genetic algorithms.

Traditional Chinese medicine (TCM) has accumulated thousands years of knowledge in herbal therapy, but the use of herbal formulas is still characterized by reliance on personal experience. Due to the complex mechanism of herbal actions, it is challenging to discover effective herbal formulas for diseases by integrating the traditional experiences and modern pharmacological mechanisms of multi-target interactions. In this study, we propose a herbal formula prediction approach (TCMFP) combined therapy experience of TCM, artificial intelligence and network science algorithms to screen optimal herbal formula for diseases efficiently, which integrates a herb score (Hscore) based on the importance of network targets, a pair score (Pscore) based on empirical learning and herbal formula predictive score (FmapScore) based on intelligent optimization and genetic algorithm. The validity of Hscore, Pscore and FmapScore was verified by functional similarity and network topological evaluation. Moreover, TCMFP was used successfully to generate herbal formulae for three diseases, i.e. the Alzheimer's disease, asthma and atherosclerosis. Functional enrichment and network analysis indicates the efficacy of targets for the predicted optimal herbal formula. The proposed TCMFP may provides a new strategy for the optimization of herbal formula, TCM herbs therapy and drug development.

Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM.

With the emergence of high-throughput technologies, computational screening based on gene expression profiles has become one of the most effective methods for drug discovery. More importantly, profile-based approaches remarkably enhance novel drug-disease pair discovery without relying on drug- or disease-specific prior knowledge, which has been widely used in modern medicine. However, profile-based systematic screening of active ingredients of traditional Chinese medicine (TCM) has been scarcely performed due to inadequate pharmacotranscriptomic data. Here, we develop the largest-to-date online TCM active ingredients-based pharmacotranscriptomic platform integrated traditional Chinese medicine (ITCM) for the effective screening of active ingredients. First, we performed unified high-throughput experiments and constructed the largest data repository of 496 representative active ingredients, which was five times larger than the previous one built by our team. The transcriptome-based multi-scale analysis was also performed to elucidate their mechanism. Then, we developed six state-of-art signature search methods to screen active ingredients and determine the optimal signature size for all methods. Moreover, we integrated them into a screening strategy, TCM-Query, to identify the potential active ingredients for the special disease. In addition, we also comprehensively collected the TCM-related resource by literature mining. Finally, we applied ITCM to an active ingredient bavachinin, and two diseases, including prostate cancer and COVID-19, to demonstrate the power of drug discovery. ITCM was aimed to comprehensively explore the active ingredients of TCM and boost studies of pharmacological action and drug discovery. ITCM is available at http://itcm.biotcm.net.

Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine.

Network pharmacology (NP) provides a new methodological perspective for understanding traditional medicine from a holistic perspective, giving rise to frontiers such as traditional Chinese medicine network pharmacology (TCM-NP). With the development of artificial intelligence (AI) technology, it is key for NP to develop network-based AI methods to reveal the treatment mechanism of complex diseases from massive omics data. In this review, focusing on the TCM-NP, we summarize involved AI methods into three categories: network relationship mining, network target positioning and network target navigating, and present the typical application of TCM-NP in uncovering biological basis and clinical value of Cold/Hot syndromes. Collectively, our review provides researchers with an innovative overview of the methodological progress of NP and its application in TCM from the AI perspective.

Predicting brain age using partition modeling strategy and atlas-based attentional enhancement in the Chinese population.

As a biomarker of human brain health during development, brain age is estimated based on subtle differences in brain structure from those under typical developmental. Magnetic resonance imaging (MRI) is a routine diagnostic method in neuroimaging. Brain age prediction based on MRI has been widely studied. However, few studies based on Chinese population have been reported. This study aimed to construct a brain age predictive model for the Chinese population across its lifespan. We developed a partition prediction method based on transfer learning and atlas attention enhancement. The participants were separated into four age groups, and a deep learning model was trained for each group to identify the brain regions most critical for brain age prediction. The Atlas attention-enhancement method was also used to help the models focus only on critical brain regions. The proposed method was validated using 354 participants from domestic datasets. For prediction performance in the testing sets, the mean absolute error was 2.218 ± 1.801 years, and the Pearson correlation coefficient (r) was 0.969, exceeding previous results for wide-range brain age prediction. In conclusion, the proposed method could provide brain age estimation to assist in assessing the status of brain health.

Phonological properties of logographic words modulate brain activation in bilinguals: a comparative study of Chinese characters and Japanese Kanji.

The brain networks for the first (L1) and second (L2) languages are dynamically formed in the bilingual brain. This study delves into the neural mechanisms associated with logographic-logographic bilingualism, where both languages employ visually complex and conceptually rich logographic scripts. Using functional Magnetic Resonance Imaging, we examined the brain activity of Chinese-Japanese bilinguals and Japanese-Chinese bilinguals as they engaged in rhyming tasks with Chinese characters and Japanese Kanji. Results showed that Japanese-Chinese bilinguals processed both languages using common brain areas, demonstrating an assimilation pattern, whereas Chinese-Japanese bilinguals recruited additional neural regions in the left lateral prefrontal cortex for processing Japanese Kanji, reflecting their accommodation to the higher phonological complexity of L2. In addition, Japanese speakers relied more on the phonological processing route, while Chinese speakers favored visual form analysis for both languages, indicating differing neural strategy preferences between the 2 bilingual groups. Moreover, multivariate pattern analysis demonstrated that, despite the considerable neural overlap, each bilingual group formed distinguishable neural representations for each language. These findings highlight the brain's capacity for neural adaptability and specificity when processing complex logographic languages, enriching our understanding of the neural underpinnings supporting bilingual language processing.

OCTOPUS regulates BIN2 to control leaf curvature in Chinese cabbage.

Heading is one of the most important agronomic traits for Chinese cabbage crops. During the heading stage, leaf axial growth is an essential process. In the past, most genes predicted to be involved in the heading process have been based on leaf development studies in Arabidopsis. No genes that control leaf axial growth have been mapped and cloned via forward genetics in Chinese cabbage. In this study, we characterize the inward curling mutant ic1 in Brassica rapa ssp. pekinensis and identify a mutation in the OCTOPUS (BrOPS) gene by map-based cloning. OPS is involved in phloem differentiation in Arabidopsis, a functionalization of regulating leaf curvature that is differentiated in Chinese cabbage. In the presence of brassinosteroid (BR) at the early heading stage in ic1, the mutation of BrOPS fails to sequester brassinosteroid insensitive 2 (BrBIN2) from the nucleus, allowing BrBIN2 to phosphorylate and inactivate BrBES1, which in turn relieves the repression of BrAS1 and results in leaf inward curving. Taken together, the results of our findings indicate that BrOPS positively regulates BR signaling by antagonizing BrBIN2 to promote leaf epinastic growth at the early heading stage in Chinese cabbage.

Differentiated adaptative genetic architecture and language-related demographical history in South China inferred from 619 genomes from 56 populations.

BACKGROUND: The underrepresentation of human genomic resources from Southern Chinese populations limited their health equality in the precision medicine era and complete understanding of their genetic formation, admixture, and adaptive features. Besides, linguistical and genetic evidence supported the controversial hypothesis of their origin processes. One hotspot case was from the Chinese Guangxi Pinghua Han people (GPH), whose language was significantly similar to Southern Chinese dialects but whose uniparental gene pool was phylogenetically associated with the indigenous Tai-Kadai (TK) people. Here, we analyzed genome-wide SNP data in 619 people from four language families and 56 geographically different populations, in which 261 people from 21 geographically distinct populations were first reported here. RESULTS: We identified significant population stratification among ethnolinguistically diverse Guangxi populations, suggesting their differentiated genetic origin and admixture processes. GPH shared more alleles related to Zhuang than Southern Han Chinese but received more northern ancestry relative to Zhuang. Admixture models and estimates of genetic distances showed that GPH had a close genetic relationship with geographically close TK compared to Northern Han Chinese, supporting their admixture origin hypothesis. Further admixture time and demographic history reconstruction supported GPH was formed via admixture between Northern Han Chinese and Southern TK people. We identified robust signatures associated with lipid metabolisms, such as fatty acid desaturases (FADS) and medically relevant loci associated with Mendelian disorder (GJB2) and complex diseases. We also explored the shared and unique selection signatures of ethnically different but linguistically related Guangxi lineages and found some shared signals related to immune and malaria resistance. CONCLUSIONS: Our genetic analysis illuminated the language-related fine-scale genetic structure and provided robust genetic evidence to support the admixture hypothesis that can explain the pattern of observed genetic diversity and formation of GPH. This work presented one comprehensive analysis focused on the population history and demographical adaptative process, which provided genetic evidence for personal health management and disease risk prediction models from Guangxi people. Further large-scale whole-genome sequencing projects would provide the entire landscape of southern Chinese genomic diversity and their contributions to human health and disease traits.

Assembly of novel sequences for Chinese domestic pigs reveals new genes and regulatory variants providing new insights into their diversity.

There is an increasing understanding that a reference genome representing an individual cannot capture all the gene repertoire of a species. Here, we conduct a population-scale missing sequences detection of Chinese domestic pigs using whole-genome sequencing data from 534 individuals. We identify 132.41 Mb of sequences absent in the reference assembly, including eight novel genes. In particular, the breeds spread in Chinese high-altitude regions perform significantly different frequencies of new sequences in promoters than other breeds. Furthermore, we dissect the role of non-coding variants and identify a novel sequence inserted in the 3'UTR of the FMO3 gene, which may be associated with the intramuscular fat phenotype. This novel sequence could be a candidate marker for meat quality. Our study provides a comprehensive overview of the missing sequences in Chinese domestic pigs and indicates that this dataset is a valuable resource for understanding the diversity and biology of pigs.