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.

Herb-CMap: a multimodal fusion framework for deciphering the mechanisms of action in traditional Chinese medicine using Suhuang antitussive capsule as a case study.

Herbal medicines, particularly traditional Chinese medicines (TCMs), are a rich source of natural products with significant therapeutic potential. However, understanding their mechanisms of action is challenging due to the complexity of their multi-ingredient compositions. We introduced Herb-CMap, a multimodal fusion framework leveraging protein-protein interactions and herb-perturbed gene expression signatures. Utilizing a network-based heat diffusion algorithm, Herb-CMap creates a connectivity map linking herb perturbations to their therapeutic targets, thereby facilitating the prioritization of active ingredients. As a case study, we applied Herb-CMap to Suhuang antitussive capsule (Suhuang), a TCM formula used for treating cough variant asthma (CVA). Using in vivo rat models, our analysis established the transcriptomic signatures of Suhuang and identified its key compounds, such as quercetin and luteolin, and their target genes, including IL17A, PIK3CB, PIK3CD, AKT1, and TNF. These drug-target interactions inhibit the IL-17 signaling pathway and deactivate PI3K, AKT, and NF-κB, effectively reducing lung inflammation and alleviating CVA. The study demonstrates the efficacy of Herb-CMap in elucidating the molecular mechanisms of herbal medicines, offering valuable insights for advancing drug discovery in TCM.

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.

Chinese Verbal Fluency Deficiency in Temporal Lobe Epilepsy with and without Hippocampal Sclerosis: A Multiscale Study.

To test a Chinese character version of the phonemic verbal fluency task in patients with temporal lobe epilepsy (TLE) and assess the verbal fluency deficiency pattern in TLE with and without hippocampal sclerosis, a cross-sectional study was conducted including 30 patients with TLE and hippocampal sclerosis (TLE-HS), 28 patients with TLE and without hippocampal sclerosis (TLE-NHS), and 29 demographically matched healthy controls (HC). Both sexes were enrolled. Participants finished a Chinese character verbal fluency (VFC) task during functional MRI. The activation/deactivation maps, functional connectivity, degree centrality, and community features of the left frontal and temporal regions were compared. A neural network classification model was applied to differentiate TLE-HS and TLE-NHS using functional statistics. The VFC scores were correlated with semantic fluency in HC while correlated with phonemic fluency in TLE-NHS. Activation and deactivation deficiency was observed in TLE-HS and TLE-NHS (p < 0.001, k ≥ 10). Functional connectivity, degree centrality, and community features of anterior inferior temporal gyri were impaired in TLE-HS and retained or even enhanced in TLE-NHS (p < 0.05, FDR-corrected). The functional connectivity was correlated with phonemic fluency (p < 0.05, FDR-corrected). The neural network classification reached an area under the curve of 0.90 in diagnosing hippocampal sclerosis. The VFC task is a Chinese phonemic verbal fluency task suitable for clinical application in TLE. During the VFC task, functional connectivity of phonemic circuits was impaired in TLE-HS and was enhanced in TLE-NHS, representing a compensative phonemic searching strategy applied by patients with TLE-NHS.

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.

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.

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.

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.

Public perceptions of international genetic information sharing for biomedical research in China: a case study of the social media debate on the article "A Pangenome Reference of 36 Chinese Populations" published in Nature.

BACKGROUND: The international disclosure of Chinese human genetic data continues to be a contentious issue in China, generating public debates in both traditional and social media channels. Concerns have intensified after Chinese scientists' research on pangenome data was published in the prestigious journal Nature. METHODS: This study scrutinized microblogs posted on Weibo, a popular Chinese social media site, in the two months immediately following the publication (June 14, 2023-August 21, 2023). Content analysis was conducted to assess the nature of public responses, justifications for positive or negative attitudes, and the users' overall knowledge of how Chinese human genetic information is regulated and managed in China. RESULTS: Weibo users displayed contrasting attitudes towards the article's public disclose of pangenome research data, with 18% positive, 64% negative, and 18% neutral. Positive attitudes came primarily from verified government and media accounts, which praised the publication. In contrast, negative attitudes originated from individual users who were concerned about national security and health risks and often believed that the researchers have betrayed China. The benefits of data sharing highlighted in the commentaries included advancements in disease research and scientific progress. Approximately 16% of the microblogs indicated that Weibo users had misunderstood existing regulations and laws governing data sharing and stewardship. CONCLUSIONS: Based on the predominantly negative public attitudes toward scientific data sharing established by our study, we recommend enhanced outreach by scientists and scientific institutions to increase the public understanding of developments in genetic research, international data sharing, and associated regulations. Additionally, governmental agencies can alleviate public fears and concerns by being more transparent about their security reviews of international collaborative research involving Chinese human genetic data and its cross-border transfer.

The representation of noun-verb distinction in left posterior middle temporal gyrus: evidence from representation similarity analyses.

Nouns and verbs are fundamental grammatical building blocks of languages. A key question is whether and where the noun-verb division was represented in the brain. Previous studies mainly used univariate analyses to examine this issue. However, the interpretation of activated brain regions in univariate analyses may be confounded with general cognitive processing and/or confounding variables. We addressed these limitations by using partial representation similarity analysis (RSA) of Chinese nouns and verbs with different levels of imageability. Participants were asked to complete the 1-back grammatical class probe (GCP; an explicit measure) and the 1-back word probe (WP; an implicit measure) tasks while undergoing functional magnetic resonance imaging. RSA results showed that the activation pattern in the left posterior middle temporal gyrus (LpMTG) was significantly correlated with the grammatical class representational dissimilarity matrix in the GCP task after eliminating the potential confounding variables. Moreover, the LpMTG did not overlap with the frontal-parietal regions that were activated by verbs vs. nouns or the task effect (CRP vs. WP) in univariate analyses. These results highlight the role of LpMTG in distinguishing nouns from verbs rather than general cognitive processing.

Functional interactions underlying visuospatial orthographic processes in Chinese reading.

As a logographic writing system, Chinese reading involves the processing of visuospatial orthographic (ORT) properties. However, this aspect has received relatively less attention in neuroimaging research, which has tended to emphasize phonological (PHO) and semantic (SEM) aspects in processing Chinese characters. Here, we compared the functional correlates supporting all these three processes in a functional MRI single-character reading study, in which 35 native Chinese adults were asked to make ORT, PHO, and SEM judgments in separate task-specific activation blocks. Our findings revealed increased involvement of the right hemisphere in processing Chinese visuospatial orthography, particularly evident in the right ventral occipito-temporal cortex (vOTC). Additionally, time course analysis revealed that the left superior parietal gyrus (SPG) was initially involved in SEM processing but contributed to the visuospatial processing of words in a later time window. Finally, ORT processing demonstrated stronger recruitment of left vOTC-SPG-middle frontal gyrus (MFG) functional connectivity compared to SEM processing. This functional coupling correlated with reduced regional engagement of the left vOTC and MFG, highlighting that visuospatial ORT processes in reading Chinese rely on functional interactions among key regions rather than local regional processes. In conclusion, these findings underscore visuospatial ORT processes as a distinctive feature of reading logographic characters.