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The Khitans lived on the steppe of Mongolia during the tenth through twelfth centuries. According to Chinese historical records, many Khitan people had names ending in the Chinese word nu 奴 ‘slave’. This study explores why this naming pattern occurred and identifies the Khitan naming systems reflected by this phenomenon. Based on data collected from materials and steles engraved in Chinese and Khitan scripts, the study shows that Khitan names are not simply dichotomized into either inherited Chinese conventions or Khitan customs. Rather, the dataset demonstrates that in their bilingual and bicultural context, the Khitan people used two linguistic approaches to personal naming. They not only continued their own kinship-based naming system by applying three strategic patterns of father–son, brother–brother, and husband–wife linkages. They also integrated elements from the Chinese lexicon and imitated the Chinese onomastic pattern of assigning nicknames containing nu. Nu thus became a Khitan naming component. This investigation draws on pragmatic and semantic theoretical frameworks in multilingual contexts, and utilizes the sociolinguistic concepts of borrowing and code–mixing. Using this multidimensional approach, this study systematically describes the Khitan naming practice and provides evidence for historical contact onomastics, thereby shedding new insights into the relationships between names, languages, and cultures.

The genetic history of Southern East Asians is not well-known, especially prior to the Neolithic period. To address this, we successfully sequenced two complete mitochondrial genomes of 11,000-year-old human individuals from Southern China, thus generating the oldest ancient DNA sequences from this area. Integrating published mitochondrial genomes, we characterized M71d, a new subhaplogroup of haplogroup M71. Our results suggest a possible early migration between Southern China and mainland Southeast Asia by at least 22,000 BP.

Human genetic history in East Asia is poorly understood. To clarify population relationships, we obtained genome-wide data from 26 ancient individuals from northern and southern East Asia spanning 9500 to 300 years ago. Genetic differentiation in this region was higher in the past than the present, which reflects a major episode of admixture involving northern East Asian ancestry spreading across southern East Asia after the Neolithic, thereby transforming the genetic ancestry of southern China. Mainland southern East Asian and Taiwan Strait island samples from the Neolithic show clear connections with modern and ancient individuals with Austronesian-related ancestry, which supports an origin in southern China for proto-Austronesians. Connections among Neolithic coastal groups from Siberia and Japan to Vietnam indicate that migration and gene flow played an important role in the prehistory of coastal Asia.

Coastal areas of northern East Asia in the ShanDong region, which show complex cultural transitions in the last 10,000 years, have helped to facilitate population interactions between more inland regions of mainland East Asia and islands such as those in the Japanese archipelago. To examine how ShanDong populations changed over time and interacted with island and inland East Asian populations, we sequenced 85 individuals from 11 ancient sites in the ShanDong region dating to ~6000-1500 BP. We found that ancestry related to ShanDong populations likely explains the mainland East Asian ancestry observed in post-Yayoi populations from the Japanese archipelago, particularly recent populations who lived in the Ryukyu Islands after ~2800 BP. In the ShanDong region, we observed gene flow from populations to the north and south of this region by at least ~7700 BP, and two waves of gene flow associated with the inland Yellow River populations into the ShanDong region during the DaWenKou cultural period (6000-4600 BP) and in the early dynastic period (3500-1500 BP). Reconstructing the genetic history of the Neolithic, Bronze, and Iron Age populations of coastal northern East Asia shows gene flow on both a north-south and an east-west (inland-coastal-island) scale. Ancient DNA can reveal evolutionary and demographic history of a region. Here, the authors analyze ancient human DNA from ShanDong, China to reveal two waves of gene flow from Yellow River populations in the DaWenKou (6-4 kBP) and early dynastic (3.5-1.5 kBP) periods.

Background Uterine corpus endometrial carcinoma (UCEC) is a prevalent gynecologic malignancy with a favorable prognosis if detected early. However, there is a lack of accurate and reliable early detection tests for UCEC. This study aims to develop a precise and non-invasive diagnostic method for UCEC using circulating cell-free DNA (cfDNA) fragmentomics. Methods Peripheral blood samples were collected from all participants, and cfDNA was extracted for analysis. Low-coverage whole-genome sequencing was performed to obtain cfDNA fragmentomics data. A robust machine learning model was developed using these features to differentiate between UCEC and healthy conditions. Results The cfDNA fragmentomics-based model showed high predictive power for UCEC detection in training ( n  = 133; AUC 0.991) and validation cohorts ( n  = 89; AUC 0.994). The model manifested a specificity of 95.5% and a sensitivity of 98.5% in the training cohort, and a specificity of 95.5% and a sensitivity of 97.8% in the validation cohort. Physiological variables and preanalytical procedures had no significant impact on the classifier’s outcomes. In terms of clinical benefit, our model would identify 99% of Chinese UCEC patients at stage I, compared to 21% under standard care, potentially raising the 5-year survival rate from 84 to 95%. Conclusion This study presents a novel approach for the early detection of UCEC using cfDNA fragmentomics and machine learning showing promising sensitivity and specificity. Using this model in clinical practice could significantly improve UCEC management and control, enabling early intervention and better patient outcomes. Further optimization and validation of this approach are warranted to establish its clinical utility.

Background: To estimate a pooled association between hearing impairment and risk of mild cognitive impairment and dementia. Methods: PubMed, Embase, and Web of Science were searched for prospective cohort studies that examined the association between hearing impairment and risk of mild cognitive impairment and/or dementia. Random-effects models were fitted to estimate the summary risk ratios (RRs) and 95% confidence interval (CIs), which represents the pooled association between hearing impairment with risk of mild cognitive impairment and dementia, compared to subjects free of hearing impairment. Results: Four studies on hearing impairment with mild cognitive impairment and 7 studies on hearing impairment with dementia were included in the meta-analysis. A total of 15,521 subjects were studied with follow-up periods between 2 and 16.8 years. Hearing impairment was associated with a greater risk of mild cognitive impairment (RR = 1.30, 95% CI: 1.12, 1.51) and dementia (RR = 2.39, 95% CI: 1.58, 3.61). Conclusions: The meta-analysis showed that hearing impairment is associated with a higher risk of mild cognitive impairment and dementia among older adults.

The soybean cyst nematode (SCN), Heterodera glycines Ichinohe, poses a serious threat to soybean production worldwide. Biological control agents have become eco-friendly candidates to control pathogens. Our previous study indicated that the biocontrol agent, Sinorhizobium fredii strain Sneb183, may induce soybean resistance to SCN. To study the mechanisms underlying induced disease resistance in the plant by Sneb183, an iTRAQ (isobaric tag for relative and absolute quantitation)-based proteomics approach was used to identify proteomic changes in SCN-infected soybean roots derived from seeds coated with the Sneb183 fermentation broth or water. Among a total of 456 identified differentially expressed proteins, 212 and 244 proteins were upregulated and downregulated, respectively, in Sneb183 treated samples in comparison to control samples. Some identified differentially expressed proteins are likely to be involved in the biosynthesis of phenylpropanoid, flavone, flavanol, and isoflavonoid and have a role in disease resistance and adaptation to environmental stresses. We used quantitative real-time PCR (qRT-PCR) to analyze key genes, including GmPAL (phenylalanine ammonia-lyase), GmCHR (chalcone reductase), GmCHS (chalcone synthase), and GmIFS (isoflavone synthase), that are involved in isoflavonoid biosynthesis in Sneb183-treated and control samples. The results showed that these targeted genes have higher expression levels in Sneb183-treated than in control samples. High performance liquid chromatography (HPLC) analysis further showed that the contents of daidzein in Sneb183-treated samples were 7.24 times higher than those in control samples. These results suggested that the Sinorhizobium fredii strain Sneb183 may have a role in inducing isoflavonoid biosynthesis, thereby resulting in enhanced resistance to SCN infection in soybean.

Background Early detection of colorectal cancer (CRC) is crucial for improving patient survival. This innovative multi-center study aims to develop a non-invasive blood-based assay using cell-free DNA (cfDNA) fragmentomics to differentiate CRC from advanced colorectal adenomas and non-cancerous colorectal and other digestive diseases. Methods A total of 167 CRC patients and 227 with benign colorectal conditions were divided into training and validation cohorts (1:1 ratio). Plasma cfDNA underwent Low-depth whole-genome sequencing to profile three fragmentomics features, which were integrated into a stacked ensemble model. The model was validated on 69 CRC patients and 96 benign controls, with an additional cohort of 31 advanced adenoma patients included to assess its performance in differentiating advanced adenomas from benign cases. Results The model achieved an AUC of 0.926, with sensitivity of 91.3% and specificity of 82.3% in validation. Sensitivities were consistently high across CRC stages (I: 94.4%, II: 86.4%, III: 91.3%, IV: 100%). Notably, the model demonstrated exceptional accuracy in distinguishing advanced adenomas from benign cases, achieving an AUC of 0.846 and sensitivity of 67.7%, outperforming traditional blood tests. Conclusions This multi-center study underscores a significant advancement in liquid biopsy technology, offering a highly accurate and non-invasive approach for early CRC detection and differentiation of advanced colorectal adenomas.

Background: The yield of soybean is limited by the soybean cyst nematode (SCN, Heterodera glycines). Soybean transformation plays a key role in gene function research but the stable genetic transformation of soybean usually takes half a year. Methods: Here, we constructed a vector, pNI-GmUbi, in an Agrobacterium rhizogenes-mediated soybean hypocotyl transformation to induce fluorescent hairy roots (FHRs). Results: We describe the operation of FHR-SCN, a fast, efficient and visual operation pathosystem to study the gene functions in the soybean-SCN interaction. With this method, FHRs were detected after 25 days in 4 cultivars (Williams 82, Zhonghuang 13, Huipizhiheidou and Peking) and at least 66.67% of the composite plants could be used to inoculate SCNs. The demographics of the SCN could be started 12 days post-SCN inoculation. Further, GmHS1pro-1 was overexpressed in the FHRs and GmHS1pro-1 provided an additional resistance in Williams 82. In addition, we found that jasmonic acid and JA-Ile increased in the transgenic soybean, implying that the resistance was mainly caused by affecting the content of JA and JA-Ile. Conclusions: In this study, we established a pathosystem, FHR-SCN, to verify the functional genes in soybeans and the SCN interaction. We also verified that GmHS1pro-1 provides additional resistance in both FHRs and transgenic soybeans, and the resistance may be caused by an increase in JA and JA-Ile contents.

Biliary tract cancer (BTC) is typically diagnosed at an advanced stage due to the lack of effective screening tools, resulting in limited therapeutic options and poor survival outcomes. Therefore, there is a critical need for non-invasive strategies that enable early detection and risk stratification. Fragmentomic profiling of cell-free DNA (cfDNA) captures genome-wide fragmentation patterns reflecting tumor-associated chromatin structure and genomic instability, providing a promising approach for non-invasive cancer detection. In this study, we developed a low-pass whole-genome sequencing (WGS)-based framework for BTC detection and postoperative risk assessment. Plasma samples were analyzed to derive three fragmentomic features, including copy number variation, fragment size distribution, and promoter fragmentation entropy, which were integrated into a machine-learning model trained using five-fold cross-validation. The model demonstrated robust performance across independent validation cohorts, outperforming individual fragmentomic features and conventional serum biomarkers, and accurately distinguished BTC from benign biliary diseases. Longitudinal analyses revealed that cfDNA fragmentomic risk scores dynamically tracked disease burden and treatment response. Importantly, postoperative risk scores were independently associated with disease-free survival, highlighting their prognostic value. Collectively, these findings establish a scalable and cost-effective framework for cfDNA-based BTC detection and monitoring using low-pass WGS data. This approach shows strong potential for targeted screening in high-risk populations and for guiding personalized postoperative surveillance and clinical management for BTC patients.