Inter-relationships among individual views of COVID-19 control measures across multi-cultural contexts.

Individual-level georeferenced data have been widely used in COVID-19 control measures around the world. Recent research observed that there is a trade-off relationship between people's privacy concerns and their acceptance of these control measures. However, whether this trade-off relationship exists across different cultural contexts is still unaddressed. Using data we collected via an international survey (n = 4260) and network analysis, our study found a substantial trade-off inter-relationship among people's privacy concerns, perceived social benefits, and acceptance across different control measures and study areas. People's privacy concerns in culturally tight societies (e.g., Japan) have the smallest negative impacts on their acceptance of pandemic control measures. The results also identify people's key views of specific control measures that can influence their views of other control measures. The impacts of these key views are heightened among participants with a conservative political view, high levels of perceived social tightness, and vertical individualism. Our results indicate that cultural factors are a key mechanism that mediate people's privacy concerns and their acceptance of pandemic control measures. These close inter-relationships lead to a double-edged sword effect: the increased positive impacts of people's acceptance and perceived social benefits also lead to increased negative impacts of privacy concerns in different combinations of control strategies. The findings highlight the importance of cultural factors as key determinants that affect people's acceptance or rejection of specific pandemic control measures.

The mechanism of cancer-depression comorbidity.

Cancer and depression are closely interrelated, particularly in patients with advanced cancer, who often present with comorbid anxiety and depression for various reasons. Recently, there has been a growing interest in the study of depression in cancer patients, with the aim of assessing the possible triggers, predictors, adverse events, and possible treatment options for depression in several common cancers. The objective of this narrative review is to synthesize the extant literature on the relationship between the occurrence and progression of depression in several common patient categories. The authors conducted a comprehensive review of 75 articles published in PubMed over the past five years. This review was further evaluated in the present paper. Ultimately, it was determined that depression is a prevalent and detrimental phenomenon among cancer patients, particularly those with advanced disease. Consequently, there is a pressing need to prioritize research and interventions aimed at improving the quality of life and psychosocial well-being of cancer patients, including those with advanced disease. The relationship between cancer and depression has been evolving dynamically in recent times. The current research findings indicate a strong association between cancer and depression. However, the direction of causality remains unclear. Focusing on depression in cancer patients may, therefore, be beneficial for these patients.

How mobility pattern shapes the association between static green space and dynamic green space exposure.

Greenspaces are crucial for enhancing mental and physical health. Recent research has shifted from static methods of assessing exposure to greenspaces, based on fixed locations, to dynamic approaches that account for individual mobility. These dynamic evaluations utilize advanced technologies like GPS tracking and remote sensing to provide more precise exposure estimates. However, little work has been conducted to compare dynamic and static exposure assessments and the effect of individual mobility on these evaluations. This study delves into how greenspaces around homes and workplaces, along with mobility patterns, affect dynamic greenspace exposure in Hong Kong. Data was collected from 787 participants in four communities in Hong Kong using GPS, portable sensors, and surveys. Using multiple statistical tests, our study revealed significant variations in participants' daily mobility patterns across socio-demographic and temporal factors. Further, using linear mixed-effects models, we identified complex and statistically significant interactions between participants' static greenspace exposure and their mobility patterns. Our findings suggest that individual mobility patterns significantly modify the relationship between static and dynamic greenspace exposure and play a critical role in explaining socio-demographic and temporal context differences in the relationship between static and dynamic greenspace exposure.

Genome assembly of Melilotus officinalis provides a new reference genome for functional genomics.

BACKGROUND: Sweet yellow clover (Melilotus officinalis) is a diploid plant (2n = 16) that is native to Europe. It is an excellent legume forage. It can both fix nitrogen and serve as a medicine. A genome assembly of Melilotus officinalis that was collected from Best corporation in Beijing is available based on Nanopore sequencing. The genome of Melilotus officinalis was sequenced, assembled, and annotated. RESULTS: The latest PacBio third generation HiFi assembly and sequencing strategies were used to produce a Melilotus officinalis genome assembly size of 1,066 Mbp, contig N50 = 5 Mbp, scaffold N50 = 130 Mbp, and complete benchmarking universal single-copy orthologs (BUSCOs) = 96.4%. This annotation produced 47,873 high-confidence gene models, which will substantially aid in our research on molecular breeding. A collinear analysis showed that Melilotus officinalis and Medicago truncatula shared conserved synteny. The expansion and contraction of gene families showed that Melilotus officinalis expanded by 565 gene families and shrank by 56 gene families. The contacted gene families were associated with response to stimulus, nucleotide binding, and small molecule binding. Thus, it is related to a family of genes associated with peptidase activity, which could lead to better stress tolerance in plants. CONCLUSIONS: In this study, the latest PacBio technology was used to assemble and sequence the genome of the Melilotus officinalis and annotate its protein-coding genes. These results will expand the genomic resources available for Melilotus officinalis and should assist in subsequent research on sweet yellow clover plants.

Comparison of laparoscopic common bile duct exploration plus cholecystectomy and endoscopic retrograde cholangiopancreatography followed by laparoscopic cholecystectomy for elderly patients with common bile duct stones and gallbladder stones.

BACKGROUND: Common bile duct (CBD) stones commonly occur in cholecystectomy cases. The management options include laparoscopic CBD exploration (LCBDE) or endoscopic retrograde cholangiopancreatography (ERCP) followed by laparoscopic cholecystectomy (LC). Although ERCP is fully developed, it has complications, and LCBDE is a proven alternative. This study aimed to evaluate the safety and efficacy of these treatments in elderly individuals aged ≥70 years. METHODS: A retrospective study between January 2015 and July 2022 included 160 elderly patients (aged ≥70 years) diagnosed with cholelithiasis and choledocholithiasis. The patients were divided into 1-stage (LCBDE [n = 80]) or 2-stage (ERCP followed by LC [n = 80]) treatment groups. Data collected encompassed comorbidities, symptoms, bile duct clearance, postoperative complications, and long-term outcomes for systematic analysis. RESULTS: This study analyzed 160 patients treated for CBD stones, comparing 1-stage and 2-stage groups. The 1-stage group had more female patients than the 2-stage group (57.5% vs 37.5%, respectively). The 1-stage group had a mean age of 80.55 ± 7.00 years, which was higher than the mean age in the 2-stage group. American Society of Anesthesiologists classification, Charlson Comorbidity Index, and laboratory findings were similar. Pancreatitis and cholangitis occurred after ERCP in the 2-stage group. Stone clearance rates (92.35% [1-stage group] vs 95.00% [2-stage group]) and biliary leakage incidence (7.5% [1-stage group] vs 3.0% [2-stage group]) were similar, as were postoperative complications and long-term recurrence rates (13.0% [1-stage group] vs 12.5% [2-stage group]). CONCLUSION: Our research indicates that both the combination of LCBDE and LC and the sequence of ERCP followed by LC are equally efficient and secure when treating CBD stones in elderly patients. Consequently, the 1-stage procedure may be considered the preferred treatment approach for this demographic.

Region fine-grained attention network for accurate bone age assessment.

Bone age assessment plays a vital role in monitoring the growth and development of adolescents. However, it is still challenging to obtain precise bone age from hand radiography due to these problems: 1) Hand bone varies greatly and is always masked by the background; 2) the hand bone radiographs with successive ages offer high similarity. To solve such issues, a region fine-grained attention network (RFGA-Net) was proposed for bone age assessment, where the region aware attention (RAA) module was developed to distinguish the skeletal regions from the background by modeling global spatial dependency; then the fine-grained feature attention (FFA) module was devised to identify similar bone radiographs by recognizing critical fine-grained feature regions. The experimental results demonstrate that the proposed RFGA-Net shows the best performance on the Radiological Society of North America (RSNA) pediatric bone dataset, achieving the mean absolute error (MAE) of 3.34 and the root mean square error (RMSE) of 4.02, respectively.

Reversible non-volatile electronic switching in a near-room-temperature van der Waals ferromagnet.

Non-volatile phase-change memory devices utilize local heating to toggle between crystalline and amorphous states with distinct electrical properties. Expanding on this kind of switching to two topologically distinct phases requires controlled non-volatile switching between two crystalline phases with distinct symmetries. Here, we report the observation of reversible and non-volatile switching between two stable and closely related crystal structures, with remarkably distinct electronic structures, in the near-room-temperature van der Waals ferromagnet Fe5-δGeTe2. We show that the switching is enabled by the ordering and disordering of Fe site vacancies that results in distinct crystalline symmetries of the two phases, which can be controlled by a thermal annealing and quenching method. The two phases are distinguished by the presence of topological nodal lines due to the preserved global inversion symmetry in the site-disordered phase, flat bands resulting from quantum destructive interference on a bipartite lattice, and broken inversion symmetry in the site-ordered phase.

Assessment of air pollution and air quality perception mismatch using mobility-based real-time exposure.

Air pollution poses a threat to human health. Public perceptions of air pollution are important for individual self-protection and policy-making. Given the uncertainty faced by residence-based exposure (RB) measurements, this study measures individuals' real-time mobility-based (MB) exposures and perceptions of air pollution by considering people's daily movement. It explores how contextual uncertainties may influence the disparities in perceived air quality by taking into account RB and MB environmental factors. In addition, we explore factors that are related to the mismatch between people's perceived air quality and actual air pollution exposure. Using K-means clustering to divide the PM2.5 values into two groups, a mismatch happens when the perceived air quality is poor but the air pollution level is lower than 15.536µg/m3 and when the perceived air quality is good but the air pollution level is higher than 15.608µg/m3. The results show that there is a mismatch between air pollution exposure and perception of air pollution. People with low income are exposed to higher air pollution. Unemployed people and people with more serious mental health symptoms (e.g., depression) have a higher chance of accurately assessing air pollution (e.g., perceiving air quality as poor when air pollution levels are high). Older people and those with a higher MB open space density tend to underestimate air pollution. Students tend to perceive air quality as good. People who are surrounded by higher MB transportation land-use density and green space density tend to perceive air quality as poor. The results can help policymakers to increase public awareness of high air pollution areas, and consider the health effects of landscapes during planning.

The interplay among individuals' distress, daily activities, and perceptions of COVID-19 and neighborhood cohesion: A study using network analysis.

The reduction of social interactions through non-pharmaceutical interventions (NPIs) has been shown to effectively curb COVID-19 transmission. However, these control measures were often accompanied by changes in people's daily routines and constraints on their activity space, which could lead to mental distress (i.e., anxiety and depression). This study examined the interplay among individuals' anxiety, depression, daily activities, and perceptions of COVID-19 and neighborhood cohesion. Taking Hong Kong as an example, an online survey (N = 376) was conducted to collect data from participants between March 14 to May 11, 2022. The data include respondents' self-reported anxiety and depressive symptoms, daily activities (e.g., smartphone use), perceptions of COVID-19 (e.g., the possibility of infecting COVID-19), and perceptions of neighborhood cohesion. Using network analysis, we found that excessive smartphone use, life disturbance by COVID-19, and a community with people getting along well with each other were significant factors associated with participants' anxiety and depression. Using critical path analysis, we observed that NPIs reduced human mobility, led to delayed bedtime, and increased smartphone use, which were associated with participants' mental distress. We also found that NPIs and COVID-19 were associated with people's perceptions of infection and the severity of COVID-19 and human mobility flexibility, which may further lead to mental distress. Our results also demonstrated that people with high education levels were vulnerable. These results provided important insights for designing appropriate interventions without generating deleterious impacts on people's mental health in the future.

Prenatal Diagnosis of Placenta Accreta Spectrum Disorders: Deep Learning Radiomics of Pelvic MRI.

BACKGROUND: Diagnostic performance of placenta accreta spectrum (PAS) by prenatal MRI is unsatisfactory. Deep learning radiomics (DLR) has the potential to quantify the MRI features of PAS. PURPOSE: To explore whether DLR from MRI can be used to identify pregnancies with PAS. STUDY TYPE: Retrospective. POPULATION: 324 pregnant women (mean age, 33.3 years) suspected PAS (170 training and 72 validation from institution 1, 82 external validation from institution 2) with clinicopathologically proved PAS (206 PAS, 118 non-PAS). FIELD STRENGTH/SEQUENCE: 3-T, turbo spin-echo T2-weighted images. ASSESSMENT: The DLR features were extracted using the MedicalNet. An MRI-based DLR model incorporating DLR signature, clinical model (different clinical characteristics between PAS and non-PAS groups), and MRI morphologic model (radiologists' binary assessment for the PAS diagnosis) was developed. These models were constructed in the training dataset and then validated in the validation datasets. STATISTICAL TESTS: The Student t-test or Mann-Whitney U, χ2 or Fisher exact test, Kappa, dice similarity coefficient, intraclass correlation coefficients, least absolute shrinkage and selection operator logistic regression, multivariate logistic regression, receiver operating characteristic (ROC) curve, DeLong test, net reclassification improvement (NRI) and integrated discrimination improvement (IDI), calibration curve with Hosmer-Lemeshow test, decision curve analysis (DCA). P < 0.05 indicated a significant difference. RESULTS: The MRI-based DLR model had a higher area under the curve than the clinical model in three datasets (0.880 vs. 0.741, 0.861 vs. 0.772, 0.852 vs. 0.675, respectively) or MRI morphologic model in training and independent validation datasets (0.880 vs. 0.760, 0.861, vs. 0.781, respectively). The NRI and IDI were 0.123 and 0.104, respectively. The Hosmer-Lemeshow test had nonsignificant statistics (P = 0.296 to 0.590). The DCA offered a net benefit at any threshold probability. DATA CONCLUSION: An MRI-based DLR model may show better performance in diagnosing PAS than a clinical or MRI morphologic model. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

People's political views, perceived social norms, and individualism shape their privacy concerns for and acceptance of pandemic control measures that use individual-level georeferenced data.

BACKGROUND: As the COVID-19 pandemic became a major global health crisis, many COVID-19 control measures that use individual-level georeferenced data (e.g., the locations of people's residences and activities) have been used in different countries around the world. Because these measures involve some disclosure risk and have the potential for privacy violations, people's concerns for geoprivacy (locational privacy) have recently heightened as a result, leading to an urgent need to understand and address the geoprivacy issues associated with COVID-19 control measures that use data on people's private locations. METHODS: We conducted an international cross-sectional survey in six study areas (n = 4260) to examine how people's political views, perceived social norms, and individualism shape their privacy concerns, perceived social benefits, and acceptance of ten COVID-19 control measures that use individual-level georeferenced data. Multilevel linear regression models were used to examine these effects. We also applied multilevel structure equation models (SEMs) to explore the direct, indirect, and mediating effects among the variables. RESULTS: We observed a tradeoff relationship between people's privacy concerns and the acceptance (and perceived social benefits) of the control measures. People's perceived social tightness and vertical individualism are positively associated with their acceptance and perceived social benefits of the control measures, while horizontal individualism has a negative association. Further, people with conservative political views and high levels of individualism (both vertical and horizontal) have high levels of privacy concerns. CONCLUSIONS: Our results first suggest that people's privacy concerns significantly affect their perceived social benefits and acceptance of the COVID-19 control measures. Besides, our results also imply that strengthening social norms may increase people's acceptance and perceived social benefits of the control measures but may not reduce people's privacy concerns, which could be an obstacle to the implementation of similar control measures during future pandemics. Lastly, people's privacy concerns tend to increase with their conservatism and individualism.

Self-Attention Mechanism-Based Head Pose Estimation Network with Fusion of Point Cloud and Image Features.

Head pose estimation serves various applications, such as gaze estimation, fatigue-driven detection, and virtual reality. Nonetheless, achieving precise and efficient predictions remains challenging owing to the reliance on singular data sources. Therefore, this study introduces a technique involving multimodal feature fusion to elevate head pose estimation accuracy. The proposed method amalgamates data derived from diverse sources, including RGB and depth images, to construct a comprehensive three-dimensional representation of the head, commonly referred to as a point cloud. The noteworthy innovations of this method encompass a residual multilayer perceptron structure within PointNet, designed to tackle gradient-related challenges, along with spatial self-attention mechanisms aimed at noise reduction. The enhanced PointNet and ResNet networks are utilized to extract features from both point clouds and images. These extracted features undergo fusion. Furthermore, the incorporation of a scoring module strengthens robustness, particularly in scenarios involving facial occlusion. This is achieved by preserving features from the highest-scoring point cloud. Additionally, a prediction module is employed, combining classification and regression methodologies to accurately estimate head poses. The proposed method improves the accuracy and robustness of head pose estimation, especially in cases involving facial obstructions. These advancements are substantiated by experiments conducted using the BIWI dataset, demonstrating the superiority of this method over existing techniques.

Evaluating the Effect of Artificial Liver Support on Acute-on-Chronic Liver Failure Using the Quantitative Difference Algorithm: Retrospective Study.

BACKGROUND: Liver failure, including acute-on-chronic liver failure (ACLF), occurs mainly in young adults and is associated with high mortality and resource costs. The prognosis evaluation is a crucial part of the ACLF treatment process and should run through the entire diagnosis process. As a recently proposed novel algorithm, the quantitative difference (QD) algorithm holds promise for enhancing the prognosis evaluation of ACLF. OBJECTIVE: This study aims to examine whether the QD algorithm exhibits comparable or superior performance compared to the Model for End-Stage Liver Disease (MELD) in the context of prognosis evaluation. METHODS: A total of 27 patients with ACLF were categorized into 2 groups based on their treatment preferences: the conventional treatment (n=12) and the double plasma molecular absorption system (DPMAS) with conventional treatment (n=15) groups. The prognosis evaluation was performed by the MELD and QD scoring systems. RESULTS: A significant reduction was observed in alanine aminotransferase (P=.02), aspartate aminotransferase (P<.001), and conjugated bilirubin (P=.002), both in P values and QD value (Lτ>1.69). A significant decrease in hemoglobin (P=.01), red blood cell count (P=.01), and total bilirubin (P=.02) was observed in the DPMAS group, but this decrease was not observed in QD (Lτ≤1.69). Furthermore, there was a significant association between MELD and QD values (P<.001). Significant differences were observed between groups based on patients' treatment outcomes. Additionally, the QD algorithm can also demonstrate improvements in patient fatigue. DPMAS can reduce alanine aminotransferase, aspartate aminotransferase, and unconjugated bilirubin. CONCLUSIONS: As a dynamic algorithm, the QD scoring system can evaluate the therapeutic effects in patients with ACLF, similar to MELD. Nevertheless, the QD scoring system surpasses the MELD by incorporating a broader range of indicators and considering patient variability.

Weyl nodal ring states and Landau quantization with very large magnetoresistance in square-net magnet EuGa4.

Magnetic topological semimetals allow for an effective control of the topological electronic states by tuning the spin configuration. Among them, Weyl nodal line semimetals are thought to have the greatest tunability, yet they are the least studied experimentally due to the scarcity of material candidates. Here, using a combination of angle-resolved photoemission spectroscopy and quantum oscillation measurements, together with density functional theory calculations, we identify the square-net compound EuGa4 as a magnetic Weyl nodal ring semimetal, in which the line nodes form closed rings near the Fermi level. The Weyl nodal ring states show distinct Landau quantization with clear spin splitting upon application of a magnetic field. At 2 K in a field of 14 T, the transverse magnetoresistance of EuGa4 exceeds 200,000%, which is more than two orders of magnitude larger than that of other known magnetic topological semimetals. Our theoretical model suggests that the non-saturating magnetoresistance up to 40 T arises as a consequence of the nodal ring state.

Angle-resolved photoemission spectroscopy with an in situ tunable magnetic field.

Angle-resolved photoemission spectroscopy (ARPES) is a powerful tool for probing the momentum-resolved single-particle spectral function of materials. Historically, in situ magnetic fields have been carefully avoided as they are detrimental to the control of photoelectron trajectory during the photoelectron detection process. However, magnetic field is an important experimental knob for both probing and tuning symmetry-breaking phases and electronic topology in quantum materials. In this paper, we introduce an easily implementable method for realizing an in situ tunable magnetic field at the sample position in an ARPES experiment and analyze magnetic-field-induced artifacts in the ARPES data. Specifically, we identified and quantified three distinct extrinsic effects of a magnetic field: constant energy contour rotation, emission angle contraction, and momentum broadening. We examined these effects in three prototypical quantum materials, i.e., a topological insulator (Bi2Se3), an iron-based superconductor (LiFeAs), and a cuprate superconductor (Pb-Bi2Sr2CuO6+x), and demonstrate the feasibility of ARPES measurements in the presence of a controllable magnetic field. Our studies lay the foundation for the future development of the technique and interpretation of ARPES measurements of field-tunable quantum phases.

How People's COVID-19 Induced-Worries and Multiple Environmental Exposures Are Associated with Their Depression, Anxiety, and Stress during the Pandemic.

This study investigates how people's perceived COVID-19 risk, worries about financial hardship, job loss, and family conflicts, and exposures to greenspace, PM2.5, and noise (in people's residential neighborhoods and daily activity locations) are related to their depression, anxiety, and stress during the COVID-19 pandemic. Using a two-day activity-travel diary, a questionnaire, and real-time air pollutant and noise sensors, a survey was conducted to collect data from 221 participants living in two residential neighborhoods of Hong Kong during the COVID-19 pandemic. Linear regression was conducted to explore the relationships. Significant associations between people's COVID-19-related worries and exposures to grassland and PM2.5 with depression, anxiety, and stress were found in the results. These associations with depression, anxiety, and stress vary depending on people's demographic attributes. These results can help direct the public authorities' efforts in dealing with the public mental health crisis during the COVID-19 pandemic.

Bone age assessment from articular surface and epiphysis using deep neural networks.

Bone age assessment is of great significance to genetic diagnosis and endocrine diseases. Traditional bone age diagnosis mainly relies on experienced radiologists to examine the regions of interest in hand radiography, but it is time-consuming and may even lead to a vast error between the diagnosis result and the reference. The existing computer-aided methods predict bone age based on general regions of interest but do not explore specific regions of interest in hand radiography. This paper aims to solve such problems by performing bone age prediction on the articular surface and epiphysis from hand radiography using deep convolutional neural networks. The articular surface and epiphysis datasets are established from the Radiological Society of North America (RSNA) pediatric bone age challenge, where the specific feature regions of the articular surface and epiphysis are manually segmented from hand radiography. Five convolutional neural networks, i.e., ResNet50, SENet, DenseNet-121, EfficientNet-b4, and CSPNet, are employed to improve the accuracy and efficiency of bone age diagnosis in clinical applications. Experiments show that the best-performing model can yield a mean absolute error (MAE) of 7.34 months on the proposed articular surface and epiphysis datasets, which is more accurate and fast than the radiologists. The project is available at https://github.com/YameiDeng/BAANet/, and the annotated dataset is also published at https://doi.org/10.5281/zenodo.7947923.

Few-Shot Multi-Agent Perception With Ranking-Based Feature Learning.

In this article, we focus on performing few-shot learning (FSL) under multi-agent scenarios in which participating agents only have scarce labeled data and need to collaborate to predict labels of query observations. We aim at designing a coordination and learning framework in which multiple agents, such as drones and robots, can collectively perceive the environment accurately and efficiently under limited communication and computation conditions. We propose a metric-based multi-agent FSL framework which has three main components: an efficient communication mechanism that propagates compact and fine-grained query feature maps from query agents to support agents; an asymmetric attention mechanism that computes region-level attention weights between query and support feature maps; and a metric-learning module which calculates the image-level relevance between query and support data fast and accurately. Furthermore, we propose a specially designed ranking-based feature learning module, which can fully utilize the order information of training data by maximizing the inter-class distance, while minimizing the intra-class distance explicitly. We perform extensive numerical studies and demonstrate that our approach can achieve significantly improved accuracy in visual and acoustic perception tasks such as face identification, semantic segmentation, and sound genre recognition, consistently outperforming the state-of-the-art baselines by 5%-20%.

High-quality chromosome-level de novo assembly of the Trifolium repens.

BACKGROUND: White clover (Trifolium repens L.), an excellent perennial legume forage, is an allotetraploid native to southeastern Europe and southern Asia. It has high nutritional, ecological, genetic breeding, and medicinal values and exhibits excellent resistance to cold, drought, trample, and weed infestation. Thus, white clover is widely planted in Europe, America, and China; however, the lack of reference genome limits its breeding and cultivation. This study generated a white clover de novo genome assembly at the chromosomal level and annotated its components. RESULTS: The PacBio third-generation Hi-Fi assembly and sequencing methods generated a 1096 Mb genome size of T. repens, with contigs of N50 = 14 Mb, scaffolds of N50 = 65 Mb, and BUSCO value of 98.5%. The newly assembled genome has better continuity and integrity than the previously reported white clover reference genome; thus provides important resources for the molecular breeding and evolution of white clover and other forage. Additionally, we annotated 90,128 high-confidence gene models from the genome. White clover was closely related to Trifolium pratense and Trifolium medium but distantly related to Glycine max, Vigna radiata, Medicago truncatula, and Cicer arietinum. The expansion, contraction, and GO functional enrichment analysis of the gene families showed that T. repens gene families were associated with biological processes, molecular function, cellular components, and environmental resistance, which explained its excellent agronomic traits. CONCLUSIONS: This study reports a high-quality de novo assembly of white clover genome obtained at the chromosomal level using PacBio Hi-Fi sequencing, a third-generation sequencing. The generated high-quality genome assembly of white clover provides a key basis for accelerating the research and molecular breeding of this important forage crop. The genome is also valuable for future studies on legume forage biology, evolution, and genome-wide mapping of quantitative trait loci associated with the relevant agronomic traits.