Identify gestational diabetes mellitus by deep learning model from cell-free DNA at the early gestation stage.

Gestational diabetes mellitus (GDM) is a common complication of pregnancy, which has significant adverse effects on both the mother and fetus. The incidence of GDM is increasing globally, and early diagnosis is critical for timely treatment and reducing the risk of poor pregnancy outcomes. GDM is usually diagnosed and detected after 24 weeks of gestation, while complications due to GDM can occur much earlier. Copy number variations (CNVs) can be a possible biomarker for GDM diagnosis and screening in the early gestation stage. In this study, we proposed a machine-learning method to screen GDM in the early stage of gestation using cell-free DNA (cfDNA) sequencing data from maternal plasma. Five thousand and eighty-five patients from north regions of Mainland China, including 1942 GDM, were recruited. A non-overlapping sliding window method was applied for CNV coverage screening on low-coverage (~0.2×) sequencing data. The CNV coverage was fed to a convolutional neural network with attention architecture for the binary classification. The model achieved a classification accuracy of 88.14%, precision of 84.07%, recall of 93.04%, F1-score of 88.33% and AUC of 96.49%. The model identified 2190 genes associated with GDM, including DEFA1, DEFA3 and DEFB1. The enriched gene ontology (GO) terms and KEGG pathways showed that many identified genes are associated with diabetes-related pathways. Our study demonstrates the feasibility of using cfDNA sequencing data and machine-learning methods for early diagnosis of GDM, which may aid in early intervention and prevention of adverse pregnancy outcomes.

Uncovering the complexity of childhood undernutrition through strain-level analysis of the gut microbiome.

BACKGROUND: Undernutrition (UN) is a critical public health issue that threatens the lives of children under five in developing countries. While evidence indicates the crucial role of the gut microbiome (GM) in UN pathogenesis, the strain-level inspection and bacterial co-occurrence network investigation in the GM of UN children are lacking. RESULTS: This study examines the strain compositions of the GM in 61 undernutrition patients (UN group) and 36 healthy children (HC group) and explores the topological features of GM co-occurrence networks using a complex network strategy. The strain-level annotation reveals that the differentially enriched species between the UN and HC groups are due to discriminated strain compositions. For example, Prevotella copri is mainly composed of P. copri ASM1680343v1 and P. copri ASM345920v1 in the HC group, but it is composed of P. copri ASM346549v1 and P. copri ASM347465v1 in the UN group. In addition, the UN-risk model constructed at the strain level demonstrates higher accuracy (AUC = 0.810) than that at the species level (AUC = 0.743). With complex network analysis, we further discovered that the UN group had a more complex GM co-occurrence network, with more hub bacteria and a higher clustering coefficient but lower information transfer efficiencies. Moreover, the results at the strain level suggested the inaccurate and even false conclusions obtained from species level analysis. CONCLUSIONS: Overall, this study highlights the importance of examining the GM at the strain level and investigating bacterial co-occurrence networks to advance our knowledge of UN pathogenesis.

Prematurely delivering mothers show reductions of lachnospiraceae in their gut microbiomes.

BACKGROUND: Preterm birth is the leading cause of perinatal morbidity and mortality. Despite evidence shows that imbalances in the maternal microbiome associates to the risk of preterm birth, the mechanisms underlying the association between a perturbed microbiota and preterm birth remain poorly understood. METHOD: Applying shotgun metagenomic analysis on 80 gut microbiotas of 43 mothers, we analyzed the taxonomic composition and metabolic function in gut microbial communities between preterm and term mothers. RESULTS: Gut microbiome of mothers delivering prematurely showed decreased alpha diversity and underwent significant reorganization, especially during pregnancy. SFCA-producing microbiomes, particularly species of Lachnospiraceae, Ruminococcaceae, and Eubacteriaceae, were significantly depleted in preterm mothers. Lachnospiraceae and its species were the main bacteria contributing to species' differences and metabolic pathways. CONCLUSION: Gut microbiome of mothers delivering prematurely has altered and demonstrates the reduction of Lachnospiraceae.

Icaritin Derivative IC2 Induces Cytoprotective Autophagy of Breast Cancer Cells via SCD1 Inhibition.

Breast cancer is one of the most prevalent malignancies and the leading cause of cancer-associated mortality in China. Icaritin (ICT), a prenyl flavonoid derived from the Epimedium Genus, has been proven to inhibit the proliferation and stemness of breast cancer cells. Our previous study demonstrated that IC2, a derivative of ICT, could induce breast cancer cell apoptosis by Stearoyl-CoA desaturase 1 (SCD1) inhibition. The present study further investigated the mechanism of the inhibitory effects of IC2 on breast cancer cells in vitro and in vivo. Our results proved that IC2 could stimulate autophagy in breast cancer cells with the activation of adenosine monophosphate (AMP)-activated protein kinase (AMPK) signaling and mitogen-activated protein kinase (MAPK) signaling. Combination treatment of the AMPK inhibitor decreased IC2-induced autophagy while it markedly enhanced IC2-induced apoptosis. In common with IC2-induced apoptosis, SCD1 overexpression or the addition of exogenous oleic acid (OA) could also alleviate IC2-induced autophagy. In vivo assays additionally demonstrated that IC2 treatment markedly inhibited tumor growth in a mouse breast cancer xenograft model. Overall, our study was the first to demonstrate that IC2 induced cytoprotective autophagy by SCD1 inhibition in breast cancer cells and that the autophagy inhibitor markedly enhanced the anticancer activity of IC2. Therefore, IC2 was a potential candidate compound in combination therapy for breast cancer.

Remaining Useful Life Prediction of Rolling Bearings Based on Multi-scale Permutation Entropy and ISSA-LSTM.

The performance of bearings plays a pivotal role in determining the dependability and security of rotating machinery. In intricate systems demanding exceptional reliability and safety, the ability to accurately forecast fault occurrences during operation holds profound significance. Such predictions serve as invaluable guides for crafting well-considered reliability strategies and executing maintenance practices aimed at enhancing reliability. In the real operational life of bearings, fault information often gets submerged within the noise. Furthermore, employing Long Short-Term Memory (LSTM) neural networks for time series prediction necessitates the configuration of appropriate parameters. Manual parameter selection is often a time-consuming process and demands substantial prior knowledge. In order to ensure the reliability of bearing operation, this article investigates the application of three advanced techniques-Maximum Correlation Kurtosis Deconvolution (MCKD), Multi-Scale Permutation Entropy (MPE), and Long Short-Term Memory (LSTM) recurrent neural networks-for the prediction of the remaining useful life (RUL) of rolling bearings. The improved sparrow search algorithm (ISSA) is employed for configuring parameters in the Long Short-Term Memory (LSTM) network. Each technique's principles, methodologies, and applications are comprehensively reviewed, offering insights into their respective strengths and limitations. Case studies and experimental evaluations are presented to assess their performance in RUL prediction. Findings reveal that MCKD enhances fault signatures, MPE captures complexity, and LSTM excels in modeling temporal patterns. The root mean square error of the prediction results is 0.007. The fusion of these techniques offers a comprehensive approach to RUL prediction, leveraging their unique attributes for more accurate and reliable predictions.

eSMC: a statistical model to infer admixture events from individual genomics data.

BACKGROUND: Inferring historical population admixture events yield essential insights in understanding a species demographic history. Methods are available to infer admixture events in demographic history with extant genetic data from multiple sources. Due to the deficiency in ancient population genetic data, there lacks a method for admixture inference from a single source. Pairwise Sequentially Markovian Coalescent (PSMC) estimates the historical effective population size from lineage genomes of a single individual, based on the distribution of the most recent common ancestor between the diploid's alleles. However, PSMC does not infer the admixture event. RESULTS: Here, we proposed eSMC, an extended PSMC model for admixture inference from a single source. We evaluated our model's performance on both in silico data and real data. We simulated population admixture events at an admixture time range from 5 kya to 100 kya (5 years/generation) with population admix ratio at 1:1, 2:1, 3:1, and 4:1, respectively. The root means the square error is [Formula: see text] kya for all experiments. Then we implemented our method to infer the historical admixture events in human, donkey and goat populations. The estimated admixture time for both Han and Tibetan individuals range from 60 kya to 80 kya (25 years/generation), while the estimated admixture time for the domesticated donkeys and the goats ranged from 40 kya to 60 kya (8 years/generation) and 40 kya to 100 kya (6 years/generation), respectively. The estimated admixture times were concordance to the time that domestication occurred in human history. CONCLUSION: Our eSMC effectively infers the time of the most recent admixture event in history from a single individual's genomics data. The source code of eSMC is hosted at https://github.com/zachary-zzc/eSMC .

LDscaff: LD-based scaffolding of de novo genome assemblies.

BACKGROUND: Genome assembly is fundamental for de novo genome analysis. Hybrid assembly, utilizing various sequencing technologies increases both contiguity and accuracy. While such approaches require extra costly sequencing efforts, the information provided millions of existed whole-genome sequencing data have not been fully utilized to resolve the task of scaffolding. Genetic recombination patterns in population data indicate non-random association among alleles at different loci, can provide physical distance signals to guide scaffolding. RESULTS: In this paper, we propose LDscaff for draft genome assembly incorporating linkage disequilibrium information in population data. We evaluated the performance of our method with both simulated data and real data. We simulated scaffolds by splitting the pig reference genome and reassembled them. Gaps between scaffolds were introduced ranging from 0 to 100 KB. The genome misassembly rate is 2.43% when there is no gap. Then we implemented our method to refine the Giant Panda genome and the donkey genome, which are purely assembled by NGS data. After LDscaff treatment, the resulting Panda assembly has scaffold N50 of 3.6 MB, 2.5 times larger than the original N50 (1.3 MB). The re-assembled donkey assembly has an improved N50 length of 32.1 MB from 23.8 MB. CONCLUSIONS: Our method effectively improves the assemblies with existed re-sequencing data, and is an potential alternative to the existing assemblers required for the collection of new data.

Uncovering rearrangements in the Tibetan antelope via population-derived genome refinement and comparative analysis with homologous species.

Introduction: The Tibetan antelope (Pantholops hodgsonii) is a remarkable mammal thriving in the extreme Qinghai-Tibet Plateau conditions. Despite the availability of its genome sequence, limitations in the scaffold-level assembly have hindered a comprehensive understanding of its genomics. Moreover, comparative analyses with other Bovidae species are lacking, along with insights into genome rearrangements in the Tibetan antelope. Methods: Addressing these gaps, we present a multifaceted approach by refining the Tibetan Antelope genome through linkage disequilibrium analysis with data from 15 newly sequenced samples. Results: The scaffold N50 of the refined reference is 3.2 Mbp, surpassing the previous version by 1.15-fold. Our annotation analysis resulted in 50,750 genes, encompassing 29,324 novel genes not previously study. Comparative analyses reveal 182 unique rearrangements within the scaffolds, contributing to our understanding of evolutionary dynamics and species-specific adaptations. Furthermore, by conducting detailed genomic comparisons and reconstructing rearrangements, we have successfully pioneered the reconstruction of the X-chromosome in the Tibetan antelope. Discussion: This effort enhances our comprehension of the genomic landscape of this species.

Causal role of immune cells on cervical cancer onset revealed by two-sample Mendelian randomization study.

Cervical cancer (CC) is a prevalent gynecological cancer worldwide that significantly impacts the quality of life and the physical and mental well-being of women. However, there have been limited studies utilizing Mendelian randomization (MR) analysis to investigate the connection between immune cells and CC. This study is to investigate the causal effects of immune traits on CC and non-neoplastic conditions of the cervix. The GWAS data for 731 immunophenotypes and six GWAS data for CC from the FinnGen database were downloaded. Subsequently, a two-sample MR analysis was conducted using the MR Egger, Weighted median, Inverse variance weighted (IVW), Simple mode, and Weighted mode methods. Our study has identified the potential causal effects of immune traits on inflammatory diseases of the cervix, other noninflammatory disorders of the cervix uteri, carcinoma in situ of cervix uteri, adenocarcinomas of cervix, squamous cell neoplasms and carcinoma of cervix, as well as malignant neoplasm of the cervix uteri, with the respective numbers being 8, 6, 11, 8, 23, and 12, respectively. A strong correlation between classic monocytes and various cervical diseases was revealed. Furthermore, we discovered that B cells expressing BAFF-R have the ability to impede the advancement of malignant CC, specifically squamous cell neoplasms and carcinoma of cervix. Our study has demonstrated a significant association between immune traits and both CC and non-neoplastic conditions of the cervix through two-sample Mendelian randomization, providing valuable insights for future clinical research.

Coumestrol facilitates apoptosis in colorectal cancer cells by interacting with ZIP8 protein via the ferroptosis pathway.

Objective: So far, there have been no reports of coumestrol inhibiting colorectal cancer (CRC) through the ferroptosis pathway. This study is to investigate the mechanism of the traditional Chinese medicine monomer coumestrol in the treatment of CRC. Methods: Data on CRC transcriptome sequencing was obtained from the GEO database and TCGA database. Bioinformatics analyses were conducted to screen for CRC prognostic-related key genes and their potential binding monomers in traditional Chinese medicine. The inhibitory effect of coumestrol on CRC cell lines (COLO 205 & HCT 116) was determined using the CCK-8 assay, and cell apoptosis was assessed by flow cytometry. The content of ferrous ions was measured using the Ferrous Ion Content Assay Kit. The expression of ferroptosis pathway-related genes SLC39A8, NCOA4, VDAC2, and NOX2 before and after small interference RNA (siRNA) was examined through real-time PCR and Western blotting. Results: SLC39A8 was found to be associated with CRC clinical progression staging, and its encoded protein ZIP8 may bind to coumestrol. KEGG enrichment analysis suggested that ZIP8 plays a role in iron transmembrane transport and may affect the expression of ferroptosis pathway-related genes NCOA4, VDAC2, and NOX2. Coumestrol was found to induce apoptosis in CRC cell lines by upregulating the expression of ferroptosis pathway-related genes SLC39A8, NCOA4, VDAC2, and NOX2. However, coumestrol was unable to upregulate the expression of ferroptosis pathway-related genes in CRC cell lines after SLC39A8 interference. Conclusion: Coumestrol facilitates apoptosis in CRC cells by interacting with ZIP8 protein via the ferroptosis pathway.

Inspecting mother-to-infant microbiota transmission: disturbance of strain inheritance by cesarian section.

Introduction: The initial acquisition and subsequent development of the microbiota in early life is crucial to future health. Cesarean-section (CS) birth is considered to affect early microbial transmission from mother to infant. Methods: In this study, we collected fecal samples from 34 CS infants and their mothers from West China Second Hospital, Sichuan University to assess the microbiota developmental trajectory of mothers and infants. We explored mother-infant gut microbiome transmission via comparison with corresponding Finnish data. Results: Metagenomic analysis of gut microbiota profiles indicated that the communities of mothers and infants were distinct. The composition of the infant gut microbiome was highly variable but also followed predictable patterns in the early stages of life. Maternal communities were stable and mainly dominated by species from Bacteroidacea spp. We used PStrain to analyze and visualize strain transmission in each mother-infant pair. Excluding missing data, we included 32 mother-infant pairs for analysis of strain transmission. Most CS deliveries (65.6%, 21/32) did not demonstrate transmission of strains from mother to infant. To further explore the mother-infant strain transmission, we analyzed metagenomics data from Finnish mother-infant pairs. A total of 32 mother-infant pairs were included in the analysis, including 28 vaginal delivery (VD) infants and four CS infants. Strain transmission was observed in 30 infants, including 28 VD infants and two CS infants. All VD infants received transmitted stains from their mothers. Finally, a total of 193 strain transmission events were observed, comprising 131 strains and 45 species. Discussion: Taken together, our data suggested that delivery mode was an important factor influencing the mother-infant strain transmission.

Essential role of lattice oxygen in hydrogen sensing reaction.

Understanding the sensing mechanism of metal oxide semiconductors is imperative to the development of high-performance sensors. The traditional sensing mechanism only recognizes the effect of surface chemisorbed oxygen from the air but ignores surface lattice oxygen. Herein, using in-situ characterizations, we provide direct experimental evidence that the surface chemisorbed oxygen participated in the sensing process can come from lattice oxygen of the oxides. Further density functional theory (DFT) calculations prove that the p-band center of O serves as a state of art for regulating the participation of lattice oxygen in gas-sensing reactions. Based on our experimental data and theoretical calculations, we discuss mechanisms that are fundamentally different from the conventional mechanism and show that the easily participation of lattice oxygen is helpful for the high response value of the materials.

Value of intralesional and perilesional radiomics for predicting the bioactivity of hepatic alveolar echinococcosis.

Objectives: To investigate the value of intralesional and perilesional radiomics based on computed tomography (CT) in predicting the bioactivity of hepatic alveolar echinococcosis (HAE). Materials and methods: In this retrospective study, 131 patients who underwent surgical resection and diagnosed HAE in pathology were included (bioactive, n=69; bioinactive, n=62). All patients were randomly assigned to the training cohort (n=78) and validation cohort (n=53) in a 6:4 ratio. The gross lesion volume (GLV), perilesional volume (PLV), and gross combined perilesional volume (GPLV) radiomics features were extracted on CT images of portal vein phase. Feature selection was performed by intra-class correlation coefficient (ICC), univariate analysis, and least absolute shrinkage and selection operator (LASSO). Radiomics models were established by support vector machine (SVM). The Radscore of the best radiomics model and clinical independent predictors were combined to establish a clinical radiomics nomogram. Receiver operating characteristic curve (ROC) and decision curves were used to evaluate the predictive performance of the nomogram model. Results: In the training cohort, the area under the ROC curve (AUC) of the GLV, PLV, and GPLV radiomic models was 0.774, 0.729, and 0.868, respectively. GPLV radiomic models performed best among the three models in training and validation cohort. Calcification type and fibrinogen were clinical independent predictors (p<0.05). The AUC of the nomogram-model-based clinical and GPLV radiomic signatures was 0.914 in the training cohort and 0.833 in the validation cohort. The decision curve analysis showed that the nomogram had greater benefits compared with the single radiomics model or clinical model. Conclusion: The nomogram model based on clinical and GPLV radiomic signatures shows the best performance in prediction of the bioactivity of HAE. Radiomics including perilesional tissue can significantly improve the prediction efficacy of HAE bioactivity.

Insights from metagenomics into gut microbiome associated with acute coronary syndrome therapy.

Acute coronary syndrome (ACS) is a predominant cause of mortality, and the prompt and precise identification of this condition is crucial to minimize its impact. Recent research indicates that gut microbiota is associated with the onset, progression, and treatment of ACS. To investigate its role, we sequenced the gut microbiota of 38 ACS patients before and after percutaneous coronary intervention and statin therapy at three time points, examining differential species and metabolic pathways. We observed a decrease in the abundance of Parabacteroides, Escherichia, and Blautia in patients after treatment and an increase in the abundance of Gemalla, Klebsiella variicola, Klebsiella pneumoniae, and others. Two pathways related to sugar degradation were more abundant in patients before treatment, possibly correlated with disorders of sugar metabolism and risk factors, such as hyperglycemia, insulin resistance, and insufficient insulin secretion. Additionally, seven pathways related to the biosynthesis of vitamin K2 and its homolog were reduced after treatment, suggesting that ACS patients may gradually recover after therapy. The gut microbiota of patients treated with different statins exhibited notable differences after treatment. Rosuvastatin appeared to promote the growth of anti-inflammatory bacteria while reducing pro-inflammatory bacteria, whereas atorvastatin may have mixed effects on pro-inflammatory and anti-inflammatory bacteria while increasing the abundance of Bacteroides. Our research will provide valuable insights and enhance comprehension of ACS, leading to better patient diagnosis and therapy.

Protocol to analyze population structure and migration history based on human genome variation data.

We describe a protocol to integrate genome variation data from different datasets and explore the population structure and migration history of human populations. This protocol provides semi-automated scripts to perform and visualize the effect of variant filtering strategy on eliminating batch effects, principal component analysis, ancestry component analysis, historical population effective size inference, and migration and isolation analysis based on independent biallelic SNPs, genotype likelihoods, and haplotypes. The protocol can be adapted to variation data from other sources. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2022).1.

An organ-effective modulation for non-contrast chest computed tomography imaging: effect on image quality and thyroid exposure reduction.

We investigate the efficacy of organ-effective modulation (OEM) technique for thyroid dose reduction among various body habitus and its impact on image quality in chest non-contrast computed tomography (CT). We prospectively enrolled 64 patients who underwent non-contrast chest CT from January to May 2022. The skin-absorbed radiation dose over the thyroid (Dthyroid) was obtained using a thermoluminescence dosemeter. Signal-to-noise ratio and image noise was also quantitatively assessed. In subjective analyses, two radiologists independently evaluated images based on a 5-point scale. The OEM group showed a markedly decrease in Dthyroid when compared with the non-OEM group (p < 0.05). No significant difference was observed regarding the image noise (p < 0.05), except for the ventral air space. The subjective scores of two radiologists showed no significant differences between the non-OEM and OEM groups. OEM can effectively reduce the radiation exposure of thyroid without compromising on image quality in non-contrast chest CT.

Clinical Application of Dark-blood Imaging in Head and Neck CT Angiography: Effect on Image Quality and Plaque Visibility.

RATIONALE AND OBJECTIVES: The aim of this study was to explore the potential of a newly developed dark-blood imaging technique to improve image quality and plaque visibility in head and neck computed tomography (CT) angiography. MATERIALS AND METHODS: Patients who underwent triphasic head and neck CT angiography scans from August 2021 to March 2023 were retrospectively enrolled (mean age 67.23 ± 10.81 [SD] years, range 43-85 years, 64.7% male). The CT protocol consists of pre-contrast, arterial and delayed phases. Dark-blood images were postprocessed with the contrast-enhancement boost (CE-boost) technique. The quantitative assessment involved evaluating the CT value, image noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of calcified plaque and non-calcified plaque. The plaque CNR relative to the vessel lumen (CNRplaque-lumen), vessel wall (CNRplaque-wall), and adjacent muscle (CNRplaque-muscle) was respectively calculated. Two experienced radiologists independently evaluated the CT images (5, best; 1, worst) by four characteristics including calcified plaque visibility, non-calcified plaque visibility, diagnostic confidence, and overall image quality. Inter-rater variability was also evaluated. The artery stenosis rate and plaque burden on dark-blood images were measured and compared with arterial phases. The intraclass correlation coefficient (ICC) was used for consistency analysis. The diagnostic accuracy of dark-blood images for the stenosis rate was evaluated by the area under the curve (AUC). RESULTS: A total of 43 patients with 54 calcified plaques and 34 non-calcified plaques were assessed in this study. When compared with pre-contrast and delayed phase, dark-blood images yielded significantly higher CNRplaque-lumen and CNRplaque-muscle of calcified (219.79 ± 159.20 and 181.23 ± 112.12, respectively) and non-calcified (30.30 ± 29.11 and 6.28 ± 4.75, respectively) plaques (all p < 0.001). Calcified plaque SNR of dark-blood showed equal or slightly lower than other phases (p > 0.05 or p = 0.02). A major increase was observed in the non-calcified plaque SNR of dark-blood compared to the arterial phase (5.56 ± 3.71 vs. 4.23 ± 3.56, p = 0.02), although there were no apparent differences compared to pre-contrast and delayed phases (p > 0.05). In subjective analyzes, the calcified plaque visibility (4.99 ± 0.07), non-calcified plaque visibility (4.62 ± 0.48), overall image quality (4.81 ± 0.34), and diagnostic confidence (4.74 ± 0.36) in dark-blood images dominated the highest scores (p < 0.001). The subjective scores of radiologists exhibited good consistency (all kappa value>0.7). The dark-blood image and the arterial phase image exhibited good consistency in identifying the stenosis rate (p < 0.001). In the evaluation of plaque burden, the interobserver agreement for dark-blood images was higher compared to arterial phase images (ICC = 0.870 vs. 0.729). CONCLUSIONS: Compared to conventional triphasic head and neck CT angiography, the CE-boost derived dark-blood imaging demonstrated a significant improvement in image quality and visibility for both calcified and non-calcified plaque assessment.

Chromosome-Level Haplotype Assembly for Equus asinu.

Background: Haplotype provides significant insights into understanding genomes at both individual and population levels. However, research on many non-model organisms is still based on independent genetic variations due to the lack of haplotype. Results: We conducted haplotype assembling for Equus asinu, a non-model organism that plays a vital role in human civilization. We described the hybrid single individual assembled haplotype of the Dezhou donkey based on the high-depth sequencing data from single-molecule real-time sequencing (×30), Illumina short-read sequencing (×211), and high-throughput chromosome conformation capture (×56). We assembled a near-complete haplotype for the high-depth sequenced Dezhou donkey individual and a phased cohort for the resequencing data of the donkey population. Conclusion: Here, we described the complete chromosome-scale haplotype of the Dezhou donkey with more than a 99.7% phase rate. We further phased a cohort of 156 donkeys to form a donkey haplotype dataset with more than 39 million genetic variations.

Synthesis and characterization of tung oil-based UV curable for three-dimensional printing resins.

Using tung oil as the raw material, a new bio-based prepolymer was successfully synthesized by reacting with acrylic-modified rosin (ß-acryloyl nutrient ethyl) ester (ARA)/acrylic-2-hydroxyethyl ester (HEA) followed by the use of the above composite material as the matrix and then reacting with the active diluent (2-HEMA, TPGDA) and the photoinitiator TPO and Irgacure1173 to successfully synthesize a new type of bio-based prepolymer-acrylate-epoxy tung oil polypolymer (AETP). The tung oil monomer before and after the epoxy formation was compared by proton NMR spectroscopy, and the chemical structure of AETP was analyzed by Fourier transform spectroscopy. Tung oil has an acid value of 1.5 mg KOH per g, an epoxy value of 5.38%, an iodine value of 11.28 g/100 g, and a refractive index of n 25 = 1.475. Composite-based 3D printing resins (like AETP) were cured using digital light treatment, while some samples were also post-treated via ultraviolet (UV) light treatment. The AETP-based 3D printing resin has excellent thermal and mechanical properties, and the viscosity of its system is 313 mPa s; exposure time 4.5 s; the tensile strength, flexural strength and flexural modulus were 62 MPA, 63.84 MPa and 916.708 MPa, respectively; Shore hardness was 80 HD and shrinkage was 4.00%. The good performance of the AETP-based 3D printing resin is attributed to the rigidity of their tightly crosslinked structure. This study pioneered a method for producing photoactive acrylates (e.g., tung oil-based acrylate oligomer resins) from renewable, low-cost biomass for light-curing 3D printing.