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Background Proprotein convertase subtilisin/kexin type 9 (PCSK9) plays a crucial role in lipid metabolism. The factors influencing PCSK9 in acute myocardial infarction (AMI) patients have not been fully understood. Objective To investigate the influence factors of PCSK9 among AMI patients. Methods Patients with an admission diagnosis of AMI between 2010 and 2018 were consecutively enrolled in the Department of Cardiology, Peking University First Hospital. Baseline information was collected from patients' current history, past history, and physical examination in the electronic medical record system at admission, and baseline plasma PCSK9 levels were measured. The related factors of PCSK9 were analyzed using univariate and multivariate linear regressions. The LASSO method was employed, the selected variables were subsequently included in the analysis of a multivariate linear regression model. Results 996 AMI patients were consecutively enrolled from 2010 to 2018, and 37 patients with no PCSK9 measurement results due to insufficient volume of retained blood samples were excluded. A total of 959 patients were included in the analysis. The median PCSK9 levels were 543.1 (425.4, 692.1) ng/mL. The multivariate linear regression analysis of non-genetic factors of PCSK9 showed that gender had the greatest effect (standardized regression coefficient was 0.13). Other significant factors related to PCSK9 levels were history of atrial fibrillation (standardized regression coefficient was 0.09), white blood cell count (standardized regression coefficient was 0.07) and uric acid levels (standardized regression coefficient was 0.07) . Conclusion In patients with AMI, gender, history of atrial fibrillation, blood white blood cell count, and uric acid levels were significant independent factors related to plasma PCSK9 levels.

To ensure national food and ecological security in China, it is essential to develop various reserve resources for cultivated land based on existing farmland advantages. Owing to abundant saline-alkali land reserves, the Yellow River Delta has enormous potentials for agriculture development. However, water scarcity and imbalance between water and land resources allocation have significantly limited the efficient utilization of saline-alkali land in this region. According to the development status regarding salinealkali soil amelioration and crop production, this study analyzes water and land resource conditions, clarifies major challenges for the sustainable development of local agriculture from the perspectives of water resource supply, groundwater table regulation, management and utilization system of saline-alkali land, technological innovation, and follow-up engineering maintenance, and proposes strategies for efficient use and coordinated allocation of water and land resources in the region. The strategies include: (1) increasing water supply from the Yellow River and connecting the coastal hydrographic net to make optimal allocation of regional water resources; (2) improving irrigation and drainage systems and auxiliary projects to enhance the quality of arable lands; and (3) accelerating demonstration and promotion of key technologies for saline-alkali soil amelioration to fully leverage the supporting function of sciences and technologies. The research results can provide a reference for comprehensive governance and utilization of saline-alkali land and agricultural sustainable development.

Accurate and timely acquisition of soil information is crucial for precision agriculture, food security, and environmental protection. Proximal visible near-infrared reflectance (vis–NIR) spectroscopy has been widely employed for rapid and accurate soil measurement, but its point measurement nature limits its direct applicability for large-scale soil surveys. On the other hand, remote sensing techniques can provide soil information at a larger scale, but their resolution is relatively coarse. While both techniques have been used independently for soil analyses, integrating vis–NIR spectroscopy with remote sensing remains a challenge and is underexplored, especially at the field scale. This study addresses this gap by combining field vis–NIR spectra with Gaofen-1 remote sensing data to spatially analyze soil organic matter and total nitrogen at the field scale. Unlike previous work, we first applied Gaofen-1 data and 10 derived spectral indices to estimate soil organic matter and total nitrogen using partial least squares regression and random forest, identifying the optimal combination of spectral indices. Then, we integrated the proximal vis–NIR spectra with this optimal spectral index combination for improved soil property estimation. This integration advanced existing methodologies by leveraging the high spatial resolution of Gaofen-1 data and the detailed spectral information from vis–NIR spectroscopy. The results showed the following: (1) the coefficient of variation across different crop growth stages of Gaofen-1 data was more crucial for modeling these two properties compared to bare soil Gaofen-1 data; (2) integrating proximal vis–NIR spectra with Gaofen-1 data improved model performance, yielding Lin's concordance correlation coefficient ( ρ c ) values of 0.63 and 0.72 and ratios of performance to interquartile distance (RPIQ) of 1.99 and 1.59 for soil organic matter and total nitrogen, respectively; and (3) the combined use of vis–NIR spectra and Gaofen-1 data provided higher spatial estimation accuracy (R2 of 0.68 and 0.57 for soil organic matter and total nitrogen) compared to ordinary kriging (R2 of 0.63 and 0.31 for soil organic matter and total nitrogen). These results demonstrate that the synergistic use of remote sensing and proximal soil sensing is a practical approach for spatially estimating soil organic matter and total nitrogen at the field scale. [ABSTRACT FROM AUTHOR]

Objective: Osteoporosis increases the risk of fragility fractures, impacting patients' lives. This study aimed to investigate whether LINC01271 was involved in the process of fragility fractures and healing, providing a new perspective for its diagnosis and treatment. Methods: This study included 94 healthy individuals, 82 patients with osteoporosis, and 85 patients with fragility fractures as subjects. RT-qPCR was used to measure the levels of LINC01271, miR-19a-3p, PIK3CA, and osteogenic differentiation markers in osteoblasts and subjects' serum. Luciferase reporter assays, RIP experiments, and RNA pull-down assays were utilized to verify the target relationships between LINC01271 and miR-19a-3p, as well as between miR-19a-3p and PIK3CA. Cell proliferation and apoptosis were assessed using CCK-8 assays and flow cytometry. Results: Compared to the healthy control group, the serum levels of LINC01271 were significantly reduced in patients with osteoporosis and fragility fractures. Furthermore, LINC01271 levels increased with time-dependent fracture healing. In vitro studies indicated that LINC01271 boosted osteoblast proliferation, inhibited apoptosis, and augmented osteogenic differences, whereas its inhibition reverses their effects. LINC01271 and PIK3CA were identified as targets of miR-19a-3p, and overexpression of miR-19a-3p could antagonize the effect of LINC01271 in promoting fracture healing. Conclusion: The results indicated that LINC01271 may play a key role in osteoblast function and fracture healing through its interaction with miR-19a-3p and regulation of PIK3CA. [ABSTRACT FROM AUTHOR]

The pursuit of complete telomere-to-telomere (T2T) genome assembly in plants, challenged by genomic complexity, has been advanced by Oxford Nanopore Technologies (ONT), which offers ultra-long, real-time sequencing. Despite its promise, sequencing length and gap filling remain significant challenges. This study optimized DNA extraction and library preparation, achieving DNA lengths exceeding 485 kb; average N50 read lengths of 80.57 kb, reaching up to 440 kb; and maximum reads of 5.83 Mb. Importantly, we demonstrated that combining ultra-long sequencing and adaptive sampling can effectively fill gaps during assembly, evidenced by successfully filling the remaining gaps of a near-complete Arabidopsis genome assembly and resolving the sequence of an unknown telomeric region in watermelon genome. Collectively, our strategies improve the feasibility of complete T2T genomic assemblies across various plant species, enhancing genome-based research in diverse fields. This study efficiently addresses the assembly of telomere-to-telomere (T2T) genomes in plants using the ultra-long sequencing of Oxford Nanopore Technologies. By optimizing DNA extraction and library preparation, ultra-long read lengths with an N50 of up to 440 kb was achieved. Combining ultra-long sequencing with adaptive sampling could effectively fill assembly gaps, facilitating comprehensive genomic research across various plant species. [ABSTRACT FROM AUTHOR]

【Background】 Along with drought, soil salinization is one of the most important abiotic stresses facing agricultural production in arid and semi-arid regions like Xinjiang in northwest China. Saline soil is estimated to have reached 11 million hm2 in Xinjiang, and is a major limiting factor in cotton industry in this region. Accurately measuring soil salinity is hence important to safeguard cotton production in Xinjiang. 【Objective】 The purpose of this paper is to present a new method to estimate salinity distribution at field and regional scales to help improve irrigation and cultivation management. 【Method】 The experiments were conducted at Alar reclamation area in southern Xinjiang. Electrical conductivity of 540 soil samples taken from 30 cotton fields with different salinization levels were measured in soil profile at 0~0.375, 0~0.750, and 0~1.000 m depth. Using the electromagnetic induction data, linear and nonlinear models were constructed to inversely calculate the electrical conductivity of soil at field and regional scales respectively. Stability and reliability of the models was verified against ground-true data using the sample-size-reduction method. 【Result】 At field scale, the coefficient of determination R2 associated with the multiple linear regression model (MLR), the partial least square regression (PLSR) model and the principal component regression model (PCR) varied from 0.88 to 0.95, while their associated R2 for regional scale was from 0.34 to 0.53. The R2 of the nonlinear model built on the random forest (RF), neural network (NN) and support vector machine (SVM) varied between 0.60 and 0.85, with the RF most accurate. The R2 of the RF model for calculating the electrical conductivity of the soil profile at 0~0.375, 0~0.750, 0~1.000 m was 0.80, 0.85 and 0.84, respectively. Compared with the linear model, the model for regional scale significantly improved the accuracy. The sample size in the RF model for regional scale was reduced from 540 to 240, while the accuracy remained almost unchanged, indicating the regional-scale model can reduce the number of soil profile without compromising modelling accuracy. 【Conclusion】 For constructing the inversion model for estimating soil-profile electrical conductivity at regional scale, the random forest method is most accurate.

National identity constitutes the psychological basis and important force for the survival and development of a country. How to shape and construct national identity and maintain the unity and stability of multi-ethnic countries is an important subject that multi-ethnic countries must face. Citizenship education is considered to be the basic path to cultivate and strengthen national identity. The construction of national identity affects the direction of citizenship education, which in turn affects the cultivation of national identity. National identity and citizenship education interact and shape each other. It should be pointed out that both national identity and citizenship education are related to citizenship, and citizenship is the connection point between national identity and citizenship education.