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Large-scale meta-analyses of genome-wide association studies (GWAS) have identified >175 loci associated with fasting cholesterol levels, including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG). With differences in linkage disequilibrium (LD) structure and allele frequencies between ancestry groups, studies in additional large samples may detect new associations. We conducted staged GWAS meta-analyses in up to 69,414 East Asian individuals from 24 studies with participants from Japan, the Philippines, Korea, China, Singapore, and Taiwan. These meta-analyses identified (P

Estrogen receptor α (ER-α) is a nuclear hormone receptor that controls selected genes, thereby regulating proliferation and differentiation of target tissues, such as breast. Gene expression controlled by ER-α is modulated by Ca2+ via calmodulin (CaM). Here we present the NMR structure of Ca2+-CaM bound to two molecules of ER-α (residues 287-305). The two lobes of CaM bind to the same site on two separate ER-α molecules (residues 292, 296, 299, 302, and 303), which explains why CaM binds two molecules of ER-α in a 1:2 complex and stabilizes ER-α dimerization. Exposed glutamate residues in CaM (Glu-11, Glu-14, Glu-84, and Glu-87) form salt bridges with key lysine residues in ER-α (Lys-299, Lys-302, and Lys-303), which is likely to prevent ubiquitination at these sites and inhibit degradation of ER-α. Transfection of cells with full-length CaM slightly increased the ability of estrogen to enhance transcriptional activation by ER-α of endogenous estrogen-responsive genes. By contrast, expression of either the N- or C-lobe of CaM abrogated estrogen-stimulated transcription of the estrogen responsive genes pS2 and progesterone receptor. These data suggest that CaM-induced dimerization of ER-α is required for estrogen-stimulated transcriptional activation by the receptor. In light of the critical role of ER-α in breast carcinoma, our data suggest that small molecules that selectively disrupt the interaction of ER-α with CaM may be useful in the therapy of breast carcinoma.

Lithium‐ion batteries play an integral role in various aspects of daily life, yet there is a pressing need to enhance their safety and cycling stability. In this study, we have successfully developed a highly secure and flexible solid‐state polymer electrolyte (SPE) through the in situ polymerization of allyl acetoacetate (AAA) monomers. This SPE constructed an efficient Li+ transport channel inside and effectively improved the solid‐solid interface contact of solid‐state batteries to reduce interfacial impedance. Furthermore, it exhibited excellent thermal stability, an ionic conductivity of 3.82×10−4 S cm−1 at room temperature (RT), and a Li+ transport number (tLi+) of 0.66. The numerous oxygen vacancies on layered inorganic SiO2 created an excellent environment for TFSI− immobilization. Free Li+ migrated rapidly at the C=O equivalence site with the poly(allyl acetoacetate) (PAAA) matrix. Consequently, when cycled at 0.5C and RT, it displayed an initial discharge specific capacity of 140.6 mAh g−1 with a discharge specific capacity retention rate of 70 % even after 500 cycles. Similarly, when cycled at a higher rate of 5C, it demonstrated an initial discharge specific capacity of 132.3 mAh g−1 while maintaining excellent cycling stability. [ABSTRACT FROM AUTHOR]

A copper‐catalyzed one‐pot three‐component aminophosphinoylation reaction of 1,4‐naphthoquinone with disubstituted phosphine oxides and arylamines has been developed. Various disubstituted phosphine oxides and arylamines with diverse structures and electronic properties can work smoothly with 1,4‐naphthoquinone, afforded a series of corresponding aminophosphinoylated 1,4‐naphthoquinones with up to 96 % isolated yield. The present method features easily available reagents, operational simplicity, broad substrate scope, high atom and step economy. The potential practicality of this protocol was further demonstrated by gram‐scale synthesis. [ABSTRACT FROM AUTHOR]

The precipitation estimation with high spatial and temporal resolution over Tibet is of great significance for monitoring the stability of the plateau ecosystem and early warning of natural disasters. In response to the problem of the unsatisfactory accuracy in traditional real-time precipitation inversion methods and quantitative accuracy estimation products in this region. This paper adopts deep-learning methods to explore the corresponding relationship between precipitation and multi-band observation data from geostationary satellite imagery. The U-High Resolution Network (U-HRNet) as backbone is fully exploited to generate multi-scale features and fused using attention gates to extract high-resolution spatial representations representing precipitation clouds, thereby achieving precipitation estimation of target clouds at different scales. In addition, in response to the characteristics of high precipitation intensity and drastic cloud cluster changes in summer convective weather within a short period of time, a multi-head attention mechanism is introduced to extract temporal information from time series inputs and fuse it with spatial features to improve the accuracy of real-time precipitation estimation. Compared with the operational precipitation products and baseline deep-learning models, the classification metrics such as Probability of detection (POD), False Alarm Ratio (FAR) and Critical success index (CSI) show that the proposed model (U-HRNet+) has better performance in test set and has high generalization ability for estimating short-term heavy rainfall over Tibet and its surrounding areas in summer. Therefore, the proposed model has the potential to serve as a more accurate and reliable satellite-based precipitation estimation product. [ABSTRACT FROM AUTHOR]

Purpose: The aim of our study was to investigate the relationship between albumin-bilirubin (ALBI) grade and recurrence in patients who underwent TACE sequential ablation. We developed and validated a nomogram to predict low levels of ALBI patients' recurrence. Patients and Methods: A total of 880 patients undergoing TACE combined ablation at Beijing Youan Hospital from January 2014 to December 2021 were retrospectively enrolled, including 415 patients with L-ALBI (≤-2.6) and 465 patients with high levels (>-2.6) of ALBI (H-ALBI). L-ALBI patients were randomized in a 7:3 ratio into the training cohort (N=289) and validation cohort (N=126). Multivariate Cox regression followed by random survival forest was carried out to identify independent risk factors for prediction nomogram construction. An examination of nomogram accuracy was performed using the C-index, receiver operating characteristic (ROC), calibration curves, and decision curve analysis (DCA) curves. According to the nomogram, the patients were divided into low-risk, intermediate-risk, and high-risk groups. Kaplan-Meier (KM) curves were applied to compare the difference in recurrence-free survival (RFS) among the three groups. Results: The median RFS in L-ALBI patients was significantly longer than the H-ALBI patients (40.8m vs 20.1m, HR:1.71, 95% CI:1.44– 2.04, P< 0.0001). The nomogram was composed of five variables, such as age, Barcelona Clinic Liver Cancer (BCLC) stage, globulin, gamma-glutamyl transferase to lymphocyte ratio (GLR), and international normalized ratio (INR). The C-index (0.722 and 0.731) and 1-, 3-, and 5-year AUCs (0.725, 0.803, 0.870, and 0.764, 0.816, 0.798) of the training and validation cohorts proved the good predictive performance of the nomogram. Calibration curves and DCA curves demonstrated good consistency and good clinical utility. There were significant differences in RFS between the low-risk, intermediate-risk, and high-risk groups (P< 0.0001). Conclusion: L-ALBI Patients who underwent TACE combined ablation had better recurrence-free survival than patients with H-ALBI. The nomogram developed and validated in our study had good predictive ability in recurrence for L-ALBI patients. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has posed a severe challenge to the global economic recovery and China's economic growth. Although China has achieved stage victory against the epidemic, the impact and influence of the epidemic on China's economy will linger. On the positive side, the epidemic has led to a dramatic reduction in air pollution levels and a sharp slowdown in greenhouse gas emissions such as CO2. Forecasting China's CO2 emissions under the impact of the COVID-19 epidemic is crucial for formulating policies that contribute to smooth economic development and rational energy consumption. To this end, this paper improves on the traditional grey model, GM(1, N), by developing a novel fractional multivariate nonlinear grey model, FMNGM(q, N). These improvements include two key points. First, different power exponents are assigned to each relevant factor variable to explain their nonlinear, uncertain, and complicated relationships with the system characteristic variables. Second, the integer accumulation is changed to fractional accumulation to preprocess the data, and the Caputo-type fractional derivative is used to characterize the endogenous relationships among the system characteristic data. In addition, the quantum particle swarm optimization (QPSO) algorithm is used to optimize the above parameters with the goal of minimizing MAPE. The collected data on China's CO2 emission from 2001 to 2018 were divided into two parts according to different stages of economic development: 2001–2009 and 2010–2018. Both sets of data were modelled and analysed separately and compared with other models. Results showed that the proposed model had better prediction accuracy than other models. Finally, the model built using the 2010–2018 data was used to forecast China's CO2 emissions. Results show that China's CO2 emissions in 2020 under the impact of COVID-19 will decrease by approximately 3.15% from 2019 to 9893 million tons. The results bear important policy implications for planners in investment in clean energy and infrastructure to achieving the goal of a low-carbon transition. [ABSTRACT FROM AUTHOR]

The complete mitochondrial genome of Pealius mori (Hemiptera: Aleyrodidae) was determined in this study. The mitogenome was 15,654 bp long with 37 typical Insecta mitochondrial genes and one non-coding control region. Its gene content and order were different to other Hemiptera mitochondrial genomes. The overall nucleotide composition of the mitogenome was 42.62% A, 32.73% T, 11.12% G and 13.54% C, with an A + T bias of 75.34%. Phylogenetic analyses of 14 species in Aleyrodidae, 2 species in Lepidoptera and 1 species in Thysanoptera by Maximum Likelihood showed that P. mori China had been more closely related to P. mori France, closely related to Pealius machili. This result well supported the taxonomic position of Aleyrodidae and their close relationship with the Pealius category. [ABSTRACT FROM AUTHOR]

The integrity of a pressure vessel (PRV) is affected by the decay heat of the molten pool in the lower head, so it is very important to study the natural convection heat transmission in the lower head. Scholars from all over the world have carried out a lot of experimental studies and calculations to determine the convection mechanism and heat transmission characteristics of the molten pool. Most of them were based on the empirical formula of convective heat transmission on the wall of a hemispherical molten pool based on two-dimensional slice experiments and simulation calculations. In this study, FLUENT 2021R1 software was used to simulate the three-dimensional convective heat transmission process of the hemispherical molten pool, and the temperature, velocity and wall heat transmission coefficients of the flow field were analyzed. The results were in good agreement with experimental data from UCLA (University of California, Los Angeles). Through research on the heat transmission of the lower head, the results showed that in the region where the wall angle was θ between approximately 72 and 90 degrees, heat transmission coefficients had larger fluctuations, and a more reasonable empirical relation was proposed. Comparing between the simulation results of CFD and the two-dimensional empirical formula, it was found that the latter one was smaller under the same θ angle condition. Finally, the convection phenomena of different external temperatures were simulated, and the main factors affecting the flow field by temperature were analyzed. [ABSTRACT FROM AUTHOR]

Purpose: We explored the role of tumor size and number in the prognosis of HCC patients who underwent ablation and created a nomogram based on machine learning to predict the recurrence. Patients and Methods: A total of 990 HCC patients who underwent transcatheter arterial chemoembolization (TACE) combined ablation at Beijing Youan Hospital from January 2014 to December 2021 were prospectively enrolled, including 478 patients with single small HCC (S-S), 209 patients with single large (≥ 30mm) HCC (S-L), 182 patients with multiple small HCC (M-S), and 121 patients with multiple large HCC (M-L). S-S patients were randomized in a 7:3 ratio into the training cohort (N=334) and the validation cohort (N=144). Lasso-Cox regression analysis was carried out to identify independent risk factors, which were used to construct a nomogram. The performance of the nomogram was evaluated by C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) curves. Patients in the training and validation cohorts were divided into low-risk, intermediate-risk, and high-risk groups based on the risk scores of the nomogram. Results: The median recurrence-free survival (mRFS) in S-S patients was significantly longer than the S-L, M-S, and S-L patients (P< 0.0001). The content of the nomogram includes age, monocyte-to-lymphocyte (MLR), gamma-glutamyl transferase-to-lymphocyte (GLR), International normalized ratio (INR), and Erythrocyte (RBC). The C-index (0.704 and 0.71) and 1-, 3-, and 5-year AUCs (0.726, 0.800, 0.780, and 0.752, 0.761, 0.760) of the training and validation cohorts proved the excellent predictive performance of the nomogram. Calibration curves the DCA curves showed that the nomogram had good consistency and clinical utility. There were apparent variances in RFS between the low-risk, intermediate-risk, and high-risk groups (P< 0.0001). Conclusion: S-S patients who underwent ablation had the best prognosis. The nomogram developed and validated in the study had good predictive ability for S-S patients. [ABSTRACT FROM AUTHOR]

Background: To demonstrate and analyze the 18F-FDG positron emission tomography/computed tomography (PET/CT) findings in this rare nevoid basal cell carcinoma syndrome (NBCCS). Case presentation: A 71-year-old woman with the left invasive breast cancer was treated with hormone therapy for six months and underwent the 18F-FDG PET/CT examination for efficacy evaluation. 18F-FDG PET/CT revealed the improvement after treatment and other unexpected findings, including multiple nodules on the skin with 18F-FDG uptake, bone expansion of cystic lesions in the bilateral ribs, ectopic calcifications and dilated right ureter. She had no known family history. Then, the patient underwent surgical excision of the all skin nodules and the postoperative pathology were multiple basal cell carcinomas. Finally, the comprehensive diagnosis of NBCCS was made. The patient was still in follow-up. Additionally, we have summarized the reported cases (n = 3) with 18F-FDG PET/CT from the literature. Conclusions: It is important to recognize this syndrome on 18F-FDG PET/CT because of different diagnoses and therapeutic consequences. [ABSTRACT FROM AUTHOR]

Subsurface pipes covered with geotextiles and filters are essential for preventing clogging and ensuring efficient drainage. To address low salt discharge efficiency due to subsurface drainage pipes (SDPs) clogging easily, sand gravel, straw, and combined sand gravel–straw were set above SDPs, respectively, within a setting of uniform geotextiles. The influences of different filter materials on the drainage efficiency and salt discharge effect of the SDPs, as well as the effects of different filter materials on the salt drainage efficiency and anti-siltation effect of the SDPs were studied by performing simulation experiments in a laboratory. The results confirmed the following: (1) The salt removal rates of the SDPs externally wrapped with materials exceeded 95%. The subsurface pipe treated with the sand gravel filter material had the highest desalting rate (93.69%) and soil profiles with total salt contents that were 17.7% and 20.5% lower than those treated with the straw and combined sand gravel–straw materials, respectively. (2) The soil salinity of the sand gravel filter material around the SDPs was between 1.57 and 3.6 g/kg, and the drainage rate (R) was 0.97, so its salt-leaching effect was the best. (3) The sand gravel filter material increased the characteristic particle size of the soil above the SDP by 8.4%. It could effectively intercept coarse particles, release fine particles, and facilitate the formation of a highly permeable soil skeleton consisting of coarse particles, such as sand particles surrounding the soil. (4) The use of the straw filter material produced dense filter cake layers on the upstream surfaces of the geotextiles. When the sand gravel and combined sand gravel–straw filter materials were used, soil particles remained in the geotextile fiber structure, and a large number of pores were still retained. Therefore, the sand gravel filter material was the most suitable for the treatment of Yinbei saline–alkali soil in Ningxia Hui Autonomous Region. [ABSTRACT FROM AUTHOR]

Background: Traditional Chinese medicine (TCM) has been garnering ever-increasing worldwide attention as the herbal extracts and formulas prove to have potency against disease. Fuzhengjiedu San (FZJDS), has been extensively used to treat viral diseases in pigs, but its bioactive components and therapeutic mechanisms remain unclear. Methods: In this study, we conducted an integrative approach of network pharmacology and experimental study to elucidate the mechanisms underlying FZJDS's action in treating porcine reproductive and respiratory syndrome virus (PRRSV). We constructed PPI network and screened the core targets according to their degree of value. GO and KEGG enrichment analyses were also carried out to identify relevant pathways. Lastly, qRT-PCR, flow cytometry and western blotting were used to determine the effects of FZJDS on core gene expression in PRRSV-infected monkey kidney (MARC-145) cells to further expand the results of network pharmacological analysis. Results: Network pharmacology data revealed that quercetin, kaempferol, and luteolin were the main active compounds of FZJDS. The phosphatidylinositol-3-kinase (PI3K)/Akt pathway was deemed the cellular target as it has been shown to participate most in PRRSV replication and other PRRSV-related functions. Analysis by qRT-PCR and western blotting demonstrated that FZJDS significantly reduced the expression of P65, JNK, TLR4, N protein, Bax and IĸBa in MARC-145 cells, and increased the expression of Bcl-2, consistent with network pharmacology results. This study provides that FZJDS has significant antiviral activity through its effects on the PI3K/AKT signaling pathway. Conclusion: We conclude that FZJDS is a promising candidate herbal formulation for treating PRRSV and deserves further investigation. [ABSTRACT FROM AUTHOR]

This paper proposes a spatiotemporal attention convolutional network (STAC-Pred) that leverages deep learning techniques to model the spatiotemporal features of tropical cyclones (TCs) and enable real-time prediction of their intensity. The proposed model employs dual branches to concurrently extract and integrate features from intensity heatmaps and satellite cloud imagery. Additionally, a residual attention (RA) module is integrated into the three-channel cloud imagery convolution process to automatically respond to high wind speed regions. TC's longitude, latitude, and radius of winds are injected into the multi-timepoint prediction model to assist in the prediction task. Furthermore, a rolling mechanism (RM) is employed to smooth the fluctuation of losses, achieving accurate prediction of TC intensity. We use several TC records to evaluate and validate the universality and effectiveness of the model. The results indicate that STAC-Pred achieves satisfactory performance. Specifically, the STAC-Pred model improves prediction performance by 47.69% and 28.26% compared to the baseline (official institutions) at 3- and 6-h intervals, respectively. Significance Statement: Tropical cyclones are one of the most deadly and damaging natural disasters in coastal areas worldwide. Early prediction can significantly reduce casualties and property losses. This study innovatively conducts dimensionality augmentation on one-dimensional intensity numerical sequences and proposes a new network model for rolling forecast of their future intensity. The proposed prototype model (not yet incorporating any atmospheric conditions) shows promising results for 3- and 6-h advance forecasts, providing valuable guidance for forecasters regarding real-time operational predictions of short-term tropical cyclone intensity. [ABSTRACT FROM AUTHOR]