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The seismic performance of earth-rock dams on deep overburdens has long been a central focus in dam engineering. Understanding the propagation law of seismic waves within a deep overburden is key to revealing the mechanisms of the dynamic response. In this study, the propagation characteristics of horizontal and vertical seismic waves in a deep overburden were systematically investigated using large-scale shaking table tests, with analysis based on acceleration and pore pressure data combined with stockwell transforms and coherence function methods. The results show clear differences in how the deep overburden modulates seismic waves in different directions. Horizontally, seismic waves exhibit an amplitude pattern that first decreases and then increases during propagation, with low-frequency components dominating and high-frequency components being strongly filtered. Vertically, the peak acceleration at the overburden top was higher than that in the lower layers; however, the frequency content changed slightly, and high-frequency filtering was weak. The weak interlayer has an obvious filtering effect on high-frequency horizontal seismic waves. For the pore pressure response, the accumulation of excess pore pressure across different depths was generally similar; however, liquefaction was only observed at the top of the overburden.

Metabolic reprogramming is one of the main characteristics of malignant tumors. The metabolic reprogramming of tumors is not only related to the characteristics of cancer cells, but also closely related to the tumor microenvironment (TME). 'Aerobic glycolysis' is considered to be the classic metabolic mode of tumor cells. However, recent experiments have shown that the TME plays a key role in carcinogenesis and epithelial-mesenchymal transition. Cancer-associated fibroblasts (CAFs) dominate in the microenvironment and affect the homeostasis of the TME. The interaction between cancer cells and the surrounding CAFs markedly affects the growth, metabolism, metastasis, and progression of cancer. Based on this, a 'dual-chamber' model, also known as the 'Reverse Warburg effect', is proposed. Specifically, cancer cells secrete hydrogen peroxide into the TME to induce oxidative stress in neighboring stromal cells. CAFs undergo aerobic glycolysis and produce high levels of energy-rich 'fuels' (such as pyruvate, ketone bodies, fatty acids, and lactic acid). In turn, these energy-rich 'fuels' then 'feed' cancer cells. The mitochondrial oxidative phosphorylation system produces a large quantity of ATP, such that tumor cells have a higher proliferation ability. The proposed 'Reverse Warburg effect' redefines the tumor cell microenvironment and tumor metabolic reprogramming. Therefore, understanding the 'Reverse Warburg effect' of CAFs and its related mechanisms will help us to understand the association between the microenvironment, the matrix, and cancer cells, and may lead to new treatment strategies and targets.

Many statistical applications involve models for which it is difficult to evaluate the likelihood, but from which it is relatively easy to sample. Approximate Bayesian computation is a likelihood-free method for implementing Bayesian inference in such cases. We present results on the asymptotic variance of estimators obtained using approximate Bayesian computation in a large data limit. Our key assumption is that the data are summarized by a fixed-dimensional summary statistic that obeys a central limit theorem. We prove asymptotic normality of the mean of the approximate Bayesian computation posterior. This result also shows that, in terms of asymptotic variance, we should use a summary statistic that is of the same dimension as the parameter vector, p, and that any summary statistic of higher dimension can be reduced, through a linear transformation, to dimension p in a way that can only reduce the asymptotic variance of the posterior mean. We look at how the Monte Carlo error of an importance sampling algorithm that samples from the approximate Bayesian computation posterior affects the accuracy of estimators. We give conditions on the importance sampling proposal distribution such that the variance of the estimator will be of the same order as that of the maximum likelihood estimator based on the summary statistics used. This suggests an iterative importance sampling algorithm, which we evaluate empirically on a stochastic volatility model.

To study the liquefaction problem of the dam foundation of an ultra-deep overburden in the strong earthquake area, a large-scale shaking table model test is carried out based on a 500 m rockfill dam project on an ultra-deep overburden. A laminar shear box was used to simulate the seismic response of the free field dam foundation under earthquake action. Under the action of 0.1–0.8 bidirectional ground motion, the changes in excess pore-water pressure in different positions of the ultra-deep overburden are analyzed, and the liquefaction characteristics of the ultra-deep overburden dam foundation are discussed. The results show that the excess pore-water pressure decreases first and then increases from the bottom to the top of the overburden when the PGA reaches 0.48 . In addition, the excess pore-water pressure is small in the clay layer and begins to increase in the surface near the ground. With the increase of the PGA, the excess pore-water pressure increases at each position, and the maximum value is located at the bottom of the overburden. The excess pore-water pressure ratio is always small below the clay layer but increases above the clay layer. When the PGA reaches 0.48 , the excess pore-water pressure ratio exceeds 0.8, and the soil liquefies. With the increase of the PGA, from the bottom to the top of the overburden, the maximum dynamic soil pressure in the horizontal direction increases first, then decreases, and then increases, while the vertical direction is just the opposite.

Accurately obtaining accurate information about the future traffic flow of all roads in the transportation network is essential for traffic management and control applications. In order to address the challenges of acquiring dynamic global spatial correlations between transportation links and modeling time dependencies in multi-step prediction, we propose a spatial linear transformer and temporal convolution network (SLTTCN). The model is using spatial linear transformers to aggregate the spatial information of the traffic flow, and bidirectional temporal convolution network to capture the temporal dependency of the traffic flow. The spatial linear transformer effectively reduces the complexity of data calculation and storage while capturing spatial dependence, and the time convolutional network with bidirectional and gate fusion mechanisms avoids the problems of gradient vanishing and high computational cost caused by long time intervals during model training. We conducted extensive experiments using two publicly available large-scale traffic data sets and compared SLTTCN with other baselines. Numerical results show that SLTTCN achieves the best predictive performance in various error measurements. We also performed attention visualization analysis on the spatial linear transformer, verifying its effectiveness in capturing dynamic global spatial dependency.

The development of Alzheimer’s disease (AD) is implicated with the dysregulation of numerous circular RNAs (circRNAs). However, the function of several circRNAs remains unclear. The aim of this study was to investigate the role of circular AXL receptor tyrosine kinase (circAXL) in AD. Cell models of AD were constructed by treating SK-N-SH cells with amyloid-β (Aβ 1-42 ). The expression of circAXL, miR-1306-5p and phosphodiesterase 4A (PDE4A) mRNA was detected by quantitative real-time PCR (qPCR). Cell viability was checked by CCK-8 assay. The production of inflammatory factors was monitored by ELISA. Cell apoptosis was checked by flow cytometry assay. Oxidative stress was assessed by ROS level, MDA level and SOD activity using commercial kits. Endoplasmic reticulum (ER) stress was assessed by ER-related protein markers using western blotting. The relationship between miR-1306-5p and circAXL or PDE4A was validated by RIP assay and dual-luciferase reporter assay. Serum exosomes were isolated by centrifugation to assess the diagnostic value of exosomal circAXL, miR-1306-5p and PDE4A. CircAXL was overexpressed in Aβ 1-42 -treated SK-N-SH cells. CircAXL knockdown alleviated Aβ 1-42 -induced cell cytotoxicity, cell apoptosis, inflammation, oxidative stress and endoplasmic reticulum (ER) stress in SK-N-SH cells. MiR-1306-5p was screened as a target of circAXL, and miR-1306-5p inhibition abolished the effects of circAXL knockdown. MiR-1306-5p inhibited the expression of PDE4A, and circAXL regulated PDE4A expression by targeting miR-1306-5p. MiR-1306-5p restoration also alleviated Aβ 1-42 -induced cell injuries, while PDE4A reintroduction abolished the effects of miR-1306-5p restoration. Exosomal circAXL and exosomal miR-1306-5p had diagnostic values for AD. CircAXL knockdown alleviates Aβ 1-42 -induced neurotoxicity in AD pathology via repressing PDE4A by releasing miR-1306-5p.

The emergence of the novel human coronavirus SARS-CoV-2 in Wuhan, China has caused a worldwide epidemic of respiratory disease (COVID-19). Vaccines and targeted therapeutics for treatment of this disease are currently lacking. Here we report a human monoclonal antibody that neutralizes SARS-CoV-2 (and SARS-CoV) in cell culture. This cross-neutralizing antibody targets a communal epitope on these viruses and may offer potential for prevention and treatment of COVID-19. Vaccines and targeted therapeutics for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are currently lacking. Here, the authors report a human monoclonal antibody capable of neutralizing both authentic SARS-CoV and SARS-CoV-2 by targeting a common epitope.

Computer-aided diagnosis techniques based on deep learning in skin cancer classification have disadvantages such as unbalanced datasets, redundant information in the extracted features and ignored interactions of partial features among different convolutional layers. In order to overcome these disadvantages, we propose a skin cancer classification model named EFFNet, which is based on feature fusion and random forests. Firstly, the model preprocesses the HAM10000 dataset to make each category of training set images balanced by image enhancement technology. Then, the pre-training weights of the EfficientNetV2 model on the ImageNet dataset are fine-tuned on the HAM10000 skin cancer dataset. After that, an improved hierarchical bilinear pooling is introduced to capture the interactions of some features between the layers and enhance the expressive ability of features. Finally, the fused features are passed into the random forests for classification prediction. The experimental results show that the accuracy, recall, precision and F1-score of the model reach 94.96%, 93.74%, 93.16% and 93.24% respectively. Compared with other models, the accuracy rate is improved to some extent and the highest accuracy rate can be increased by about 10%.

Background Accumulating evidence shows that long noncoding RNAs (lncRNAs) are important regulator molecules involved in diverse biological processes. Acquired drug resistance is a major challenge in the clinical treatment of glioblastoma (GBM), and lncRNAs have been shown to play a role in chemotherapy resistance. However, the underlying mechanisms by which lncRNA mediates TMZ resistance in GBM remain poorly characterized. Methods Quantitative reverse transcription PCR (qRT-PCR) and fluorescence in situ hybridization assays were used to detect small nucleolar RNA host gene 12 (SNHG12) levels in TMZ-sensitive and TMZ-resistant GBM cells and tissues. The effects of SNHG12 on TMZ resistance were investigated through in vitro assays (western blots, colony formation assays, flow cytometry assays, and TUNEL assays). The mechanism mediating the high expression of SNHG12 in TMZ-resistant cells and its relationships with miR-129-5p, mitogen-activated protein kinase 1 (MAPK1), and E2F transcription factor 7 (E2F7) were determined by bioinformatic analysis, bisulfite amplicon sequencing, methylation-specific PCR, dual luciferase reporter assays, chromatin immunoprecipitation assays, RNA immunoprecipitation assays, immunofluorescence, qRT-PCR, and western blot. For in vivo experiments, an intracranial xenograft tumor mouse model was used to investigate SNHG12 function. Results SNHG12 was upregulated in TMZ-resistant cells and tissues. Overexpression of SNHG12 led to the development of acquired TMZ resistance, while knockdown of SNHG12 restored TMZ sensitivity. An abnormally low level of DNA methylation was detected within the promoter region of SNHG12, and loss of DNA methylation made this region more accessible to the Sp1 transcription factor (SP1); this indicated that methylation and SP1 work together to regulate SNHG12 expression. In the cytoplasm, SNHG12 served as a sponge for miR-129-5p, leading to upregulation of MAPK1 and E2F7 and endowing the GBM cells with TMZ resistance. Disinhibition of MAPK1 regulated TMZ-induced cell apoptosis and the G1/S cell cycle transition by activating the MAPK/ERK pathway, while E2F7 dysregulation was primarily associated with G1/S cell cycle transition. Clinically, SNHG12 overexpression was associated with poor survival of GBM patients undergoing TMZ treatment. Conclusion Our results suggest that SNHG12 could serve as a promising therapeutic target to surmount TMZ resistance, thereby improving the clinical efficacy of TMZ chemotherapy.

PurposeTo assess the impact of cardiac motion during percutaneous computed tomography (CT)-guided core needle biopsy (PCT-CNB) of small lung lesions near pericardium, focusing on safety and diagnostic accuracy.Materials and methodsSeventy-eight PCT-CNBs were performed between March 2010 and June 2018 in 78 patients with small (≤ 20 mm) lung nodules, each within 10 mm of pericardium. Shifts in distance and length of interface separating lesions from pericardium were calculated and compared by cardiac chambers (left atrium, left ventricle, right atrium, or right ventricle). Risk factors for complications were subjected to univariate analysis, and diagnostic accuracy was assessed.ResultsThe respective mean values were 0.8 ± 1.1 mm (range 0–5.1 mm) for shifts in distance and 1.5 ± 2.1 mm (range 0–10.8 mm) for length of interface. Neither parameter shifted significantly with respect to cardiac chambers (p > 0.05, both). Pneumothorax ensued in 28 patients (35.9%), and pulmonary hemorrhage occurred in 41 (52.6%). The overall sensitivity, specificity, and accuracy of PCT-CNB were 91.2%, 100%, and 93.2%, respectively.ConclusionOur data indicate that cardiac motion has no impact on either the incidence of complications or the diagnostic accuracy of PCT-CNB in patients with small (≤ 20 mm) lung lesions near pericardium.