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Background. Paeoniflorin is an active component of a widely used traditional Chinese medicine with antitumor activity through ferroptosis induction. It has been reported recently that ferroptosis is emerging in certain types of cancer; however, its relevance in glioma is still not well studied. Methods. CCK8 assay was performed for cell proliferation. Expression of mRNA and protein was tested by qPCR and western blot, respectively. Clinical section samples were detected by IHC. The relationship between NEDD4L and STAT3 was validated by a coimmunoprecipitation assay. Apoptosis was identified by TUNEL assay. A xenograft mouse model was utilized to validate the potential of paeoniflorin toward glioma cancer cells. Results. The data suggested that paeoniflorin could increase NEDD4L expression in glioma cells. The NEDD4L expression level was lower in glioma cancer tissues compared to adjacent normal tissues, and it correlates with poor prognosis. Meanwhile, NEDD4L mediates the ubiquitination of STAT3. Furthermore, increased NEDD4L significantly inhibited cell viability and induced accumulation of intracellular ROS levels, accompanied by decreased expression of key ferroptosis factors Nrl2 and GPX4, while NEDD4L knockdown had a reverse effect, suggesting that ferroptosis could be involved. NEDD4L-induced ferroptosis could be rescued by forced expression of STAT3. A xenograft nude mouse model showed that paeoniflorin inhibits tumor growth and further sensitizes glioma cells to RSL3, another well-known ferroptosis inducer. Conclusions. In summary, this study demonstrated that paeoniflorin might function as an effective drug for glioma by inducing ferroptosis via upregulation of NEDD4L and repression of Nrl2, GPX4, and STAT3.

Offshore wind turbine is facing with extremely complicated climatic environment. The accurate prediction of wave movement caused by strong typhoons and its action on foundation of wind turbine are crucial. To disclose hydrodynamic characteristics of foundation structure of wind turbine under typhoon-wave-current coupling effect on the sea, a 10 MW super-large offshore wind turbine in Wailuo Wind Farm, Guangdong was chosen as a research object and a real-time meso-scale WRF-SWAN-FVCOM (W-S-F) coupling simulation platform was constructed by using Model Coupling Toolkit The spatial-temporal evolution of typhoon-wave-current in the offshore wind farm was simulated when a super typhoon “Rammasun” passed through. Next, the hydrodynamic load distribution characteristics of single pile foundation of wind turbine were analyzed by combining the meso-micro scale nested method. Extreme load model of foundation piles under different wave phases was proposed. Results demonstrated that the constructed W-S-F platform increased the simulation precision of typhoon path by 42.51% than single WRF model. The horizontal wave force of the foundation pile reached the negative and positive peaks at phases T0 and T4 under typhoon-wave-current coupling, and it presented symmetric distribution circumferentially around the 180° angle of wave attack. The phase T4 was the most adverse phase for the strength design of single pile foundation of offshore wind turbine. At the bottom of foundation, the maximum shear reached the 7.68 × 106 magnitude and the maximum bending moment reached the 5.2 × 108 magnitude.

Tannic acid (TA) is a natural phenolic compound abundant in plants. Its characteristics of low combustion and good absorption make it useful in the flame retardant field. On this basis, a new expansive flame retardant system (ACT) composed of ammonium polyphosphate (APP)/TA functional clay (CT) was used to study the synergistic flame retardancy and smoke suppression of natural rubber (NR). Because of their unique flame retardancy and better mechanical properties compared with the traditional expansive flame retardant system (IFR), new flame retardants have attracted much attention in various fields. The results of the cone calorimeter showed that the ACT system can significantly influence the decomposition behavior of NR and form a highly graphitized and phosphorous carbon layer to protect the composite material, thus a synergistic effect is produced on the flame retardancy and smoke suppression performance of the composite material. In addition, within the effective additive quality range of the ACT system, TC can give the NR composite excellent mechanical properties.

In 2009, an emerging citrus viral disease caused by Citrus chlorotic dwarf-associated virus (CCDaV) was discovered in Yunnan Province of China. However, the occurrence and spread of CCDaV in other citrus-growing provinces in China is unknown to date. To better understand the distribution and molecular diversity of CCDaV in China, a total of 1 772 citrus samples were collected from 11 major citrus-growing provinces and were tested for CCDaV by PCR. Among these, 134 citrus samples from Guangxi, Yunnan and Guangdong were tested positive for CCDaV, demonstrating that the occurrence and spread of CCDaV are increasing in China. The complete genome sequences of 17 CCDaV isolates from different provinces and hosts were sequenced. Comparisons of the whole-genome sequences of the 17 CCDaV isolates as well as the 15 isolates available in GenBank revealed that the sequence identity was about 99–100%, showing that the CCDaV isolates were highly conserved. Phylogenetic studies showed that the 32 CCDaV isolates belonged to four different groups based on geographical origins and host species, and that CCDaV isolates from China and Turkey were clustered into different groups. The results provide important information for clarifying the distribution and genetic diversity of CCDaV in China.

The steerability of a quantum state can be detected by steering inequalities. The linear steering inequalities show that more steerable states can be discovered with the increase of measurements. In order to detect more steerable states in two-photon systems, we first theoretically derive an optimized steering criterion based on infinity measurements for an arbitrary two-qubit state. The steering criterion is only determined by the spin correlation matrix of the state, and do not require infinity measurements. Then, we prepared the Werner-like states in two-photon systems, and measure their spin correlation matrices. Finally, we apply three steering criteria, which include our steering criterion, the three-measurement steering criterion and the geometric Bell-like inequality, to distinguish the steerability of these states. The results show that our steering criterion can detect the most steerable states under the same experimental conditions. Thus, our work provides a valuable reference for detecting the steerability of quantum states.

Bronsted acid-catalyzed direct C(sp2)-H alkylation of N-heteroaromatics with cyclic ethers via a cross-dehydrogenative coupling reaction in the presence of benzoyl peroxide was developed. This methodology successfully provided an easy access to a variety of alkyl-substituted quinoxaline, benzoimidazole, pyrazine, pyrimidine, quinoline, isoquinoline, and pyridine derivatives in up to 94% yield under metal-free conditions.

The LIM domain only 1 (LMO1) gene belongs to the LMO family of genes that encodes a group of transcriptional cofactors. This group of transcriptional cofactors regulates gene transcription by acting as a key “connector” or “scaffold” in transcription complexes. All LMOs, including LMO1, are important players in the process of tumorigenesis. Unique biological features of LMO1 distinct from other LMO members, such as its tissue-specific expression patterns, interacting proteins, and transcriptional targets, have been increasingly recognized. Studies indicated that LMO1 plays a critical oncogenic role in various types of cancers, including T-cell acute lymphoblastic leukemia, neuroblastoma, gastric cancer, lung cancer, and prostate cancer. The molecular mechanisms underlying such functions of LMO1 have also been investigated, but they are currently far from being fully elucidated. Here, we focus on reviewing the current findings on the role of LMO1 in tumorigenesis, the mechanisms of its oncogenic action, and the mechanisms that drive its aberrant activation in cancers. We also briefly review its roles in the development process and non-cancer diseases. Finally, we discuss the remaining questions and future investigations required for promoting the translation of laboratory findings to clinical applications, including cancer diagnosis and treatment.

BACKGROUND: Traditional least mean square algorithm (LMS) tends to converge faster and thus the larger the steady-state error of the algorithm. OBJECTIVE: In order to solve this issue, an improved adaptive normalized least mean square (NLMS) ECG signal denoising algorithm is proposed through utilizing the NLMS and the least mean square algorithm with added momentum term (MLMS). METHODS: The algorithm firstly performs LMS adaptive filtering on the original ECG signal. Then, the algorithm uses the relative error of the prior error signal and the posterior error signal before and after filtering to adaptively determine the iteration step factor. Finally, the expected error is set to determine whether the denoising meets the expected requirements. This method is applied to the MIT-BIH ECG database established by the Massachusetts Institute of Technology. RESULTS: Experimental results have shown that the proposed algorithm can achieve good denoising for the target signal, and the average signal to noise ratio (SNR) of the proposed method is 17.6016, the RMSE is only 0.0334, and the average smoothness index R is only 0.0325. CONCLUSION: The proposed algorithm effectively removes the original ECG signal noise, and improves the smoothness of the signal the denoising efficiency.

International audience; This study examines the influence of surface roughness on the capillarity of liquid bridges between two solid surfaces and the wettability of droplets on solid surfaces. For this purpose, different surface roughnesses were prepared by gluing waterproof sandpaper onto flat glass surfaces. The effects of surface roughness on liquid bridge capillarity were investigated by using a method that is based on an exact analytical solution of the Young-Laplace equation for a liquid bridge between two parallel planes coupled with capillary force measurement. The capillary forces between two parallel planes were measured by a micro-balance, while the geometries of the capillary bridge were recorded using a high-resolution camera. Using the images of capillary bridges, the meridional profiles of capillary bridges were determined by a high-resolution image processing technique and correlated to the measured capillary forces. The effects of surface roughness on droplet wetting were measured by employing the same high-resolution image processing technique. The measured results show that as the roughness increases, the wetting angle increases, whereas the capillary forces of the liquid bridge decrease.

Entanglement and coherence are two important resources in quantum information theory. A question naturally arises: Is there some connection between them? We prove that the entanglement of formation and the first-order coherence of two-qubit states satisfy an inequality relation. Two-qubit pure state reaches the upper bound of this inequality. A large number of randomly generated states are used to intuitively verify the complementarity between the entanglement of formation and the first-order coherence. We give the maximum accessible coherence of two-qubit states. Our research results will provide a reliable theoretical basis for conversion of the two quantum resources.

Because of the nonlinearity and nonstationarity in the vibration signals of some rotating machinery, the analysis of these signals using conventional time- or frequency-domain methods has some drawbacks, and the results can be misleading. In this paper, a couple of features derived from multivariate empirical mode decomposition (MEMD) are introduced, which overcomes the shortcomings of the traditional features. A wind turbine gearbox and its bearings are investigated as rotating machinery. In this method, two types of feature structures are extracted from the decomposed signals resulting from the MEMD algorithm, called intrinsic mode function (IMF). The first type of feature vector element is the energy moment of effective IMFs. The other type of vector elements is amplitudes of a signal spectrum at the characteristic frequencies. A correlation factor is used to detect effective IMFs and eliminate the redundant IMFs. Since the basic MEMD algorithm is sensitive to noise, a noise-assisted extension of MEMD, NA-MEMD, is exploited to reduce the effect of noise on the output results. The capability of the proposed feature vector in health condition monitoring of the system is evaluated and compared with traditional features by using a discrimination factor. The proposed feature vector is utilized in the input layer of the classical three-layer backpropagation neural network. The results confirm that these features are appropriate for intelligent fault detection of complex rotating machinery and can diagnose the occurrence of early faults.

With the increasing demand of higher travelling speed, a new streamlined high-speed maglev train has been designed to reach a speed of 600 km/h. To better capture the flow field structures around the maglev train, an improved delayed detached eddy simulation (IDDES) is adopted to model the turbulence. Results show that the new maglev train has good aerodynamic load performance such as small drag coefficient contributing to energy conservation. The main frequencies of aerodynamic forces for each car have a scattered distribution. There are two pairs of counter-rotating large vortices in the non-streamlined part of the train that make the boundary layer thicker. Many high-intensity vortices are distributed in the narrow space between skirt plates or train floor and track. In the gap between the train floor and track (except near the tail car nose), the main frequency of vortex shedding remains constant and its strength increases exponentially in the streamwise direction. In the wake, the counter-rotating vortices gradually expand and reproduce some small vortices that move downward. The vortex has quite random and complex frequencydomain distribution characteristics in the wake. The maximum time-averaged velocity of the slipstream occurs near the nose of the head car, based on which, the track-side safety domain is divided.

The normalized difference vegetation index (NDVI) is an important agricultural parameter that is closely correlated with crop growth. In this study, a novel method combining the dynamic time warping (DTW) model and the long short-term memory (LSTM) deep recurrent neural network model was developed to predict the short and medium-term winter wheat NDVI. LSTM is well-suited for modelling long-term dependencies, but this method may be susceptible to overfitting. In contrast, DTW possesses good predictive ability and is less susceptible to overfitting. Therefore, by utilizing the combination of these two models, the prediction error caused by overfitting is reduced, thus improving the final prediction accuracy. The combined method proposed here utilizes the historical MODIS time series data with an 8-day time resolution from 2015 to 2020. First, fast Fourier transform (FFT) is used to decompose the time series into two parts. The first part reflects the inter-annual and seasonal variation characteristics of winter wheat NDVI, and the DTW model is applied for prediction. The second part reflects the short-term change characteristics of winter wheat NDVI, and the LSTM model is applied for prediction. Next, the results from both models are combined to produce a final prediction. A case study in Hebei Province that predicts the NDVI of winter wheat at five prediction horizons in the future indicates that the DTW–LSTM model proposed here outperforms the LSTM model according to multiple evaluation indicators. The results of this study suggest that the DTW–LSTM model is highly promising for short and medium-term NDVI prediction.

The number of malicious traffic in the real network is growing rapidly, such as common DoS attacks, web attacks, DDoS attacks, and so on. These attacks may cause huge economic losses to enterprises, countries, and individuals. Traditional machine learning methods are hardly meeting the accuracy and real-time requirements of malicious traffic identification technology in the face of huge and multi-dimensional online data. Large-scale data can provides training data for deep learning, and deep learning can directly obtain advanced features from the data. Therefore, the malicious traffic identification technology based on a convolutional neural network (CNN) is adopted. The outliers and useless features in the data set are removed by preprocessing, and then the model is obtained by multiple training and optimization of the convolutional neural network. The experimental results show that CNN performs well in the malicious traffic detection tasks, and the metric F1 values reach 95%, which is better than the deep learning models such as RNN, and CRNN.

Eggshell translucency is a ubiquitous external eggshell quality problem caused by variations of eggshell ultrastructure or shell membrane. In previous studies, researchers have widely investigated this phenomenon with nutritional, environmental, and genetic perspectives in many breeds. However, most studies referring to phenotypic measurement of shell translucency have been performed using a relatively subjective two-, three-, or four-grading methods, which made it impossible to compare distribution of shell translucency among different breeds. In this study, we aimed to explore variations of translucent eggshell spots in different breeds and their distribution in blunt, middle, and sharp ends of eggshell using a relatively objective grayscale recognition method. We selected 45 eggs from each flock of pure lines, commercial strains, and Chinese local breeds (10 flocks, aged 60 to 70 wk), and stored them in a constant environment for 5 d. Then measured eggshell translucency using grayscale recognition method. Indicators of shell translucency included sum of spot areas on the whole eggshell (SUSA), sum area of the whole eggshell (SUSHA), RSS (ratio of SUSA to SUSHA), quantity of spots (QS), average spot area in eggshell (AAES), and diameter of spots in eggshell (DS). As results, in Hy-Line Brown, Brown-Egg Dwarf Layer, and Taihang (pink-shelled) breeds, phenotypic intensity of eggshell translucency was slight; in Rhode Island Red, Jingfen-1, and Dongxiang breeds, phenotypic intensity of eggshell translucency was relatively extensive; and in Jinghong-1, Hy-Line Sonia, White Leghorn, and Taihang (blue-shelled), phenotypic intensity of eggshell translucency was at an intermediate level. In general, the larger the RSS, the larger the QS, AAES, and DS. Of 3 ends for most breeds, eggshell translucency of blunt and sharp ends was usually greater than that of middle ends, and blunt ends seemed to have the most extensive eggshell translucency. Findings from this study could provide a reference for population selection to locate genes regulating shell translucency and to explore the physical structure mechanism for eggshell translucency formation.

Clarifying the current situation of regional water pollutants and the relationship between pollutants and pollution sources is considered essential for managing the water environment. Water quality identification index (WQI), cluster analysis (CA), positive matrix factorization (PMF), and stable isotope analysis in R (SIAR) were employed to interpret a large and complex water quality data set of the Qinhuai River catchment generated during 2015 to 2019 to monitor of 11 parameters at 29 different sampling sites. WQI analysis indicated that water quality in Qinhuai River catchment is considered to have "moderate pollution," and an improving trend of water quality was observed at the interannual scale. TN was the most deteriorated of all pollution parameters. CA and PMF results on the spatial scale revealed that sampling sites located at downtown of Nanjing and Lishui District or Jangling University town were highly polluted due to the sewage from domestic sewage and business service sewage (28.88%) as well as industrial wastewater (27.43%), while sampling sites located at Hushu Street Administrative District, Ergan River, and Sangan River were slightly polluted by rural domestic wastewater and garbage (28.79%), and agricultural non-point source pollution (24.3%). The middle-lower reaches (Jiangning Development Zone and Moling Street) and middle reaches (Lukou Street Administrative District) were moderately polluted by industrial wastewater (27.25%), sewage from domestic wastewater and business service wastewater (31.62%) as well as inner sources (24.76%). The SIAR results showed that NO