Deregulation of Polycomb Repressive Complex-2 in Mantle Cell Lymphoma Confers Growth Advantage by Epigenetic Suppression of

The polycomb repressive complex 2 (PRC2) maintains the transcriptional repression of target genes through its catalytic component enhancer of zeste homolog 2 (EZH2). Through modulating critical gene expression, EZH2 also plays a role in cancer development and progression by promoting cancer cell survival and invasion. Mutations in EZH2 are prevalent in certain B-cell lymphoma subtypes such as diffuse large cell lymphoma and follicular lymphoma; while no EZH2 mutation has been reported in the mantle cell lymphoma (MCL). Here we demonstrate that the PRC2 components EZH2, EED and SUZ12 are upregulated in the MCL cells as compared to normal B-cells. Moreover, stably transfected cells with wild-type EZH2 or-EED showed increased cell growth and H3K27-trimehtylation. However, unlike wild-type EZH2, ectopic expression of a deletion construct of EZH2 (EZH

Conductivity Prediction Method of Carbon Nanotube Resin Composites Considering the Quantum Tunnelling Effect

Understanding and predicting the conductivity of carbon nanotube resin composites are essential for structural health detection and monitoring applications. Due to the complexity in the composition of carbon nanotube resin composites, it is of practical significance to develop a method for predicting the conductivity with a view to design and making of the composite. In this paper, the influence of carbon nanotube tunnelling on the conductivity was investigated thoroughly, where the tunnelling conductivity effect is considered as an independent conductive phase. Then, the effective medium model and the Hashin–Shtrikman (H–S) boundary model are used to predict the conductivity of carbon nanotube resin composites. The results presented in this paper show that the developed method can reduce the prediction range of the H–S boundary model and improve the prediction accuracy of the lower bound of the H–S boundary model. The results also show that the tunnelling has little effect on conductivity prediction based on the effective medium model. Based on the results, the effects of nanotube conductivity, the aspect ratio and the barrier height on the prediction of the effective conductivity are discussed to provide a guidance for the design and making of the composites

Study on the relationship and related factors between physical fitness and health behavior of preschool children in southwest China

Abstract Objective To investigate the physical fitness level and health behavior status of preschool children in China, explore the relationship between physical fitness and health behavior, and further reveal the main factors affecting health behavior, to provide a reference for improving the physical fitness level of preschool children and maintaining healthy behavior. Methods A total of 755 preschool children (394 boys and 361 girls, aged 4.52 ± 1.11 years) were selected from Chongqing and Liupanshui in China by cluster random sampling method for questionnaire survey and physical monitoring, and SPSS21.0 software was used to process and analyze the data. Results (1) Heart rate (p = 0.015), protein content (p < 0.001), and time spent on the balance beam (p < 0.001) were significantly lower in boys than in girls, while BMI (p = 0.012), muscle mass (p < 0.001), and distance of standing long jump (p < 0.001) were significantly higher in boys than in girls. Meanwhile, systolic blood pressure (p = 0.004) and diastolic blood pressure (p = 0.001) of rural children were significantly higher than those of urban children, while BMI (p < 0.001) and sitting forward flexion (p = 0.019) were significantly lower than those of urban children. (2) The light-intensity physical activity (LPA) and moderate to vigorous physical activity (MVPA) of boys were significantly higher than that of girls (p < 0.001), and the MVPA of urban children was significantly higher than that of rural children (p = 0.001), and the former participated in sports classes more frequently (p < 0.001). (3) There was a significant correlation between physical activity (PA) and physical fitness indicators of preschoolers. Participating in sports interest classes was only significantly correlated with systolic blood pressure (r = 0.08) and sitting forward flexion (r = 0.09). (4) The PA level of preschool children was related to gender, household registration, kindergarten nature, age, residence environment, parental support, and participation degree. Participation in sports interest classes was related to gender, the nature of the kindergarten, household registration, age, and parent participation. Daily screen time was related to household registration, the nature of the kindergarten, the environment of residence, and the value perception of parents. Conclusions There were different degrees of correlation between preschool children’s physical fitness and health behaviors, and children’s health behaviors were closely related to gender, environment, parents, and other factors. Therefore, how to increase the protective factors of children’s health behaviors and controlling the risk factors may be crucial to promoting the development of good health behaviors and improving the physical fitness of preschool children

Data from: Spatial variation of soil respiration in a cropland under winter wheat and summer maize rotation in the North China Plain

Spatial variation of soil respiration (Rs) in cropland ecosystems must be assessed to evaluate the global terrestrial carbon budget. This study aims to explore the spatial characteristics and controlling factors of Rs in a cropland under winter wheat and summer maize rotation in the North China Plain. We collected Rs data from 23 sample plots in the cropland. At the late jointing stage, the daily mean Rs of summer maize (4.74 μmol CO2 m-2 s-1) was significantly higher than that of winter wheat (3.77μmol CO2 m-2 s-1). However, the spatial variation of Rs in summer maize (coefficient of variation, CV = 12.2%) was lower than that in winter wheat (CV = 18.5%). A similar trend in CV was also observed for environmental factors but not for biotic factors, such as leaf area index, aboveground biomass, and canopy chlorophyll content. Pearson’s correlation analyses based on the sampling data revealed that the spatial variation of Rs was poorly explained by the spatial variations of biotic factors, environmental factors, or soil properties alone for winter wheat and summer maize. The similarly non-significant relationship was observed between Rs and the enhanced vegetation index (EVI), which was used as surrogate for plant photosynthesis. EVI was better correlated with field-measured leaf area index than the normalized difference vegetation index and red edge chlorophyll index. All the data from the 23 sample plots were categorized into three clusters based on the cluster analysis of soil carbon/nitrogen and soil organic carbon content. An apparent improvement was observed in the relationship between Rs and EVI in each cluster for both winter wheat and summer maize. The spatial variation of Rs in the cropland under winter wheat and summer maize rotation could be attributed to the differences in spatial variations of soil properties and biotic factors. The results indicate that applying cluster analysis to minimize differences in soil properties among different clusters can improve the role of remote sensing data as a proxy of plant photosynthesis in semi-empirical Rs models and benefit the acquisition of Rs in cropland ecosystems at large scales

Data from: Spatial variation of soil respiration in a cropland under winter wheat and summer maize rotation in the North China Plain

Spatial variation of soil respiration (Rs) in cropland ecosystems must be assessed to evaluate the global terrestrial carbon budget. This study aims to explore the spatial characteristics and controlling factors of Rs in a cropland under winter wheat and summer maize rotation in the North China Plain. We collected Rs data from 23 sample plots in the cropland. At the late jointing stage, the daily mean Rs of summer maize (4.74 μmol CO2 m-2 s-1) was significantly higher than that of winter wheat (3.77μmol CO2 m-2 s-1). However, the spatial variation of Rs in summer maize (coefficient of variation, CV = 12.2%) was lower than that in winter wheat (CV = 18.5%). A similar trend in CV was also observed for environmental factors but not for biotic factors, such as leaf area index, aboveground biomass, and canopy chlorophyll content. Pearson’s correlation analyses based on the sampling data revealed that the spatial variation of Rs was poorly explained by the spatial variations of biotic factors, environmental factors, or soil properties alone for winter wheat and summer maize. The similarly non-significant relationship was observed between Rs and the enhanced vegetation index (EVI), which was used as surrogate for plant photosynthesis. EVI was better correlated with field-measured leaf area index than the normalized difference vegetation index and red edge chlorophyll index. All the data from the 23 sample plots were categorized into three clusters based on the cluster analysis of soil carbon/nitrogen and soil organic carbon content. An apparent improvement was observed in the relationship between Rs and EVI in each cluster for both winter wheat and summer maize. The spatial variation of Rs in the cropland under winter wheat and summer maize rotation could be attributed to the differences in spatial variations of soil properties and biotic factors. The results indicate that applying cluster analysis to minimize differences in soil properties among different clusters can improve the role of remote sensing data as a proxy of plant photosynthesis in semi-empirical Rs models and benefit the acquisition of Rs in cropland ecosystems at large scales

Magnolia officinalis Extract Contains Potent Inhibitors against PTP1B and Attenuates Hyperglycemia in db/db Mice

Protein tyrosine phosphatase 1B (PTP1B) is an established therapeutic target for type 2 diabetes mellitus (T2DM) and obesity. The aim of this study was to investigate the inhibitory activity of Magnolia officinalis extract (ME) on PTP1B and its anti-T2DM effects. Inhibition assays and inhibition kinetics of ME were performed in vitro. 3T3-L1 adipocytes and C2C12 myotubes were stimulated with ME to explore its bioavailability in cell level. The in vivo studies were performed on db/db mice to probe its anti-T2DM effects. In the present study, ME inhibited PTP1B in a reversible competitive manner and displayed good selectivity against PTPs in vitro. Furthermore, ME enhanced tyrosine phosphorylation levels of cellular proteins, especially the insulin-induced tyrosine phosphorylations of insulin receptor β-subunit (IRβ) and ERK1/2 in a dose-dependent manner in stimulated 3T3-L1 adipocytes and C2C12 myotubes. Meanwhile, ME enhanced insulin-stimulated GLUT4 translocation. More importantly, there was a significant decrease in fasting plasma glucose level of db/db diabetic mice treated orally with 0.5 g/kg ME for 4 weeks. These findings indicated that improvement of insulin sensitivity and hypoglycemic effects of ME may be attributed to the inhibition of PTP1B. Thereby, we pioneered the inhibitory potential of ME targeted on PTP1B as anti-T2DM drug discovery

An Efficient Codebook Search Algorithm for Line Spectrum Frequency (LSF) Vector Quantization in Speech Codec

A high-performance vector quantization (VQ) codebook search algorithm is proposed in this paper. VQ is an important data compression technique that has been widely applied to speech, image, and video compression. However, the process of the codebook search demands a high computational load. To solve this issue, a novel algorithm that consists of training and encoding procedures is proposed. In the training procedure, a training speech dataset was used to build the squared-error distortion look-up table for each subspace. In the encoding procedure, firstly, an input vector was quickly assigned to a search subspace. Secondly, the candidate code word group was obtained by employing the triangular inequality elimination (TIE) equation. Finally, a partial distortion elimination technique was employed to reduce the number of multiplications. The proposed method reduced the number of searches and computation load significantly, especially when the input vectors were uncorrelated. The experimental results show that the proposed algorithm provides a computational saving (CS) of up to 85% in the full search algorithm, up to 76% in the TIE algorithm, and up to 63% in the iterative TIE algorithm. Further, the proposed method provides CS and load reduction of up to 29–33% and 67–69%, respectively, over the BSS-ITIE algorithm

An Efficient Codebook Search Algorithm for Line Spectrum Frequency (LSF) Vector Quantization in Speech Codec

A high-performance vector quantization (VQ) codebook search algorithm is proposed in this paper. VQ is an important data compression technique that has been widely applied to speech, image, and video compression. However, the process of the codebook search demands a high computational load. To solve this issue, a novel algorithm that consists of training and encoding procedures is proposed. In the training procedure, a training speech dataset was used to build the squared-error distortion look-up table for each subspace. In the encoding procedure, firstly, an input vector was quickly assigned to a search subspace. Secondly, the candidate code word group was obtained by employing the triangular inequality elimination (TIE) equation. Finally, a partial distortion elimination technique was employed to reduce the number of multiplications. The proposed method reduced the number of searches and computation load significantly, especially when the input vectors were uncorrelated. The experimental results show that the proposed algorithm provides a computational saving (CS) of up to 85% in the full search algorithm, up to 76% in the TIE algorithm, and up to 63% in the iterative TIE algorithm. Further, the proposed method provides CS and load reduction of up to 29–33% and 67–69%, respectively, over the BSS-ITIE algorithm

Lightning Identification Method Based on Deep Learning

In this study, a deep learning method called Lightning-SN was developed and used for cloud-to-ground (CG) lightning identification. Based on artificial scenarios, this network model selects radar products that exhibit characteristic factors closely related to lightning. Advanced time of arrival and direction lightning positioning data were used as the labeling factors. The Lightning-SN model was constructed based on an encoder–decoder structure with 25 convolutional layers, five pooling layers, five upsampling layers, and a sigmoid activation function layer. Additionally, the maximum pooling index method was adopted in Lightning-SN to avoid characteristic boundary information loss in the pooling process. The gradient harmonizing mechanism was used as the loss function to improve the model performance. The evaluation results showed that the Lightning-SN improved the segmentation accuracy of the CG lightning location compared with the traditional threshold method, according to the 6-minute operating period of the current S-band Doppler radar, exhibiting a better performance in terms of lightning location identification based on high-resolution radar data. The model was applied to the Ningbo area of Zhejiang Province, China. It was applied to the lightning hazard prevention in the hazardous chemical park in Ningbo. The composite reflectivity and radial velocity were the two dominant factors, with a greater influence on the model performance than other factors