Glutathione S-Transferase alpha 4 promotes proliferation and chemoresistance in colorectal cancer cells

Glutathion

The COP9 signalosome complex regulates fungal development and virulence in the wheat scab fungus Fusarium graminearum

The COP9 signalosome (Csn) complex is an evolutionarily conserved complex that regulates various important cellular processes. However, the function of the Csn complex in pathogenic fungi remains elusive. Here, the distribution of Csn subunits in the fungal kingdom was surveyed, and their biological functions were systematically characterized in the fungal pathogen Fusarium graminearum, which is among the top 10 plant fungal pathogens. The results obtained from bioinformatic analyses suggested that the F. graminearum Csn complex consisted of seven subunits (Csn1–Csn7) and that Csn5 was the most conserved subunit across the fungi kingdom. Yeast two-hybrid assays demonstrated that the seven Csn subunits formed a complex in F. graminearum. The Csn complex was localized to both the nucleus and cytoplasm and necessary for hyphal growth, asexual and sexual development and stress response. Transcriptome profiling revealed that the Csn complex regulated the transcription abundance of TRI genes necessary for mycotoxin deoxynivalenol (DON) biosynthesis, subsequently regulating DON production to control fungal virulence. Collectively, the roles of the Csn complex in F. graminearum were comprehensively analyzed, providing new insights into the functions of the Csn complex in fungal virulence and suggesting that the complex may be a potential target for combating fungal diseases

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Does the Belt and Road Initiative facilitate China’s corporate overseas investment: Based on a sustainable development perspective

Corporate overseas investment is a pivotal element of the Belt and Road Initiative (BRI). As an all-round opening-up strategy, the BRI has brought new ideas to international cooperation, and Chinese enterprises should seize this opportunity to promote global sustainable development. Adopting the data of Chinese listed enterprises from 2011-2020, this paper investigates the impact of the BRI on corporate overseas investment (COI) and its mechanisms via exploiting the difference-in-differences model (DID). Results show that the BRI has significantly facilitated the COI along the routes. We observe that the findings still hold after a series of robustness tests. Mechanism analysis verifies that tax incentives and credit environment improvement are the main channels by which BRI enhances COI. Heterogeneity results reveal that this initiative is more prominent for small and medium-sized enterprises and enterprises in dominant industries. The extensive analysis suggests that from a sustainable development perspective, the BRI facilitates more overseas investment of enterprises in polluting or high energy-consuming industries; the COI is more affected by BRI in regions with more stringent environmental regulations. This study provides empirical evidence for BRI construction and regional development

Medical Image Classification Algorithm Based on Visual Attention Mechanism-MCNN

Due to the complexity of medical images, traditional medical image classification methods have been unable to meet the actual application needs. In recent years, the rapid development of deep learning theory has provided a technical approach for solving medical image classification. However, deep learning has the following problems in the application of medical image classification. First, it is impossible to construct a deep learning model with excellent performance according to the characteristics of medical images. Second, the current deep learning network structure and training strategies are less adaptable to medical images. Therefore, this paper first introduces the visual attention mechanism into the deep learning model so that the information can be extracted more effectively according to the problem of medical images, and the reasoning is realized at a finer granularity. It can increase the interpretability of the model. Additionally, to solve the problem of matching the deep learning network structure and training strategy to medical images, this paper will construct a novel multiscale convolutional neural network model that can automatically extract high-level discriminative appearance features from the original image, and the loss function uses the Mahalanobis distance optimization model to obtain a better training strategy, which can improve the robust performance of the network model. The medical image classification task is completed by the above method. Based on the above ideas, this paper proposes a medical classification algorithm based on a visual attention mechanism-multiscale convolutional neural network. The lung nodules and breast cancer images were classified by the method in this paper. The experimental results show that the accuracy of medical image classification in this paper is not only higher than that of traditional machine learning methods but also improved compared with other deep learning methods, and the method has good stability and robustness

Urban Traffic Flow Prediction Model with CPSO/SSVM Algorithm under the Edge Computing Framework

Urban traffic flow prediction has always been an important realm for smart city build-up. With the development of edge computing technology in recent years, the network edge nodes of smart cities are able to collect and process various types of urban traffic data in real time, which leads to the possibility of deploying intelligent traffic prediction technology with real-time analysis and timely feedback on the edge. In view of the strong nonlinear characteristics of urban traffic flow, multiple dynamic and static influencing factors involved, and increasing difficulty of short-term traffic flow prediction in a metropolitan area, this paper proposes an urban traffic flow prediction model based on chaotic particle swarm optimization algorithm-smooth support vector machine (CPSO/SSVM). The prediction model has built a new second-order smooth function to achieve better approximation and regression effects and has further improved the computational efficiency of the smooth support vector machine algorithm through chaotic particle swarm optimization. Simulation experiment results show that this model can accurately predict urban traffic flow

A Novel Real-Time Image Restoration Algorithm in Edge Computing

Owning to the high processing complexity, the image restoration can only be processed offline and hardly be applied in the real-time production life. The development of edge computing provides a new solution for real-time image restoration. It can upload the original image to the edge node to process in real time and then return results to users immediately. However, the processing capacity of the edge node is still limited which requires a lightweight image restoration algorithm. A novel real-time image restoration algorithm is proposed in edge computing. Firstly, 10 classical functions are used to determine the population size and maximum iteration times of traction fruit fly optimization algorithm (TFOA). Secondly, TFOA is used to optimize the optimal parameters of least squares support vector regression (LSSVR) kernel function, and the error function of image restoration is taken as an adaptive function of TFOA. Thirdly, the LLSVR algorithm is used to restore the image. During the image restoration process, the training process is to establish a mapping relationship between the degraded image and the adjacent pixels of the original image. The relationship is established; the degraded image can be restored by using the mapping relationship. Through the comparison and analysis of experiments, the proposed method can meet the requirements of real-time image restoration, and the proposed algorithm can speed up the image restoration and improve the image quality

Morphological Evolution Characteristics of River Cross-Sections in the Lower Weihe River and Their Response to Streamflow and Sediment Changes

River cross-section morphology and water and sediment conditions are deeply connected. In recent years, the lower Wei River has experienced regular flooding and drastic changes in river channel shape, causing significant harm to the economy and development of the lower reaches. This research investigated the morphological evolution features based on annual extensive cross-section data and water and sediment data from the hydrological stations of Xianyang, Lintong, and Huaxian in the lower Weihe River from 2006 to 2018 of river cross-sections and the reaction to water and sediment variations. The findings indicated that the lower Wei River&rsquo;s cross-sectional alterations between 2006 and 2018 exhibited a trend of &ldquo;flushing at both ends and siltation in the middle&rdquo; while continuing to exhibit &ldquo;non-flood flushing and flood siltation&rdquo; features. The incoming sediment coefficient in the lower Weihe River declined dramatically, whereas the median diameter of suspended sediment particles grew significantly at the Lintong station. The average elevation of the river channel was highly synchronized with the change in the coming sediment coefficient, and the impact of big floods dramatically influenced the shape of the river cross-section. Human activities such as river management have directly affected the morphology of the river cross-section at Lintong station and caused a significant increase in the median diameter of suspended sediment particles, resulting in siltation in the Lintong river. The study&rsquo;s findings can serve as a theoretical foundation for water and sediment regulation and river training in the lower Weihe River, reducing flooding damage

User Access Management Based on Network Pricing for Social Network Applications

Social applications play a very important role in people’s lives, as users communicate with each other through social networks on a daily basis. This presents a challenge: How does one receive high-quality service from social networks at a low cost? Users can access different kinds of wireless networks from various locations. This paper proposes a user access management strategy based on network pricing such that networks can increase its income and improve service quality. Firstly, network price is treated as an optimizing access parameter, and an unascertained membership algorithm is used to make pricing decisions. Secondly, network price is adjusted dynamically in real time according to network load. Finally, selecting a network is managed and controlled in terms of the market economy. Simulation results show that the proposed scheme can effectively balance network load, reduce network congestion, improve the user's quality of service (QoS) requirements, and increase the network’s income

Greening-induced increase in evapotranspiration over Eurasia offset by CO2-induced vegetational stomatal closure

Evapotranspiration (ET), as a key exchanging component of the land energy, water and carbon cycles, is expected to increase in response to greening land under a warming climate. However, the relative importance of major drivers (e.g. leaf area index (LAI), climate forcing, atmospheric CO _2 , etc) to long-term ET change remain largely unclear. Focusing on the Eurasia which experienced the strong vegetational greening, we aim to estimate the long-term ET trend and its drivers’ relative contributions by applying a remote sensing-based water-carbon coupling model— Penman–Monteith–Leuning version 2 (PML-V2) driven by observational climate forcing and CO _2 records, and satellite-based LAI, albedo and emissivity. The PML-V2 estimated an increasing ET trend (6.20 ± 1.13 mm year ^−1 decade ^−1 , p < 0.01) over Eurasia during 1982–2014, which is close to the ensemble mean (6.51 ± 3.10 mm year ^−1 decade ^−1 ) from other three ET products (GLEAMv3.3a, ERA5 and CRv1.0). The PML-based ET overall agrees well with water-balance derived ET in detecting the trend directions. We find that the Eurasian ET increasing trend was mostly from vegetated regions over central and eastern Europe, Indian and southeast China where ET trends were larger than 20 mm year ^−1 decade ^−1 . Modeling sensitivity experiments indicate that the Eurasian ET trend was mainly dominated by positive contributions from climate forcing change (40%) and increased LAI (22%), but largely offset by a negative contribution of increased CO _2 (26%). Our results highlight the importance of the suppression effect of increasing CO _2 -induced stomatal closure on transpiration. This effect was rarely considered in diagnostic ET products but plays a key role to ensure that the long-term ET trend should not be overestimated by only accounting for greening-induced increases in transpiration and rainfall interception