In the paper a novel hybrid model combining air mass trajectory analysis and wavelet transformation to improve the artificial neural network (ANN) forecast accuracy of daily average concentrations of ...PM2.5 two days in advance is presented. The model was developed from 13 different air pollution monitoring stations in Beijing, Tianjin, and Hebei province (Jing-Jin-Ji area). The air mass trajectory was used to recognize distinct corridors for transport of “dirty” air and “clean” air to selected stations. With each corridor, a triangular station net was constructed based on air mass trajectories and the distances between neighboring sites. Wind speed and direction were also considered as parameters in calculating this trajectory based air pollution indicator value. Moreover, the original time series of PM2.5 concentration was decomposed by wavelet transformation into a few sub-series with lower variability. The prediction strategy applied to each of them and then summed up the individual prediction results. Daily meteorological forecast variables as well as the respective pollutant predictors were used as input to a multi-layer perceptron (MLP) type of back-propagation neural network. The experimental verification of the proposed model was conducted over a period of more than one year (between September 2013 and October 2014). It is found that the trajectory based geographic model and wavelet transformation can be effective tools to improve the PM2.5 forecasting accuracy. The root mean squared error (RMSE) of the hybrid model can be reduced, on the average, by up to 40 percent. Particularly, the high PM2.5 days are almost anticipated by using wavelet decomposition and the detection rate (DR) for a given alert threshold of hybrid model can reach 90% on average. This approach shows the potential to be applied in other countries’ air quality forecasting systems.
•We propose a novel hybrid model to forecast PM2.5 pollution.•Using trajectory based geographic parameter as an extra input to ANN model.•Applying prediction strategy at different scales and then sum them up.•The model is capable to predict the high peaks of PM2.5 concentrations.
The gut vascular barrier (GVB) is the deepest layer of the gut barrier. It mainly comprised gut vascular endothelial cells, enteric glial cells, and pericytes. The GVB facilitates nutrient absorption ...and blocks bacterial translocation through its size-restricted permeability. Accumulating evidence suggests that dysfunction of this barrier correlates with several clinical pathologies including Crohn's disease (CD). Significant progress has been made to elucidate the mechanism of GVB dysfunction and to confirm the participation of disrupted GVB in the course of CD. However, further analyses are required to pinpoint the specific roles of GVB in CD pathogenesis. Many preclinical models and clinical trials have demonstrated that various agents are effective in protecting the GVB integrity and thus providing a potential CD treatment strategy. Through this review, we established a systemic understanding of the role of GVB in CD pathogenesis and provided novel insights for GVB-targeting strategies in CD treatment.
•Gut vascular barrier enables nutrient absorption and blocks bacterial translocation.•Wnt/β-catenin signaling and microbiota modulates gut vascular barrier.•Endothelial dysfunction disrupts gut vascular barrier and leads to Crohn's Disease.•Agents targeting gut vascular barrier have potential to treat Crohn's Disease.
As the Software Define Network (SDN) adopts centralized control logic, it is vulnerable to various types of Distributed Denial of Service (DDoS) attacks. At present, almost all the research work ...focuses on high-rate DDoS attack against the SDN control layer. Moreover, most of the existing detection methods are effective for high-rate DDoS attack detection of the control layer, while a low-rate DDoS attack against the SDN data layer is highly concealed, and the detection accuracy against this kind of attack is low. In order to improve the detection accuracy of the low-rate DDoS attack against the SDN data layer, this paper studies the mechanism of such attacks, and then proposes a multi-feature DDoS attack detection method based on Factorization Machine (FM). The features extracted from the flow rules are used to detect low-rate DDoS attacks, and the detection of low-rate DDoS attacks based on FM machine learning algorithms is implemented. The experimental results show that the method can effectively detect the low-rate DDoS attack against the SDN data layer, and the detection accuracy reaches 95.80 percent. Because FM algorithm can achieve fine-grained detection for low-rate DDoS attack, which provides a reliable condition for defending against such attacks. Finally, this paper proposes a defense method based on dynamic deletion of flow rules, and carries out experimental simulation and analysis to prove the effectiveness of the defense method, and the success rate of forwarding normal packets reached 97.85 percent.
Nuclear factor E2-related factor 2 (Nrf2 or NFE2L2) is abundantly expressed in cancer cells and relates to proliferation, invasion, and chemoresistance. Our early observations also found that ...expression of Nrf2 was up-regulated in kinds of cancer including human hepatocellular carcinoma (HCC) cells. But there are limited reports about the expression, significance, function of Nrf2 in HCC.
First, Nrf2 expression was analyzed in HCC cell lines and tumor samples. Then, the relationship of Nrf2 with clinicopathological factors and survival were analyzed. Further, the effect of Nrf2 on cell proliferation, apoptosis, and metastasis was examined in vitro by modulating expression of Nrf2 through specific shRNA or expression plasmid. Last, the potential mechanisms were also investigated.
Nrf2 was up-regulated in HCC, and expression of Nrf2 was correlated with tumor differentiation, metastasis, and tumor size. Univariate and multivariate analyses indicated that high Nrf2 expression might be a poor prognostic factor. Further studies demonstrated that inhibition of Nrf2 expression inhibited proliferation by inducing apoptosis and repressed invasion, and up-regulation of Nrf2 expression resulted in opposite phenotypes. Moreover, there are positive correlation between Nrf2 expression and that of Bcl-xL and MMP-9.
Nrf2 is a potential prognostic marker and promotes proliferation and invasion in human hepatocellular carcinoma partly through regulating expression of Bcl-xL and MMP-9.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Increasingly, blockchain technology is attracting significant attentions in various agricultural applications. These applications could satisfy the diverse needs in the ecosystem of agricultural ...products, e.g., increasing transparency of food safety and IoT based food quality control, provenance traceability, improvement of contract exchanges, and transactions efficiency. As multiple untrusted parties, including small-scale farmers, food processors, logistic companies, distributors and retailers, are involved into the complex farm-to-fork pipeline, it becomes vital to achieve optimal trade-off between efficiency and integrity of the agricultural management systems as required in contexts. In this paper, we provide a survey to study both techniques and applications of blockchain technology used in the agricultural sector. First, the technical elements, including data structure, cryptographic methods, and consensus mechanisms are explained in detail. Secondly, the existing agricultural blockchain applications are categorized and reviewed to demonstrate the use of the blockchain techniques. In addition, the popular platforms and smart contract are provided to show how practitioners use them to develop these agricultural applications. Thirdly, we identify the key challenges in many prospective agricultural systems, and discuss the efforts and potential solutions to tackle these problems. Further, we conduct an improved food supply chain in the post COVID-19 pandemic economy as an illustration to demonstrate an effective use of blockchain technology.
Several studies have shown an important role for long non‐coding RNA (lncRNA) in breast cancer progression. The present study investigated the role of lncRNA Opa interacting protein 5‐antisense RNA 1 ...(OIP5‐AS1) in the progression of breast cancer. OIP5‐AS1 was significantly upregulated in breast cancer tissues and in breast cancer cell lines, and OIP5‐AS1 downregulation inhibited the malignant behavior of breast cancer in vitro and in vivo. For in‐depth exploration of the mechanism of OIP5‐AS1 in breast cancer, we found that expression of microRNA‐129‐5p(miR‐129‐5p), which was found to bind sites in the sequence of OIP5‐AS1, in breast cancer tissues was negatively correlated with OIP5‐AS1. Also, luciferase assays indicated that OIP5‐AS1 acted as a miR‐129‐5p sponge, resulting in upregulated expression of the sex‐determining region Y‐box 2 (SOX2) transcription factor. Our study showed that OIP5‐AS1 plays a critical role in promoting breast cancer progression and that OIP5‐AS1 downregulation targets SOX2 by miR‐129‐5p upregulation.
lncRNA OIP5‐AS1 promotes breast cancer malignant phenotype. 2. lncRNA OIP5‐AS1 acts as ceRNA, targeting SOX2 by regulating miR‐129‐5p.
Abstract
The role of 5-methylcytosine (m5C) in tumor initiation and progression has been increasingly recognized. However, the precise association between the regulation of m5C and the progression, ...metastasis, and prognosis of head and neck squamous cell carcinoma (HNSCC) has not yet been fully explored. Data from 545 HNSCC patients obtained from The Cancer Genome Atlas (TCGA) database were analyzed. Unsupervised cluster analysis was conducted using the expression levels of m5C regulatory genes. Additionally, gene set variation analysis (GSVA), single-sample gene set enrichment analysis (ssGSEA), and Cox regression analysis were utilized. Quantitative reverse transcription polymerase chain reaction (RT-qPCR), colony formation assay, transwell experiments and western blots were performed in the HNSCC cell line UM-SCC-17B to assess the expression and functional role of one of the novel signatures, CNFN. Significant expression differences were found in m5C regulatory genes between tumor and normal tissues in HNSCC. Two distinct m5C modification patterns, characterized by substantial prognostic differences, were identified. Cluster-2, which exhibited a strong association with epithelial-mesenchymal transition (EMT), was found to be associated with a poorer prognosis. Based on the m5C clusters and EMT status, differentially expressed genes (DEGs) were identified. Using DEGs, an 8-gene signature (CAMK2N1, WNT7A, F2RL1, AREG, DEFB1, CNFN, TGFBI, and CAV1) was established to develop a prognostic model. The performance of this signature was validated in both the training and external validation datasets, demonstrating its promising efficacy. Furthermore, additional investigations using RT-qPCR on clinical specimens and experimental assays in cell lines provided compelling evidence suggesting that CNFN, one of the genes in the signature, could play a role in HNSCC progression and metastasis through the EMT pathway. This study highlighted the role of m5C in HNSCC progression and metastasis. The relationship between m5C and EMT has been elucidated for the first time. A robust prognostic model was developed for accurately predicting HNSCC patients’ survival outcomes. Potential molecular mechanisms underlying these associations have been illuminated through this research.
Emergency cooperative social networks (ECSNs) play a very important role in emergency management for magnitude emergencies in China recently. Based on the data set of cooperative fight against ...COVID-19 of the Beijing-Tianjin-Hebei region in China, using social network analysis (SNA) and asymmetric evolutionary game model, this study finds that the asymmetry between regions is comprehensively determined by resource endowment, administrative level, geographical distance, regional vulnerability, political pressure and other factors; vertical control is still the main operating mechanism of ECSNs; network derivation is caused by the superposition of multiple factors, of which political factors are very important, and asymmetry may become an obstacle.
Circular RNA (CircRNA) is a type of non-coding RNAs in which both ends are covalently linked. Researchers have demonstrated that many circRNAs can act as biomarkers of diseases. However, traditional ...experimental methods for circRNA-disease associations identification are labor-intensive. In this work, we propose a novel method based on the heterogeneous graph neural network and metapaths for circRNA-disease associations prediction termed as HMCDA. First, a heterogeneous graph consisting of circRNA-disease associations, circRNA-miRNA associations, miRNA-disease associations and disease-disease associations are constructed. Then, six metapaths are defined and generated according to the biomedical pathways. Afterwards, the entity content transformation, intra-metapath and inter-metapath aggregation are implemented to learn the embeddings of circRNA and disease entities. Finally, the learned embeddings are used to predict novel circRNA-disase associations. In particular, the result of extensive experiments demonstrates that HMCDA outperforms four state-of-the-art models in fivefold cross validation. In addition, our case study indicates that HMCDA has the ability to identify novel circRNA-disease associations.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
As a common social network, the SNEM plays an important role in emergency management. Magnitude emergencies are characterized by high complexity and uncertainty, and it is impossible to rely on the ...government for emergency management alone. We should absorb multiple subjects to build the SNEM and carry out extensive emergency mobilization in the whole society. The SNEM can integrate resources, gather consensus, promote participation, and reduce risks. The analysis of the types, generation mechanism, subject behavior, and strategy selection of the SNEM aid in adopting appropriate mobilization strategy based on magnitude emergencies, achieving the adaptation of the SNEM and emergency scenarios. By constructing the evolutionary game model of the SNEM for magnitude emergencies, taking China as an empirical sample, this paper explores the behavior evolution law and stable strategy of the government, social organizations, and the public. The results showed that the symbiotic SNEM with a positive response of social organizations and the public under the path of high-intensity mobilization by the government is the best strategy combination, and it is conducive to maximizing the emergency joint force.
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