A hybrid EDA for load balancing in multicast with network coding

Load balancing is one of the most important issues in the practical deployment of multicast with network coding. However, this issue has received little research attention. This paper studies how traffic load of network coding based multicast (NCM) is disseminated in a communications network, with load balancing considered as an important factor. To this end, a hybridized estimation of distribution algorithm (EDA) is proposed, where two novel schemes are integrated into the population based incremental learning (PBIL) framework to strike a balance between exploration and exploitation, thus enhance the efficiency of the stochastic search. The first scheme is a bi-probability-vector coevolution scheme, where two probability vectors (PVs) evolve independently with periodical individual migration. This scheme can diversify the population and improve the global exploration in the search. The second scheme is a local search heuristic. It is based on the problem-specific domain knowledge and improves the NCM transmission plan at the expense of additional computational time. The heuristic can be utilized either as a local search operator to enhance the local exploitation during the evolutionary process, or as a follow-up operator to improve the best-so-far solutions found after the evolution. Experimental results show the effectiveness of the proposed algorithms against a number of existing evolutionary algorithms

LncRNA gas5 regulates granulosa cell apoptosis and viability following radiation by x-ray via sponging miR-205- 5p and Wnt/β-catenin signaling pathway in granulosa cell tumor of ovary

Purpose: To investigate the role of lncRNA gas5 in ovarian granulosa cells exposed to x-ray in granulosa cell tumor of ovary (GCTO). Methods: KGN cell line was exposed to X-ray to mimic the radiotherapy for GCSO patients in vitro, cell viability was checked by CCK8 assays. RNA expression of apoptosis-related genes was determined by quantitative reverse transcriptase-polymerase reaction (qRT-PCR) while Western Blot for biomarkers in wnt/β-catenin signaling. Differential expressions of lncRNA gas5 were examined after cells were exposed to a ray for 0,24,48hs. We over expressed gas5 and assessed resultant cell viability, apoptosis and signaling. The sponging between gas5 and miR-205-5p was verified by luciferase assay. CCK8, qRT-PCR and Western blot were applied to investigate the correlation between miR-205-5p, cell viability, and apoptosis after miR-205-5p augmentation. Similarly, interaction between gas5 and miR-205-5p was assessed after co-transfection of miR-205-5p mimics and oe-gas5. Finally, wnt inhibitor was used to study the role of signaling pathway in KGN cells. Results: Exposure of KGN to x-ray reduced cell viability and increased apoptosis. Gas5showed reduced expression in the cells, while miR-205-5p  expression increased. Gas5 upregulation protected the cells against apoptosis and contributed to cell viability and activation of wnt//β-catenin signaling. lncRNA gas5 targeted miR-205-5p and miR-205-5p mimics counteracted the functions of up-regulated lncRNA gas5, regulating Wnt/β-catenin signaling pathway. Inactivation of Wnt/β-catenin suppressed cell viability. Conclusions: lncRNA gas5 regulates cell apoptosis and viability following cellular radiation, thus presenting a potential therapeutic target for the application radiotherapy in GCTO patients. Keywords: Ovary, Proliferation, Apoptosis, lncRNA gas5, Radiotherapy, β-catenin signalin

LncRNA gas5 regulates granulosa cell apoptosis and viability following radiation by X-ray through sponging miR-205-5p and Wnt/β-catenin signaling pathway ingranulosa cell tumor of ovary

Purpose: The study explored the role of lncRNA gas5 in ovarian granulosa cells exposed to X-ray in granulosa cell tumor of  ovary(GCTO). Methods:Exposed the KGN cell line (KALANG, Beijing, China) to X-ray to mimic the radiotherapy for GCSO patients in vitro, cell viability was checked by CCK8 assays. RT-qPCR detected the RNA expression of apoptosis-related genes while Western Blot for biomarkers in wnt/β-catenin signaling. Differential expressions of lncRNA gas5 were examined after cells exposed to X ray for 0,24,48hs. We over expressed gas5 and assessed resultant cell viabilities, apoptosis and signaling. The sponging between gas5 and miR-205-5p was verified through Luciferase Assay. CCK8, RT-qPCR and Western Blot were applied for investigations into the correlation between miR-205-5p and cell viability and apoptosis after miR-205-5p augmentation. Similarly, the interactions between the gas5 and  miR-205-5p were assessed after co-transfection of miR-205-5p mimics and oe-gas5. Last, wnt inhibitor was used to study the role of signaling pathway in KGN cells. Results: Exposure of KGN toX-ray reduced cell viabilities and increased apoptosis. Gas5 had reduced expression in cells while  miR-205-5p increased. Gas5 upregulation could protect the cells from apoptosis and add to the cell viability and activation of wnt//β-catenin signaling. lncRNA gas5 targeted miR-205-5p and miR-205-5p mimics could counteract functions of up-regulated lncRNA gas5, regulating Wnt/β-catenin signaling pathway. Inactivation in Wnt/β-catenin could suppress cell viability. Conclusions: lncRNA gas5 regulated the cell apoptosis and viability after cellular radiation, which might be a potential therapeutic target to combine into radiotherapy for GCTO patients in clinical stage. Keywords: Ovary, proliferation, apoptosis, lncRNA gas5, x-ra

Circulating Fractalkine Levels Predict the Development of the Metabolic Syndrome

The fractalkine/CX3CR1 axis plays an important role in regulating glucose and lipid metabolism. However, the role of fractalkine in metabolic disorders remains to be fully elucidated. We selected 887 Chinese (40–65 years old) at baseline, with a subgroup of 459 participants examined again 2 years later. The relationship of serum fractalkine levels with the metabolic syndrome (MetS) and its components was investigated. At baseline, participants with MetS had higher fractalkine concentrations than their counterparts without MetS (P<0.001). At the 2-year follow-up, participants in the highest quartile of baseline fractalkine exhibited higher values for body mass index, waist circumference, waist-to-hip ratio, body fat percentage, glucose, insulin, total cholesterol, triglycerides (TG), and homeostasis model assessment of insulin resistance (HOMA-IR) and lower value for high density lipoprotein-cholesterol (HDL-c) (all P<0.05). Among 390 participants without MetS at baseline, 45 developed it at year 2. Even after multiple adjustments for visceral adipose tissue area, HOMA-IR, C-reactive protein (CRP), or TG and HDL-c, baseline fractalkine predicted the development of MetS (OR = 7.18, 95%CI: 2.28–18.59). In conclusion, circulating fractalkine predicts the development of the MetS independently of central obesity, CRP, insulin resistance, and dyslipidemia

Development of Ling-zhi industry in China – emanated from the artificial cultivation in the Institute of Microbiology, Chinese Academy of Sciences (IMCAS)

Ling-zhi is a medicinal herb that generally refers to a fungus in the genus Ganoderma. It has been used as a medicinal mushroom in traditional Chinese medicine for more than 2000 years. Mycologists at the Institute of Microbiology, Chinese Academy of Sciences (IMCAS) first artificially cultivated the Ling-zhi fruiting body in the late 1960s (X.J. Liu’s team). In IMCAS, different research teams have extensively studied Ling-zhi in the aspects of national resource surveys, systematic taxonomy, chemical analysis, and processing for medicinal and health applications. The research results from IMCAS have provided essential support and prompted the development of the Ling-zhi industry in China to some extent. This review aims to summarize the history of research on Ling-zhi in IMCAS and its role in the development of the Ling-zhi economy

JSSTR: A Joint Server Selection and Traffic Routing Algorithm for the Software-Defined Data Center

Server load balancing technology makes services highly functional by distributing the incoming user requests to different servers. Thus, it plays a key role in data centers. However, most of the current server load balancing schemes are designed without considering the impact on the network. More specifically, when using these schemes, the server selection and routing path calculation are usually executed sequentially, which may result in inefficient use of network resources or even cause some issues in the network. As an emerging architecture, Software-Defined Networking (SDN) provides new solutions to overcome these shortcomings. Therefore, taking advantages of SDN, this paper proposes a Joint Server Selection and Traffic Routing algorithm (JSSTR) based on improving the Shuffle Frog Leaping Algorithm (SFLA) to achieve high network utilization, network load balancing and server load balancing. Evaluation results validate that the proposed algorithm can significantly improve network efficiency and balance the network load and server load

Assessment of Creep Properties Using Small Punch Test for a 9%Cr-Mo-Co-B Power Plant Steel

The present study provides a feasible method to evaluate creep properties for a 9%Cr-Mo-Co-B power plant steel by comparing two sets of data obtained from small punch tests and conventional uniaxial creep tests. The method includes three steps: firstly, conduct a series of small punch tests and conventional creep tests in different load and temperature conditions; secondly, convert the load and central deflection data obtained from the small punch test to stress and strain data; thirdly, determinate the best fit correlation factor by comparing the two sets of data in selected creep models. It is found that two sets of data show a similar trend in stress–rupture time relation, stress–minimum strain rate relation and LMP–stress relation. The correlation factor, ksp, can effectively bridge the gap between the load in small punch test and the stress in conventional creep test. For a high-Cr martensitic heat-resistant steel named as CB2, the ksp value 1.4 can make a good prediction for rupture time, while for minimum creep rate and the Larson–Miller parameter, the ksp value 1.4 will lead a conservative prediction in the low-stress range

Assessment of Creep Properties Using Small Punch Test for a 9%Cr-Mo-Co-B Power Plant Steel

The present study provides a feasible method to evaluate creep properties for a 9%Cr-Mo-Co-B power plant steel by comparing two sets of data obtained from small punch tests and conventional uniaxial creep tests. The method includes three steps: firstly, conduct a series of small punch tests and conventional creep tests in different load and temperature conditions; secondly, convert the load and central deflection data obtained from the small punch test to stress and strain data; thirdly, determinate the best fit correlation factor by comparing the two sets of data in selected creep models. It is found that two sets of data show a similar trend in stress&ndash;rupture time relation, stress&ndash;minimum strain rate relation and LMP&ndash;stress relation. The correlation factor, ksp, can effectively bridge the gap between the load in small punch test and the stress in conventional creep test. For a high-Cr martensitic heat-resistant steel named as CB2, the ksp value 1.4 can make a good prediction for rupture time, while for minimum creep rate and the Larson&ndash;Miller parameter, the ksp value 1.4 will lead a conservative prediction in the low-stress range

Frequency-Temporal Disagreement Adaptation for Robotic Terrain Classification via Vibration in a Dynamic Environment

The accurate terrain classification in real time is of great importance to an autonomous robot working in field, because the robot could avoid non-geometric hazards, adjust control scheme, or improve localization accuracy, with the aid of terrain classification. In this paper, we investigate the vibration-based terrain classification (VTC) in a dynamic environment, and propose a novel learning framework, named DyVTC, which tackles online-collected unlabeled data with concept drift. In the DyVTC framework, the exterior disagreement (ex-disagreement) and interior disagreement (in-disagreement) are proposed novely based on the feature diversity and intrinsic temporal correlation, respectively. Such a disagreement mechanism is utilized to design a pseudo-labeling algorithm, which shows its compelling advantages in extracting key samples and labeling; and consequently, the classification accuracy could be retrieved by incremental learning in a changing environment. Since two sets of features are extracted from frequency and time domain to generate disagreements, we also name the proposed method feature-temporal disagreement adaptation (FTDA). The real-world experiment shows that the proposed DyVTC could reach an accuracy of 89.5%, but the traditional time- and frequency-domain terrain classification methods could only reach 48.8% and 71.5%, respectively, in a dynamic environment