Grouping and sponsoring centric green coverage model for Internet of Things

Recently, green computing has received significant attention for Internet of Things (IoT) environments due to the growing computing demands under tiny sensor enabled smart services. The related literature on green computing majorly focuses on a cover set approach that works efficiently for target coverage, but it is not applicable in case of area coverage. In this paper, we present a new variant of a cover set approach called a grouping and sponsoring aware IoT framework (GS-IoT) that is suitable for area coverage. We achieve non-overlapping coverage for an entire sensing region employing sectorial sensing. Non-overlapping coverage not only guarantees a sufficiently good coverage in case of large number of sensors deployed randomly, but also maximizes the life span of the whole network with appropriate scheduling of sensors. A deployment model for distribution of sensors is developed to ensure a minimum threshold density of sensors in the sensing region. In particular, a fast converging grouping (FCG) algorithm is developed to group sensors in order to ensure minimal overlapping. A sponsoring aware sectorial coverage (SSC) algorithm is developed to set off redundant sensors and to balance the overall network energy consumption. GS-IoT framework effectively combines both the algorithms for smart services. The simulation experimental results attest to the benefit of the proposed framework as compared to the state-of-the-art techniques in terms of various metrics for smart IoT environments including rate of overlapping, response time, coverage, active sensors, and life span of the overall network

Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering

This paper presents a comprehensive survey of the meta-heuristic optimization algorithms on the text clustering applications and highlights its main procedures. These Artificial Intelligence (AI) algorithms are recognized as promising swarm intelligence methods due to their successful ability to solve machine learning problems, especially text clustering problems. This paper reviews all of the relevant literature on meta-heuristic-based text clustering applications, including many variants, such as basic, modified, hybridized, and multi-objective methods. As well, the main procedures of text clustering and critical discussions are given. Hence, this review reports its advantages and disadvantages and recommends potential future research paths. The main keywords that have been considered in this paper are text, clustering, meta-heuristic, optimization, and algorithm

Nature-inspired optimization algorithms for text document clustering—a comprehensive analysis

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Text clustering is one of the efficient unsupervised learning techniques used to partition a huge number of text documents into a subset of clusters. In which, each cluster contains similar documents and the clusters contain dissimilar text documents. Nature-inspired optimization algorithms have been successfully used to solve various optimization problems, including text document clustering problems. In this paper, a comprehensive review is presented to show the most related nature-inspired algorithms that have been used in solving the text clustering problem. Moreover, comprehensive experiments are conducted and analyzed to show the performance of the common well-know nature-inspired optimization algorithms in solving the text document clustering problems including Harmony Search (HS) Algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Algorithm, Ant Colony Optimization (ACO), Krill Herd Algorithm (KHA), Cuckoo Search (CS) Algorithm, Gray Wolf Optimizer (GWO), and Bat-inspired Algorithm (BA). Seven text benchmark datasets are used to validate the performance of the tested algorithms. The results showed that the performance of the well-known nurture-inspired optimization algorithms almost the same with slight differences. For improvement purposes, new modified versions of the tested algorithms can be proposed and tested to tackle the text clustering problems

Transglutaminase 2 facilitates the distant hematogenous metastasis of breast cancer by modulating interleukin-6 in cancer cells

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Abstract Introduction Inflammation has been implicated in cancer aggressiveness. As transglutaminase 2 (TG2), which has been associated with inflammatory signaling, has been suggested to play a role in tumor behavior, we propose that TG2 may be an important linker inducing interleukin (IL)-6-mediated cancer-cell aggressiveness, including distant hematogenous metastasis. Methods To investigate the role for TG2 and IL-6, TG2-knocked-down and IL-6-knocked-down cancer cells were generated by using shRNA. Human breast cancer cell xenograft model in highly immunocompromised mice and human advanced breast cancer primary tumor tissue microarrays were used in this study. Results IL-6 production in human breast cancer cells was dependent on their TG2 expression level. In vitro tumor-sphere formation was dependent on TG2 and downstream IL-6 production from cancer cells. Primary tumor growth in the mammary fat pads and distant hematogenous metastasis into the lung was also dependent on TG2 and downstream IL-6 expression levels. The effect of TG2 expression on human breast cancer distant metastasis was investigated by analyzing a tissue microarray of primary tumors from 412 patients with their clinical data after 7 years. TG2 expression in primary tumor tissue was inversely correlated with recurrence-free survival (P = 0.019) and distant metastasis-free survival (DMFS) (P = 0.006) in patients with advanced breast cancer. Furthermore, by using public datasets that included a total of 684 breast cancer patients, we found that the combined high expression of TG2 and IL-6 was associated with shorter DMFS, compared with the high expression of IL-6 only (P = 0.013). Conclusions We provide evidence that TG2 is an important link in IL-6-mediated tumor aggressiveness, and that TG2 could be an important mediator of distant metastasis, both in a xenograft animal model and in patients with advanced breast cancer

How local is local? Evidence from bank competition and corporate innovation in U.S.

This paper aims to fill in a research gap in the effects of bank competition on corporate innovation. In addition to the evidence on the favorable effects of bank competition on corporate innovation, we show novel evidence on the substitution effects of bank competition in a wider region and neighbor-state to local bank competition in financing corporate innovation activities. In banking market, we show ‘how local is local’ depends on the operating scope and information transparency of firms. Local banks have an information advantage over distant banks in financing local businesses and informationally opaque corporate innovation activities

Glutathione and Adaptive Immune Responses against Mycobacterium tuberculosis Infection in Healthy and HIV Infected Individuals

Glutathione (GSH), a tripeptide antioxidant, is essential for cellular homeostasis and plays a vital role in diverse cellular functions. Individuals who are infected with Human immuno deficiency virus (HIV) are known to be susceptible to Mycobacterium tuberculosis (M. tb) infection. We report that by enhancing GSH levels, T-cells are able to inhibit the growth of M. tb inside macrophages. In addition, those GSH-replenished T cell cultures produced increased levels of Interleukin-2 (IL-2), Interleukin-12 (IL-12), and Interferon-gamma (IFN-γ), cytokines, which are known to be crucial for the control of intracellular pathogens. Our study reveals that T lymphocytes that are derived from HIV infected individuals are deficient in GSH, and that this deficiency correlates with decreased levels of Th1 cytokines and enhanced growth of M. tb inside human macrophages

Site-Directed Mutations and the Polymorphic Variant Ala160Thr in the Human Thromboxane Receptor Uncover a Structural Role for Transmembrane Helix 4

The human thromboxane A2 receptor (TP), belongs to the prostanoid subfamily of Class A GPCRs and mediates vasoconstriction and promotes thrombosis on binding to thromboxane (TXA2). In Class A GPCRs, transmembrane (TM) helix 4 appears to be a hot spot for non-synonymous single nucleotide polymorphic (nsSNP) variants. Interestingly, A160T is a novel nsSNP variant with unknown structure and function. Additionally, within this helix in TP, Ala1604.53 is highly conserved as is Gly1644.57. Here we target Ala1604.53 and Gly1644.57 in the TP for detailed structure-function analysis. Amino acid replacements with smaller residues, A160S and G164A mutants, were tolerated, while bulkier beta-branched replacements, A160T and A160V showed a significant decrease in receptor expression (Bmax). The nsSNP variant A160T displayed significant agonist-independent activity (constitutive activity). Guided by molecular modeling, a series of compensatory mutations were made on TM3, in order to accommodate the bulkier replacements on TM4. The A160V/F115A double mutant showed a moderate increase in expression level compared to either A160V or F115A single mutants. Thermal activity assays showed decrease in receptor stability in the order, wild type>A160S>A160V>A160T>G164A, with G164A being the least stable. Our study reveals that Ala1604.53 and Gly1644.57 in the TP play critical structural roles in packing of TM3 and TM4 helices. Naturally occurring mutations in conjunction with site-directed replacements can serve as powerful tools in assessing the importance of regional helix-helix interactions