Economic Levers for Mitigating Interest Flooding Attack in Named Data Networking

As a kind of unwelcome, unavoidable, and malicious behavior, distributed denial of service (DDoS) is an ongoing issue in today’s Internet as well as in some newly conceived future Internet architectures. Recently, a first step was made towards assessing DDoS attacks in Named Data Networking (NDN)—one of the promising Internet architectures in the upcoming big data era. Among them, interest flooding attack (IFA) becomes one of the main serious problems. Enlightened by the extensive study on the possibility of mitigating DDoS in today’s Internet by employing micropayments, in this paper we address the possibility of introducing economic levers, say, dynamic pricing mechanism, and so forth, for regulating IFA in NDN

Economic Levers for Mitigating Interest Flooding Attack in Named Data Networking

As a kind of unwelcome, unavoidable, and malicious behavior, distributed denial of service (DDoS) is an ongoing issue in today’s Internet as well as in some newly conceived future Internet architectures. Recently, a first step was made towards assessing DDoS attacks in Named Data Networking (NDN)—one of the promising Internet architectures in the upcoming big data era. Among them, interest flooding attack (IFA) becomes one of the main serious problems. Enlightened by the extensive study on the possibility of mitigating DDoS in today’s Internet by employing micropayments, in this paper we address the possibility of introducing economic levers, say, dynamic pricing mechanism, and so forth, for regulating IFA in NDN

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, NLP based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely-used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.Comment: Accepted by Briefings in Bioinformatic

The expression patterns and correlations of claudin-6, methy-CpG binding protein 2, DNA methyltransferase 1, histone deacetylase 1, acetyl-histone H3 and acetyl-histone H4 and their clinicopathological significance in breast invasive ductal carcinomas

<p>Abstract</p> <p>Background</p> <p>Claudin-6 is a candidate tumor suppressor gene in breast cancer, and has been shown to be regulated by DNA methylation and histone modification in breast cancer lines. However, the expression of claudin-6 in breast invasive ductal carcinomas and correlation with clinical behavior or expression of other markers is unclear. We considered that the expression pattern of claudin-6 might be related to the expression of DNA methylation associated proteins (methyl-CpG binding protein 2 (MeCP2) and DNA methyltransferase 1 (DNMT1)) and histone modification associated proteins (histone deacetylase 1 (HDAC1), acetyl-histone H3 (H3Ac) and acetyl- histone H4 (H4Ac)).</p> <p>Methods</p> <p>We have investigated the expression of claudin-6, MeCP2, HDAC1, H3Ac and H4Ac in 100 breast invasive ductal carcinoma tissues and 22 mammary gland fibroadenoma tissues using immunohistochemistry.</p> <p>Results</p> <p>Claudin-6 protein expression was reduced in breast invasive ductal carcinomas (<it>P </it>< 0.001). In contrast, expression of MeCP2 (<it>P </it>< 0.001), DNMT1 (<it>P </it>= 0.001), HDAC1 (<it>P </it>< 0.001) and H3Ac (<it>P </it>= 0.004) expressions was increased. Claudin-6 expression was inversely correlated with lymph node metastasis (<it>P </it>= 0.021). Increased expression of HDAC1 was correlated with histological grade (<it>P </it>< 0.001), age (<it>P </it>= 0.004), clinical stage (<it>P </it>= 0.007) and lymph node metastasis (<it>P </it>= 0.001). H3Ac expression was associated with tumor size (<it>P </it>= 0.044) and clinical stage of cancers (<it>P </it>= 0.034). MeCP2, DNMT1 and H4Ac expression levels did not correlate with any of the tested clinicopathological parameters (<it>P </it>> 0.05). We identified a positive correlation between MeCP2 protein expression and H3Ac and H4Ac protein expression.</p> <p>Conclusions</p> <p>Our results show that claudin-6 protein is significantly down-regulated in breast invasive ductal carcinomas and is an important correlate with lymphatic metastasis, but claudin-6 down-regulation was not correlated with upregulation of the methylation associated proteins (MeCP2, DNMT1) or histone modification associated proteins (HDAC1, H3Ac, H4Ac). Interestingly, the expression of MeCP2 was positively correlated with the expression of H3Ac and H3Ac protein expression was positively correlated with the expression of H4Ac in breast invasive ductal carcinoma</p> <p>Virtual slides</p> <p>The virtual slide(s) for this article can be found here: <url>http://www.diagnosticpathology.diagnomx.eu/vs/4549669866581452</url></p

Construction, validation and, visualization of a web-based nomogram to identify the best candidates for primary tumor resection in advanced cutaneous melanoma patients

BackgroundExisting studies have shown whether primary site resection (PSR) in cutaneous melanoma (CM) patients with stage IV is controversial. Our study aimed to identify the clinical characteristics of CM patients with stage IV who benefited from PSR on a population-based study.MethodsWe retrospectively reviewed stage IV CM patients in the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015. Patients were divided into surgical and non-surgical groups according to whether PSR was performed or not. According to the median cancer-specific survival (CSS) time of the non-surgery group, the surgical group was divided into the surgery-benefit group and the non-surgery-benefit group. Multivariate cox regression analysis was used to explore independent CSS prognostic factors in the surgical group. Then, based on the independent prognostic factors of the surgical group, we established a web-based nomogram based on logistics regression.ResultsA total of 574 stage IV CM patients were included in our study, and 491 (85.60%) patients were included in the surgical group. The clinical characteristics (benefit group and non-benefit group) included age, M stage, lesion location, and ulceration status. These independent prognostic factors were includeed to construct a web-based nomogram.ConclusionsWe constructed a web-based nomogram. This model was suitable for identifying the best candidates suitable for PSR in stage IV CM patients

Analysis of the dermatophyte Trichophyton rubrum expressed sequence tags

BACKGROUND: Dermatophytes are the primary causative agent of dermatophytoses, a disease that affects billions of individuals worldwide. Trichophyton rubrum is the most common of the superficial fungi. Although T. rubrum is a recognized pathogen for humans, little is known about how its transcriptional pattern is related to development of the fungus and establishment of disease. It is therefore necessary to identify genes whose expression is relevant to growth, metabolism and virulence of T. rubrum. RESULTS: We generated 10 cDNA libraries covering nearly the entire growth phase and used them to isolate 11,085 unique expressed sequence tags (ESTs), including 3,816 contigs and 7,269 singletons. Comparisons with the GenBank non-redundant (NR) protein database revealed putative functions or matched homologs from other organisms for 7,764 (70%) of the ESTs. The remaining 3,321 (30%) of ESTs were only weakly similar or not similar to known sequences, suggesting that these ESTs represent novel genes. CONCLUSION: The present data provide a comprehensive view of fungal physiological processes including metabolism, sexual and asexual growth cycles, signal transduction and pathogenic mechanisms

The use of global transcriptional analysis to reveal the biological and cellular events involved in distinct development phases of Trichophyton rubrum conidial germination

<p>Abstract</p> <p>Background</p> <p>Conidia are considered to be the primary cause of infections by <it>Trichophyton rubrum</it>.</p> <p>Results</p> <p>We have developed a cDNA microarray containing 10250 ESTs to monitor the transcriptional strategy of conidial germination. A total of 1561 genes that had their expression levels specially altered in the process were obtained and hierarchically clustered with respect to their expression profiles. By functional analysis, we provided a global view of an important biological system related to conidial germination, including characterization of the pattern of gene expression at sequential developmental phases, and changes of gene expression profiles corresponding to morphological transitions. We matched the EST sequences to GO terms in the <it>Saccharomyces </it>Genome Database (SGD). A number of homologues of <it>Saccharomyces cerevisiae </it>genes related to signalling pathways and some important cellular processes were found to be involved in <it>T. rubrum </it>germination. These genes and signalling pathways may play roles in distinct steps, such as activating conidial germination, maintenance of isotropic growth, establishment of cell polarity and morphological transitions.</p> <p>Conclusion</p> <p>Our results may provide insights into molecular mechanisms of conidial germination at the cell level, and may enhance our understanding of regulation of gene expression related to the morphological construction of <it>T. rubrum</it>.</p

Genome dynamics and diversity of Shigella species, the etiologic agents of bacillary dysentery

The Shigella bacteria cause bacillary dysentery, which remains a significant threat to public health. The genus status and species classification appear no longer valid, as compelling evidence indicates that Shigella, as well as enteroinvasive Escherichia coli, are derived from multiple origins of E.coli and form a single pathovar. Nevertheless, Shigella dysenteriae serotype 1 causes deadly epidemics but Shigella boydii is restricted to the Indian subcontinent, while Shigella flexneri and Shigella sonnei are prevalent in developing and developed countries respectively. To begin to explain these distinctive epidemiological and pathological features at the genome level, we have carried out comparative genomics on four representative strains. Each of the Shigella genomes includes a virulence plasmid that encodes conserved primary virulence determinants. The Shigella chromosomes share most of their genes with that of E.coli K12 strain MG1655, but each has over 200 pseudogenes, 300∼700 copies of insertion sequence (IS) elements, and numerous deletions, insertions, translocations and inversions. There is extensive diversity of putative virulence genes, mostly acquired via bacteriophage-mediated lateral gene transfer. Hence, via convergent evolution involving gain and loss of functions, through bacteriophage-mediated gene acquisition, IS-mediated DNA rearrangements and formation of pseudogenes, the Shigella spp. became highly specific human pathogens with variable epidemiological and pathological features

The mechanisms of Yu Ping Feng San in tracking the cisplatin-resistance by regulating ATP-binding cassette transporter and glutathione S-transferase in lung cancer cells

Cisplatin is one of the first line anti-cancer drugs prescribed for treatment of solid tumors; however, the chemotherapeutic drug resistance is still a major obstacle of cisplatin in treating cancers. Yu Ping Feng San (YPFS), a well-known ancient Chinese herbal combination formula consisting of Astragali Radix, Atractylodis Macrocephalae Rhizoma and Saposhnikoviae Radix, is prescribed as a herbal decoction to treat immune disorders in clinic. To understand the fast-onset action of YPFS as an anti-cancer drug to fight against the drug resistance of cisplatin, we provided detailed analyses of intracellular cisplatin accumulation, cell viability, and expressions and activities of ATP-binding cassette transporters and glutathione S-transferases (GSTs) in YPFS-treated lung cancer cell lines. In cultured A549 or its cisplatin-resistance A549/DDP cells, application of YPFS increased accumulation of intracellular cisplatin, resulting in lower cell viability. In parallel, the activities and expressions of ATP-binding cassette transporters and GSTs were down-regulated in the presence of YPFS. The expression of p65 subunit of NF-κB complex was reduced by treating the cultures with YPFS, leading to a high ratio of Bax/Bcl-2, i.e. increasing the rate of cell death. Prim-O-glucosylcimifugin, one of the abundant ingredients in YPFS, modulated the activity of GSTs, and then elevated cisplatin accumulation, resulting in increased cell apoptosis. The present result supports the notion of YPFS in reversing drug resistance of cisplatin in lung cancer cells by elevating of intracellular cisplatin, and the underlying mechanism may be down regulating the activities and expressions of ATP-binding cassette transporters and GSTs