Military Academy

About: Military Academy is a based out in . It is known for research contribution in the topics: Population & Fuzzy logic. The organization has 2478 authors who have published 3003 publications receiving 33188 citations.

Plasma miR-22-3p, miR-642b-3p and miR-885-5p as diagnostic biomarkers for pancreatic cancer.

Abstract: Diagnosis of pancreatic cancer (PC) by using sensitive and specific biomarkers is considered necessary. MiRNAs are master regulators of gene expression and several biological processes, and they are dysregulated in various cancers, where they play a vital role in either cancer progression or suppression. So, this study was designed to investigate the role of plasma miR-22-3p, miR-642b-3p and miR-885-5p expression as possible diagnostic markers in PC patients as compared to serum CA19-9. In addition, the correlation of those miRNAs and CA19-9 with clinical characteristics of PC patients was analyzed. The expression levels of selected miRNAs and serum CA19-9 concentration were determined for 35 patients with PDAC and 15 healthy controls by quantitative real-time RT-PCR and electro-chemiluminescence immune assay, respectively. The sensitivities of miRNAs as biomarkers of PC were evaluated and compared with CA19-9 using a receiver operating characteristic analysis. The levels of three miRNAs (miR-22-3p, miR-642b-3p and miR-885-5p) and CA19-9 were significantly higher in PC patients, even those with early-stage disease (IB and IIB), than in healthy control. Both miRNAs and CA19-9 were associated with tumor stage. The high sensitivities of the three selected miRNAs and CA19-9 were observed. The measurement of miR-22-3p, miR-642b-3p and miR-885-5p may prove to have clinical utility in diagnosis of PC. Those miRNAs are ideal early biomarkers for PC diagnosis. So, they can effectively be used with serum CA19-9 for PC screening in early tumor stage.

A scalable heuristic for viral marketing under the tipping model

Abstract: In a “tipping” model, each node in a social network, representing an individual, adopts a property or behavior if a certain number of his incoming neighbors currently exhibit the same. In viral marketing, a key problem is to select an initial “seed” set from the network such that the entire network adopts any behavior given to the seed. Here we introduce a method for quickly finding seed sets that scales to very large networks. Our approach finds a set of nodes that guarantees spreading to the entire network under the tipping model. After experimentally evaluating 31 real-world networks, we found that our approach often finds seed sets that are several orders of magnitude smaller than the population size and outperform nodal centrality measures in most cases. In addition, our approach scales well—on a Friendster social network consisting of 5.6 million nodes and 28 million edges we found a seed set in under 3.6 h. Our experiments also indicate that our algorithm provides small seed sets even if high-degree nodes are removed. Last, we find that highly clustered local neighborhoods, together with dense network-wide community structures, suppress a trend’s ability to spread under the tipping model.

A Computational Study Of A New Heuristic For The Site-Dependent Vehicle Routing Problem

Abstract: In the site-dependent vehicle routing problem, a fleet of heterogeneous vehicles services a set of customers. Typically, there are several vehicle types (for example, small-, medium-, and large-cap...