Background: Lung cancer is a leading cause of cancer-related death worldwide and is the most commonly diagnosed cancer. Like other cancers, it is a complex and highly heterogeneous disease involving multiple signaling pathways. Identifying potential therapeutic targets is critical for the development of effective treatment strategies. Methods: We used a systems biology approach to identify potential key regulatory factors in smoking-induced lung cancer. We first identified genes that were differentially expressed between smokers with normal lungs and those with cancerous lungs, then integrated

The multiplicity per rapidity of the well-identified particles π-, π+, k-, k+, p¯ , p, and p- p¯ measured in different high-energy experiments, at energies ranging from 6.3 to 5500 GeV, is successfully compared with the Cosmic Ray Monte Carlo event generator. For these rapidity distributions, we introduce a theoretical approach based on fluctuations and correlations (Carruthers approach) and another one based on statistical thermal assumptions (hadron resonance gas approach). Both approaches are fitted to both sets of results deduced from experiments and simulations. We found that the

A novel algorithm for view-invariant human action recognition is presented. This approach is based on Two-Dimensional Principal Component Analysis (2DPCA) applied directly on the Motion Energy Image (MEI) or the Motion History Image (MHI) in both the spatial domain and the transform domain. This method reduces the computational complexity by a factor of at least 66, achieving the highest recognition accuracy per camera, while maintaining minimum storage requirements, compared with the most recent reports in the field. Experimental results performed on the Weizmann action and the INIRIA IXMAS

Objectives: The purpose of this study was to determine the prognostic value of strain-encoded magnetic resonance imaging (SENC) during high-dose dobutamine stress cardiac magnetic resonance imaging (DS-MRI) compared with conventional wall motion readings. Background: Detection of inducible ischemia by DS-MRI on the basis of assessing cine images is subjective and depends on the experience of the readers, which may influence not only the diagnostic classification but also the risk stratification of patients with ischemic heart disease. Methods: In all, 320 consecutive patients with suspected or

The aim of our study was to investigate the ability of Strain-Encoded magnetic resonance imaging (MRI) to detect cardiac allograft vasculopathy (CAV) in heart transplantation (HTx)-recipients. In consecutive subjects (n = 69), who underwent cardiac catheterization, MRI was performed for quantification of myocardial strain and perfusion reserve. Based on angiographic findings subjects were classified: group A including patients with normal vessels; group B, patients with stenosis

Lactic acid is used for the preparation of poly-lactic acid. The objective of this research was to produce lactic acid from agro-industrial wastes as cheap, renewable substrates, and also reduce the pollution burden on the environment. Sixteen bacterial isolates were isolated from agro-industrial wastes. The chemical hydrolysis of agro-industrial wastes was achieved with hydrochloric acid, sulfuric acid, and sodium hydroxide. The highest yield of lactic acid produced was identified using 16S rRNA. The optimum conditions for lactic acid production were determined. Calcium lactate, produced from

Cyber crime is a growing threat affecting all business sectors. Stock Exchanges, a financial services sector, are not far from it. Trading stocks via Internet exposes the process to cyber threats that might take advantage of a system defect to breach security and cause possible harm. Online Trading websites are protected by various security systems. Digital Certificate, which is based on Secure Socket Layer (SSL) protocol, is an example. This research examines implementation of Digital Certificate in online trading servers. This evaluation helps to identify security weaknesses and take actions

Time series classification is a supervised learning problem used in many vital applications. Classification of data varying with time is considered an important and challenging pattern recognition task. The temporal aspect and lack of features in time series data makes the learning process different from traditional classification problems. In this paper we propose a multiple classifier system approach for time series classification. The proposed approach adaptively integrates extracted local and global features together with distance similarity based methods. A feature extraction process is

In this paper, we report our experience in working with Network Address Translators (NATs). Traditionally, there were only 4 types of NATs. For each type, the (im)possibility of traversal is well-known. Recently, the NAT community has provided a deeper dissection of NAT behaviors resulting into at least 27 types and documented the (im)possibility of traversal for some types. There are, however, two fundamental issues that were not previously tackled by the community. First, given the more elaborate set of behaviors, it is incorrect to reason about traversing a single NAT, instead combinations

In this paper, we describe an application-level emulator for P2P systems with a special focus on high reproducibil-ity. We achieve reproduciblity by taking control over the scheduling ofconcurrent events from the operating system. We accomplish that for inter-and intra-peer concurrency. The development ofthe system was driven by the need to enhance the testing process ofan already-developed industrial product. Therefore, we were constrained by the architecture ofthe overlying application. However, we managed to provide highly transparent emulation by wrapping standard/widely-used networking