Evaluation of a novel saliva-based epidermal growth factor receptor mutation detection for lung cancer: A pilot study.

BackgroundThis article describes a pilot study evaluating a novel liquid biopsy system for non-small cell lung cancer (NSCLC) patients. The electric field-induced release and measurement (EFIRM) method utilizes an electrochemical biosensor for detecting oncogenic mutations in biofluids.MethodsSaliva and plasma of 17 patients were collected from three cancer centers prior to and after surgical resection. The EFIRM method was then applied to the collected samples to assay for exon 19 deletion and p.L858 mutations. EFIRM results were compared with cobas results of exon 19 deletion and p.L858 mutation detection in cancer tissues.ResultsThe EFIRM method was found to detect exon 19 deletion with an area under the curve (AUC) of 1.0 in both saliva and plasma samples in lung cancer patients. For L858R mutation detection, the AUC of saliva was 1.0, while the AUC of plasma was 0.98. Strong correlations were also found between presurgery and post-surgery samples for both saliva (0.86 for exon 19 and 0.98 for L858R) and plasma (0.73 for exon 19 and 0.94 for L858R).ConclusionOur study demonstrates the feasibility of utilizing EFIRM to rapidly, non-invasively, and conveniently detect epidermal growth factor receptor mutations in the saliva of patients with NSCLC, with results corresponding perfectly with the results of cobas tissue genotyping

A Location Selection Policy of Live Virtual Machine Migration for Power Saving and Load Balancing

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. And load balancing has also been one of the most important goals in cloud data centers. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on location selection (migration policy) of live VM migration for power saving and load balancing. We propose a novel approach MOGA-LS, which is a heuristic and self-adaptive multiobjective optimization algorithm based on the improved genetic algorithm (GA). This paper has presented the specific design and implementation of MOGA-LS such as the design of the genetic operators, fitness values, and elitism. We have introduced the Pareto dominance theory and the simulated annealing (SA) idea into MOGA-LS and have presented the specific process to get the final solution, and thus, the whole approach achieves a long-term efficient optimization for power saving and load balancing. The experimental results demonstrate that MOGA-LS evidently reduces the total incremental power consumption and better protects the performance of VM migration and achieves the balancing of system load compared with the existing research. It makes the result of live VM migration more high-effective and meaningful

VMs Placement Strategy based on Distributed Parallel Ant Colony Optimization Algorithm

Cloud computing is at the forefront of information technology. Cloud computing led to abandon the use of expensive mainframe. It becomes a trend that data center uses the cluster which is relatively cheap and virtualization technologies to provide infrastructure services. To improve the utilization rate of the cloud center and decrease the operating cost, the cloud center provides services to users as required by sharding the resources with virtualization. Because consideration should be given to both QoS for users and cost saving for cloud computing providers, cloud providers try to maximize performance and minimize energy cost as well. In this paper, we propose a Distributed Parallel Ant Colony Optimization (DPACO) Algorithm of placement strategy for live virtual machines Live Migration on cloud platform. It executes the ant colony optimization algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the second stage ant colony optimization algorithm with solutions calculated by the first stage. The solution calculated by the second stage ant colony optimization algorithm is the optimal solution of our approach. The experimental results have shown that the proposed placement strategy of VM live migration is more effective and energy-efficient with ensuring QoS for users than other placement strategies on the cloud platform

DSMC: A Novel Distributed Store-Retrieve Approach of Internet Data Using MapReduce Model and Community Detection in Big Data

The processing of big data is a hotspot in the scientific research. Data on the Internet is very large and also very important for the scientific researchers, so the capture and store of Internet data is a priority among priorities. The traditional single-host web spider and data store approaches have some problems such as low efficiency and large memory requirement, so this paper proposes a big data store-retrieve approach DSMC (distributed store-retrieve approach using MapReduce model and community detection) based on distributed processing. Firstly, the distributed capture method using MapReduce to deduplicate big data is presented. Secondly, the storage optimization method is put forward; it uses the hash functions with light-weight characteristics and the community detection to address the storage structure and solve the data retrieval problems. DSMC has achieved the high performance of large web data comparison and storage and gets the efficient data retrieval at the same time. The experimental results show that, in the Cloudsim platform, comparing with the traditional web spider, the proposed DSMC approach shows better efficiency and performance

SNP-Target Genes Interaction Perturbing the Cancer Risk in the Post-GWAS

Cancer ranks as the second leading cause of death worldwide, and, being a genetic disease, it is highly heritable. Over the past few decades, genome-wide association studies (GWAS) have identified many risk-associated loci harboring hundreds of single nucleotide polymorphisms (SNPs). Some of these cancer-associated SNPs have been revealed as causal, and the functional characterization of the mechanisms underlying the cancer risk association has been illuminated in some instances. In this review, based on the different positions of SNPs and their modes of action, we discuss the mechanisms underlying how SNPs regulate the expression of target genes to consequently affect tumorigenesis and the development of cancer

Inorganic/Organic Doped Carbon Aerogels As Biosensing Materials for the Detection of Hydrogen Peroxide

In this article, three different inorganic/organic doped carbon aerogel (CA) materials (Ni-CA, Pd-CA, and Ppy-CA) were, respectively, mixed with ionic liquid (IL) to form three stable composite films, which were used as enhanced elements for an integrated sensing platform to increase the surface area and to improve the electronic transmission rate. Subsequently, the effect of the materials performances such as adsorption, specific surface area and conductivity on electrochemistry for myoglobin (Mb) was discussed using N₂ adsorption–desorption isotherm measurements, scanning electron microscopy (SEM), and electrochemical impedance spectroscopy (EIS). Moreover, they could act as sensors toward the detection of hydrogen peroxide (H₂O₂) with lower detection limits (1.68 μM, 1.02 μM, and 0.85 μM, for Ni-CA/IL/Mb-CPE, Pd-CA/IL/Mb-CPE, and Ppy-CA/IL/Mb-CPE, respectively) and smaller apparent Michaelis–Menten constants <i>K</i>_M. The results indicated that the electroconductibility of the doped CA materials would become dominant, thus playing an important role in facilitating the electron transfer. Meanwhile, the synergetic effect with [BMIm]­BF₄ IL improved the capability of the composite inorganic/organic doped CA/IL matrix for protein immobilization. This work demonstrates the feasibility and the potential of a series of CA-based hybrid materials as biosensors, and further research and development are required to prepare other functional CAs and make them valuable for more extensive application in biosensing

An Efficient Fau It-Tolerant Multicast Routing Protocol with Core-Based Tree Techniques

Abstract-In this paper, we design and analyze an efficient fault-tolerant multicast routing protocol. Reliable multicast communication is critical for the success of many Internet applications. Multicast routing protocols with core-based tree techniques (CBT) have been widely used because of their scalability and simplicity. We enhance the CBT protocol with fault tolerance capability and improve its efficiency and effectiveness. With our strategy, when a faulty Component is detected, some pre-defined backup path(s) is (are) used to bypass the faulty component and enable the multicast communication to continue. Our protocol only requires that routers near the faulty component be reconfigured, thus reducing the runtime overhead without compromising much of the performance. Our approach is in contrast to other approaches that often require relativeiy large tree reformation when faults occur. These global methods are usually costly and complicated in their attempt to achieve theoretically optimal performance. Our performance evaluation shows that our new protocol performs nearly as well as the best possible global method while utilizing much less runtime overhead and implementation cost. Index Terms-Multicast routing, fault tolerance, core-based trees.