A survey on MAC-based physical layer security over wireless sensor network

Physical layer security for wireless sensor networks (WSNs) is a laborious and highly critical issue in the world. Wireless sensor networks have great importance in civil and military fields or applications. Security of data/information through wireless medium remains a challenge. The data that we transmit wirelessly has increased the speed of transmission rate. In physical layer security, the data transfer between source and destination is not confidential, and thus the user has privacy issues, which is why improving the security of wireless sensor networks is a prime concern. The loss of physical security causes a great threat to a network. We have various techniques to resolve these issues, such as interference, noise, fading in the communications, etc. In this paper we have surveyed the different parameters of a security design model to highlight the vulnerabilities. Further we have discussed the various attacks on different layers of the TCP/IP model along with their mitigation techniques. We also elaborated on the applications of WSNs in healthcare, military information integration, oil and gas. Finally, we have proposed a solution to enhance the security of WSNs by adopting the alpha method and handshake mechanism with encryption and decryption

A genome-wide DNA methylation study in colorectal carcinoma

<p>Abstract</p> <p>Background</p> <p>We performed a genome-wide scan of 27,578 CpG loci covering 14,475 genes to identify differentially methylated loci (DML) in colorectal carcinoma (CRC).</p> <p>Methods</p> <p>We used Illumina's Infinium methylation assay in paired DNA samples extracted from 24 fresh frozen CRC tissues and their corresponding normal colon tissues from 24 consecutive diagnosed patients at a tertiary medical center.</p> <p>Results</p> <p>We found a total of 627 DML in CRC covering 513 genes, of which 535 are novel DML covering 465 genes. We also validated the Illumina Infinium methylation data for top-ranking genes by non-bisulfite conversion q-PCR-based methyl profiler assay in a subset of the same samples. We also carried out integration of genome-wide copy number and expression microarray along with methylation profiling to see the functional effect of methylation. Gene Set Enrichment Analysis (GSEA) showed that among the major "gene sets" that are hypermethylated in CRC are the sets: "inhibition of adenylate cyclase activity by G-protein signaling", "Rac guanyl-nucleotide exchange factor activity", "regulation of retinoic acid receptor signaling pathway" and "estrogen receptor activity". Two-level nested cross validation showed that DML-based predictive models may offer reasonable sensitivity (around 89%), specificity (around 95%), positive predictive value (around 95%) and negative predictive value (around 89%), suggesting that these markers may have potential clinical application.</p> <p>Conclusion</p> <p>Our genome-wide methylation study in CRC clearly supports most of the previous findings; additionally we found a large number of novel DML in CRC tissue. If confirmed in future studies, these findings may lead to identification of genomic markers for potential clinical application.</p

A Genome-Wide Study of Cytogenetic Changes in Colorectal Cancer Using SNP Microarrays: Opportunities for Future Personalized Treatment

In colorectal cancer (CRC), chromosomal instability (CIN) is typically studied using comparative-genomic hybridization (CGH) arrays. We studied paired (tumor and surrounding healthy) fresh frozen tissue from 86 CRC patients using Illumina's Infinium-based SNP array. This method allowed us to study CIN in CRC, with simultaneous analysis of copy number (CN) and B-allele frequency (BAF) - a representation of allelic composition. These data helped us to detect mono-allelic and bi-allelic amplifications/deletion, copy neutral loss of heterozygosity, and levels of mosaicism for mixed cell populations, some of which can not be assessed with other methods that do not measure BAF. We identified associations between CN abnormalities and different CRC phenotypes (histological diagnosis, location, tumor grade, stage, MSI and presence of lymph node metastasis). We showed commonalities between regions of CN change observed in CRC and the regions reported in previous studies of other solid cancers (e.g. amplifications of 20q, 13q, 8q, 5p and deletions of 18q, 17p and 8p). From Therapeutic Target Database, we identified relevant drugs, targeted to the genes located in these regions with CN changes, approved or in trials for other cancers and common diseases. These drugs may be considered for future therapeutic trials in CRC, based on personalized cytogenetic diagnosis. We also found many regions, harboring genes, which are not currently targeted by any relevant drugs that may be considered for future drug discovery studies. Our study shows the application of high density SNP arrays for cytogenetic study in CRC and its potential utility for personalized treatment

Use of p-nitrobenzoic acid in 7H10 agar for identification of Mycobacterium tuberculosis complex: a field study

We report the efficiency and cost-effectiveness of p-nitrobenzoic acid (PNB) testing in Middlebrook 7H10 agar medium for the identification of Mycobacterium tuberculosis complex (MTC). PNB-7H10 was compared with PNB-MGIT and BACTEC-NAP using 200 clinical mycobacterial isolates. PNB-7H10 showed 100% agreement with PNB-MGIT and BACTEC-NAP tests, and reduced the cost of PNB-MGIT test by 80%. PNB-7H10 agar is therefore an effective alternative to the costly PNB-MGIT and BACTEC-NAP tests, especially in resource-poor settings

Trends in Esophageal Adenocarcinoma and Esophageal Squamous Cell Carcinoma Incidence in the United States from 1992 to 2019

Background: Esophageal cancer (EC) incidence rates overall have declined in recent decades; however, the two main subtypes, esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC), show divergent secular trends. Methods: Age-adjusted EC incidence rates were calculated using data from the Surveillance Epidemiology and End Results (SEER) 12 Program. We examined secular trends from 1992 to 2019 overall and by age group, sex, race/ethnicity, tumor location, and SEER registry. Joinpoint regression was used to compute annual percent changes (APC) and average annual percent changes (AAPC). We used age-period-cohort models to examine the potential impact of period and birth cohort effects on trends. Results: Between 1992 and 2019, overall EC incidence rates declined by 0.54% annually (95% confidence interval [CI]: −0.75%, −0.33%). While ESCC rates declined linearly throughout the study period (AAPC = −2.85; 95%CI: −3.05%, −2.65%), EAC rates increased by over 5% annually from 1992 to 2000 (APC = 5.17; 95%CI: 3.28%, 7.10%), before stabilizing from 2000 to 2019 (APC = 0.22; 95%CI: −0.16%, 0.60%). Trends in ESCC and EAC varied by age group, sex, and race/ethnicity. Relative to ESCC rates among cohorts born circa 1950, the rates were 81% lower in cohorts born circa 1985 (rate ratio, 0.19; 95%CI: 0.04, 0.96). For EAC, rates have remained stable across successive birth cohorts since 1950. Conclusions: We observed linear declines in EC rates overall and for ESCC across age, sex, and race/ethnicity subgroups, but an inconsistent pattern for EAC. The trends in EAC cohorts born after 1955 were stable and suggest that EAC rates may have peaked in the U.S

Designing of a Simulation Tool for the Performance Analysis of Hybrid Data Center Networks (DCNs)

Data center (DC) technology changes the mode of computing. Traditional DCs consist of a single layer and only have Ethernet connections among switches. Those old-fashioned DCs cannot fulfill the high resource demand compared with today’s DCs. The architectural design of the DCs is getting substantial importance and acting as the backbone of the network because of its essential feature of supporting and maintaining the rapidly increasing Internet-based applications which include search engines (e.g., Google and Yandex) and social networking applications (e.g., YouTube, Twitter, and Facebook). Every application has its parameters, like latency and blocking in the DC network. Every data center network (DCN) has its specialized architecture. It has a specific arrangement of layers and switches, which increase or decrease the DC network’s efficiency. We develop a simulation tool that comprises two different DC architectures: basic tree architecture and c-Through architecture. Using this simulation, we analyze the traffic behavior and the performance of the simulated DCN. Our main purpose is to focus on mean waiting time, load, and blocking with respect to the traffic within the DCN

Precision Measurement for Industry 4.0 Standards towards Solid Waste Classification through Enhanced Imaging Sensors and Deep Learning Model

Achievement of precision measurement is highly desired in a current industrial revolution where a significant increase in living standards increased municipal solid waste. The current industry 4.0 standards require accurate and efficient edge computing sensors towards solid waste classification. Thus, if waste is not managed properly, it would bring about an adverse impact on health, the economy, and the global environment. All stakeholders need to realize their roles and responsibilities for solid waste generation and recycling. To ensure recycling can be successful, the waste should be correctly and efficiently separated. The performance of edge computing devices is directly proportional to computational complexity in the context of nonorganic waste classification. Existing research on waste classification was done using CNN architecture, e.g., AlexNet, which contains about 62,378,344 parameters, and over 729 million floating operations (FLOPs) are required to classify a single image. As a result, it is too heavy and not suitable for computing applications that require inexpensive computational complexities. This research proposes an enhanced lightweight deep learning model for solid waste classification developed using MobileNetV2, efficient for lightweight applications including edge computing devices and other mobile applications. The proposed model outperforms the existing similar models achieving an accuracy of 82.48% and 83.46% with Softmax and support vector machine (SVM) classifiers, respectively. Although MobileNetV2 may provide a lower accuracy if compared to CNN architecture which is larger and heavier, the accuracy is still comparable, and it is more practical for edge computing devices and mobile applications