Validating sensor nodes in Wireless sensor networks using scoring algorithm

Sensor networks are frequently used to collect data in the environment such as agriculture, forest monitoring, healthcare, and military battlefield. In Wireless Sensor Networks (WSN), nodes are used to monitor the environment and gather data where sinks can be used to collect the data from the sensor nodes and transfer them to the back-end server for processing. These sensible data are moved from one node to another node in the network. Such data should not be considered for public accessibility by the nodes in the network where the visibility and ease of access can only be achieved through either authenticated nodes or right authenticated persons. As sensor node can collect an important data (such as medical or military data), security is a critical issue. Hence, the sensor network needs a secure authentication mechanism to solve this problem and protects the unauthorized access. Therefore, the authentication mechanism used by the node and the sink node must be very efficient in terms of both computational time and energy consumptions. This is especially important for nodes with computing capabilities and battery lifetime is very low. Moreover, for extremely lightweight devices, efficient security solutions with simple mathematics operation and low energy consumptions are still required. To make an authentication decision in real-time, a scoring algorithm examines the user model and the user’s recent behavior, and outputs a score indicating the likelihood that the correct user is using the device. The score is used to make an authentication decision

Pulmonary Tumor Detection by virtue of GLCM

132–134As per the technical evolution and latest trend, Image processing techniques has become a boon in medical domain especially for tumor detection. Presence of tumor in Lungs which leads to lung cancer is a prominent and trivial disease at 18%. This is important to be detected at early stage thereby decreasing the mortality rate. The survival rate among people increased by early diagnosis of lung tumor. Detection of tumor cell will improve the survival rate from 14 to 49%. The aim of this research work is to design a lung tumor detection system based on analysis of microscopic image of biopsy using digital image processing. This can be done using Gray Level Co-Occurrence Matrix (GLCM) method and classified using back propagation neural network. This method is used for extracting texture features based on parameters such as contrast, correlation, energy, and homogeneity from the lung nodule. The microscopic lung biopsy images are classified into either cancer or non-cancer class using the artificial neural network algorithm. The proposed system has proven results in lung tumor detection and diagnosis

Hydrostatic pressure effect on Tc of new BiS2 based Bi4O4S3 and NdO0.5F0.5BiS2 layered superconductors

We investigate the external hydrostatic pressure effect on the superconducting transition temperature (Tc) of new layered superconductors Bi4O4S3 and NdO0.5F0.5BiS2. Though the Tc is found to have moderate decrease from 4.8 K to 4.3 K (dTconset/dP = -0.28 K/GPa) for Bi4O4S3 superconductor, the same increases from 4.6 K to 5 K (dTconset/dP = 0.44 K/GPa) upto 1.31 GPa followed by a sudden decrease from 5 K to 4.7 K upto 1.75 GPa for NdO0.5F0.5BiS2 superconductor. The variation of Tc in these systems may be correlated to increase or decrease of the charge carriers in the density of states under externally applied pressure.Comment: 3 pages text +Fig

Effects of quercetin on insulin-like growth factors (IGFs) and their binding protein-3 (IGFBP-3) secretion and induction of apoptosis in human prostate cancer cells

BACKGROUND: Quercetin, the predominant flavonoid, has been reported to lower the risk of several cancers. This flavonoid found in onion, grapes, green vegetables, etc. has been shown to possess potent antiproliferative effects against various malignant cells. This study was designed to investigate its effects on insulin-like growth factors (IGFs) and their binding protein-3 (IGFBP-3) proteins secretion and also apoptosis induction in the human prostate cancer cell line, PC-3. METHODS: We evaluated the secretion of IGF-I, -II and IGFBP-3 in quercetin treated cells by immunoradiometric (IRMA) method. Apoptosis was studied in quercetin treated cells by TUNEL and DNA fragmentation. Protein expressions of Bcl-2, Bcl-x(L), Bax and caspase-3 were studied by western blot. RESULTS: At a dose of 100 μM concentration, we observed increased IGFBP-3 accumulation in PC-3 cells conditioned medium with a dose dependent increase with 2 fold over a base line, and significantly reduced the both IGF-I and IGF-II levels. Apoptosis induction was also confirmed by TUNEL assay. Bcl-2 and Bcl-x(L )protein expressions were significantly decreased and Bax and caspase-3 were increased. CONCLUSION: These results suggest that the decreased level of IGFs could be due to the increased levels of IGFBP-3, because of the high binding affinity towards IGFs, thereby decreasing the cell proliferation. The increased level of IGFBP-3 was associated with increased pro-apoptotic proteins and apoptosis in response to quercetin, suggesting it may be a p53-independent effector of apoptosis in prostate cancer cells

Pulmonary Tumor Detection by virtue of GLCM

As per the technical evolution and latest trend, Image processing techniques has become a boon in medical domain especially for tumor detection. Presence of tumor in Lungs which leads to lung cancer is a prominent and trivial disease at 18%. This is important to be detected at early stage thereby decreasing the mortality rate. The survival rate among people increased by early diagnosis of lung tumor. Detection of tumor cell will improve the survival rate from 14 to 49%. The aim of this research work is to design a lung tumor detection system based on analysis of microscopic image of biopsy using digital image processing. This can be done using Gray Level Co-Occurrence Matrix (GLCM) method and classified using back propagation neural network. This method is used for extracting texture features based on parameters such as contrast, correlation, energy, and homogeneity from the lung nodule. The microscopic lung biopsy images are classified into either cancer or non-cancer class using the artificial neural network algorithm. The proposed system has proven results in lung tumor detection and diagnosis

Tool speed and polarity effects in micro-EDM drilling of 316L stainless steel

This paper focuses on the issues of Resistor-Capacitor-based Electrical Discharge Micro-Machining process and investigates the effects of tool speed and polarity on the performance measures such as Tool Wear Rate, Material Removal Rate, Overcut and Taper Angle by drilling on 316L Stainless Steel. Taguchi’s L54 mixed orthogonal array design is employed to conduct experiments by varying tool polarity at two levels and voltage, capacitance, spindle speed at three levels. The cause and effect relationship between the experimental factors and responses are analysed and discussed using Factorial Analysis of Variance technique. Optimum combinations of machining parameters are also evaluated using Taguchi-based Grey Relational Analysis, by considering grey relational grade matrix and influence of process parameters on the responses. Further, microscopic analysis is done to identify the micro-voids, globular formation, and cracks present on the surface of the hole produced under various machining conditions

Excessive reactive oxygen species induce transcription-dependent replication stress.

Elevated levels of reactive oxygen species (ROS) reduce replication fork velocity by causing dissociation of the TIMELESS-TIPIN complex from the replisome. Here, we show that ROS generated by exposure of human cells to the ribonucleotide reductase inhibitor hydroxyurea (HU) promote replication fork reversal in a manner dependent on active transcription and formation of co-transcriptional RNA:DNA hybrids (R-loops). The frequency of R-loop-dependent fork stalling events is also increased after TIMELESS depletion or a partial inhibition of replicative DNA polymerases by aphidicolin, suggesting that this phenomenon is due to a global replication slowdown. In contrast, replication arrest caused by HU-induced depletion of deoxynucleotides does not induce fork reversal but, if allowed to persist, leads to extensive R-loop-independent DNA breakage during S-phase. Our work reveals a link between oxidative stress and transcription-replication interference that causes genomic alterations recurrently found in human cancer. [Abstract copyright: © 2023. The Author(s).

The conserved C-terminus of the PcrA/UvrD helicase interacts directly with RNA polymerase

Copyright: © 2013 Gwynn et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by a Wellcome Trust project grant to MD (Reference: 077368), an ERC starting grant to MD (Acronym: SM-DNA-REPAIR) and a BBSRC project grant to PM, NS and MD (Reference: BB/I003142/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Strong Neutral Spatial Effects Shape Tree Species Distributions across Life Stages at Multiple Scales

Traditionally, ecologists use lattice (regional summary) count data to simulate tree species distributions to explore species coexistence. However, no previous study has explicitly compared the difference between using lattice count and basal area data and analyzed species distributions at both individual species and community levels while simultaneously considering the combined scenarios of life stage and scale. In this study, we hypothesized that basal area data are more closely related to environmental variables than are count data because of strong environmental filtering effects. We also address the contribution of niche and the neutral (i.e., solely dependent on distance) factors to species distributions. Specifically, we separately modeled count data and basal area data while considering life stage and scale effects at the two levels with simultaneous autoregressive models and variation partitioning. A principal coordinates of neighbor matrix (PCNM) was used to model neutral spatial effects at the community level. The explained variations of species distribution data did not differ significantly between the two types of data at either the individual species level or the community level, indicating that the two types of data can be used nearly identically to model species distributions. Neutral spatial effects represented by spatial autoregressive parameters and the PCNM eigenfunctions drove species distributions on multiple scales, different life stages and individual species and community levels in this plot. We concluded that strong neutral spatial effects are the principal mechanisms underlying the species distributions and thus shape biodiversity spatial patterns