VSF: An Energy-Efficient Sensing Framework Using Virtual Sensors

In this paper, we describe virtual sensing framework (VSF), which reduces sensing and data transmission activities of nodes in a sensor network without compromising on either the sensing interval or data quality. VSF creates virtual sensors (VSs) at the sink to exploit the temporal and spatial correlations amongst sensed data. Using an adaptive model at every sensing iteration, the VSs can predict multiple consecutive sensed data for all the nodes with the help of sensed data from a few active nodes. We show that even when the sensed data represent different physical parameters (e.g., temperature and humidity), our proposed technique still works making it independent of physical parameter sensed. Applying our technique can substantially reduce data communication among the nodes leading to reduced energy consumption per node yet maintaining high accuracy of the sensed data. In particular, using VSF on the temperature data from IntelLab and GreenOrb data set, we have reduced the total data traffic within the network up to 98% and 79%, respectively. Corresponding average root mean squared error of the predicted data per node is as low as 0.36 °C and 0.71 °C, respectively. This paper is expected to support deployment of many sensors as part of Internet of Things in large scales

Murphy loves CI: Unfolding and Improving Constructive Interference in WSNs

Constructive Interference (CI) phenomenon has been exploited by Glossy, a mechanism for low-latency and reliable network flooding and time synchronization for wireless sensor networks. Recently, CI has also been used for other applications such as data collection and multicasting in static and mobile WSNs. These applications base their working on the high reliability promised by Glossy regardless of the physical conditions of deployment, number of nodes in the network, and unreliable wireless channels that may be detrimental for CI. There are several works that study the working of CI, but they present inconsistent views. We study CI from a receiver's viewpoint, list factors that affect CI and also specify how and why they affect. We validate our arguments with results from extensive and rigorous experimentation in real-world settings. This paper presents comprehensive insights into CI phenomenon. With this understanding, we improve the performance of CI through an energy-efficient and distributed algorithm. We cause destructive interference on a designated byte to provide negative feedback. We leverage this to adapt transmission powers. Compared to Glossy, we achieve 25% lesser packet losses while using only half of its transmission power

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Not AvailablePigeonpea is one of the important legume crops of India which is affected by Fusarium wilt (Fusarium udum) disease causing severe yield loss. Four different races of Fusarium wilt have reported been with pathogenic race present in Bangalore being most virulent. Hence in the present study nature of inheritance of wilt disease was studied in segregating generations (F2 and F3) of crosses namely BRG-1 × ICP-8863 and TTB-7 × ICP-8863. Digenic ratio of 9 (susceptible): 7 (resistant) and 13 (susceptible): 3 (resistant) was obtained in F2 generation of two crosses BRG-1 × ICP-8863 and TTB-7 × ICP-8863, respectively. Frequency distribution of F3 generation showed normal curve, skewed towards susceptibility. This indicates that susceptibility was dominant over resistance and is governed by two or more genes. Probable loci responsible for disease reaction have been designated as FuB1, FuB2 and FuB3. Susceptible parents (TTB 7 and BRG 1) shared one common dominant gene whereas ICP 8863 had recessive resistant gene. Characterisation of these genes will help in marker assisted breeding programmeNot Availabl

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Not AvailableAn experiment was conducted to identify markers linked to Fusarium wilt disease resistance, Parents namely TTB 7 and ICP 8863 were screened using 151 SSRs markers and 16 AFLP primer combinations. Parental screening revealed five SSR primers and 12 AFLP primer combinations polymorphic between parents. Bulk segregant analysis identified five AFLP primer combinations generating seven markers polymorphic between resistant and susceptible bulks while, none of the SSR markers were polymorphic. This indicates that, these markers are putatively linked to wilt disease. Screening of F2 segregating population of cross TTB 7 x ICP 8863 with these putatively linked markers revealed four markers (E-AAT/M-CTG850, ETCG/ M-CTT650, E-TCG/M-CTA730 and E-TCG/M-CTT230) which segregated in 3:1 mendelian pattern. Simple linear regression performed on these four markers had identified two markers namely E-TCG/M-CTT650 and E-TCG/M-CTA730 linked to diseaseNot Availabl

Synovial fluid proteome in rheumatoid arthritis

BACKGROUND: Rheumatoid arthritis (RA) is a chronic autoinflammatory disorder that affects small joints. Despite intense efforts, there are currently no definitive markers for early diagnosis of RA and for monitoring the progression of this disease, though some of the markers like anti CCP antibodies and anti vimentin antibodies are promising. We sought to catalogue the proteins present in the synovial fluid of patients with RA. It was done with the aim of identifying newer biomarkers, if any, that might prove promising in future. METHODS: To enrich the low abundance proteins, we undertook two approaches—multiple affinity removal system (MARS14) to deplete some of the most abundant proteins and lectin affinity chromatography for enrichment of glycoproteins. The peptides were analyzed by LC–MS/MS on a high resolution Fourier transform mass spectrometer. RESULTS: This effort was the first total profiling of the synovial fluid proteome in RA that led to identification of 956 proteins. From the list, we identified a number of functionally significant proteins including vascular cell adhesion molecule-1, S100 proteins, AXL receptor protein tyrosine kinase, macrophage colony stimulating factor (M-CSF), programmed cell death ligand 2 (PDCD1LG2), TNF receptor 2, (TNFRSF1B) and many novel proteins including hyaluronan-binding protein 2, semaphorin 4A (SEMA4D) and osteoclast stimulating factor 1. Overall, our findings illustrate the complex and dynamic nature of RA in which multiple pathways seems to be participating actively. CONCLUSIONS: The use of high resolution mass spectrometry thus, enabled identification of proteins which might be critical to the progression of RA. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12014-016-9113-1) contains supplementary material, which is available to authorized users