Points of Interest Coverage with Connectivity Constraints using Wireless Mobile Sensors

Part 7: Network Topology ConfigurationInternational audienceThe coverage of Points of Interest (PoI) is a classical requirement in mobile wireless sensor applications. Optimizing the sensors self-deployment over a PoI while maintaining the connectivity between the sensors and the sink is thus a fundamental issue. This article addresses the problem of autonomous deployment o f mobile sensors that need to cover a predefined PoI with a connectivity constraints and provides the solution to it using Relative Neighborhood Graphs (RNG). Our deployment scheme minimizes the number of sensors used for connectivity thus increasing the number of monitoring sensors. Analytical results, simulation results and real implementation are provided to show the efficiency of our algorithm

Nicotine, IFN-γ and retinoic acid mediated induction of MUC4 in pancreatic cancer requires E2F1 and STAT-1 transcription factors and utilize different signaling cascades

BACKGROUND: The membrane-bound mucins are thought to play an important biological role in cell–cell and cell–matrix interactions, in cell signaling and in modulating biological properties of cancer cell. MUC4, a transmembrane mucin is overexpressed in pancreatic tumors, while remaining undetectable in the normal pancreas, thus indicating a potential role in pancreatic cancer pathogenesis. The molecular mechanisms involved in the regulation of MUC4 gene are not yet fully understood. Smoking is strongly correlated with pancreatic cancer and in the present study; we elucidate the molecular mechanisms by which nicotine as well as agents like retinoic acid (RA) and interferon-γ (IFN-γ) induce the expression of MUC4 in pancreatic cancer cell lines CD18, CAPAN2, AsPC1 and BxPC3. RESULTS: Chromatin immunoprecipitation assays and real-time PCR showed that transcription factors E2F1 and STAT1 can positively regulate MUC4 expression at the transcriptional level. IFN-γ and RA could collaborate with nicotine in elevating the expression of MUC4, utilizing E2F1 and STAT1 transcription factors. Depletion of STAT1 or E2F1 abrogated the induction of MUC4; nicotine-mediated induction of MUC4 appeared to require α7-nicotinic acetylcholine receptor subunit. Further, Src and ERK family kinases also mediated the induction of MUC4, since inhibiting these signaling molecules prevented the induction of MUC4. MUC4 was also found to be necessary for the nicotine-mediated invasion of pancreatic cancer cells, suggesting that induction of MUC4 by nicotine and other agents might contribute to the genesis and progression of pancreatic cancer. CONCLUSIONS: Our studies show that agents that can promote the growth and invasion of pancreatic cancer cells induce the MUC4 gene through multiple pathways and this induction requires the transcriptional activity of E2F1 and STAT1. Further, the Src as well as ERK signaling pathways appear to be involved in the induction of this gene. It appears that targeting these signaling pathways might inhibit the expression of MUC4 and prevent the proliferation and invasion of pancreatic cancer cells

Fishery, population dynamics and stock structure of frigate tuna Auxis thazard (Lacepede, 1800) exploited from Indian waters

Auxis thazard, commonly known as frigate tuna represents an important group of coastal tuna species occurring in the Indian waters. The species is landed all along the Indian coastline and the major landing is along the south-west coast with Kerala contributing the most. The species is exploited by a variety of gears viz., drift gill nets, shore seines, ring seines and hooks and lines. Though there is recent information on the fishery and the exploitation status of Auxis thazard from Tuticorin (Kasim, 2002; Abdussamad et al., 2005) and Veraval (Ghosh et al., 2010), studies on the catch, population characteristics and stock estimates covering the entire coasts of India are lacking after the work of Silas et al. (1985) and James et al. (1993). These studies date back to two decades, after which there has been a change in the fishing pattern of coastal tunas throughout the country. Therefore, the present study was undertaken to provide an insight into the fishery, population dynamics and stock structure of A. thazard exploited from Indian water

Automatic Identification System (AIS): An initiative in purse seine fisheries along Mumbai coast

Automatic Identification System (AIS) is a significant development in navigation safety since the introduction of RADAR. It was originally developed as a collision avoidance tool for commercial vessels to improve the helmsman’s information about his surrounding environment. AIS does this by continuously transmitting a vessels identity, position, speed and course along with other relevant information to all other AIS equipped vessels within range

Indian tuna fishery - production trend during yesteryears and scope for the future

Fishery for tuna and tuna like fishes in the country has been in vogue from time immemorial and presently involves fishery by coastal based fleets of varying specifications with different craft-gear combinations and longline fishery by large oceanic fishing vessels. The former undertakes short duration fishing trips and exploit mainly surface tunas in the outer shelf and adjacent oceanic waters. The tuna landings though nominal during 1950-2005, registered a continuous increase over the years from a minimum of 848 t (1951) to 46,334 t (2000). With the introduction of targeted fishing for oceanic tunas during 2005-‘06, the landings improved and reached the maximum of 129,801 t in 2008. The fishery was supported by nine species, five coastal/neritic species and four oceanic species. Coastal tunas formed 57% of the tuna catch during 2006-’10 and was represented by the little tuna (Euthynnus affinis), frigate tuna (Auxis thazard), bullet tuna (Auxis rochei), longtail tuna (Thunnus tonggol) and bonito (Sarda orientalis). The oceanic species, which formed 43% of tuna catch, were yellowfin tuna (Thunnus albacares), skipjack tuna (Katsuwonus pelamis), dogtooth tuna (Gymnosarda unicolor) and bigeye tuna (Thunnus obesus). Information collected from different sources suggested that longliners operating in Indian EEZ and adjacent international waters caught around 87,000 t of tuna annually during 2006-'10. Catch was supported by three species dominated by yellowfin tuna and small proportion of big-eye and dogtooth tuna. Since fishery by coastal based units restricted to small areas and share of the catch by longliners from EEZ are not clearly known, systematic assessment of tuna stock in Indian EEZ is very difficult. However, the evaluation of the fishery scenario indicated only limited scope for improving tuna production from certain areas of coastal waters; whereas enormous scope remain for increasing tuna production from the oceanic waters of EEZ. However, since tunas being straddling resources shared by several nations, exploitation at one area will influence the fishery in other areas

Fishery and bionomics of the little tuna, Euthynnus affinis (Cantor, 1849) exploited from Indian waters

Euthynnus affinis, with an average annual landing of 40,757 t during 2006-2010 formed the bulk (65.1%) of the total coastal tuna catch of the country. The fishery, biology, growth and stock structure of E. affinis was studied in detail. Hooks and lines, gillnets and purseseines were the major gears exploiting the fish. Fishery was sustained mainly by 1 - 2 year old fishes (34 to 50 cm). Size at first maturity was estimated at 37.7 cm and fecundity was 3,08,150 eggs. Spawning was observed round the year with peaks during July-August and November-January. E. affinis was found to be a nonselective generalist feeder foraging on fishes, crustaceans and molluscs. The length–weight is given by the relationship 0.0254 L2.889 with no significant difference between males and females. Age and growth were estimated using length based methods. The von Bertalanffy growth parameters estimated were L∞ = 81.92 cm, annual K= 0.56 and t0 = -0.0317. Mortality estimates were M= 0.93 and Z = 1.68 and F= 0.75 with the exploitation rate E=0.45. The maximum sustainable yield estimated was higher than the average annual catch indicating scope for further exploitation

Online Survival Analysis Software to Assess the Prognostic Value of Biomarkers Using Transcriptomic Data in Non-Small-Cell Lung Cancer

In the last decade, optimized treatment for non-small cell lung cancer had lead to improved prognosis, but the overall survival is still very short. To further understand the molecular basis of the disease we have to identify biomarkers related to survival. Here we present the development of an online tool suitable for the real-time meta-analysis of published lung cancer microarray datasets to identify biomarkers related to survival. We searched the caBIG, GEO and TCGA repositories to identify samples with published gene expression data and survival information. Univariate and multivariate Cox regression analysis, Kaplan-Meier survival plot with hazard ratio and logrank P value are calculated and plotted in R. The complete analysis tool can be accessed online at: www.kmplot.com/lung. All together 1,715 samples of ten independent datasets were integrated into the system. As a demonstration, we used the tool to validate 21 previously published survival associated biomarkers. Of these, survival was best predicted by CDK1 (p<1E-16), CD24 (p<1E-16) and CADM1 (p = 7E-12) in adenocarcinomas and by CCNE1 (p = 2.3E-09) and VEGF (p = 3.3E-10) in all NSCLC patients. Additional genes significantly correlated to survival include RAD51, CDKN2A, OPN, EZH2, ANXA3, ADAM28 and ERCC1. In summary, we established an integrated database and an online tool capable of uni- and multivariate analysis for in silico validation of new biomarker candidates in non-small cell lung cancer

Marine climate and fisheries scenario of Maharashtra Climcard-4

Marine climate and fisheries scenario of Maharashtra Climcard-

Analysis of gene expression data from non-small celllung carcinoma cell lines reveals distinct sub-classesfrom those identified at the phenotype level

Microarray data from cell lines of Non-Small Cell Lung Carcinoma (NSCLC) can be used to look for differences in gene expression between the cell lines derived from different tumour samples, and to investigate if these differences can be used to cluster the cell lines into distinct groups. Dividing the cell lines into classes can help to improve diagnosis and the development of screens for new drug candidates. The micro-array data is first subjected to quality control analysis and then subsequently normalised using three alternate methods to reduce the chances of differences being artefacts resulting from the normalisation process. The final clustering into sub-classes was carried out in a conservative manner such that subclasses were consistent across all three normalisation methods. If there is structure in the cell line population it was expected that this would agree with histological classifications, but this was not found to be the case. To check the biological consistency of the sub-classes the set of most strongly differentially expressed genes was be identified for each pair of clusters to check if the genes that most strongly define sub-classes have biological functions consistent with NSCLC