Patholigical And Prognostic Role Of Mdig In Pancreatic Cancer

Pancreatic cancer is a highly aggressive malignant disease having very limited therapeutic options that ultimately results in its poor prognosis. It is still elusive on the etiology and tumorigenic mechanisms of pancreatic cancer. In the present report, we provide evidence showing involvement of the mineral dust-induced gene (mdig) in the pathogenesis and prognosis of the pancreatic cancer. Using immunohistochemistry approach on human pancreatic cancer tissue microarray, we found differential expression of mdig in pancreatic adenocarcinoma and normal pancreas. Based on the staining intensities of mdig in these tissue samples, we found that 12% of the cancer tissues were strongly positive for mdig, 39% and 31% were moderately and weakly positive respectively. Several alternatively spliced mdig mRNAs were detected in the selected pancreatic cancer cell line. Through R2 platform for the patient survival analysis (http://r2.amc.nl), we found that enrichment of some specific exon of mdig predicates different survival rate of the pancreatic cancer patients. In summary, our findings may help in assessing the role of mdig in the pathogenesis of the pancreatic cancer and the prognosis of the pancreatic cancer patients

Socioeconomic characteristics of cancer mortality in the United States of America: a spatial data mining approach

Cancer is the second leading cause of death in the United States of America. Though it is generally known that cancer is influenced by environment, its relation to socioeconomic conditions is still widely debated. This research analyzed the spatial distribution of cancer mortalities of breast, colorectal, lung, and prostate, and their associated socioeconomic characteristics using association rule mining technique. The mortality patterns were analyzed at the county and health service area levels that corresponded to the years between 1999 – 2002 and 1988 – 1992, respectively. Distinct socioeconomic characteristics of cancer mortality were revealed by the association rule mining technique. The counties that had very high rates of breast cancer mortality also had very low percent of whites who walked to work; very high rates of colorectal cancer mortality was associated with very low percentage of foreign born population; very high rates of lung cancer mortality was associated with very low percent of whites who walked to work; and counties that had very high prostate cancer mortality rates had a very low percentage of their residents born in the west. The cancer mortality and socioeconomic variables were discretized using equal interval, natural breaks, and quantile discretization methods to analyze the impact discretization techniques have on the cancer mortality and socioeconomic patterns obtained using association rule mining. The three discretization techniques produced patterns that involved different rates of cancer mortality and socioeconomic characteristics. Results of this analysis showed that a 5-class interval natural breaks discretization technique achieved the highest discretization accuracy, while the equal interval method produced association rules that had the highest support value. The research also analyzed the effect of scale on the patterns produced by the association rule technique. At the county level breast and lung cancers associated with mode of transportation to work, whereas colorectal and prostate cancers associated with place of birth. At the health service area level, the association rule with the highest support value among the breast-, colorectal-, and prostate-cancer mortality rates involved a household family characteristics, whereas high lung cancer mortality rates were associated with low educational attainment

A Study On Influence Of Real Municipal Solid Waste Leachate On Properties Of Soils In Warangal, India

Warangal city generates three hundred tons of garbage daily which is dropped into the Rampur dump yard by Warangal Municipal Corporation (WMC). Dumping of wastes will lead to the formation of leachate which in turn will cause environmental issues like soil and ground water contamination. Chemical analysis of leachate indicates that calcium, chloride, sodium and magnesium are the major ions, along with organic content. This leads to contamination of soil as well as ground water bodies. In this study, authors have attempted to know the behavior of soil under the influence of leachate. Contaminated specimens were prepared and tested for Atterberg limits, shear strength, swell potential and hydraulic conductivity of CH and SC which are present in the dumping yard. Index properties, hydraulic conductivity and swell potential decreased with increase in leachate concentration. Unconfined compressive strength also showed an increase. The decrease in hydraulic conductivity indicated the clogging of pores. In a nutshell, the present work deals with the impact of leachate on the index and engineering properties of CH and red soil

Image Inpainting and Enhancement using Fractional Order Variational Model

The intention of image inpainting is to complete or fill the corrupted or missing zones of an image by considering the knowledge from the source region. A novel fractional order variational image inpainting model in reference to Caputo definition is introduced in this article. First, the fractional differential, and its numerical methods are represented according to Caputo definition. Then, a fractional differential mask is represented in 8-directions. The complex diffusivity function is also defined to preserve the edges. Finally, the missing regions are filled by using variational model with fractional differentials of 8-directions. The simulation results and analysis display that the new model not only inpaints the missing regions, but also heightens the contrast of the image. The inpainted images have better visual quality than other fractional differential filters

Multiple-Target Tracking in Complex Scenarios

In this dissertation, we develop computationally efficient algorithms for multiple-target tracking: MTT) in complex scenarios. For each of these scenarios, we develop measurement and state-space models, and then exploit the structure in these models to propose efficient tracking algorithms. In addition, we address design issues such as sensor selection and resource allocation. First, we consider MTT when the targets themselves are moving in a time-varying multipath environment. We develop a sparse-measurement model that allows us to exploit the inherent joint delay-Doppler diversity offered by the environment. We then reformulate the problem of MTT as a block-support recovery problem using the sparse measurement model. We exploit the structure of the dictionary matrix to develop a computationally efficient block support recovery algorithm: and thereby a multiple-target tracking algorithm) under the assumption that the channel state describing the time-varying multipath environment is known. Further, we also derive an upper bound on the overall error probability of wrongly identifying the support of the sparse signal. We then relax the assumption that the channel state is known. We develop a new particle filter called the Multiple Rao-Blackwellized Particle Filter: MRBPF) to jointly estimate both the target and the channel states. We also compute the posterior Cramér-Rao bound: PCRB) on the estimates of the target and the channel states and use the PCRB to find a suitable subset of antennas to be used for transmission in each tracking interval, as well as the power transmitted by these antennas. Second, we consider the problem of tracking an unknown number and types of targets using a multi-modal sensor network. In a multi-modal sensor network, different quantities associated with the same state are measured using sensors of different kinds. Hence, an efficient method that can suitably combine the diverse information measured by each sensor is required. We first develop a Hierarchical Particle Filter: HPF) to estimate the unknown state from the multi-modal measurements for a special class of problems which can be modeled hierarchically. We then model our problem of tracking using a hierarchical model and then use the proposed HPF for joint initiation, termination and tracking of multiple targets. The multi-modal data consists of the measurements collected from a radar, an infrared camera and a human scout. We also propose a unified framework for multi-modal sensor management that comprises sensor selection: SS), resource allocation: RA) and data fusion: DF). Our approach is inspired by the trading behavior of economic agents in commercial markets. We model the sensors and the sensor manager as economic agents, and the interaction among them as a double sided market with both consumers and producers. We propose an iterative double auction mechanism for computing the equilibrium of such a market. We relate the equilibrium point to the solutions of SS, RA and DF. Third, we address MTT problem in the presence of data association ambiguity that arises due to clutter. Data association corresponds to the problem of assigning a measurement to each target. We treat the data association and state estimation as separate subproblems. We develop a game-theoretic framework to solve the data association, in which we model each tracker as a player and the set of measurements as strategies. We develop utility functions for each player, and then use a regret-based learning algorithm to find the correlated equilibrium of this game. The game-theoretic approach allows us to associate measurements to all the targets simultaneously. We then use particle filtering on the reduced dimensional state of each target, independently