Noise Removal in Microarray Images Using Variational Mode Decomposition Technique

Microarray technology allows the simultaneous monitoring of thousands of genes in parallel. Based on the gene expression measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Enhancement, Gridding, Segmentation and Intensity extraction are important steps in microarray image analysis. This paper presents a noise removal method in microarray images based on Variational Mode Decomposition (VMD). VMD is a signal processing method which decomposes any input signal into discrete number of sub-signals (called Variational Mode Functions) with each mode chosen to be its band width in spectral domain. First the noisy image is processed using 2-D VMD to produce 2-D VMFs. Then Discrete Wavelet Transform (DWT) thresholding technique is applied to each VMF for denoising.  The denoised microarray image is reconstructed by the summation of VMFs.  This method is named as 2-D VMD and DWT thresholding method. The proposed method is compared with DWT thresholding and BEMD and DWT thresholding methods. The qualitative and quantitative analysis shows that 2-D VMD and DWT thresholding method produces better noise removal than other two methods

Different Techniques to Transfer Big data: a Survey

ABSTRACT Now a days the world is moving around the social networks, i.e most of the people are interacting with each other via internet only. Transmitting data via the Internet is a routine and common task for users today. Transferring a gigabyte of data in an entire day was normal, however users are now transmitting multiple gigabytes in a single hour. The Big Data is the combination of structured, semi-structured, unstructured, homogeneous and heterogeneous data. With the influx of big data and massive scientific data sets that are measured in tens of petabytes, a user has the propensity to transfer even larger amounts of data. When transferring data sets of this magnitude on public or shared networks, the performance of all workloads in the system will be impacted. This paper addresses the issues and challenges inherent with transferring big data over networks. A survey of current transfer techniques is provided in this paper

The BRD9/7 Inhibitor TP-472 Blocks Melanoma Tumor Growth by Suppressing ECM-Mediated Oncogenic Signaling and Inducing Apoptosis

Melanoma accounts for the majority of all skin cancer-related deaths and only 1/3rd of melanoma patients with distal metastasis survive beyond five years. However, current therapies including BRAF/MEK targeted therapies or immunotherapies only benefit a subset of melanoma patients due to the emergence of intrinsic or extrinsic resistance mechanisms. Effective treatment of melanoma will thus require new and more effective therapeutic agents. Towards the goal of identifying new therapeutic agents, we conducted an unbiased, druggable epigenetic drug screen using a library of 32 epigenetic inhibitors obtained from the Structural Genome Consortium that targets proteins encoding for epigenetic regulators. This chemical genetic screening identified TP-472, which targets bromodomain-7/9, as the strongest inhibitor of melanoma growth in both short- and long-term survival assays and in mouse models of melanoma tumor growth. Mechanistically, using a transcriptome-wide mRNA sequencing profile we identified TP-472 treatment downregulates genes encoding various extracellular matrix (ECM) proteins, including integrins, collagens, and fibronectins. Reactome-based functional pathway analyses revealed that many of the ECM proteins are involved in extracellular matrix interactions required for cancer cell growth and proliferation. TP-472 treatment also upregulated several pro-apoptotic genes that can inhibit melanoma growth. Collectively, our results identify BRD7/9 inhibitor TP-472 as a potentially useful therapeutic agent for melanoma therapy