DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structure. In this paper, we propose an automatic feature discovery framework for extracting discriminative class-specific features and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific features which are suitable for representing samples from the same class while are poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian lung images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, show the significance of DFDL model in a variety problems over state-of-the-art methodsComment: Accepted to IEEE International Symposium on Biomedical Imaging (ISBI), 201

Orthogonally Regularized Deep Networks For Image Super-resolution

Deep learning methods, in particular trained Convolutional Neural Networks (CNNs) have recently been shown to produce compelling state-of-the-art results for single image Super-Resolution (SR). Invariably, a CNN is learned to map the low resolution (LR) image to its corresponding high resolution (HR) version in the spatial domain. Aiming for faster inference and more efficient solutions than solving the SR problem in the spatial domain, we propose a novel network structure for learning the SR mapping function in an image transform domain, specifically the Discrete Cosine Transform (DCT). As a first contribution, we show that DCT can be integrated into the network structure as a Convolutional DCT (CDCT) layer. We further extend the network to allow the CDCT layer to become trainable (i.e. optimizable). Because this layer represents an image transform, we enforce pairwise orthogonality constraints on the individual basis functions/filters. This Orthogonally Regularized Deep SR network (ORDSR) simplifies the SR task by taking advantage of image transform domain while adapting the design of transform basis to the training image set

Folate-conjugated nanoparticles as a potent therapeutic approach in targeted cancer therapy

The selective and efficient drug delivery to tumor cells can remarkably improve different cancer therapeutic approaches. There are several nanoparticles (NPs) which can act as a potent drug carrier for cancer therapy. However, the specific drug delivery to cancer cells is an important issue which should be considered before designing new NPs for in vivo application. It has been shown that cancer cells over-express folate receptor (FR) in order to improve their growth. As normal cells express a significantly lower levels of FR compared to tumor cells, it seems that folate molecules can be used as potent targeting moieties in different nanocarrier-based therapeutic approaches. Moreover, there is evidence which implies folate-conjugated NPs can selectively deliver anti-tumor drugs into cancer cells both in vitro and in vivo. In this review, we will discuss about the efficiency of different folate-conjugated NPs in cancer therapy. © 2015, International Society of Oncology and BioMarkers (ISOBM)

Interprocedural Reachability for Flat Integer Programs

We study programs with integer data, procedure calls and arbitrary call graphs. We show that, whenever the guards and updates are given by octagonal relations, the reachability problem along control flow paths within some language w1* ... wd* over program statements is decidable in Nexptime. To achieve this upper bound, we combine a program transformation into the same class of programs but without procedures, with an Np-completeness result for the reachability problem of procedure-less programs. Besides the program, the expression w1* ... wd* is also mapped onto an expression of a similar form but this time over the transformed program statements. Several arguments involving context-free grammars and their generative process enable us to give tight bounds on the size of the resulting expression. The currently existing gap between Np-hard and Nexptime can be closed to Np-complete when a certain parameter of the analysis is assumed to be constant.Comment: 38 pages, 1 figur

Reducing radar cross section by investigation electromagnetic materials

Decreasing the Radar Cross Section (RCS) is investigated in electromagnetic materials, i.e. double-positive (DPS) , double-negative (DNG) , epsilon-negative (ENG) and mu-negative (MNG) materials. The interesting properties of these materials lead to a great flexibility in manufacturing structures with unusual electromagnetic characteristics. The valid conditions for achieving the transparency and gaining resonance for an electrically small cylinder are established, in this corresponding The effect of incidence direction on RCS inclusive of transparency and resonance conditions is also explored ,through computer simulations for an electrically small cylinder

The intricate interplay between epigenetic events, alternative splicing and noncoding RNA deregulation in colorectal cancer

Colorectal cancer (CRC) results from a transformation of colonic epithelial cells into adenocarcinoma cells due to genetic and epigenetic instabilities, alongside remodelling of the surrounding stromal tumour microenvironment. Epithelial-specific epigenetic variations escorting this process include chromatin remodelling, histone modifications and aberrant DNA methylation, which influence gene expression, alternative splicing and function of non-coding RNA. In this review, we first highlight epigenetic modulators, modifiers and mediators in CRC, then we elaborate on causes and consequences of epigenetic alterations in CRC pathogenesis alongside an appraisal of the complex feedback mechanisms realized through alternative splicing and non-coding RNA regulation. An emphasis in our review is put on how this intricate network of epigenetic and post-transcriptional gene regulation evolves during the initiation, progression and metastasis formation in CRC

Wnt5a induces ROR1 to associate with 14-3-3ζ for enhanced chemotaxis and proliferation of chronic lymphocytic leukemia cells.

Wnt5a can activate Rho GTPases in chronic lymphocytic leukemia (CLL) cells by inducing the recruitment of ARHGEF2 to ROR1. Mass spectrometry on immune precipitates of Wnt5a-activated ROR1 identified 14-3-3ζ, which was confirmed by co-immunoprecipitation. The capacity of Wnt5a to induce ROR1 to complex with 14-3-3ζ could be blocked in CLL cells by treatment with cirmtuzumab, a humanized mAb targeting ROR1. Silencing 14-3-3ζ via small interfering RNA impaired the capacity of Wnt5a to: (1) induce recruitment of ARHGEF2 to ROR1, (2) enhance in vitro exchange activity of ARHGEF2 and (3) induce activation of RhoA and Rac1 in CLL cells. Furthermore, CRISPR/Cas9 deletion of 14-3-3ζ in ROR1-negative CLL cell-line MEC1, and in MEC1 cells transfected to express ROR1 (MEC1-ROR1), demonstrated that 14-3-3ζ was necessary for the growth/engraftment advantage of MEC1-ROR1 over MEC1 cells. We identified a binding motif (RSPS857SAS) in ROR1 for 14-3-3ζ. Site-directed mutagenesis of ROR1 demonstrated that serine-857 was required for the recruitment of 14-3-3ζ and ARHGEF2 to ROR1, and activation of RhoA and Rac1. Collectively, this study reveals that 14-3-3ζ plays a critical role in Wnt5a/ROR1 signaling, leading to enhanced CLL migration and proliferation