A Review of Object Detection Models based on Convolutional Neural Network

Convolutional Neural Network (CNN) has become the state-of-the-art for object detection in image task. In this chapter, we have explained different state-of-the-art CNN based object detection models. We have made this review with categorization those detection models according to two different approaches: two-stage approach and one-stage approach. Through this chapter, it has shown advancements in object detection models from R-CNN to latest RefineDet. It has also discussed the model description and training details of each model. Here, we have also drawn a comparison among those models.Comment: 17 pages, 11 figures, 1 tabl

Human Factor IX Binds to Specific Sites on the Collagenous Domain of Collagen IV

The primary region of factor IX that mediates binding to bovine aortic endothelial cells resides in residues 3-11 of the N-terminal region known as the Gla domain. Recently, it was proposed that the observed binding to endothelial cells is actually a measure of the interaction between factor IX and collagen IV (Cheung, W. F., van den Born, J., Kuhn, K., Kjellen, L., Hudson, B. G., and Stafford, D. W. (1996) Proc. Natl. Acad. Sci. U. S. A. 93, 11068-11073). To confirm that factor IX binds to collagen IV and to examine the specificity of this interaction, we used scanning force microscopy to examine factor IX binding to collagen IV. We imaged collagen IV in the presence and the absence of factor IX and observed specific interactions between factor IX and collagen IV. Our results demonstrate that factor IX binds to collagen IV at specific sites in the collagenous domain approximately 98 and approximately 50 nm from the C-terminal pepsin-cleaved end

Algorithm Selection Framework for Cyber Attack Detection

The number of cyber threats against both wired and wireless computer systems and other components of the Internet of Things continues to increase annually. In this work, an algorithm selection framework is employed on the NSL-KDD data set and a novel paradigm of machine learning taxonomy is presented. The framework uses a combination of user input and meta-features to select the best algorithm to detect cyber attacks on a network. Performance is compared between a rule-of-thumb strategy and a meta-learning strategy. The framework removes the conjecture of the common trial-and-error algorithm selection method. The framework recommends five algorithms from the taxonomy. Both strategies recommend a high-performing algorithm, though not the best performing. The work demonstrates the close connectedness between algorithm selection and the taxonomy for which it is premised.Comment: 6 pages, 7 figures, 1 table, accepted to WiseML '2

Air Pollution Upregulates Endothelial Cell Procoagulant Activity via Ultrafine Particle-Induced Oxidant Signaling and Tissue Factor Expression

Air pollution exposure is associated with cardiovascular events triggered by clot formation. Endothelial activation and initiation of coagulation are pathophysiological mechanisms that could link inhaled air pollutants to vascular events. Here we investigated the underlying mechanisms of increased endothelial cell procoagulant activity following exposure to soluble components of ultrafine particles (soluble UF). Human coronary artery endothelial cells (HCAEC) were exposed to soluble UF and assessed for their ability to trigger procoagulant activity in platelet-free plasma. Exposed HCAEC triggered earlier thrombin generation and faster fibrin clot formation, which was abolished by an anti-tissue factor (TF) antibody, indicating TF-dependent effects. Soluble UF exposure increased TF mRNA expression without compensatory increases in key anticoagulant proteins. To identify early events that regulate TF expression, we measured endothelial H2O2 production following soluble UF exposure and identified the enzymatic source. Soluble UF exposure increased endothelial H2O2 production, and antioxidants attenuated UF-induced upregulation of TF, linking the procoagulant responses to reactive oxygen species (ROS) formation. Chemical inhibitors and RNA silencing showed that NOX-4, an important endothelial source of H2O2, was involved in UF-induced upregulation of TF mRNA. These data indicate that soluble UF exposure induces endothelial cell procoagulant activity, which involves de novo TF synthesis, ROS production, and the NOX-4 enzyme. These findings provide mechanistic insight into the adverse cardiovascular effects associated with air pollution exposure

The fibrinogen γA/γ′ isoform does not promote acute arterial thrombosis in mice

Elevated plasma fibrinogen associates with arterial thrombosis in humans and promotes thrombosis in mice by increasing fibrin formation and thrombus fibrin content. Fibrinogen is composed of six polypeptide chains: (Aα, Bβ, and γ)2. Alternative splicing of the γ chain leads to a dominant form (γA/γA) and a minor species (γA/γ’). Epidemiologic studies have detected elevated γA/γ’ fibrinogen in patients with arterial thrombosis, suggesting this isoform promotes thrombosis. However, in vitro data show that γA/γ’ is anticoagulant due to its ability to sequester thrombin, and suggest its expression is upregulated in response to inflammatory processes

Analysis of the potential of cancer cell lines to release tissue factor-containing microvesicles: correlation with tissue factor and PAR2 expression

BackgroundDespite the association of cancer-derived circulating tissue factor (TF)-containing microvesicles and hypercoagulable state, correlations with the incidence of thrombosis remain unclear.MethodsIn this study the upregulation of TF release upon activation of various cancer cell lines, and the correlation with TF and PAR2 expression and/or activity was examined. Microvesicle release was induced by PAR2 activation in seventeen cell lines and released microvesicle density, microvesicle-associated TF activity, and phoshpatidylserine-mediated activity were measured. The time-course for TF release was monitored over 90 min in each cell line. In addition, TF mRNA expression, cellular TF protein and cell-surface TF activities were quantified. Moreover, the relative expression of PAR2 mRNA and cellular protein were analysed. Any correlations between the above parameters were examined by determining the Pearson’s correlation coefficients.ResultsTF release as microvesicles peaked between 30–60 min post-activation in the majority of cell lines tested. The magnitude of the maximal TF release positively correlated with TF mRNA (c = 0.717; p

Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer

BACKGROUND: Tumor classification is inexact and largely dependent on the qualitative pathological examination of the images of the tumor tissue slides. In this study, our aim was to develop an automated computational method to classify Hematoxylin and Eosin (H&E) stained tissue sections based on cancer tissue texture features. METHODS: Image processing of histology slide images was used to detect and identify adipose tissue, extracellular matrix, morphologically distinct cell nuclei types, and the tubular architecture. The texture parameters derived from image analysis were then applied to classify images in a supervised classification scheme using histologic grade of a testing set as guidance. RESULTS: The histologic grade assigned by pathologists to invasive breast carcinoma images strongly correlated with both the presence and extent of cell nuclei with dispersed chromatin and the architecture, specifically the extent of presence of tubular cross sections. The two parameters that differentiated tumor grade found in this study were (1) the number density of cell nuclei with dispersed chromatin and (2) the number density of tubular cross sections identified through image processing as white blobs that were surrounded by a continuous string of cell nuclei. Classification based on subdivisions of a whole slide image containing a high concentration of cancer cell nuclei consistently agreed with the grade classification of the entire slide. CONCLUSION: The automated image analysis and classification presented in this study demonstrate the feasibility of developing clinically relevant classification of histology images based on micro- texture. This method provides pathologists an invaluable quantitative tool for evaluation of the components of the Nottingham system for breast tumor grading and avoid intra-observer variability thus increasing the consistency of the decision-making process

Abnormal clot microstructure formed in blood containing HIT-like antibodies

IntroductionThrombosis is a severe and frequent complication of heparin-induced thrombocytopenia (HIT). However, there is currently no knowledge of the effects of HIT-like antibodies on the resulting microstructure of the formed clot, despite such information being linked to thrombotic events. We evaluate the effect of the addition of pathogenic HIT-like antibodies to blood on the resulting microstructure of the formed clot.Materials and methodsPathogenic HIT-like antibodies (KKO) and control antibodies (RTO) were added to samples of whole blood containing Unfractionated Heparin and Platelet Factor 4. The formed clot microstructure was investigated by rheological measurements (fractal dimension; df) and scanning electron microscopy (SEM), and platelet activation was measured by flow cytometry.Results and conclusionsOur results revealed striking effects of KKO on clot microstructure. A significant difference in df was found between samples containing KKO (df = 1.80) versus RTO (df = 1.74; p < 0.0001). This increase in df was often associated with an increase in activated platelets. SEM images of the clots formed with KKO showed a network consisting of a highly branched and compact arrangement of thin fibrin fibres, typically found in thrombotic disease. This is the first study to identify significant changes in clot microstructure formed in blood containing HIT-like antibodies. These observed alterations in clot microstructure can be potentially exploited as a much-needed biomarker for the detection, management and monitoring of HIT-associated thrombosis

InterFace : A software package for face image warping, averaging, and principal components analysis

We describe InterFace, a software package for research in face recognition. The package supports image warping, reshaping, averaging of multiple face images, and morphing between faces. It also supports principal components analysis (PCA) of face images, along with tools for exploring the “face space” produced by PCA. The package uses a simple graphical user interface, allowing users to perform these sophisticated image manipulations without any need for programming knowledge. The program is available for download in the form of an app, which requires that users also have access to the (freely available) MATLAB Runtime environment