Clinical and magnetic resonance imaging characteristics of thoracolumbar intervertenral disk extrusions and protrusions in large breed dogs

It has recently been shown that the fat-derived hormone adiponectin has the ability to decrease hyperglycemia and to reverse insulin resistance. However, bacterially produced full-length adiponectin is functionally inactive. Here, we show that endogenous adiponectin secreted by adipocytes is post-translationally modified into eight different isoforms, as shown by two-dimensional gel electrophoresis. Carbohydrate detection revealed that six of the adiponectin isoforms are glycosylated. The glycosylation sites were mapped to several lysines (residues 68, 71, 80, and 104) located in the collagenous domain of adiponectin, each having the surrounding motif of GXKGE(D). These four lysines were found to be hydroxylated and subsequently glycosylated. The glycosides attached to each of these four hydroxylated lysines are possibly glucosylgalactosyl groups. Functional analysis revealed that full-length adiponectin produced by mammalian cells is much more potent than bacterially generated adiponectin in enhancing the ability of subphysiological concentrations of insulin to inhibit gluconeogenesis in primary rat hepatocytes, whereas this insulin-sensitizing ability was significantly attenuated when the four glycosylated lysines were substituted with arginines. These results indicate that full-length adiponectin produced by mammalian cells is functionally active as an insulin sensitizer and that hydroxylation and glycosylation of the four lysines in the collagenous domain might contribute to this activity.link_to_subscribed_fulltex

Effect of screening abdominal ultrasound examination on the decision to pursue advanced diagnostic tests and treatment in dogs with neurologic disease.

BackgroundAbdominal ultrasound examinations (AUS) are commonly performed before advanced neurodiagnostics to screen for diseases that might affect diagnostic plans and prognosis.ObjectivesDescribe the type and frequency of abnormalities found by AUS in dogs presenting with a neurological condition, identify risk factors associated with abnormalities, and evaluate treatment decisions based on findings.AnimalsSeven hundred and fifty-nine hospitalized dogs.MethodsRetrospective study. Medical records of dogs presented from 2007 to 2009 for neurologic disease were searched for signalment, neuroanatomic localization, and AUS findings. Whether dogs had advanced neurodiagnostics and treatment was analyzed.ResultsFifty-eight percent of dogs had abnormal findings on AUS. Probability of abnormalities increased with age (P < 0.001). Nondachshund breeds had higher probability of abnormal AUS than dachshunds (odds ratio [OR] = 1.87). Eleven percent of dogs did not have advanced neurodiagnostics and in 1.3%, this was because of abnormal AUS. Dogs with ultrasonographic abnormalities were less likely than dogs without to have advanced neurodiagnostics (OR = 0.3 [95% confidence interval [CI]: 0.17, 0.52]), however, the probability of performing advanced diagnostics was high regardless of normal (OR = 0.95 [95% CI: 0.92, 0.97]) or abnormal (OR = 0.85 [95% CI: 0.81, 0.88]) AUS. Treatment was more often pursued in small dogs and less often in dogs with brain disease.Conclusions and clinical importanceFindings from screening AUS had a small negative effect on the likelihood of pursuing advanced neurodiagnostics. Although it should be included in the extracranial diagnostic workup in dogs with significant history or physical examination abnormalities, AUS is considered a low-yield diagnostic test in young dogs and dachshunds

Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks

In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input. CLEAR facilitates the visualization of attentive regions and levels of interest of DNNs during the decision-making process. It also enables the visualization of the most dominant classes associated with these attentive regions of interest. As such, CLEAR can mitigate some of the shortcomings of heatmap-based methods associated with decision ambiguity, and allows for better insights into the decision-making process of DNNs. Quantitative and qualitative experiments across three different datasets demonstrate the efficacy of CLEAR for gaining a better understanding of the inner workings of DNNs during the decision-making process.Comment: Accepted at Computer Vision and Patter Recognition Workshop (CVPR-W) on Explainable Computer Vision, 201

Object Part Localization Using Exemplar-based Models

​Object part localization is a fundamental problem in computer vision, which aims to let machines understand object in an image as a configuration of parts. As the visual features at parts are usually weak and misleading, spatial models are needed to constrain the part configuration, ensuring that the estimated part locations respect both image cue and shape prior. Unlike most of the state-of-the-art techniques that employ parametric spatial models, we turn to non-parametric exemplars of part configurations. The benefit is twofold: instead of assuming any parametric yet imprecise distributions on the spatial relations of parts, exemplars literally encode such relations present in the training samples; exemplars allow us to prune the search space of part configurations with high confidence. This thesis consists of two parts: fine-grained classification and object part localization. We first verify the efficacy of parts in fine-grained classification, where we build working systems that automatically identify dog breeds, fish species, and bird species using localized parts on the object. Then we explore multiple ways to enhance exemplar-based models, such that they can be well applied to deformable objects such as bird and human body. Specifically, we propose to enforce pose and subcategory consistency in exemplar matching, thus obtaining more reliable hypotheses of configuration. We also propose part-pair representation that features novel shape composing with multiple promising hypotheses. In the end, we adapt exemplars to hierarchical representation, and design a principled formulation to predict the part configuration based on multi-scale image cues and multi-level exemplars. These efforts consistently improve the accuracy of object part localization