Identification and Characterization of New Small Molecule Inhibitors of Picornavirus Replication

The Picornaviridae family consists of positive-strand RNA viruses that are the causative agents of a variety of diseases in humans and animals. Few drugs targeting picornaviruses are available, making the discovery of new antivirals a high priority. Here, we identified and characterized three compounds from a library of kinase inhibitors that block replication of poliovirus, coxsackievirus B3, and encephalomyocarditis virus. The antiviral effect of these compounds is not likely related to their known cellular targets because other inhibitors targeting the same pathways did not inhibit viral replication. Using an in vitro translation-replication system, we showed that these drugs inhibit different stages of the poliovirus life cycle. A4(1) inhibited the formation of a functional replication complex, while E5(1) and E7(2) affected replication after the replication complex had formed. A4(1) demonstrated partial protection from paralysis in a murine model of poliomyelitis. Poliovirus resistant to E7(2) had a single mutation in the 3A protein. This mutation was previously found to confer resistance to enviroxime-like compounds, which target either PI4KIIIβ (major enviroxime-like compounds) or OSBP (minor enviroxime-like compounds), cellular factors involved in lipid metabolism and shown to be important for replication of diverse positive-strand RNA viruses. We classified E7(2) as a minor enviroxime-like compound, because the localization of OSBP changed in the presence of this inhibitor. Interestingly, both E7(2) and major enviroxime-like compound GW5074 interfered with the viral polyprotein processing. Multiple attempts to isolate resistant mutants in the presence of A4(1) or E5(1) were unsuccessful, showing that effective broad-spectrum antivirals could be developed on the basis of these compounds. Studies with these compounds shed light on pathways shared by diverse picornaviruses that could be potential targets for the development of broad-spectrum antiviral drugs.

A NOVEL NON-LETHAL LAPAROSCOPIC APPROACH TO DETECT INTERSEX (TESTICULAR OOCYTES) IN LARGEMOUTH BASS (MICROPTERUS SALMOIDES) AND SMALLMOUTH BASS (MICROPTERUS DOLOMIEU)

The appearance of testicular oocytes (TO) in wild fish populations has received considerable attention in the scientific literature and public media. Current methods to quantify TO are lethal; instead, a non-lethal alternative was examined. Laparoscopic insertion into the genital pore allowed internal visualization of the gonad and detection of TO by collecting five testis biopsies in smallmouth bass Micropterus dolomieu and largemouth bass Micropterus salmoides. Overall, biopsies quantified similar levels of TO detection and severity to conventional transverse sectioning with less than 10% mortality. Suitability of surgical anesthetics, tricaine methanesulfonate and electronarcosis were examined in laboratory and field applications. Electronarcosis had the added benefit of rapid sex identification and immediate release of female fish with minimal trauma, representing significant benefits when sampling small or compromised populations. Laparoscopy may be useful for monitoring the prevalence and severity of TO in these fish species when lethal sampling is not a desired outcome.

Leveraging Multiple Features for Image Retrieval and Matching

The goal of image retrieval and matching is to find and locate object instances in images from a large-scale image database. While visual features are abundant, how to combine them to improve performance by individual features remains a challenging task. In this work, we focus on leveraging multiple features for accurate and efficient image retrieval and matching. We first propose two graph-based approaches to rerank initially retrieved images for generic image retrieval. In the graph, vertices are images while edges are similarities between image pairs. Our first approach employs a mixture Markov model based on a random walk model on multiple graphs to fuse graphs. We introduce a probabilistic model to compute the importance of each feature for graph fusion under a naive Bayesian formulation, which requires statistics of similarities from a manually labeled dataset containing irrelevant images. To reduce human labeling, we further propose a fully unsupervised reranking algorithm based on a submodular objective function that can be efficiently optimized by greedy algorithm. By maximizing an information gain term over the graph, our submodular function favors a subset of database images that are similar to query images and resemble each other. The function also exploits the rank relationships of images from multiple ranked lists obtained by different features. We then study a more well-defined application, person re-identification, where the database contains labeled images of human bodies captured by multiple cameras. Re-identifications from multiple cameras are regarded as related tasks to exploit shared information. We apply a novel multi-task learning algorithm using both low level features and attributes. A low rank attribute embedding is joint learned within the multi-task learning formulation to embed original binary attributes to a continuous attribute space, where incorrect and incomplete attributes are rectified and recovered. To locate objects in images, we design an object detector based on object proposals and deep convolutional neural networks (CNN) in view of the emergence of deep networks. We improve a Fast RCNN framework and investigate two new strategies to detect objects accurately and efficiently: scale-dependent pooling (SDP) and cascaded rejection classifiers (CRC). The SDP improves detection accuracy by exploiting appropriate convolutional features depending on the scale of input object proposals. The CRC effectively utilizes convolutional features and greatly eliminates negative proposals in a cascaded manner, while maintaining a high recall for true objects. The two strategies together improve the detection accuracy and reduce the computational cost.