Transcription of nanos-1 in Zebrafish Embryos is not Affected by Bisphenol A: Evaluated Using Quantitative Real-Time PCR

The presence of primordial germ cells (PGCs) is crucial for proper gonad formation in zebrafish (Danio rerio). The many aspects of PGC migration that allow these cells to reach the proper location at the gonadal ridge include receptors, ligands, germ plasm components, and internal maintenance of PGCs. Any one of these factors could be affected by endocrine-disrupting chemicals (EDCs), which have been shown to alter the directed migration of these cells during early embryonic development. Based on recent research wherein the EDC bisphenol A (BPA) inhibited normal PGC migration, we have used the same dose of BPA to determine the impact of BPA on a gene central to proper germ cell migration. Zebrafish embryos were exposed to BPA, and the levels of the target gene nanos-1 were analyzed using quantitative real-time PCR (q-PCR). The target gene nanos-1 is a critically important germplasm component that allows for survival and proper migration of PGCs. The q-PCR results showed that BPA did not affect the transcription level of nanos-1 in zebrafish embryos

Unsupervised Discovery of Parts, Structure, and Dynamics

Humans easily recognize object parts and their hierarchical structure by watching how they move; they can then predict how each part moves in the future. In this paper, we propose a novel formulation that simultaneously learns a hierarchical, disentangled object representation and a dynamics model for object parts from unlabeled videos. Our Parts, Structure, and Dynamics (PSD) model learns to, first, recognize the object parts via a layered image representation; second, predict hierarchy via a structural descriptor that composes low-level concepts into a hierarchical structure; and third, model the system dynamics by predicting the future. Experiments on multiple real and synthetic datasets demonstrate that our PSD model works well on all three tasks: segmenting object parts, building their hierarchical structure, and capturing their motion distributions.Comment: ICLR 2019. The first two authors contributed equally to this wor

Ariel - Volume 11 Number 4

Executive Editor Ellen Feldman Leonardo S. Nasca, Jr. Business Managers Barbara L. Davies Martin B. Getzow News Editor Hugh A. Gelabert Features Editor Aaron D. Bleznak CAHS Editor Joan M. Greco Editorial Page Editor Samuel Markind Photography Editor Todd L. Demmy Sports Editor Paul F. Mansfiel

Law Enforcement and the 1033 Program

Law enforcement agencies are faced with situations and circumstances within the communities they serve that if they had specialized equipment, they could resolve those situations more effectively. However, because cost is a determining factor, departments are unable to purchase items that would otherwise assist them with performing the variety of tasks their communities expect from them. Some of these roles may be from a tactical perspective such as providing ballistic protection for officers involved in an active shooter event or that of a search and rescue role during a natural disaster. An ever present argument from those in the citizenry that oppose law enforcement agencies acquiring military equipment is the perception that police agencies are no longer police, but rather military in appearance. To offset this often negative perception by the public, it would necessitate that agencies should not only engage in educating their communities as to how these resources would be utilized by the agency, but also how these resources would serve the citizens in the event of critical incidents. Agencies that choose to participate in the Law Enforcement Support Office (LESO) or the 1033 program as it is frequently referred are choosing a more cost effective method to acquire specialized equipment and are also exercising forward thinking strategies to serve their communities

Sharing visual features for multiclass and multiview object detection

We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and scales. This can be slow and can require a lot of training data, since each classifier requires the computation of many different image features. In particular, for independently trained detectors, the (run-time) computational complexity, and the (training-time) sample complexity, scales linearly with the number of classes to be detected. It seems unlikely that such an approach will scale up to allow recognition of hundreds or thousands of objects. We present a multi-class boosting procedure (joint boosting) that reduces the computational and sample complexity, by finding common features that can be shared across the classes (and/or views). The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required, and therefore the computational cost, is observed to scale approximately logarithmically with the number of classes. The features selected jointly are closer to edges and generic features typical of many natural structures instead of finding specific object parts. Those generic features generalize better and reduce considerably the computational cost of an algorithm for multi-class object detection