DNA modification by sulfur: analysis of the sequence recognition specificity surrounding the modification sites

The Dnd (DNA degradation) phenotype, reflecting a novel DNA modification by sulfur in Streptomyces lividans 1326, was strongly aggravated when one (dndB) of the five genes (dndABCDE) controlling it was mutated. Electrophoretic banding patterns of a plasmid (pHZ209), reflecting DNA degradation, displayed a clear change from a preferential modification site in strain 1326 to more random modifications in the mutant. Fourteen randomly modifiable sites on pHZ209 were localized, and each seemed to be able to be modified only once. Residues in a region (5′-c–cGGCCgccg-3′) including a highly conserved 4-bp central core (5′-GGCC-3′) in a well-documented preferential modification site were assessed for their necessity by site-directed mutagenesis. While the central core (GGCC) was found to be stringently required in 1326 and in the mutant, ‘gccg’ flanking its right could either abolish or reduce the modification frequency only in the mutant, and two separate nucleotides to the left had no dramatic effect. The lack of essentiality of DndB for S-modification suggests that it might only be required for enhancing or stabilizing the activity of a protein complex at the required preferential modification site, or resolving secondary structures flanking the modifiable site(s), known to constitute an obstacle for efficient modification

Multiple Object Detection by Sequential Monte Carlo and Hierarchical Detection Network

In this paper, we propose a novel framework for detecting multiple objects in 2D and 3D images. Since a joint multi-object model is difficult to obtain in most practical situations, we focus here on detecting the objects sequentially, one-by-one. The interdependence of object poses and strong prior information embedded in our domain of medical images results in better performance than detecting the objects individually. Our approach is based on Sequential Estimation techniques, frequently applied to visual tracking. Unlike in tracking, where the sequential order is naturally determined by the time sequence, the order of detection of multiple objects must be selected, leading to a Hierarchical Detection Network (HDN). We present an algorithm that optimally selects the order based on probability of states (object poses) within the ground truth region. The posterior distribution of the object pose is approximated at each step by sequential Monte Carlo. The samples are propagated within the sequence across multiple objects and hierarchical levels. We show on 2D ultrasound images of left atrium, that the automatically selected sequential order yields low mean detection error. We also quantitatively evaluate the hierarchical detection of fetal faces and three fetal brain structures in 3D ultrasound images

Abstract Synthesis of Progressively-Variant Textures on Arbitrary Surfaces

Permission to make digital/hard copy of part of all of this work for personal or classroom use is granted without fee provided that the copies are not made or distributed for profit or commercial advantage, the copyright notice, the title of the publication, and its date appear, and notice is given that copying is by permissio

Joint real-time object detection and pose estimation using probabilistic boosting network

In this paper, we present a learning procedure called probabilistic boosting network (PBN) for joint real-time object detection and pose estimation. Grounded on the law of total probability, PBN integrates evidence from two building blocks, namely a multiclass boosting classifier for pose estimation and a boosted detection cascade for object detection. By inferring the pose parameter, we avoid the exhaustive scanning for the pose, which hampers real time requirement. In addition, we only need one integral image/volume with no need of image/volume rotation. We implement PBN using a graph-structured network that alternates the two tasks of foreground/background discrimination and pose estimation for rejecting negatives as quickly as possible. Compared with previous approaches, we gain accuracy in object localization and pose estimation while noticeably reducing the computation. We invoke PBN to detect the left ventricle from a 3D ultrasound volume, processing about 10 volumes per second, and the left atrium from 2D images in real time. 1

DETECTION AND RETRIEVAL OF CYSTS IN JOINT ULTRASOUND B-MODE AND ELASTICITY BREAST IMAGES

Distinguishing cysts from other tumors is a routine clinical practice for diagnosing breast cancer. It has shown that more accurate diagnosis can be achieved by combining elasticity images with traditional B-mode ultrasound images [1]. In this paper, we propose a fully automatic system to detect cysts jointly in both B-mode and elasticity images. It is based on database-guided techniques that learn the knowledge of cyst appearance automatically from B-mode and elasticity images in a database. Further, for a detected cyst in a query image, the cysts with similar image appearance in the database are retrieved to improve diagnostic accuracy and confidence. In the experiment, we show that our system achieves high sensitivity and specificity in cyst diagnosis. Index Terms — breast cyst detection and retrieval, ultrasound (US) imaging, elasticity imaging 1