The crystal structure of the immunity protein of colicin E7 suggests a possible colicin-interacting surface

The immunity protein of colicin E7 (ImmE7) can bind specifically to the DNase-type colicin E7 and inhibit its bactericidal activity. Here we report the 1.8-angstrom crystal structure of the ImmE7 protein. This is the first x-ray structure determined in the superfamily of colicin immunity proteins. The ImmE7 protein consists of four antiparallel alpha-helices, folded in a topology similar to the architecture of a four-helix bundle structure. A region rich in acidic residues is identified. This negatively charged area has the greatest variability within the family of DNase-type immunity proteins; thus, it seems likely that this area is involved in specific binding to colicin. Based on structural, genetic, and kinetic data, we suggest that all the DNase-type immunity proteins, as well as colicins, share a "homologous-structural framework" and that specific interaction between a colicin and its cognate immunity protein relies upon how well these two proteins' charged residues match on the interaction surface, thus leading to specific immunity of the colicin.     

Some sexological characteristics of stress incontinent women

The goal of stress incontinence surgery is to prevent opening of the urethra during increases in intra-abdominal pressure. Greater refinements in the understanding of the pathophysiology of incontinence and experience with newer treatments have extended surgical thinking beyond the familiar paradigm "to place the urethra in a high retropubic position." When incontinence is associated with vaginal hypermobility, vaginal support may be sufficient to restore continence if the suburethral vaginal wall is sufficiently strong, an evaluation which must often be made by physical examination alone. However, when the vaginal wall is weak, the urethra will require an alternative form of support, usually a sling. If the urethra is intrinsically deficient, vaginal support may not be sufficient to prevent opening during increased intra-abdominal pressure, and coaptation by sling obstruction or periurethral bulking injection may be required. Most laparoscopic approaches to stress incontinence use Burch's method, which offers excellent urethral stability provided the suburethral vaginal wall is strong. Newer insights into the relation between vaginal mobility and urethral closure are discussed, as well as anatomic aspects of the Burch suspension relevant to laparoscopic repair.     

Inexpensive high availability solutions for the SIP-based VoIP service

In the era of IP-based service, people expect a simple, cheap, and competent Voice over IP (VoIP) service as an alternative of the traditional voice over PSTN. The introduction of the SIP protocol realizes the expectation. Following the cost saving spirit of VoIP, we focus on studying inexpensive high availability solutions for the SIP-based VoIP Service. In this paper, Peer-to-Peer (P2P) based and DN-LB based schemes are mainly compared in the paper. A P2P-based scheme enables an inexpensive high availability solution to the VoIP service by the shared computation resources form P2P nodes. Such a P2P-based solution may be appropriate for an individual VoIP user. However, a caller may take a large volume of messages to find out a callee via the proxy nodes in the P2P network. This inherent property of a P2P network may induce the message overhead and long call setup delay. Based on above, another inexpensive scheme, which is a probing-based name resolution solution, is proposed to achieve high availability and load balancing for the VoIP service. We tag the probing mechanism onto the open source project Domain Name Relay Daemon (DNRD) to become a domain name resolution based load balancer (DN-LB). With DN-LB, all request messages from clients can be fairly distributed to all failure-proof proxy servers in the server farm without using any additional costly intermediate network device and changing the standard SIP architecture. Such a DN-LB based solution may be a good choice for a VoIP service provider.     

Scalable continuous object detection and tracking in sensor networks

With the advancement of MEMS technologies, sensor networks have opened up broad application prospects. An important issue in wireless sensor networks is object detection and tracking, which typically involves two basic components, collaborative data processing and object location reporting. The former aims to have sensors collaborating in determining a concise digest of object location information, while the latter aims to transport a concise digest to sink in a timely manner. This issue has been intensively studied in individual objects, such as intruders. However, the characteristic of continuous objects has posed new challenges to this issue. Continuous objects can diffuse, increase in size, or split into multiple continuous objects, such as a noxious gas. In this paper, a scalable, topology-control-based approach for continuous object detection and tracking is proposed. Extensive simulations are conducted, which show a significant improvement over existing solutions.     

Extending 3D Lucas–Kanade tracking with adaptive templates for head pose estimation

The Lucas–Kanade tracker (LKT) is a commonly used method to track target objects over 2D images. The key principle behind the object tracking of an LKT is to warp the object appearance so as to minimize the difference between the warped object’s appearance and a pre-stored template. Accordingly, the 2D pose of the tracked object in terms of translation, rotation, and scaling can be recovered from the warping. To extend the LKT for 3D pose estimation, a model-based 3D LKT assumes a 3D geometric model for the target object in the 3D space and tries to infer the 3D object motion by minimizing the difference between the projected 2D image of the 3D object and the pre-stored 2D image template. In this paper, we propose an extended model-based 3D LKT for estimating 3D head poses by tracking human heads on video sequences. In contrast to the original model-based 3D LKT, which uses a template with each pixel represented by a single intensity value, the proposed model-based 3D LKT exploits an adaptive template with each template pixel modeled by a continuously updated Gaussian distribution during head tracking. This probabilistic template modeling improves the tracker’s ability to handle temporal fluctuation of pixels caused by continuous environmental changes such as varying illumination and dynamic backgrounds. Due to the new probabilistic template modeling, we reformulate the head pose estimation as a maximum likelihood estimation problem, rather than the original difference minimization procedure. Based on the new formulation, an algorithm to estimate the best head pose is derived. The experimental results show that the proposed extended model-based 3D LKT achieves higher accuracy and reliability than the conventional one does. Particularly, the proposed LKT is very effective in handling varying illumination, which cannot be well handled in the original LKT.     

Application of a remote image surveillance system in a robotic weapon

As one of the major steps toward a fully intelligent autonomous robotic weapon, we have made progress in three major areas, (1) design of the surveillance system by an AVR microcontroller, (2) implementation of the design mechanism, and (3) performance of the human- machine interface surveillance system via the LabVIEW graphical programming environment, so that the supervisor can control the vehicle with a keyboard or a specially adapted mouse. In order to accomplish all these achievements, there have been major additions and overhauls in both system software codes and system circuit board developments. All these developments, including a new algorithm and hardware implementation, are described in this article. The experimental results have shown the practicality of the AVR microcontroller, the LabVIEW graphical programming environment, and ZigBee wireless technology applied to a robotic weapon.     

Comparing e-Learning tools’ success: The case of instructor–student interactive vs. self-paced tools

e-Learning tools have profoundly transformed modern pedagogical approaches. Vendors provide different types of systems, such as self-paced (SP) and instructor–student interactive (ISI) e-Learning tools. Although both types of tools represent promising solutions to facilitate the learning process, it is important to theoretically identify a framework to evaluate the success of these tools and assess whether one type of tool is more effective than another. Toward this end, we (1) propose a model to evaluate e-Learning tools’ success by extending and contextualizing Seddon’s information systems (IS) success model for the e-Learning environment and (2) formulate four hypotheses to predict the differences in the success factors between SP and ISI tools. We test the model and hypotheses using data from 783 students across seven higher education institutions in Hong Kong. The results support the proposed e-Learning tool success model and three of the four hypotheses. ISI tools outperform SP tools in terms of system quality, perceived usefulness, satisfaction, and learning outcome.     

A multi-strategy knowledge interoperability framework for heterogeneous learning objects

This paper presents a knowledge exchange framework that can leverage the interoperability among semantically heterogeneous learning objects. With the release of various e-Learning standards, learning contents and digital courses are easy to achieve cross-platform sharing, exchanging, and even reorganizing. However, knowledge sharing in semantic level is still a challenge due to that the learning materials can be presented in any form, such as audios, videos, web pages, and even flash files. The proposed knowledge exchange framework allows users to share their learning materials (also called “learning objects”) in semantic level automatically. This framework contains two methodologies: the first is a semantic mapping between knowledge bases (i.e. ontologies) which have essentially similar concepts, and the second is an ontology-based classification algorithm for sharable learning objects. The proposed algorithm adopts the IMS DRI standard and classifies the sharable learning objects from heterogeneous repositories into a local knowledge base by their inner meaning instead of keyword matching. Significance of this research lies in the semantic inferring rules for ontology mapping and learning objects classification as well as the full automatic processing and self-optimizing capability. Focused on digital learning materials and contrasted to other traditional technologies, the proposed approach has experimentally demonstrated significantly improvement in performance.     

Hybrid feature selection by combining filters and wrappers

Feature selection aims at finding the most relevant features of a problem domain. It is very helpful in improving computational speed and prediction accuracy. However, identification of useful features from hundreds or even thousands of related features is a nontrivial task. In this paper, we introduce a hybrid feature selection method which combines two feature selection methods – the filters and the wrappers. Candidate features are first selected from the original feature set via computationally-efficient filters. The candidate feature set is further refined by more accurate wrappers. This hybrid mechanism takes advantage of both the filters and the wrappers. The mechanism is examined by two bioinformatics problems, namely, protein disordered region prediction and gene selection in microarray cancer data. Experimental results show that equal or better prediction accuracy can be achieved with a smaller feature set. These feature subsets can be obtained in a reasonable time period.     

A two-stage hierarchical regression model for meta-analysis of epidemiologic nonlinear dose–response data

To estimate a summarized dose–response relation across different exposure levels from epidemiologic data, meta-analysis often needs to take into account heterogeneity across studies beyond the variation associated with fixed effects. We extended a generalized-least-squares method and a multivariate maximum likelihood method to estimate the summarized nonlinear dose–response relation taking into account random effects. These methods are readily suited to fitting and testing models with covariates and curvilinear dose–response relations.