Interaction of Botulinum Toxin with the Epithelial Barrier

Botulinum neurotoxin (BoNT) is a protein toxin (~150 kDa), which possesses a metalloprotease activity. Food-borne botulism is manifested when BoNT is absorbed from the digestive tract to the blood stream and enters the peripheral nerves, where the toxin cleaves core proteins of the neuroexocytosis apparatus and elicits the inhibition of neurotransmitter release. The initial obstacle to orally ingested BoNT entering the body is the epithelial barrier of the digestive tract. Recent cell biology and molecular biology studies are beginning to elucidate the mechanism by which this large protein toxin crosses the epithelial barrier. In this review, we provide an overview of the structural features of botulinum toxins (BoNT and BoNT complex) and the interaction of these toxins with the epithelial barrier

Digital Prosopography for Renaissance Musicians: Discovery of Social and Professional Networks

As part of Web 2.0 (Semantic Web), there is a new technology called FOAF (Friend of a Friend), describing relationships between people. We will investigate the applicability of FOAF for describing relationships between musicians of the past, thereby establishing a new biographical tool. Musicians have complex relationships,particularly those between teachers and students and those within ensembles of various sizes. Visual artists may have similar teacher-student relationships, but typically do not create their work together. Dancers may perform together, but they are usually taught in groups. Similarly, athletes may compete in groups, but they do not usually perform in public with their coaches. For this project we will focus specifically on relationships among Renaissance musicians and how to extract the biographical and relational data automatically from existing documents using natural language processing technology, creating a model applicable to other time periods and disciplines

Digitizing musical scores : challenges and opportunities for libraries

Musical scores and manuscripts are essential resources for music theory research. Although many libraries are such documents from their collections, these online resources are dispersed and the functionalities for exploiting their content remain limited. In this paper, we present a qualitative study based on interviews with librarians on the challenges libraries of all types face when they wish to digitize musical scores. In the light of a literature review on the role libraries can play in supporting digital humanities research, we conclude by briefly discussing the opportunities new technologies for optical music recognition and computer-aided music analysis could create for libraries

Plane Formation by Synchronous Mobile Robots in the Three Dimensional Euclidean Space

Creating a swarm of mobile computing entities frequently called robots, agents or sensor nodes, with self-organization ability is a contemporary challenge in distributed computing. Motivated by this, we investigate the plane formation problem that requires a swarm of robots moving in the three dimensional Euclidean space to land on a common plane. The robots are fully synchronous and endowed with visual perception. But they do not have identifiers, nor access to the global coordinate system, nor any means of explicit communication with each other. Though there are plenty of results on the agreement problem for robots in the two dimensional plane, for example, the point formation problem, the pattern formation problem, and so on, this is the first result for robots in the three dimensional space. This paper presents a necessary and sufficient condition for fully-synchronous robots to solve the plane formation problem that does not depend on obliviousness i.e., the availability of local memory at robots. An implication of the result is somewhat counter-intuitive: The robots cannot form a plane from most of the semi-regular polyhedra, while they can form a plane from every regular polyhedron (except a regular icosahedron), whose symmetry is usually considered to be higher than any semi-regular polyhedrdon

Mixed-effects location-scale model based on generalized hyperbolic distribution

Motivated by better modeling of intra-individual variability in longitudinal data, we propose a class of location-scale mixed effects models, in which the data of each individual is modeled by a parameter-varying generalized hyperbolic distribution. We first study the local maximum-likelihood asymptotics and reveal the instability in the numerical optimization of the log-likelihood. Then, we construct an asymptotically efficient estimator based on the Newton-Raphson method based on the original log-likelihood function with the initial estimator being naive least-squares-type. Numerical experiments are conducted to show that the proposed one-step estimator is not only theoretically efficient but also numerically much more stable and much less time-consuming compared with the maximum-likelihood estimator