Regional and Hemispheric Determinants of Language Laterality: Implications for Preoperative fMRI

Language is typically a function of the left hemisphere but the right hemisphere is also essential in some healthy individuals and patients. This inter-subject variability necessitates the localization of language function, at the individual level, prior to neurosurgical intervention. Such assessments are typically made by comparing left and right hemisphere language function to determine “language lateralization” using clinical tests or fMRI. Here, we show that language function needs to be assessed at the region and hemisphere specific level, because laterality measures can be misleading. Using fMRI data from 82 healthy participants, we investigated the degree to which activation for a semantic word matching task was lateralized in 50 different brain regions and across the entire cortex. This revealed two novel findings. First, the degree to which language is lateralized across brain regions and between subjects was primarily driven by differences in right hemisphere activation rather than differences in left hemisphere activation. Second, we found that healthy subjects who have relatively high left lateralization in the angular gyrus also have relatively low left lateralization in the ventral precentral gyrus. These findings illustrate spatial heterogeneity in language lateralization that is lost when global laterality measures are considered. It is likely that the complex spatial variability we observed in healthy controls is more exaggerated in patients with brain damage. We therefore highlight the importance of investigating within hemisphere regional variations in fMRI activation, prior to neuro-surgical intervention, to determine how each hemisphere and each region contributes to language processing. Hum Brain Mapp, 2010. © 2010 Wiley-Liss, Inc

Mapping the Interacting Regions between Troponins T and C. Binding of TnT and TnI peptides to TnC and NMR mapping of the TnT-binding site on TnC

Muscular contraction is triggered by an increase in calcium concentration, which is transmitted to the contractile proteins by the troponin complex. The interactions among the components of the troponin complex (troponins T, C, and I) are essential to understanding the regulation of muscle contraction. While the structure of TnC is well known, and a model for the binary TnC·TnI complex has been recently published (Tung, C.-S., Wall, M. E., Gallagher, S. C., and Trewhella, J. (2000)Protein Sci. 9, 1312–1326), very little is known about TnT. Using non-denaturing gels and NMR spectroscopy, we have analyzed the interactions between TnC and five peptides from TnT as well as how three TnI peptides affect these interactions. Rabbit fast skeletal muscle peptide TnT-(160–193) binds to TnC with a dissociation constant of 30 ± 6 µm. This binding still occurs in the presence of TnI-(1–40) but is prevented by the presence of TnI-(56–115) or TnI-(96–139), both containing the primary inhibitory region of TnI. TnT-(228–260) also binds TnC. The binding site for TnT-(160–193) is located on the C-terminal domain of TnC and was mapped to the surface of TnC using NMR chemical shift mapping techniques. In the context of the model for the TnC·TnI complex, we discuss the interactions between TnT and the other troponin subunits

Atomic resolution insight into host cell recognition by Toxoplasma gondii.

Accepted versio

An Approach to Multi-robot Site Exploration Based on Principles of Self-organisation

This paper presents a novel approach to multi-robot site exploration and map building considering the robot team as a self-organising system. The approach has been developed within the framework of the project GUARDIANS. The Map Building process represents not a separate activity, but an inherent by-product of self-organisation. The system consists of an (heterogeneous) robot swarm, a mobile ad-hoc network and an (evolving) topological map of the environment. The proposed map building approach takes advantage of a cooperating robot team (as opposed to a single robot) allowing accurate deployment and localisation in a structured, yet dynamic manner. A topological graph representation of the environment is formed, from which an initial metric representation is elicitable as edges are assigned lengths. This reasonable sketch of the environment can be further developed to a full metric map and be used as the basis of building ad-hoc mobile wireless communication and sensor networks. The presented algorithms also take into consideration sensor limitation and are tested on a group of Khepera III robots, specially upgraded to fulfil the needs of our approach