The Evolution and Diversity of SALMFamide Neuropeptides

The SALMFamides are a family of neuropeptides that act as muscle relaxants in echinoderms. Two types of SALMFamides have been identified: L-type (e.g. the starfish neuropeptides S1 and S2) with the C-terminal motif LxFamide (x is variable) and F-type with the C-terminal motif FxFamide. In the sea urchin Strongylocentrotus purpuratus (class Echinoidea) there are two SALMFamide genes, one encoding L-type SALMFamides and a second encoding F-type SALMFamides, but hitherto it was not known if this applies to other echinoderms. Here we report the identification of SALMFamide genes in the sea cucumber Apostichopus japonicus (class Holothuroidea) and the starfish Patiria miniata (class Asteroidea). In both species there are two SALMFamide genes: one gene encoding L-type SALMFamides (e.g. S1 in P. miniata) and a second gene encoding F-type SALMFamides plus one or more L-type SALMFamides (e.g. S2-like peptide in P. miniata). Thus, the ancestry of the two SALMFamide gene types traces back to the common ancestor of echinoids, holothurians and asteroids, although it is not clear if the occurrence of L-type peptides in F-type SALMFamide precursors is an ancestral or derived character. The gene sequences also reveal a remarkable diversity of SALMFamide neuropeptides. Originally just two peptides (S1 and S2) were isolated from starfish but now we find that in P. miniata, for example, there are sixteen putative SALMFamide neuropeptides. Thus, the SALMFamides would be a good model system for experimental analysis of the physiological significance of neuropeptide "cocktails" derived from the same precursor protein

Stability, Structure and Scale: Improvements in Multi-modal Vessel Extraction for SEEG Trajectory Planning

Purpose Brain vessels are among the most critical landmarks that need to be assessed for mitigating surgical risks in stereo-electroencephalography (SEEG) implantation. Intracranial haemorrhage is the most common complication associated with implantation, carrying signi cant associated morbidity. SEEG planning is done pre-operatively to identify avascular trajectories for the electrodes. In current practice, neurosurgeons have no assistance in the planning of electrode trajectories. There is great interest in developing computer assisted planning systems that can optimise the safety pro le of electrode trajectories, maximising the distance to critical structures. This paper presents a method that integrates the concepts of scale, neighbourhood structure and feature stability with the aim of improving robustness and accuracy of vessel extraction within a SEEG planning system. Methods The developed method accounts for scale and vicinity of a voxel by formulating the problem within a multi-scale tensor voting framework. Feature stability is achieved through a similarity measure that evaluates the multi-modal consistency in vesselness responses. The proposed measurement allows the combination of multiple images modalities into a single image that is used within the planning system to visualise critical vessels. Results Twelve paired datasets from two image modalities available within the planning system were used for evaluation. The mean Dice similarity coe cient was 0.89 ± 0.04, representing a statistically signi cantly improvement when compared to a semi-automated single human rater, single-modality segmentation protocol used in clinical practice (0.80 ±0.03). Conclusions Multi-modal vessel extraction is superior to semi-automated single-modality segmentation, indicating the possibility of safer SEEG planning, with reduced patient morbidity

SEEG trajectory planning: combining stability, structure and scale in vessel extraction

StereoEEG implantation is performed in patients with epilepsy to determine the site of the seizure onset zone. Intracranial haemorrhage is the most common complication associated to implantation carrying a risk that ranges from 0.6 to 2.7%, with significant associated morbidity. SEEG planning is done pre-operatively to identify avascular trajectories for the electrodes. In current practice neurosurgeons have no assistance in the planning of the electrode trajectories. There is great interest in developing computer assisted planning systems that can optimize the safety profile of electrode trajectories, maximizing the distance to critical brain structures. In this work, we address the problem of blood vessel extraction for SEEG trajectory planning. The proposed method exploits the availability of multi-modal images within a trajectory planning system to formulate a vessel extraction framework that combines the scale and the neighbouring structure of an object. We validated the proposed method in twelve multi-modal patient image sets. The mean Dice similarity coefficient (DSC) was 0.88 ± 0.03, representing a statistically significantly improvement when compared to the semi-automated single rater, single modality segmentation protocol used in current practice (DSC = 0.78 ± 0.02)