Channel-Forming (Porin) Activity in Herpetosiphon aurantiacus Hp a2

Detergent extracts of cell envelopes of the gliding bacterium Herpetosiphon aurantiacus formed channels in lipid bilayers. Fast protein liquid chromatography across a HiTrap-Q cation-exchange column demonstrated that a 45-kDa protein forms the channel. The observation of a channel-forming protein suggests that Herpetosiphon aurantiacus Hp a2 has a permeability barrier on its surface

Atlas-Guided Cluster Analysis of Large Tractography Datasets

<div><p>Diffusion Tensor Imaging (DTI) and fiber tractography are important tools to map the cerebral white matter microstructure in vivo and to model the underlying axonal pathways in the brain with three-dimensional fiber tracts. As the fast and consistent extraction of anatomically correct fiber bundles for multiple datasets is still challenging, we present a novel atlas-guided clustering framework for exploratory data analysis of large tractography datasets. The framework uses an hierarchical cluster analysis approach that exploits the inherent redundancy in large datasets to time-efficiently group fiber tracts. Structural information of a white matter atlas can be incorporated into the clustering to achieve an anatomically correct and reproducible grouping of fiber tracts. This approach facilitates not only the identification of the bundles corresponding to the classes of the atlas; it also enables the extraction of bundles that are not present in the atlas. The new technique was applied to cluster datasets of 46 healthy subjects. Prospects of automatic and anatomically correct as well as reproducible clustering are explored. Reconstructed clusters were well separated and showed good correspondence to anatomical bundles. Using the atlas-guided cluster approach, we observed consistent results across subjects with high reproducibility. In order to investigate the outlier elimination performance of the clustering algorithm, scenarios with varying amounts of noise were simulated and clustered with three different outlier elimination strategies. By exploiting the multithreading capabilities of modern multiprocessor systems in combination with novel algorithms, our toolkit clusters large datasets in a couple of minutes. Experiments were conducted to investigate the achievable speedup and to demonstrate the high performance of the clustering framework in a multiprocessing environment.</p></div

Inferior fronto-occipital fasciculus (IFO) of one dataset clustered with different methods.

<p>The IFO of one dataset is shown, which was clustered with all three methods (atlas-guided CATSER, CATSER, HAC) and both similarity measures (CD, HD). Bundles are shown in the atlas space and are superimposed with the corresponding class of the atlas (in semi-transparent blue). The spatial agreement for the bundles clustered using CD is (from left to right): 0.58; 0.62; 0.69 and for the bundles clustered using HD: 0.75; 0.67; 0.73.</p

Effects of different weighting factors.

<p>The weighting factors guide the clustering by modulating the distance between the clusters and according to their anatomical correspondence in the atlas. While a weighting factor has no effect, a weighting factor will decrease the cluster distance (attraction). Contrary, a weighting factor will increase the distance between the clusters (repulsion).</p

Left uncinate fasciculus (UNC) of two datasets clustered with atlas-guided CATSER clustering.

<p>Both bundles reside in the atlas space and are superimposed with the corresponding class of the atlas (in semi-transparent green). While the bundle on the left follow the anticipated trajectory of the UNC, the bundle on the right side contains additional tracts that leave the bundle and follow other paths. The spatial agreement for the left bundle is s and for the right bundle s.</p

Influence of Local Outlier Factors on intra-cluster distances.

<p>Given one cluster and the set of tracts , the influence of LOFs on the intra-cluster distances between and the exemplary tracts is unveiled. In the example, the LOF of , is approximately one, the LOF of is slightly increased and the LOF of is considerably elevated. Since a reciprocal relation is used for the computation of intra-cluster distances compared to inter-cluster distances, high LOFs result in reduced distances between tracts – an attraction effect. Therefore, the LOF-corrected distance between , is considerably reduced, while the correction only slightly reduces the distance between , . Since , are not outlying (LOF ), the LOF correction has almost no effect on the distance between , .</p

Example of whole brain fiber tractography and fiber tract clustering.

<p>Fiber tractography is performed to generate streamlines that approximate the underlying axonal pathways of the white-matter architecture (left). Tracts are color-coded according to their orientation with red = left-right, green = anterior-posterior and blue = inferior-superior. Clustering methods can be used to cluster the fiber tracts and to group similar tracts into fiber bundles or set of tracts (right). By employing a white matter atlas that consists of several white matter bundles (middle), the automatic extraction can be improved to retrieve anatomically correct fiber bundles.</p

Overview of the three fundamental stages of the clustering and their sequential and parallel sub-stages.

<p>The figure depicts the way how the data is processed during the stages of the clustering process. It is illustrated, which parts of the clustering are performed either in serial or in parallel and how the data is distributed across multiple threads.</p

Parameters for the three different outlier elimination strategies.

<p>The table shows the outlier elimination parameters for the three outlier elimination strategies (low, moderate, high). The outlier elimination is performed during the preclustering (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083847#pone-0083847-g002" target="_blank">Figure 2</a>, step 4) as well as during the final clustering (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083847#pone-0083847-g002" target="_blank">Figure 2</a>, step 6). Clusters that contain no more tracts than the critical cluster size () after () of the clustering has been finished are considered outliers and are removed from the subsequent clustering.</p

Determination of the number of cluster representatives.

<p>To determine the number of representatives for a cluster we use a two stage approach. As long as the number of tracts in cluster is smaller than , is selected in dependency of using either a linear or a nonlinear function (stage 1). If is larger than , is set to a constant value (stage 2).</p