The skin barrier is fundamental to terrestrial life and its evolution; it upholds homeostasis and protects against the environment. Skin barrier capacity is controlled by lipids that fill the extracellular space of the skin's surface layer--the stratum corneum. Here we report on the determination of the molecular organization of the skin's lipid matrix in situ, in its near-native state, using a methodological approach combining very high magnification cryo-electron microscopy (EM) of vitreous skin section defocus series, molecular modeling, and EM simulation. The lipids are organized in an arrangement not previously described in a biological system-stacked bilayers of fully extended ceramides (CERs) with cholesterol molecules associated with the CER sphingoid moiety. This arrangement rationalizes the skin's low permeability toward water and toward hydrophilic and lipophilic substances, as well as the skin barrier's robustness toward hydration and dehydration, environmental temperature and pressure changes, stretching, compression, bending, and shearing.

ANACONDA performs well in comparison with other algorithms. By including CT/CBCT data in the validation, the various aspects of the algorithm such as its ability to handle different modalities, large deformations, and air pockets are shown.

Structural analyses reveal that oligomerization between cell adhesion molecules in the same membrane is influenced by their interactions across opposing membranes (see also in this issue the accompanying paper by Müller et al.).

International audienceBiomass can be converted into liquid and gaseous biofuels with good efficiency. In this study, the conversion of industrial hemp ( L.), a biomass source that can be cultivated with a high biomass yield per hectare, was used. Steam pretreatment of dry and ensiled hemp was investigated prior to ethanol production. The pretreatment efficiency was evaluated in terms of sugar recovery and polysaccharide conversion in the enzymatic hydrolysis step. For both materials, impregnation with 2% SO followed by steam pretreatment at 210°C for five minutes were found to be the optimal conditions leading to the highest overall yield of glucose. Simultaneous saccharification and fermentation experiments carried out with optimised pretreatment conditions resulted in ethanol yields of 163 g kg ensiled hemp (dry matter) (71% of the theoretical maximum) and 171 g kg dry hemp (74%), which corresponds to 206-216 l Mg ethanol based on initial dry material

Purpose The accuracy of deformable image registration tools can vary widely between imaging modalities and specific implementations of the same algorithms. A biomechanical model-based algorithm initially developed in-house at an academic institution was translated into a commercial radiotherapy treatment planning system and validated for multiple imaging modalities and anatomic sites. Methods Biomechanical deformable registration (Morfeus) is a geometry driven algorithm based on the finite element method. Boundary conditions are derived from the model-based segmentation of controlling structures in each image which establishes a point-to-point surface correspondence. For each controlling structure, material properties and fixed or sliding interfaces are assigned. The displacements of internal volumes for controlling structures and other structures implicitly deformed are solved with finite element analysis. Registration was performed for 74 patients with images (mean vector resolution) of thoracic and abdominal 4DCT (2.8 mm) and MR (5.3 mm), liver CT-MR (4.5 mm) and prostate MR (2.6 mm). Accuracy was quantified between deformed and actual target images using distance-to-agreement (DTA) for structure surfaces and the target registration error (TRE) for internal point landmarks. Results The results of the commercial implementation were as follows. The mean DTA was ≤1.0 mm for controlling structures and 1.0–3.5 mm for implicitly deformed structures on average. TRE ranged from 2.0 mm on prostate MR to 5.1 mm on lung MR on average, within 0.1 mm or lower than the image voxel sizes. Accuracy was not overly sensitive to changes in the material properties or variability in structure segmentations, as changing these inputs affected DTA and TRE by ≤0.8 mm. Maximum DTA >5 mm occurred for 88% of the structures evaluated although these were within the inherent segmentation uncertainty for 82% of structures. Differences in accuracy between the commercial and in-house research implementations were ≤0.5 mm for mean DTA and ≤0.7 mm for mean TRE. Conclusions Accuracy of biomechanical deformable registration evaluated on a large cohort of images in the thorax, abdomen and prostate was similar to the image voxel resolution on average across multiple modalities. Validation of this treatment planning system implementation supports biomechanical deformable registration as a versatile clinical tool to enable accurate target delineation at planning and treatment adaptation.

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Purpose: We describe a public dataset with MR and CT images of patients performed in the same position with both multiobserver and expert consensus delineations of relevant organs in the male pelvic region. The purpose was to provide means for training and validation of segmentation algorithms and methods to convert MR to CT like data, i.e., so called synthetic CT (sCT). Acquisition and validation methods: T1-and T2-weighted MR images as well as CT data were collected for 19 patients at three different departments. Five experts delineated nine organs for each patient based on the T2-weighted MR images. An automatic method was used to fuse the delineations. Starting from each fused delineation, a consensus delineation was agreed upon by the five experts for each organ and patient. Segmentation overlap between user delineations with respect to the consensus delineations was measured to describe the spread of the collected data. Finally, an open-source software was used to create deformation vector fields describing the relation between MR and CT images to further increase the usability of the dataset. Data format and usage notes: The dataset has been made publically available to be used for academic purposes, and can be accessed from https://zenodo.org/record/583096. Potential applications: The dataset provides a useful source for training and validation of segmentation algorithms as well as methods to convert MR to CT-like data (sCT). To give some examples: The T2-weighted MR images with their consensus delineations can directly be used as a template in an existing atlas-based segmentation engine; the expert delineations are useful to validate the performance of a segmentation algorithm as they provide a way to measure variability among users which can be compared with the result of an automatic segmentation; and the pairwise deformably registered MR and CT images can be a source for an atlas-based sCT algorithm or for validation of sCT algorithm.