Structure Analysis of Ice-Embedded Single Particles

The conventional negative-stain preparation method for electron microscopy, in which biological macromolecules are contrasted using heavy metal salts (such as uranyl-acetate), is a simple and fast technique which has helped visualize hundreds of different molecular structures. Computer analysis of such negatively stained images of individual (i.e., non-crystalline) macromolecules using statistical pattern-recognition techniques has revealed considerable new structural information. Negative staining, however, has some disadvantages: the specimens are often severely flattened (as much as 25%-75%), they often exhibit strong preferential attachment of the molecules to the supporting carbon foil, and the molecular images may be difficult to interpret due to the relatively complex nature of the interaction between molecules and stain. Embedding biological macromolecules in a layer of vitreous ice (actually: vitreous water ) represents an attractive alternative preparation method which mimics the natural environment of these molecules. The processing of ice-images often requires special computational approaches such as: multivariate statistical classification of aligned images or of invariant functions derived from the unaligned images; alignment of images belonging to a specific class of images, determination of the spatial orientations of the projection images relative to each other ( angular reconstitution ). In this paper, we discuss our own overall single-particle structure analysis approach and highlight some new methodological developments in this context

Maritime cognitive workload assessment

The human factor plays the key role for safety in many industrial and civil every-day operations in our technologized world. Human failure is more likely to cause accidents than technical failure, e.g. in the challenging job of tugboat captains. Here, cognitive workload is crucial, as its excess is a main cause of dangerous situations and accidents while being highly participant and situation dependent. However, knowing the captain’s level of workload can help to improve man-machine interaction. The main contributions of this paper is a successful workload indication and a transfer of cognitive workload knowledge from laboratory to realistic settings

A case report of a blueberry muffin baby caused by congenital self-healing indeterminate cell histiocytosis

Background: Blueberry muffin is a descriptive term for a neonate with multiple purpuric skin lesions. Many causes are known, amongst them life-threatening diseases like congenital infections or leukemia. Indeterminate cell histiocytosis (ICH) is an exceptionally rare cause of blueberry muffin rash. ICH is a histiocytic disorder which can be limited to the skin or can present with systemic involvement. A mutation that has been described in histiocytic disorders is a MAP2K1 mutation. In ICH, this mutation has previously been described in merely one case. Case presentation: A term male neonate was admitted to the neonatology ward directly after birth because of a blueberry muffin rash. ICH was diagnosed on skin biopsy. The lesions resolved spontaneously. The patient is currently 3 years old and has had no cutaneous lesions or systemic involvement so far. This disease course is similar to that of the Hashimoto-Pritzker variant of LCH. Conclusions: ICH can manifest in neonates as resolving skin lesions. It is limited to the skin in most cases, but systemic development is possible. Therefore, it is essential to confirm the diagnosis with a biopsy before the lesions resolve and to monitor these patients closely with routine follow-up.</p

A posteriori correction of camera characteristics from large image data sets

Large datasets are emerging in many fields of image processing including: electron microscopy, light microscopy, medical X-ray imaging, astronomy, etc. Novel computer-controlled instrumentation facilitates the collection of very large datasets containing thousands of individual digital images. In single-particle cryogenic electron microscopy (“cryo-EM”), for example, large datasets are required for achieving quasi-atomic resolution structures of biological complexes. Based on the collected data alone, large datasets allow us to precisely determine the statistical properties of the imaging sensor on a pixel-by-pixel basis, independent of any “a priori” normalization routinely applied to the raw image data during collection (“flat field correction”). Our straightforward “a posteriori” correction yields clean linear images as can be verified by Fourier Ring Correlation (FRC), illustrating the statistical independence of the corrected images over all spatial frequencies. The image sensor characteristics can also be measured continuously and used for correcting upcoming images