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The ALICE Zero Degree Calorimeter system (ZDC) is composed of two identical sets of calorimeters, placed at opposite sides with respect to the interaction point, 114 meters away from it, complemented by two small forward electromagnetic calorimeters (ZEM). Each set of detectors consists of a neutron (ZN) and a proton (ZP) ZDC. They are placed at zero degrees with respect to the LHC axis and allow to detect particles emitted close to beam direction, in particular neutrons and protons emerging from hadronic heavy-ion collisions (spectator nucleons) and those emitted from electromagnetic processes. For neutrons emitted by these two processes, the ZN calorimeters have nearly 100% acceptance.
During the √sNN = 2.76 TeV Pb-Pb data-taking, the ALICE Collaboration studied forward neutron emission with a dedicated trigger, requiring a minimum energy deposition in at least one of the two ZN. By exploiting also the information of the two ZEM calorimeters it has been possible to separate the contributions of electromagnetic and hadronic processes and to study single neutron vs. multiple neutron emission.
The measured cross sections of single and mutual electromagnetic dissociation of Pb nuclei at √sNN = 2.76 TeV, with neutron emission, are σsingle EMD = 187:4 ± 0.2 (stat.)−11.2+13.2 (syst.) b and σmutual EMD = 5.7 ± 0.1 (stat.) ±0.4 (syst.) b, respectively [1]. This is the first measurement of electromagnetic dissociation of 208Pb nuclei at the LHC energies, allowing a test of electromagnetic dissociation theory in a new energy regime. The experimental results are compared to the predictions from a relativistic electromagnetic dissociation model.
Cryo-electron tomography (CET) is a unique technique to visualize biological objects under near-to-native conditions at near-atomic resolution. CET provides three-dimensional (3D) snapshots of the cellular proteome, in which the spatial relations between macromolecular complexes in their near native cellular context can be explored. Due to the limitation of the electron dose applicable on biological samples, the achievable resolution of a tomogram is restricted to a few nanometers, higher resolution can be achieved by averaging of structures occurring in multiples. For this purpose, computational techniques such as template matching, sub-tomogram averaging and classification are essential for a meaningful processing of CET data.
This thesis introduces the techniques of template matching and sub-tomogram averaging and their applications on real biological data sets. Subsequently, the problem of reference bias, which restricts the applicability of those techniques, is addressed. Two methods that estimate the reference bias in Fourier and real space are demonstrated. The real space method, which we have named the “M-free” score, provides a reliable estimation of the reference bias, which gives access to the reliability of the template matching or sub-tomogram averaging process. Thus, the “M-free” score makes those approaches more applicable to structural biology. Furthermore, a classification algorithm based on Neural Networks (NN) called “KerDenSOM3D” is introduced, which is implemented in 3D and compensates for the missing-wedge. This approach helps extracting different structural states of macromolecular complexes or increasing the class purity of data sets by eliminating outliers. A comprehensive comparison with other classification methods shows superior performance of KerDenSOM3D.
Cryo-electron tomography provides a snapshot of the cellular proteome. With template matching, the spatial positions of various macromolecular complexes within their native cellular context can be detected. However, the growing awareness of the reference bias introduced by the cross-correlation based approaches, and more importantly the lack of a reliable confidence measurement in the selection of these macromolecular complexes, has restricted the use of these applications. Here we propose a heuristic, in which the reference bias is measured in real space in an analogous way to the R-free value in X-ray crystallography. We measure the reference bias within the mask used to outline the area of the template, and do not modify the template itself. The heuristic works by splitting the mask into a working and a testing area in a volume ratio of 9:1. While the working area is used during the calculation of the cross-correlation function, the information from both areas is explored to calculate the M-free score. We show using artificial data, that the M-free score gives a reliable measure for the reference bias. The heuristic can be applied in template matching and in sub-tomogram averaging. We further test the applicability of the heuristic in tomograms of purified macromolecules, and tomograms of whole Mycoplasma cells.
Correlative microscopy incorporates the specificity of fluorescent protein labeling into high-resolution electron micrographs. Several approaches exist for correlative microscopy, most of which have used the green fluorescent protein (GFP) as the label for light microscopy. Here we use chemical tagging and synthetic fluorophores instead, in order to achieve protein-specific labeling, and to perform multicolor imaging. We show that synthetic fluorophores preserve their post-embedding fluorescence in the presence of uranyl acetate. Post-embedding fluorescence is of such quality that the specimen can be prepared with identical protocols for scanning electron microscopy (SEM) and transmission electron microscopy (TEM); this is particularly valuable when singular or otherwise difficult samples are examined. We show that synthetic fluorophores give bright, well-resolved signals in super-resolution light microscopy, enabling us to superimpose light microscopic images with a precision of up to 25 nm in the x-y plane on electron micrographs. To exemplify the preservation quality of our new method we visualize the molecular arrangement of cadherins in adherens junctions of mouse epithelial cells.