Cathodoluminescence in a (S)TEM - Exploring Possibilities and Limits

Extended abstract of a paper presented at Microscopy and Microanalysis 2010 in Portland, Oregon, USA, August 1 - August 5, 201

The DeepScoresV2 dataset and benchmark for music object detection

The dataset, code and pre-trained models, as well as user instructions, are publicly available at https://zenodo.org/record/4012193.In this paper, we present DeepScoresV2, an extended version of the DeepScores dataset for optical music recognition (OMR). We improve upon the original DeepScores dataset by providing much more detailed annotations, namely (a) annotations for 135 classes including fundamental symbols of non-fixed size and shape, increasing the number of annotated symbols by 23%; (b) oriented bounding boxes; (c) higher-level rhythm and pitch information (onset beat for all symbols and line position for noteheads); and (d) a compatibility mode for easy use in conjunction with the MUSCIMA++ dataset for OMR on handwritten documents. These additions open up the potential for future advancement in OMR research. Additionally, we release two state-of-the-art baselines for DeepScoresV2 based on Faster R-CNN and the Deep Watershed Detector. An analysis of the baselines shows that regular orthogonal bounding boxes are unsuitable for objects which are long, small, and potentially rotated, such as ties and beams, which demonstrates the need for detection algorithms that naturally incorporate object angles

The left superior temporal gyrus is a shared substrate for auditory short-term memory and speech comprehension: evidence from 210 patients with stroke

Competing theories of short-term memory function make specific predictions about the functional anatomy of auditory short-term memory and its role in language comprehension. We analysed high-resolution structural magnetic resonance images from 210 stroke patients and employed a novel voxel based analysis to test the relationship between auditory short-term memory and speech comprehension. Using digit span as an index of auditory short-term memory capacity we found that the structural integrity of a posterior region of the superior temporal gyrus and sulcus predicted auditory short-term memory capacity, even when performance on a range of other measures was factored out. We show that the integrity of this region also predicts the ability to comprehend spoken sentences. Our results therefore support cognitive models that posit a shared substrate between auditory short-term memory capacity and speech comprehension ability. The method applied here will be particularly useful for modelling structure–function relationships within other complex cognitive domains

Deep learning-based simultaneous multi-phase deformable image registration of sparse 4D-CBCT

Purpose: Respiratory gated 4D-CBCT suffers from sparseness artefacts caused by the limited number of projections available for each respiratory phase/amplitude. These artefacts severely impact deformable image registration methods used to extract motion information. We use deep learning-based methods to predict displacement vector-fields (DVF) from sparse 4D-CBCT images to alleviate the impacts of sparseness artefacts. Methods: We trained U-Net-type convolutional neural network models to predict multiple (10) DVFs in a single forward pass given multiple sparse, gated CBCT and an optional artefact-free reference image as inputs. The predicted DVFs are used to warp the reference image to the different motion states, resulting in an artefact-free image for each state. The supervised training uses data generated by a motion simulation framework. The training dataset consists of 560 simulated 4D-CBCT images of 56 different patients; the generated data include fully sampled ground-truth images that are used to train the network. We compare the results of our method to pairwise image registration (reference image to single sparse image) using a) the deeds algorithm and b) VoxelMorph with image pair inputs. Results: We show that our method clearly outperforms pairwise registration using the deeds algorithm alone. PSNR improved from 25.8 to 46.4, SSIM from 0.9296 to 0.9999. In addition, the runtime of our learning-based method is orders of magnitude shorter (2 seconds instead of 10 minutes). Our results also indicate slightly improved performance compared to pairwise registration (delta-PSNR=1.2). We also trained a model that does not require the artefact-free reference image (which is usually not available) during inference demonstrating only marginally compromised results (delta-PSNR=-0.8). Conclusion: To the best of our knowledge, this is the first time CNNs are used to predict multi-phase DVFs in a single forward pass. This enables novel applications such as 4D-auto-segmentation, motion compensated image reconstruction, motion analyses, and patient motion modeling

Mitigation of motion-induced artifacts in cone beam computed tomography using deep convolutional neural networks

Background: Cone beam computed tomography (CBCT) is often employed on radiation therapy treatment devices (linear accelerators) used in image-guided radiation therapy (IGRT). For each treatment session, it is necessary to obtain the image of the day in order to accurately position the patient, and to enable adaptive treatment capabilities including auto-segmentation and dose calculation. Reconstructed CBCT images often suffer from artifacts, in particular those induced by patient motion. Deep-learning based approaches promise ways to mitigate such artifacts. Purpose: We propose a novel deep-learning based approach with the goal to reduce motion induced artifacts in CBCT images and improve image quality. It is based on supervised learning and includes neural network architectures employed as pre- and/or post-processing steps during CBCT reconstruction. Methods: Our approach is based on deep convolutional neural networks which complement the standard CBCT reconstruction, which is performed either with the analytical Feldkamp-Davis-Kress (FDK) method, or with an iterative algebraic reconstruction technique (SART-TV). The neural networks, which are based on refined U-net architectures, are trained end-to-end in a supervised learning setup. Labeled training data are obtained by means of a motion simulation, which uses the two extreme phases of 4D CT scans, their deformation vector fields, as well as time-dependent amplitude signals as input. The trained networks are validated against ground truth using quantitative metrics, as well as by using real patient CBCT scans for a qualitative evaluation by clinical experts. Results: The presented novel approach is able to generalize to unseen data and yields significant reductions in motion induced artifacts as well as improvements in image quality compared with existing state-of-the-art CBCT reconstruction algorithms (up to +6.3 dB and +0.19 improvements in peak signal-to-noise ratio, PSNR, and structural similarity index measure, SSIM, respectively), as evidenced by validation with an unseen test dataset, and confirmed by a clincal evaluation on real patient scans (up to 74% preference for motion artifact reduction over standard reconstruction). Conclusions: For the first time, it is demonstrated, also by means of clinical evaluation, that inserting deep neural networks as pre- and post-processing plugins in the existing 3D CBCT reconstruction and trained end-to-end yield significant improvements in image quality and reduction of motion artifacts

Hermeneutics and Nature

This paper contributes to the on-going research into the ways in which the humanities transformed the natural sciences in the late Eighteenth and early Nineteenth Centuries. By investigating the relationship between hermeneutics -- as developed by Herder -- and natural history, it shows how the methods used for the study of literary and artistic works played a crucial role in the emergence of key natural-scientific fields, including geography and ecology

Central nervous system rather than immune cell-derived BDNF mediates axonal protective effects early in autoimmune demyelination

Brain-derived neurotrophic factor (BDNF) is involved in neuronal and glial development and survival. While neurons and astrocytes are its main cellular source in the central nervous system (CNS), bioactive BDNF is also expressed in immune cells and in lesions of multiple sclerosis and its animal model experimental autoimmune encephalomyelitis (EAE). Previous data revealed that BDNF exerts neuroprotective effects in myelin oligodendrocyte glycoprotein-induced EAE. Using a conditional knock-out model with inducible deletion of BDNF, we here show that clinical symptoms and structural damage are increased when BDNF is absent during the initiation phase of clinical EAE. In contrast, deletion of BDNF later in the disease course of EAE did not result in significant changes, either in the disease course or in axonal integrity. Bone marrow chimeras revealed that the deletion of BDNF in the CNS alone, with no deletion of BDNF in the infiltrating immune cells, was sufficient for the observed effects. Finally, the therapeutic effect of glatiramer acetate, a well-characterized disease-modifying drug with the potential to modulate BDNF expression, was partially reversed in mice in which BDNF was deleted shortly before the onset of disease. In summary, our data argue for an early window of therapeutic opportunity where modulation of BDNF may exert neuroprotective effects in experimental autoimmune demyelination

Myelin insulation as a risk factor for axonal degeneration in autoimmune demyelinating disease

Axonal degeneration determines the clinical outcome of multiple sclerosis and is thought to result from exposure of denuded axons to immune-mediated damage. Therefore, myelin is widely considered to be a protective structure for axons in multiple sclerosis. Myelinated axons also depend on oligodendrocytes, which provide metabolic and structural support to the axonal compartment. Given that axonal pathology in multiple sclerosis is already visible at early disease stages, before overt demyelination, we reasoned that autoimmune inflammation may disrupt oligodendroglial support mechanisms and hence primarily affect axons insulated by myelin. Here, we studied axonal pathology as a function of myelination in human multiple sclerosis and mouse models of autoimmune encephalomyelitis with genetically altered myelination. We demonstrate that myelin ensheathment itself becomes detrimental for axonal survival and increases the risk of axons degenerating in an autoimmune environment. This challenges the view of myelin as a solely protective structure and suggests that axonal dependence on oligodendroglial support can become fatal when myelin is under inflammatory attack

National identity predicts public health support during a global pandemic (vol 13, 517, 2022) : National identity predicts public health support during a global pandemic (Nature Communications, (2022), 13, 1, (517), 10.1038/s41467-021-27668-9)

Publisher Copyright: © The Author(s) 2022.In this article the author name ‘Agustin Ibanez’ was incorrectly written as ‘Augustin Ibanez’. The original article has been corrected.Peer reviewe

National identity predicts public health support during a global pandemic.

Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = -0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics