Die Übersetzung der Finanzkrise in eine Wirtschaftskrise: Management- und Betriebsratsstrategien nach der Krise in einer Aktiengesellschaft

Im folgenden Beitrag werden mittels einer Ereignisstrukturanalyse die Handlungen betrieblicher AkteurInnen aus Management und Betriebsrat einer Aktiengesellschaft der IT-Branche analysiert. Die untersuchte Zeitsequenz beginnt mit der Reaktion des Vorstandes auf die Finanzkrise von 2008 und endet mit der Unterzeichnung einer Vereinbarung zwischen Vorstand und Betriebsrat zu Cost Saving Maßnahmen zum 20. Februar 2009. Entworfen wird ein historisches Argument, das die Übersetzung der Krise in die betriebliche Realität der AkteurInnen nachzeichnet und eine im Betriebsrat verlaufende Konfliktlinie zwischen einer gewerkschafts- und einer unternehmensnahen politischen Gruppierung herausarbeitet.The following paper applies an event-structure analysis to scrutinize the social ac-tions of members to the executive board and the work council in a stock corporation of the information technology sector. The analyzed sequence of events starts with the reaction of the board of directors to the Financial Crisis of 2008 and ends with the signing of a cost saving program on the 20th of February 2009. I develop a historical argument that lines out the translation of the crisis into the social reality of the actors within the corporation and sketches a line of conflict between a union and a corporation oriented political grouping

Multi-stage Biomarker Models for Progression Estimation in Alzheimer’s Disease

The estimation of disease progression in Alzheimer’s disease (AD) based on a vector of quantitative biomarkers is of high interest to clinicians, patients, and biomedical researchers alike. In this work, quantile regression is employed to learn statistical models describing the evolution of such biomarkers. Two separate models are constructed using (1) subjects that progress from a cognitively normal (CN) stage to mild cognitive impairment (MCI) and (2) subjects that progress from MCI to AD during the observation window of a longitudinal study. These models are then automatically combined to develop a multi-stage disease progression model for the whole disease course. A probabilistic approach is derived to estimate the current disease progress (DP) and the disease progression rate (DPR) of a given individual by fitting any acquired biomarkers to these models. A particular strength of this method is that it is applicable even if individual biomarker measurements are missing for the subject. Employing cognitive scores and image-based biomarkers, the presented method is used to estimate DP and DPR for subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Further, the potential use of these values as features for different classification tasks is demonstrated. For example, accuracy of 64% is reached for CN vs. MCI vs. AD classification

Dynamic Changes in White Matter Abnormalities Correlate With Late Improvement and Deterioration Following TBI: A Diffusion Tensor Imaging Study.

OBJECTIVE: Traumatic brain injury (TBI) is not a single insult with monophasic resolution, but a chronic disease, with dynamic processes that remain active for years. We aimed to assess patient trajectories over the entire disease narrative, from ictus to late outcome. METHODS: Twelve patients with moderate-to-severe TBI underwent magnetic resonance imaging in the acute phase (within 1 week of injury) and twice in the chronic phase of injury (median 7 and 21 months), with some undergoing imaging at up to 2 additional time points. Longitudinal imaging changes were assessed using structural volumetry, deterministic tractography, voxel-based diffusion tensor analysis, and region of interest analyses (including corpus callosum, parasagittal white matter, and thalamus). Imaging changes were related to behavior. RESULTS: Changes in structural volumes, fractional anisotropy, and mean diffusivity continued for months to years postictus. Changes in diffusion tensor imaging were driven by increases in both axial and radial diffusivity except for the earliest time point, and were associated with changes in reaction time and performance in a visual memory and learning task (paired associates learning). Dynamic structural changes after TBI can be detected using diffusion tensor imaging and could explain changes in behavior. CONCLUSIONS: These data can provide further insight into early and late pathophysiology, and begin to provide a framework that allows magnetic resonance imaging to be used as an imaging biomarker of therapy response. Knowledge of the temporal pattern of changes in TBI patient populations also provides a contextual framework for assessing imaging changes in individuals at any given time point

A robust similarity measure for volumetric image registration with outliers

Image registration under challenging realistic conditions is a very important area of research. In this paper, we focus on algorithms that seek to densely align two volumetric images according to a global similarity measure. Despite intensive research in this area, there is still a need for similarity measures that are robust to outliers common to many different types of images. For example, medical image data is often corrupted by intensity inhomogeneities and may contain outliers in the form of pathologies. In this paper we propose a global similarity measure that is robust to both intensity inhomogeneities and outliers without requiring prior knowledge of the type of outliers. We combine the normalised gradients of images with the cosine function and show that it is theoretically robust against a very general class of outliers. Experimentally, we verify the robustness of our measures within two distinct algorithms. Firstly, we embed our similarity measures within a proof-of-concept extension of the Lucas–Kanade algorithm for volumetric data. Finally, we embed our measures within a popular non-rigid alignment framework based on free-form deformations and show it to be robust against both simulated tumours and intensity inhomogeneities

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org

Empfehlungen der Ständigen Impfkommission (STIKO) und der Deutschen Gesellschaft für Tropenmedizin, Reisemedizin und Globale Gesundheit e.V. (DTG) zu Reiseimpfungen

Die STIKO empfiehlt Reiseimpfungen zum individuellen Schutz Reisender mit einem Expositionsrisiko gegenüber bestimmten impfpräventablen Erkrankungen und um den Import von Infektionserregern in das bereiste Land oder bei Rückreise nach Deutschland zu verhindern. Die im Epidemiologischen Bulletin 14/2022 veröffentlichten Empfehlungen zu Reiseimpfungen wurden von der STIKO-AG Reiseimpfungen in Zusammenarbeit mit externen Expertinnen und Experten erarbeitet. Neuerungen sind dabei u. a. ein Kapitel zu COVID-19, die aktualisierte Epidemiologie bei Cholera, Hepatitis A, Hepatitis B, Meningokokken und Typhus, Poliomyelitis-Impfempfehlungen gemäß dem „Statement of the 31st Polio IHR Emergency Committee“ der WHO sowie Tabellen zur Tollwut-Postexpositionsprophylaxe.Peer Reviewe

Empfehlungen der Ständigen Impfkommission (STIKO) und der Deutschen Gesellschaft für Tropenmedizin, Reisemedizin und Globale Gesundheit e.V. (DTG) zu Reiseimpfungen

Die STIKO empfiehlt Reiseimpfungen zum individuellen Schutz Reisender mit einem Expositionsrisiko gegenüber bestimmten impfpräventablen Erkrankungen und um den Import von Infektionserregern in das bereiste Land oder bei Rückreise nach Deutschland zu verhindern. Die im Epidemiologischen Bulletin 14/2022 veröffentlichten Empfehlungen zu Reiseimpfungen wurden von der STIKO-AG Reiseimpfungen in Zusammenarbeit mit externen Expertinnen und Experten erarbeitet. Neuerungen sind dabei u. a. ein Kapitel zu COVID-19, die aktualisierte Epidemiologie bei Cholera, Hepatitis A, Hepatitis B, Meningokokken und Typhus, Poliomyelitis-Impfempfehlungen gemäß dem „Statement of the 31st Polio IHR Emergency Committee“ der WHO sowie Tabellen zur Tollwut-Postexpositionsprophylaxe.Peer Reviewe