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Primary headache attributed to external compression or traction to the head : a narrative review
Get PDFBackground: The aim of this review is to synthesize the existing knowledge regarding headaches attributed to external physical
stimuli, as classified by the ICHD-3 (Group 4.6). Two forms can be distinguished in this group: (1) headache attributed to external
compression and (2) headache attributed to external traction.
Methods: A comprehensive literature review was conducted using the Medline (PubMed) database and other relevant academic
sources. All English-language articles were subjected to a relevance assessment.
Results: The prevalence of the two types of headache varies considerably, with a higher incidence observed in women or in the
presence of predisposing factors (e.g., work with compulsory helmets or long hair). An external-compression headache is typically
described as a pressing sensation, whereas an external-traction headache is characterized by a sensation of pulling. The headaches
typically persist for less than an hour after the stimulus has ceased, and the intensity is typically reported as mild to moderate.
Apart from avoiding the trigger, which is not always possible, effective therapies have not been established.
Conclusion: Both types of headaches are relatively common, yet they remain underrepresented in the scientific literature.
Furthermore, there is a dearth of comprehensive overviews. If the triggering factor cannot be eliminated, both types of headaches
can interfere with daily living and working
SynthEthics: Ensuring Digital Ethics and Performance with a Design Theory for Using Synthetic Image Data in Digital Health Deep Learning
Get PDFThis paper addresses the need for ethical and effective use of synthetic image data in digital health computer vision. It explores the design requirements and design principles for both responsible use of artificial intelligence in digital health and model robustness, focusing on privacy, ethical compliance, and domain adaptation. Using the design science research paradigm along with value-sensitive design and sociotechnical systems theory, this study presents a design theory that provides actionable guidance for the generation, selection, and integration of synthetic data in digital health. Through heuristic theorizing over two design cycles, the work provides a robust theory artifact and conceptual model to ensure ethical use and improve model performance in digital health through appropriate domain adaptation, generalization, and accuracy. In addition to contributing to theoretical knowledge, this research offers practical implications for health authorities to promote ethical standards and performance in synthetically trained AI applications
Emerging functions of Plakophilin 4 in the control of cell contact dynamics
Get PDFPlakophilin 4 (PKP4, also called p0071) is a unique armadillo family protein localized at adherens junctions that acts as a scaffold protein capable of clustering cadherins. PKP4 also regulates cadherin recycling which is vital to enable junction dynamics. In addition, PKP4 controls the mechanical properties of cells by regulating actin filament organization through small Rho-GTPases. In this setting, PKP4 controls the localization and activity of specific guanine exchange factors (GEFs) and of their opponents, the GTPase activating proteins (GAPs). Through the formation of multiprotein complexes with Rho-GTPases, their regulators and their effectors, PKP4 controls the spatio-temporal activity of Rho signaling to regulate cell adhesion and cell mechanics. In keratinocytes, PKP4 prevents differentiation and at the same time dampens proliferation. This is, in part achieved through an interaction with the Hippo pathway, which controls the activity of the transcriptional co-factors YAP and TAZ. In a feedback loop, YAP/TAZ modulate PKP4 localization and function. Here, we review the various functions of PKP4 in cell signaling, cell mechanics, cell adhesion and growth control. We discuss how these functions converge in the regulation of cell adhesion dynamics to allow cells to adapt to their changing environment and enable proliferation, delamination but, at the same time, guarantee cell barrier function
What constitutes sustainable agriculture for different audiences in Germany? : a comparative analysis of large‐scale text data
Get PDFAgriculture contributes in several ways to achieving sustainability objectives. However, there is no agreement among different societal groups, such as farmers, consumers and scientists, on what constitutes ‘sustainable agriculture’. These differences affect how the impacts of agricultural production on sustainability objectives is perceived, which solutions are considered and implemented.
In this paper, we investigate the topics discussed under the heading ‘sustainable agriculture’ in German newspapers and agricultural magazines. To this end, we apply topic modelling to extract topics discussed in these two large-scale text corpora. We complement these with scientific articles as a contrast case. We run separate topic models for each corpus and use the identified topics to compare the patterns qualitatively.
Results reveal heterogeneity in the topics across the three corpora with limited overlaps restricted to topics such as agricultural policy. This supports the assumption that farmers and the broader society in Germany have very different perceptions of sustainable agriculture. A surprising result is the absence of topics related to climate change from the agricultural magazine corpus.
These disparities may create challenges for designing and implementing democratically legitimized policies to promote sustainable agriculture
Multiple Strukturwandel
Get PDFKurzbericht zur Konferenz "Strukturwandel in den Braunkohlerevieren – Transformationsregionen als Ressource". Die Gesellschaft erlebt derzeit vielfältige strukturelle Veränderungen, die von zahlreichen Akteuren gestaltet, gesteuert und in positivie Bahnen gelenkt werden. Diese multiplen Strukturwandel diverser gesellschaftlicher Dimensionen bieten Chancen, voneinander und miteinander zu lernen und zu partizipieren. Dazu bot die Konferenz mehrere Praxisbeispiele, die in diesem Bericht vorgestellt und analysiert werden.Das Just Transition Center beschäftigt sich mit Übermorgen-Fragen für einen gerechten, nachhaltigen und klimaneutralen Übergang in der Strukturwandelregion Mitteldeutsches Revier. Dafür arbeiten 17 interdisziplinäre Forschungsteams und vier Revierscouts eng mit den Akteuren in der Fokusregion südliches Sachsen-Anhalt zusammen und entwickeln Lösungen und Strategien für die Transformationsgesellschaft
A neutropenic diet in haemato-oncological patients receiving high-dose therapy and hematopoietic stem cell transplantation : a systematic review
Get PDFBackground/Objectives: Although the benefits of low-germ diets for patients are increasingly being questioned, their application in practice is widespread. The aim of this review is to summarise the current data and evaluate the effectiveness of the neutropenic diet (ND) in adult haemato-oncological patients to provide a basis for practical guidelines. Methods: A systematic search was conducted in four electronic databases (Medline (Ovid), CINAHL (EBSCO), EMBASE (Ovid) and Cochrane CENTRAL) to identify English and German randomised controlled trials (RCTs) concerning the effectiveness of an ND in adult haematological patients. The main endpoints were fever and systemic infections, gastrointestinal (GI) infections, mortality, nutritional status and hospitalisation length. Results: A total of five RCTs with 510 adult patients were included in this systematic review. All patients received high-dose chemotherapy in order to treat haemato-oncological malignancies. None of the analysed endpoints showed a significant advantage of the ND compared to the control group. Conclusions: An ND does not have a beneficial effect on infection rates, GI health, mortality or hospitalisation length for haemato-oncological patients. On the contrary, an ND tends to negatively affect the patient’s nutritional status; therefore, an adaption in clinical routine should take place
European Federation of Organisations for medical physics (EFOMP) policy statement no 20 : The role of medical physicists and medical physics experts in physiological measurement and related therapies
Get PDFThe role of medical physics professionals (MPPs) (medical physicists and medical physics experts) in physiological measurement and related therapies (PM&T) applied in e.g., critical care, neurophysiology, neurology, physiology, audiology, and neurotology has long been acknowledged. Given that the exact role and medical specialty fields in which MPP are involved vary between countries, this policy statement aims to provide direction towards improved definition, harmonisation, and development of the role. This policy statement considers the surveyed experiences from several European countries, state-of-the-art of PM&T, and anticipated future developments. We also present an inventory of competences, and associated knowledge and skills expected of MPPs working in these areas
Liberté et soumission en Islam à travers le prisme du salafisme en Tunisie
Get PDFMots-clés : Tunisie, salafisme, liberté, Islam, sujet, sujétionKeywords: Tunisia, Salafism, freedom, Islam, subject, subjectionالكلمات المفتاحيّةُ :تونس، السّلفيّة، الحرّية، الإسلام، العبد، الخضو
Entwicklung von automatisierten Ansätzen für die Hochdurchsatz- Pflanzenbildanalyse
No full textImage-based phenotyping of whole plants and plant organs with high-resolution optical sensors became a method of choice for assessing quantitative plant traits in a non-destructive, high-throughput manner. Due to several technical and natural factors, optical appearance of plant and background structures undergoes substantial variations. Consequently, automated analysis of large phenotypic data renders a challenging problem. The intrinsic complexity of phenotypic image data requires higher-level cognitive approaches to pattern classification and segmentation, such as convolutional neural networks (CNN). However, the advanced CNN methods require a large amount of representative ground truth data, which poses a bottle-neck for their straightforward application. Consequently, this thesis deals with investigating and developing semi-and fully automated image processing approaches to plant image segmentation and pattern detection in application to high-throughput plant phenotyping. In particular, the application-oriented effort of this work is on the analysis of large image data from major IPK high-throughput imaging platforms, including multiple greenhouse shoot and root phenotyping facilities. Starting with semi-automated approaches, more than 80% accuracy of root and spike image segmentation was achieved. Based on the ground truth data from the semi-automated image segmentation, U-net CNN models were developed and trained, which achieved an accuracy of more than 90% for fully automated root and shoot segmentation. In these applications, the proposed U-net-based image segmentation models were shown to be superior compared to various CNN architectures and state-of-the-art methods. Furthermore, the proposed CNN models are less complex and more capable of segmenting different optical scenes including greenhouse and field-like images. Ultimately, this thesis shows a great potential of novel deep learning approaches to fully automated processing of large image data which essentially helps to bridging a gap in high-throughput plant phenotyping and bio-data driven research.Die bildbasierte Phänotypisierung von ganzen Pflanzen und Pflanzenorganen mit hochauflösenden optischen Sensoren hat sich als bevorzugte Methode zur nicht-destruktiven und hochdurchsatzfähigen Erfassung quantitativer Pflanzenmerkmale etabliert. Aufgrund verschiedener technischer und natürlicher Faktoren unterliegt das optische Erscheinungsbild von Pflanzen und Hintergrundstrukturen erheblichen Schwankungen. Folglich stellt die automatisierte Analyse großer phänotypischer Daten ein anspruchsvolles Problem dar. Die inhärente Komplexität phänotypischer Bilddaten erfordert höhere kognitive Ansätze für die Musterklassifikation und Segmentierung, wie beispielsweise Convolutional Neural Networks (CNN). Allerdings erfordern die fortschrittlichen CNN-Methoden eine große Menge an repräsentativen Referenzdaten (sog. ground truth), was einen Flaschenhals für ihre direkte Anwendung darstellt. Folglich befasst sich diese Arbeit mit der Untersuchung und Entwicklung halb- und vollautomatischer Bildverarbeitungsansätze zur Segmentierung und Erkennung von komplexen pflanzlichen Strukturen in Anwendung zur Hochdurchsatz-Pflanzenphänotypisierung. Der anwendungsorientierte Aufwand dieser Arbeit liegt insbesondere in der Analyse großer Bilddaten von großen Hochdurchsatz-Bildgebungsplattformen am IPK, einschließlich mehrerer Gewächshausanlagen zur Spross- und Wurzelphänotypisierung. Beginnend mit halbautomatischen Ansätzen wurde eine Genauigkeit von mehr als 80% bei der Wurzel- und Grannenbildsegmentierung erreicht. Aufbauend auf den ground truth Daten aus der semi-automatisierter Bildsegmentierung wurden U-net CNN Modelle entwickelt und trainiert, die bei der vollständigen Automatisierung eine Genauigkeit von mehr als 90% in Wurzel und Sprosssegmentierung erreicht haben. In diesen Anwendungen konnte gezeigt werden, dass die vorgeschlagenen U-net-basierten Bildsegmentierungmodelle verschiedenen modernen CNN-Methoden überlegen sind. Darüber hinaus sind die hier vorgeschlagenen CNN-Modelle weniger komplex und besser in der Lage, unterschiedliche optische Szenen zu segmentieren. Zusammenfassend zeigt diese Arbeit das große Potenzial neuartiger Deep-Learning- Ansätze für die vollautomatische Verarbeitung großer Bilddatenmengen auf, was wesentlich dazu beiträgt, eine Lücke in der Hochdurchsatzplanzenphänotypisierung und Biodaten-basierenden Pflanzenforschung zu schließen
