Hochschule für Life Sciences FHNW

Dauerhafte URI für den Bereichhttps://irf.fhnw.ch/handle/11654/22

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Bereich: Suchergebnisse

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    Publikation
    KVI-Konformität in der Nachhaltigkeitsberichterstattung der IWB. Analyse und Ergänzungen
    (Hochschule für Life Sciences FHNW, 2024) Heuberger, Noomi; Hengevoss, Dirk; Industrielle Werke Basel (IWB)
    11 - Studentische Arbeit
  • Vorschaubild
    Publikation
    Machbarkeitsstudie zur Wiederverwertung von Kupfer und Plastik aus Kabelresten
    (Hochschule für Life Sciences FHNW, 2024) Dahinden, Jonas; Lenz, Markus; Recycling Huber
    11 - Studentische Arbeit
  • Vorschaubild
    Publikation
    Automatische Datenextraktion aus Anamnesebögen
    (Hochschule für Life Sciences FHNW, 2024) Kamber, Lukas; Kahraman, Abdullah; Universität Zürich
    11 - Studentische Arbeit
  • Vorschaubild
    Publikation
    Characterization of cells for In-Vitro Fertilization
    (Hochschule für Life Sciences, 2024) Braun Ponce de Leon, Andreas; Nahum, Uri; Smart-Pick GmbH; Universitätsspital Basel, Basel
    11 - Studentische Arbeit
  • Vorschaubild
    Publikation
    GPS für das Becken. 3D Visualisierung von anatomischen Strukturen
    (Hochschule für Life Sciences FHNW, 2024) Bopp, Nicolas; Brodbeck, Dominique; Universitätsspital Zürich (USZ), Zürich
    11 - Studentische Arbeit
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    Publikation
    05 - Forschungs- oder Arbeitsbericht
  • Vorschaubild
    Publikation
    Cloud-based three-dimensional pattern analysis and classification of proximal humeral fractures – A feasibility study
    (EasyChair, 2022) Kalt, Denise; Gerber Popp, Ariane; Degen, Markus; Brodbeck, Dominique; Coigny, Florian; Suter, Thomas; Schkommodau, Erik; Rodriguez y Baena, Ferdinando; Giles, Joshua W.; Stindel, Eric
    For the complex clinical issue of treatment decision for proximal humeral fractures, dedicated software based on three-dimensional (3D) computer tomography (CT) models would potentially allow for a more accurate fracture classification and help to plan the surgical strategy needed to reduce the fracture in the operating theatre. The aim of this study was to elaborate the feasibility of implementation of such software using state-of-the-art cloud technology to enable access to its functionalities in a distributed manner. Feasibility was studied by implementation of a prototype application, which was tested in a usability study with five biomedical engineers. Implementation of a cloud-based solution was feasible using state-of-the-art technology under application of a specific software architectural approach allowing to distribute computational load between client and server. Mean System Usability Scale (SUS) Score for the developed application was determined to be 63 (StDev 20.4). These results can be interpreted as a medium low usability with high standard deviation of the measured SUS score. We conclude that more test subjects should be included in future studies and the developed application should be evaluated with a representative user group such as orthopaedic shoulder surgeons in a clinical setting.
    04B - Beitrag Konferenzschrift
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    Publikation
    Backtrainer. Computer-aided therapy system with augmented feedback for the lower back
    (SciTePress, 2009) Brodbeck, Dominique; Degen, Markus; Stanimirov, Michael; Kool, Jan; Scheermesser, Mandy; Oesch, Peter; Neuhaus, Cornelia; Azevedo, Luis; Londral, Ana
    Low back pain is an important problem in industrialized countries. Two key factors limit the effectiveness of physiotherapy: low compliance of patients with repetitive movement exercises, and inadequate awareness of patients of their own posture. The Backtrainer system addresses these problems by real-time monitoring of the spine position, by providing a framework for most common physiotherapy exercises for the low back, and by providing feedback to patients in a motivating way. A minimal sensor configuration was identified as two inertial sensors that measure the orientation of the lower back at two points with three degrees of freedom. The software was designed as a flexible platform to experiment with different hardware, and with various feedback modalities. Basic exercises for two types of movements are provided: mobilizing and stabilizing. We developed visual feedback - abstract as well as in the form of a virtual reality game - and complemented the on-screen graphics with an ambient feedback device. The system was evaluated during five weeks in a rehabilitation clinic with 26 patients and 15 physiotherapists. Subjective satisfaction of subjects was good, and we interpret the results as encouraging indication for the adoption of such a therapy support system by both patients and therapists.
    04B - Beitrag Konferenzschrift
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    Publikation
    Tracking the orientation of deep brain stimulation electrodes using an embedded magnetic sensor
    (2021) Vergne, Céline; Madec, Morgan; Hemm-Ode, Simone; Quirin, Thomas; Vogel, Dorian; Hebrard, Luc; Pascal, Joris
    This paper proposes a three-dimensional (3D) orientation tracking method of a 3D magnetic sensor embedded in a 2.5 mm diameter electrode. Our system aims to be used during intraoperative surgery to detect the orientation of directional leads (D-leads) for deep brain stimulation (DBS).
    06 - Präsentation
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    Publikation
    Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires
    (Frontiers Research Foundation, 2018) Miho, Enkelejda; Yermanos, Alexander; Weber, Cédric R.; Berger, Christoph T.; Reddy, Sai T.; Greiff, Victor
    The adaptive immune system recognizes antigens via an immense array of antigen binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires, thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity and to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic, and (iv) machine learning methods applied to dissect, quantify, and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology toward coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.
    01A - Beitrag in wissenschaftlicher Zeitschrift