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  1. Article ; Online: Towards an understanding of the chemo-mechanical influences on kidney stone failure via the material point method.

    Samuel J Raymond / Janille Maragh / Admir Masic / John R Williams

    PLoS ONE, Vol 15, Iss 12, p e

    2020  Volume 0240133

    Abstract: This paper explores the use of the meshfree computational mechanics method, the Material Point Method (MPM), to model the composition and damage of typical renal calculi, or kidney stones. Kidney stones are difficult entities to model due to their ... ...

    Abstract This paper explores the use of the meshfree computational mechanics method, the Material Point Method (MPM), to model the composition and damage of typical renal calculi, or kidney stones. Kidney stones are difficult entities to model due to their complex structure and failure behavior. Better understanding of how these stones behave when they are broken apart is a vital piece of knowledge to medical professionals whose aim is to remove these stone by breaking them within a patient's body. While the properties of individual stones are varied, the common elements and proportions are used to generate synthetic stones that are then placed in a digital experiment to observe their failure patterns. First a more traditional engineering model of a Brazil test is used to create a tensile fracture within the center of these stones to observe the effect of stone consistency on failure behavior. Next a novel application of MPM is applied which relies on an ultrasonic wave being carried by surrounding fluid to model the ultrasonic treatment of stones commonly used by medical practitioners. This numerical modeling of Extracorporeal Shock Wave Lithotripsy (ESWL) reveals how these different stones failure in a more real-world situation and could be used to guide further research in this field for safer and more effective treatments.
    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2020-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Quantifying the recarbonization of post-agricultural landscapes

    Stephen M. Bell / Samuel J. Raymond / He Yin / Wenzhe Jiao / Daniel S. Goll / Philippe Ciais / Elsa Olivetti / Victor O. Leshyk / César Terrer

    Nature Communications, Vol 14, Iss 1, Pp 1-

    2023  Volume 4

    Abstract: Despite worldwide prevalence, post-agricultural landscapes remain one of the least constrained human-induced land carbon sinks. To appraise their role in rebuilding the planet’s natural carbon stocks through ecosystem restoration, we need to better ... ...

    Abstract Despite worldwide prevalence, post-agricultural landscapes remain one of the least constrained human-induced land carbon sinks. To appraise their role in rebuilding the planet’s natural carbon stocks through ecosystem restoration, we need to better understand their spatial and temporal legacies.
    Keywords Science ; Q
    Language English
    Publishing date 2023-04-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  3. Article ; Online: A low-cost, highly functional, emergency use ventilator for the COVID-19 crisis.

    Samuel J Raymond / Sam Baker / Yuzhe Liu / Mauricio J Bustamante / Brett Ley / Michael J Horzewski / David B Camarillo / David N Cornfield

    PLoS ONE, Vol 17, Iss 3, p e

    2022  Volume 0266173

    Abstract: Respiratory failure complicates most critically ill patients with COVID-19 and is characterized by heterogeneous pulmonary parenchymal involvement, profound hypoxemia and pulmonary vascular injury. The high incidence of COVID-19 related respiratory ... ...

    Abstract Respiratory failure complicates most critically ill patients with COVID-19 and is characterized by heterogeneous pulmonary parenchymal involvement, profound hypoxemia and pulmonary vascular injury. The high incidence of COVID-19 related respiratory failure has exposed critical shortages in the supply of mechanical ventilators, and providers with the necessary skills to treat. Traditional mass-produced ventilators rely on an internal compressor and mixer to moderate and control the gas mixture delivered to a patient. However, the current emergency has energized the pursuit of alternative designs, enabling greater flexibility in supply chain, manufacturing, storage, and maintenance considerations. To achieve this, we hypothesized that using the medical gasses and flow interruption strategy would allow for a high performance, low cost, functional ventilator. A low-cost ventilator designed and built-in accordance with the Emergency Use guidance from the US Food and Drug Administration (FDA) is presented wherein pressurized medical grade gases enter the ventilator and time limited flow interruption determines the ventilator rate and tidal volume. This simple strategy obviates the need for many components needed in traditional ventilators, thereby dramatically shortening the time from storage to clinical deployment, increasing reliability, while still providing life-saving ventilatory support. The overall design philosophy and its applicability in this new crisis is described, followed by both bench top and animal testing results used to confirm the precision, safety and reliability of this low cost and novel approach to mechanical ventilation. The ventilator meets and exceeds the critical requirements included in the FDA emergency use guidelines. The ventilator has received emergency use authorization from the FDA.
    Keywords Medicine ; R ; Science ; Q
    Subject code 670
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  4. Article ; Online: A deep learning approach for designed diffraction-based acoustic patterning in microchannels

    Samuel J. Raymond / David J. Collins / Richard O’Rorke / Mahnoush Tayebi / Ye Ai / John Williams

    Scientific Reports, Vol 10, Iss 1, Pp 1-

    2020  Volume 12

    Abstract: Abstract Acoustic waves can be used to accurately position cells and particles and are appropriate for this activity owing to their biocompatibility and ability to generate microscale force gradients. Such fields, however, typically take the form of only ...

    Abstract Abstract Acoustic waves can be used to accurately position cells and particles and are appropriate for this activity owing to their biocompatibility and ability to generate microscale force gradients. Such fields, however, typically take the form of only periodic one or two-dimensional grids, limiting the scope of patterning activities that can be performed. Recent work has demonstrated that the interaction between microfluidic channel walls and travelling surface acoustic waves can generate spatially variable acoustic fields, opening the possibility that the channel geometry can be used to control the pressure field that develops. In this work we utilize this approach to create novel acoustic fields. Designing the channel that results in a desired acoustic field, however, is a non-trivial task. To rapidly generate designed acoustic fields from microchannel elements we utilize a deep learning approach based on a deep neural network (DNN) that is trained on images of pre-solved acoustic fields. We use then this trained DNN to create novel microchannel architectures for designed microparticle patterning.
    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2020-05-01T00:00:00Z
    Publisher Nature Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  5. Article ; Online: A new open-access platform for measuring and sharing mTBI data

    August G. Domel / Samuel J. Raymond / Chiara Giordano / Yuzhe Liu / Seyed Abdolmajid Yousefsani / Michael Fanton / Nicholas J. Cecchi / Olga Vovk / Ileana Pirozzi / Ali Kight / Brett Avery / Athanasia Boumis / Tyler Fetters / Simran Jandu / William M. Mehring / Sam Monga / Nicole Mouchawar / India Rangel / Eli Rice /
    Pritha Roy / Sohrab Sami / Heer Singh / Lyndia Wu / Calvin Kuo / Michael Zeineh / Gerald Grant / David B. Camarillo

    Scientific Reports, Vol 11, Iss 1, Pp 1-

    2021  Volume 10

    Abstract: Abstract Despite numerous research efforts, the precise mechanisms of concussion have yet to be fully uncovered. Clinical studies on high-risk populations, such as contact sports athletes, have become more common and give insight on the link between ... ...

    Abstract Abstract Despite numerous research efforts, the precise mechanisms of concussion have yet to be fully uncovered. Clinical studies on high-risk populations, such as contact sports athletes, have become more common and give insight on the link between impact severity and brain injury risk through the use of wearable sensors and neurological testing. However, as the number of institutions operating these studies grows, there is a growing need for a platform to share these data to facilitate our understanding of concussion mechanisms and aid in the development of suitable diagnostic tools. To that end, this paper puts forth two contributions: (1) a centralized, open-access platform for storing and sharing head impact data, in collaboration with the Federal Interagency Traumatic Brain Injury Research informatics system (FITBIR), and (2) a deep learning impact detection algorithm (MiGNet) to differentiate between true head impacts and false positives for the previously biomechanically validated instrumented mouthguard sensor (MiG2.0), all of which easily interfaces with FITBIR. We report 96% accuracy using MiGNet, based on a neural network model, improving on previous work based on Support Vector Machines achieving 91% accuracy, on an out of sample dataset of high school and collegiate football head impacts. The integrated MiG2.0 and FITBIR system serve as a collaborative research tool to be disseminated across multiple institutions towards creating a standardized dataset for furthering the knowledge of concussion biomechanics.
    Keywords Medicine ; R ; Science ; Q
    Subject code 306
    Language English
    Publishing date 2021-04-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

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