scholarly journals Perspectives on Sharing Models and Related Resources in Computational Biomechanics Research

2018 ◽  
Vol 140 (2) ◽  
Author(s):  
Ahmet Erdemir ◽  
Peter J. Hunter ◽  
Gerhard A. Holzapfel ◽  
Leslie M. Loew ◽  
John Middleton ◽  
...  

The role of computational modeling for biomechanics research and related clinical care will be increasingly prominent. The biomechanics community has been developing computational models routinely for exploration of the mechanics and mechanobiology of diverse biological structures. As a result, a large array of models, data, and discipline-specific simulation software has emerged to support endeavors in computational biomechanics. Sharing computational models and related data and simulation software has first become a utilitarian interest, and now, it is a necessity. Exchange of models, in support of knowledge exchange provided by scholarly publishing, has important implications. Specifically, model sharing can facilitate assessment of reproducibility in computational biomechanics and can provide an opportunity for repurposing and reuse, and a venue for medical training. The community's desire to investigate biological and biomechanical phenomena crossing multiple systems, scales, and physical domains, also motivates sharing of modeling resources as blending of models developed by domain experts will be a required step for comprehensive simulation studies as well as the enhancement of their rigor and reproducibility. The goal of this paper is to understand current perspectives in the biomechanics community for the sharing of computational models and related resources. Opinions on opportunities, challenges, and pathways to model sharing, particularly as part of the scholarly publishing workflow, were sought. A group of journal editors and a handful of investigators active in computational biomechanics were approached to collect short opinion pieces as a part of a larger effort of the IEEE EMBS Computational Biology and the Physiome Technical Committee to address model reproducibility through publications. A synthesis of these opinion pieces indicates that the community recognizes the necessity and usefulness of model sharing. There is a strong will to facilitate model sharing, and there are corresponding initiatives by the scientific journals. Outside the publishing enterprise, infrastructure to facilitate model sharing in biomechanics exists, and simulation software developers are interested in accommodating the community's needs for sharing of modeling resources. Encouragement for the use of standardized markups, concerns related to quality assurance, acknowledgement of increased burden, and importance of stewardship of resources are noted. In the short-term, it is advisable that the community builds upon recent strategies and experiments with new pathways for continued demonstration of model sharing, its promotion, and its utility. Nonetheless, the need for a long-term strategy to unify approaches in sharing computational models and related resources is acknowledged. Development of a sustainable platform supported by a culture of open model sharing will likely evolve through continued and inclusive discussions bringing all stakeholders at the table, e.g., by possibly establishing a consortium.

2016 ◽  
Vol 63 (10) ◽  
pp. 2080-2085 ◽  
Author(s):  
Ahmet Erdemir ◽  
Trent M. Guess ◽  
Jason P. Halloran ◽  
Luca Modenese ◽  
Jeffrey A. Reinbolt ◽  
...  

Author(s):  
Alireza Soltani ◽  
Etienne Koechlin

AbstractThe real world is uncertain, and while ever changing, it constantly presents itself in terms of new sets of behavioral options. To attain the flexibility required to tackle these challenges successfully, most mammalian brains are equipped with certain computational abilities that rely on the prefrontal cortex (PFC). By examining learning in terms of internal models associating stimuli, actions, and outcomes, we argue here that adaptive behavior relies on specific interactions between multiple systems including: (1) selective models learning stimulus–action associations through rewards; (2) predictive models learning stimulus- and/or action–outcome associations through statistical inferences anticipating behavioral outcomes; and (3) contextual models learning external cues associated with latent states of the environment. Critically, the PFC combines these internal models by forming task sets to drive behavior and, moreover, constantly evaluates the reliability of actor task sets in predicting external contingencies to switch between task sets or create new ones. We review different models of adaptive behavior to demonstrate how their components map onto this unifying framework and specific PFC regions. Finally, we discuss how our framework may help to better understand the neural computations and the cognitive architecture of PFC regions guiding adaptive behavior.


2016 ◽  
Vol 78 (5) ◽  
pp. 396-403 ◽  
Author(s):  
Samuel Potter ◽  
Rebecca M. Krall ◽  
Susan Mayo ◽  
Diane Johnson ◽  
Kim Zeidler-Watters ◽  
...  

With the looming global population crisis, it is more important now than ever that students understand what factors influence population dynamics. We present three learning modules with authentic, student-centered investigations that explore rates of population growth and the importance of resources. These interdisciplinary modules integrate biology, mathematics, and computer-literacy concepts aligned with the Next Generation Science Standards. The activities are appropriate for middle and high school science classes and for introductory college-level biology courses. The modules incorporate experimentation, data collection and analysis, drawing conclusions, and application of studied principles to explore factors affecting population dynamics in fruit flies. The variables explored include initial population structure, food availability, and space of the enclosed population. In addition, we present a computational simulation in which students can alter the same variables explored in the live experimental modules to test predictions on the consequences of altering the variables. Free web-based graphing (Joinpoint) and simulation software (NetLogo) allows students to work at home or at school.


2020 ◽  
Vol 116 (13) ◽  
pp. 2040-2054 ◽  
Author(s):  
Evangelos K Oikonomou ◽  
Musib Siddique ◽  
Charalambos Antoniades

Abstract Rapid technological advances in non-invasive imaging, coupled with the availability of large data sets and the expansion of computational models and power, have revolutionized the role of imaging in medicine. Non-invasive imaging is the pillar of modern cardiovascular diagnostics, with modalities such as cardiac computed tomography (CT) now recognized as first-line options for cardiovascular risk stratification and the assessment of stable or even unstable patients. To date, cardiovascular imaging has lagged behind other fields, such as oncology, in the clinical translational of artificial intelligence (AI)-based approaches. We hereby review the current status of AI in non-invasive cardiovascular imaging, using cardiac CT as a running example of how novel machine learning (ML)-based radiomic approaches can improve clinical care. The integration of ML, deep learning, and radiomic methods has revealed direct links between tissue imaging phenotyping and tissue biology, with important clinical implications. More specifically, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging and CT, as well as lessons that can be learned from other areas. Finally, we propose a scientific framework in order to ensure the clinical and scientific validity of future studies in this novel, yet highly promising field. Still in its infancy, AI-based cardiovascular imaging has a lot to offer to both the patients and their doctors as it catalyzes the transition towards a more precise phenotyping of cardiovascular disease.


2019 ◽  
Vol 25 (3) ◽  
pp. 263-311 ◽  
Author(s):  
Qinbing Fu ◽  
Hongxin Wang ◽  
Cheng Hu ◽  
Shigang Yue

Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging, and so forth. A good number of motion detectors have been identified in the insects' visual pathways. Computational modeling of these motion detectors has not only been providing effective solutions to artificial intelligence, but also benefiting the understanding of complicated biological visual systems. These biological mechanisms through millions of years of evolutionary development will have formed solid modules for constructing dynamic vision systems for future intelligent machines. This article reviews the computational motion perception models originating from biological research on insects' visual systems in the literature. These motion perception models or neural networks consist of the looming-sensitive neuronal models of lobula giant movement detectors (LGMDs) in locusts, the translation-sensitive neural systems of direction-selective neurons (DSNs) in fruit flies, bees, and locusts, and the small-target motion detectors (STMDs) in dragonflies and hoverflies. We also review the applications of these models to robots and vehicles. Through these modeling studies, we summarize the methodologies that generate different direction and size selectivity in motion perception. Finally, we discuss multiple systems integration and hardware realization of these bio-inspired motion perception models.


2021 ◽  
Vol 4 (4) ◽  
pp. 17257-17260
Author(s):  
Karen Karollinne Dikaua Santos Feitosa ◽  
Ingrid Lima Longo ◽  
Ana Karolina Guerreiro Costa Melo ◽  
Tiago Souza Amorim ◽  
Marco Aurelio Dantas Vieira Belem

Author(s):  
Daniel F. Hayes ◽  
Muin J. Khoury ◽  
David Ransohoff

Overview: The “omics” revolution produced great optimism that tumor biomarker tests based on high-order analysis of multiple (sometimes thousands) of factors would result in truly personalized oncologic care. Unfortunately, 10 years into the revolution, the promise of omics-based research has not yet been realized. The factors behind the slow progress in omics-based clinical care are many. First, over the last 15 years, there has been a gradual recognition of the importance of conducting tumor biomarker science with the kind of rigor that has traditionally been used for therapeutic research. However, this recognition has only recently been applied widely, and therefore most tumor biomarkers have insufficiently high levels of evidence to determine clinical utility. Second, omics-based research offers its own particular set of concerns, especially in regard to overfitting computational models and false discovery rates. Researchers and clinicians need to understand the importance of analytic validity, and the difference between clinical/biologic validity and clinical utility. The latter is required to introduce a tumor biomarker test of any kind (single analyte or omics-based), and are ideally generated by carefully planned and properly conducted “prospective retrospective” or truly prospective clinical trials. Only carefully planned studies, which take all three of these into account and in which the investigators are aware and recognize the enormous risk of unintended bias and overfitting inherent in omics-based test development, will ultimately result in translation of the exciting new technologies into better care for patients with cancer.


Author(s):  
H B Henninger ◽  
S P Reese ◽  
A E Anderson ◽  
J A Weiss

The topics of verification and validation have increasingly been discussed in the field of computational biomechanics, and many recent articles have applied these concepts in an attempt to build credibility for models of complex biological systems. Verification and validation are evolving techniques that, if used improperly, can lead to false conclusions about a system under study. In basic science, these erroneous conclusions may lead to failure of a subsequent hypothesis, but they can have more profound effects if the model is designed to predict patient outcomes. While several authors have reviewed verification and validation as they pertain to traditional solid and fluid mechanics, it is the intent of this paper to present them in the context of computational biomechanics. Specifically, the task of model validation will be discussed, with a focus on current techniques. It is hoped that this review will encourage investigators to engage and adopt the verification and validation process in an effort to increase peer acceptance of computational biomechanics models.


2016 ◽  
Vol 685 ◽  
pp. 217-220 ◽  
Author(s):  
Alexandr M. Belostosky ◽  
Sergey B. Penkovoy ◽  
Sergey V. Scherbina ◽  
Pavel A. Akimov ◽  
Taymuraz B. Kaytukov

The distinctive paper is devoted to development and verification of correct numerical methods for analysis of structural strength and stability of high-rise panel buildings. Particularly the first part of the paper contains brief introduction, description of methods of analysis and simulation software. Information about verification of corresponding computational models is presented as well.


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