Use Of Biophysical Models Research Articles

Current Trends in Biophysical Modeling of eDNA Dynamics for the Detection of Marine Species

ABSTRACTMarine pest introductions continue to occur and increase at accelerated rates, threatening the marine environment and blue economy. Environmental DNA (eDNA) is a tool for determining the presence of both indigenous and nonindigenous species, via the detection of genetic material that is shed into the local environment. Although eDNA approaches have gained widespread adoption in the last decade, fundamental knowledge gaps remain around factors that can influence the probability of detection, and how to optimize eDNA sampling in aquatic environments. Here, we partition eDNA research into four major research themes: eDNA concentration (shedding and decay), transport (advection and mixing), sampling design strategies, and the modeling of these dynamics. We review current developments and challenges in each theme with a focus on field sampling strategies and the use of biophysical models for understanding the movement of modeling eDNA in complex aquatic environments. We then introduce three modeling case studies from a large embayment where we (1) quantify the spatial and temporal variability of eDNA dispersion, (2) use biophysical models to inform a field sampling strategy, and (3) demonstrate a backtracking modeling technique to identify upstream DNA sources to an existing sample (monitoring) site. We conclude by identifying specific recommendations to help improve future eDNA studies. This work highlights how biophysical models can be applied to improve early detection and informing response and management decisions.

AAPM Task Group Report 267: A joint AAPM GEC-ESTRO report on biophysical models and tools for the planning and evaluation of brachytherapy.

Brachytherapy utilizes a multitude of radioactive sources and treatment techniques that often exhibit widely different spatial and temporal dose delivery patterns. Biophysical models, capable of modeling the key interacting effects of dose delivery patterns with the underlying cellular processes of the irradiated tissues, can be a potentially useful tool for elucidating the radiobiological effects of complex brachytherapy dose delivery patterns and for comparing their relative clinical effectiveness. While the biophysical models have been used largely in research settings by experts, it has also been used increasingly by clinical medical physicists over the last two decades. A good understanding of the potentials and limitations of the biophysical models and their intended use is critically important in the widespread use of these models. To facilitate meaningful and consistent use of biophysical models in brachytherapy, Task Group 267 (TG-267) was formed jointly with the American Association of Physics in Medicine (AAPM) and The Groupe Européen de Curiethérapie and the European Society for Radiotherapy & Oncology (GEC-ESTRO) to review the existing biophysical models, model parameters, and their use in selected brachytherapy modalities and to develop practice guidelines for clinical medical physicists regarding the selection, use, and interpretation of biophysical models. The report provides an overview of the clinical background and the rationale for the development of biophysical models in radiation oncology and, particularly, in brachytherapy; a summary of the results of literature review of the existing biophysical models that have been used in brachytherapy; a focused discussion of the applications of relevant biophysical models for five selected brachytherapy modalities; and the task group recommendations on the use, reporting, and implementation of biophysical models for brachytherapy treatment planning and evaluation. The report concludes with discussions on the challenges and opportunities in using biophysical models for brachytherapy and with an outlook for future developments.

‘Survival’: a simulation toolkit introducing a modular approach for radiobiological evaluations in ion beam therapy

One major rationale for the application of heavy ion beams in tumour therapy is their increased relative biological effectiveness (RBE). The complex dependencies of the RBE on dose, biological endpoint, position in the field etc require the use of biophysical models in treatment planning and clinical analysis. This study aims to introduce a new software, named ‘Survival’, to facilitate the radiobiological computations needed in ion therapy. The simulation toolkit was written in C++ and it was developed with a modular architecture in order to easily incorporate different radiobiological models. The following models were successfully implemented: the local effect model (LEM, version I, II and III) and variants of the microdosimetric-kinetic model (MKM). Different numerical evaluation approaches were also implemented: Monte Carlo (MC) numerical methods and a set of faster analytical approximations. Among the possible applications, the toolkit was used to reproduce the RBE versus LET for different ions (proton, He, C, O, Ne) and different cell lines (CHO, HSG). Intercomparison between different models (LEM and MKM) and computational approaches (MC and fast approximations) were performed. The developed software could represent an important tool for the evaluation of the biological effectiveness of charged particles in ion beam therapy, in particular when coupled with treatment simulations. Its modular architecture facilitates benchmarking and inter-comparison between different models and evaluation approaches. The code is open source (GPL2 license) and available at https://github.com/batuff/Survival.

Soil data for biophysical models in Victoria, Australia: Current needs and future challenges

The use of biophysical models to support increased food production and environmental protection is on the rise. This paper reviews the demand for, and trends in, soil property data for various biophysical models being used in Victoria, Australia, over the 2009–2014 period. The study used surveys, workshops and interviews with public sector modellers to examine perceptions of the soil parameters that affect model sensitivity and error. Although the data requirements of models have remained similar over the 5year period, the diversity of models used has decreased. There is evidence of increased application of models at point/site scale to support grains, dairy and livestock production industries in Victoria. Opportunities are identified to deliver finer scale soil data from digital soil mapping to better meet modelling requirements for agricultural industries in Victorian landscapes.

Deliberative processes for comprehensive evaluation of agroecological models. A review

The use of biophysical models in agroecology has increased in the last few decades for two main reasons: the need to formalize empirical knowledge and the need to disseminate model-based decision support for decision makers (such as farmers, advisors, and policy makers). The first has encouraged the development and use of mathematical models to enhance the efficiency of field research through extrapolation beyond the limits of site, season, and management. The second reflects the increasing need (by scientists, managers, and the public) for simula- tion experimentation to explore options and consequences, for example, future resource use efficiency (i.e., manage- ment in sustainable intensification), impacts of and adapta- tion to climate change, understanding market and policy responses to shocks initiated at a biophysical level under increasing demand, and limited supply capacity. Production concerns thus dominate most model applications, but there is a notable growing emphasis on environmental, economic, and policy dimensions. Identifying effective methods of assessing model quality and performance has become a challenging but vital imperative, considering the variety of factors influencing model outputs. Understanding the requirements of stakeholders, in respect of model use, logically implies the need for their inclusion in model evaluation methods. We reviewed the use of metrics of model evaluation, with a particular emphasis on the in- volvement of stakeholders to expand horizons beyond con- ventional structured, numeric analyses. Two major topics are discussed: (1) the importance of deliberative processes for model evaluation, and (2) the role computer-aided tech- niques may play to integrate deliberative processes into the evaluation of agroecological models. We point out that (i) the evaluation of agroecological models can be improved through stakeholder follow-up, which is a key for the acceptability of model realizations in practice, (ii) model credibility depends not only on the outcomes of well-struc- tured, numerically based evaluation, but also on less tangi- ble factors that may need to be addressed using comple- mentary deliberative processes, (iii) comprehensive evalua- tion of simulation models can be achieved by integrating the expectations of stakeholders via a weighting system of preferences and perception, (iv) questionnaire-based surveys can help understand the challenges posed by the delibera- tive process, and (v) a benefit can be obtained if model evaluation is conceived in a decisional perspective and evaluation techniques are developed at the same pace with which the models themselves are created and improved. Scientific knowledge hubs are also recognized as critical pillars to advance good modeling practice in relation to model evaluation (including access to dedicated software tools), an activity which is frequently neglected in the context of time-limited framework programs.

Numerical exploration of the combined effect of nutrient supply, tissue condition and deformation in the intervertebral disc

Novel strategies to heal discogenic low back pain could highly benefit from comprehensive biophysical studies that consider both mechanical and biological factors involved in intervertebral disc degeneration. A decrease in nutrient availability at the bone–disc interface has been indicated as a relevant risk factor and as a possible initiator of cell death processes. Mechanical behaviour of both healthy and degenerated discs could highly interact with cell death in these compromised situations. In the present study, a mechano-transport finite element model was used to investigate the nature of mechanical effects on cell death processes via load-induced metabolic transport variations. Cycles of static sustained compression were chosen to simulate daily human activity. Healthy and degenerated cases were simulated as well as a reduced supply of solutes and an increase in solute exchange area at the bone–disc interface. Results showed that a reduction in metabolite concentrations at the bone–disc boundaries induced cell death, even when the increased exchange area was simulated. Slight local mechanical enhancements of glucose in the disc centre were capable of decelerating cell death but occurred only with healthy mechanical properties. However, mechanical deformations were responsible for a worsening in terms of cell death in the inner annulus, a disadvantaged zone far from the boundary supply with both an increased cell demand and a strain-dependent decrease of diffusivity. Such adverse mechanical effects were more accentuated when degenerative properties were simulated. Overall, this study paves the way for the use of biophysical models for a more integrated understanding of intervertebral disc pathophysiology.

Guest Editorial: Special Issue on Noninvasive Electromagnetic Brain Stimulation

For several decades, electromagnetic brain stimulation for clinical applications has been investigated mainly with invasive techniques, such as deep brain stimulation (DBS), and this has limited its applicability. recent research has focused on transcranial magnetic stimulation (TMS) or electric current stimulation using direct current (DC), alternating current (AC), or more general current stimulation methods [i.e., generalized transcranial current stimulation (tCS)]. To explore the possibilities and limitations of these approaches, for a state-of-the-art assessment and looking into the future, we called for papers in the field and asked the authors to present new experimental and clinical findings on noninvasive transcranial electromagnetic brain stimulation. The submitted papers were subjected to a rigorous peer-review process, according to the IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE) guidelines, and the accepted papers form this special issue of TNSRE. After a general introductory article on the current state-of-the-art in the use of biophysical models and related technologies in tCS, the remaining articles are arranged in three main categories of topics: (1) specific modeling approaches for the study of noninvasive electromagnetic brain stimulation (2) techniques proposed for efficacy and reliability in transcranial magnetic stimulation (TMS) applications; and (3) specific applications of tCS and TMS in epilepsy, depression and brain-computer interfaces (BCI). We believe that this special issue, despite the relatively small number of papers, is a testament to the potential and promise that noninvasive electromagnetic brain stimulation presents for clinical and other applications. One may further comment that this issue reflects the promise of neuroengineering in general for effective applications in biomedicine.

The role of tissue microstructure and water exchange in biophysical modelling of diffusion in white matter

Biophysical models that describe the outcome of white matter diffusion MRI experiments have various degrees of complexity. While the simplest models assume equal-sized and parallel axons, more elaborate ones may include distributions of axon diameters and axonal orientation dispersions. These microstructural features can be inferred from diffusion-weighted signal attenuation curves by solving an inverse problem, validated in several Monte Carlo simulation studies. Model development has been paralleled by microscopy studies of the microstructure of excised and fixed nerves, confirming that axon diameter estimates from diffusion measurements agree with those from microscopy. However, results obtained in vivo are less conclusive. For example, the amount of slowly diffusing water is lower than expected, and the diffusion-encoded signal is apparently insensitive to diffusion time variations, contrary to what may be expected. Recent understandings of the resolution limit in diffusion MRI, the rate of water exchange, and the presence of microscopic axonal undulation and axonal orientation dispersions may, however, explain such apparent contradictions. Knowledge of the effects of biophysical mechanisms on water diffusion in tissue can be used to predict the outcome of diffusion tensor imaging (DTI) and of diffusion kurtosis imaging (DKI) studies. Alterations of DTI or DKI parameters found in studies of pathologies such as ischemic stroke can thus be compared with those predicted by modelling. Observations in agreement with the predictions strengthen the credibility of biophysical models; those in disagreement could provide clues of how to improve them. DKI is particularly suited for this purpose; it is performed using higher b-values than DTI, and thus carries more information about the tissue microstructure. The purpose of this review is to provide an update on the current understanding of how various properties of the tissue microstructure and the rate of water exchange between microenvironments are reflected in diffusion MRI measurements. We focus on the use of biophysical models for extracting tissue-specific parameters from data obtained with single PGSE sequences on clinical MRI scanners, but results obtained with animal MRI scanners are also considered. While modelling of white matter is the central theme, experiments on model systems that highlight important aspects of the biophysical models are also reviewed.

Evaluating and predicting risk to a large reptile (Varanus varius) from feral cat baiting protocols

Control of introduced predators to mitigate biodiversity impacts is a pressing conservation challenge. Across Australia feral cats (Felis catus) are a major threat to terrestrial biodiversity. Currently feral cat control is hindered by the limited utility of existing predator baiting methods. Further proposed control methods include use of the novel poison para-aminopropiophenone (PAPP) which may present a hazard to some native animal populations. Here we used experimental and predictive approaches to evaluate feral cat bait take by a large native Australian predatory reptile the Lace monitor (Varanus varius). These lizards would be expected to readily detect, ingest and consume a lethal dose (depending on toxin) from surface-laid baits intended for feral cat control if a precautionary approach was not adopted when baiting. We modelled V. varius bait take using experimental and predictive biophysical modelling approaches to evaluate temporal effects of climate variables on V. varius activity and hence potential for bait removal. Finally we conducted a pre-PAPP baiting site occupancy assessment of V. varius within Wilson Promontory National Park (WPNP) to provide a basis for monitoring any longer term population effects of cat baiting. V. varius removed 7 % of deployed baits from 73 % of bait stations across another study area in Far Eastern Victoria. Daily bait removal was positively correlated with maximum temperature and solar radiation. Biophysical modelling for Far Eastern Victoria predicted that maximum temperatures <19.5 °C prevented V. varius activity and hence opportunity for bait removal. V. varius in WPNP was undetectable suggesting aerial baiting posed limited hazard to this species at this location. Depending how climate influences annual activity patterns and the specific poison, surface-laid baits could pose a significant mortality risk to V. varius. However, use of biophysical models to predict periods of V. varius inactivity may provide a novel means to reduce non-target bait take by this predator.

Towards personalized clinical in-silico modeling of atrial anatomy and electrophysiology

Computational atrial models aid the understanding of pathological mechanisms and therapeutic measures in basic research. The use of biophysical models in a clinical environment requires methods to personalize the anatomy and electrophysiology (EP). Strategies for the automation of model generation and for evaluation are needed. In this manuscript, the current efforts of clinical atrial modeling in the euHeart project are summarized within the context of recent publications in this field. Model-based segmentation methods allow for the automatic generation of ready-to-simulate patient-specific anatomical models. EP models can be adapted to patient groups based on a-priori knowledge and to the individual without significant further data acquisition. ECG and intracardiac data build the basis for excitation personalization. Information from late enhancement (LE) MRI can be used to evaluate the success of radio-frequency ablation (RFA) procedures and interactive virtual atria pave the way for RFA planning. Atrial modeling is currently in a transition from the sole use in basic research to future clinical applications. The proposed methods build the framework for model-based diagnosis and therapy evaluation and planning. Complex models allow to understand biophysical mechanisms and enable the development of simplified models for clinical applications.

A cerebrovascular response model for functional neuroimaging including dynamic cerebral autoregulation

Functional neuroimaging techniques such as functional magnetic resonance imaging (fMRI) and near-infrared spectroscopy (NIRS) can be used to isolate an evoked response to a stimulus from significant background physiological fluctuations. Data analysis approaches typically use averaging or linear regression to remove this physiological baseline with varying degrees of success. Biophysical model-based analysis of the functional hemodynamic response has also been advanced previously with the Balloon and Windkessel models. In the present work, a biophysical model of systemic and cerebral circulation and gas exchange is applied to resting state NIRS neuroimaging data from 10 human subjects. The model further includes dynamic cerebral autoregulation, which modulates the cerebral arteriole compliance to control cerebral blood flow. This biophysical model allows for prediction, from noninvasive blood pressure measurements, of the background hemodynamic fluctuations in the systemic and cerebral circulations. Significantly higher correlations with the NIRS data were found using the biophysical model predictions compared to blood pressure regression and compared to transfer function analysis (multifactor ANOVA, p < 0.0001). This finding supports the further development and use of biophysical models for removing baseline activity in functional neuroimaging analysis. Future extensions of this work could model changes in cerebrovascular physiology that occur during development, aging, and disease.