Multi-compartment Model Research Articles

Pressure–Poisson equation in numerical simulation of cerebral arterial circulation and its effect on the electrical conductivity of the brain

Background and ObjectiveThis study considers dynamic modeling of the cerebral arterial circulation and reconstructing an atlas for the electrical conductivity of the brain. Electrical conductivity is a governing parameter in several electrophysiological modalities applied in neuroscience, such as electroencephalography (EEG), transcranial electrical stimulation (tES), and electrical impedance tomography (EIT). While high-resolution 7-Tesla (T) Magnetic Resonance Imaging (MRI) data allow for reconstructing the cerebral arteries with a cross-sectional diameter larger than the voxel size, electrical conductivity cannot be directly inferred from MRI data. Brain models of electrophysiology typically associate each brain tissue compartment with a constant electrical conductivity, omitting any dynamic effects of cerebral blood circulation. Incorporating those effects poses the challenge of solving a system of incompressible Navier–Stokes equations (NSEs) in a realistic multi-compartment head model. However, using a simplified circulation model is well-motivated since, on the one hand, the complete system does not always have a numerically stable solution and, on the other hand, the full set of arteries cannot be perfectly reconstructed from the MRI data, meaning that any solution will be approximative. MethodsWe postulate that circulation in the distinguishable arteries can be estimated via the pressure–Poisson equation (PPE), which is coupled with Fick's law of diffusion for microcirculation. To establish a fluid exchange model between arteries and microarteries, a boundary condition derived from the Hagen–Poisseuille model is applied. The relationship between the estimated volumetric blood concentration and the electrical conductivity of the brain tissue is approximated through Archie's law for fluid flow in porous media. ResultsThrough the formulation of the PPE and a set of boundary conditions (BCs) based on the Hagen–Poisseuille model, we obtained an equivalent formulation of the incompressible Stokes equation (SE). Thus, allowing effective blood pressure estimation in cerebral arteries segmented from open 7T MRI data. ConclusionsAs a result of this research, we developed and built a useful modeling framework that accounts for the effects of dynamic blood flow on a novel MRI-based electrical conductivity atlas. The electrical conductivity perturbation obtained in numerical experiments has an appropriate overall match with previous studies on this subject. Further research to validate these results will be necessary.

A multicompartment population PK model to predict tenofovir and emtricitabine mucosal tissue concentrations for HIV prevention.

A priori use of mathematical modeling and simulation to predict outcomes from incomplete adherence or reduced frequency dosing strategies may mitigate the risk of clinical trial failure with HIV pre-exposure prophylaxis regimens. We developed a semi-physiologic population pharmacokinetic model for two antiretrovirals and their active intracellular metabolites in three mucosal tissues using pharmacokinetic data from a phase I, dose-ranging study. Healthy female volunteers were given a single oral dose of tenofovir disoproxil fumarate (150, 300, or 600 mg) or emtricitabine (100, 200, or 400 mg). Simultaneous co-modeling of all data was performed on a Linux cluster. A 16 compartment, bolus input, linear kinetic model best described the data, containing 986 observations in 23 individuals across three matrices and four analytes. Combined with a defined efficacious concentration target in mucosal tissues, this model can be used to optimize the dose and dosing frequency through Monte-Carlo simulations.

Radiation Protection Considerations for Cancer Patients with End-stage Renal Disease Receiving 131 I Treatment.

Differentiated thyroid cancer (DTC) is commonly treated first with a partial or complete thyroidectomy, followed by radioiodine (RAI) ablative therapy to eliminate remaining cancer cells. In such treatments, physical decay and urinary excretion are the primary means of 131 I. As such, patients with impaired urinary ability clearance, such as patients with end-stage renal disease (ESRD) whose urinary ability is impaired by dysfunction, can retain abnormally high activities of RAI, posing a concern to both the patient and those with whom the patient interacts. Additionally, ESRD patients are commonly administered dialysis therapy, wherein their blood is externally cycled through a dialyzer (hemodialysis) or filtered by instilling a dialysate fluid into the peritoneum (peritoneal dialysis) to filter uremic toxins from their blood that accumulate due to kidney dysfunction. These factors make determining release and dosing for ESRD patients receiving RAI therapy dependent on a plurality of variables. An evaluation of the current patient release guidelines, as given in US Nuclear Regulatory Commission (US NRC) Regulatory Guide 8.39 Rev. 1 for ESRD patients receiving RAI, has yet to be addressed. In this study, a biokinetic model for 131 I in ESRD patients receiving dialysis has been developed, improving on traditional two-compartment models, reflective of kinetics from multi-compartment models with updated transfer coefficients modified to reflect the different physiological functions of compartments. This updated biokinetic model was integrated with Monte Carlo radiation transport calculations using stylized computational hermaphroditic phantoms to calculate dose rate coefficients in exposure scenarios and compared with those of the point source models of NRC Reg Guide 8.39 Rev. 1 (and the proposed verbiage in Rev. 2). Results demonstrated that the baseline models of Rev. 1 and Rev. 2 overestimated the effective dose rate to an exposed individual for the majority of time post-administration, where both models overestimated the total dose to the maximally exposed individual. However, the application of several patient-specific modifying factors to the Rev. 2 model resulted in an overestimation by only a factor of 1.25, and in general, the results produced with the patient-specific modifications provide improved convergence with the dose rate coefficients computed in this study for ESRD patients.

A variable heart rate multi-compartmental coupled model of the cardiovascular and respiratory systems.

Current mathematical models of the cardiovascular system that are based on systems of ordinary differential equations are limited in their ability to mimic important features of measured patient data, such as variable heart rates (HR). Such limitations present a significant obstacle in the use of such models for clinical decision-making, as it is the variations in vital signs such as HR and systolic and diastolic blood pressure that are monitored and recorded in typical critical care bedside monitoring systems. In this paper, novel extensions to well-established multi-compartmental models of the cardiovascular and respiratory systems are proposed that permit the simulation of variable HR. Furthermore, a correction to current models is also proposed to stabilize the respiratory behaviour and enable realistic simulation of vital signs over the longer time scales required for clinical management. The results of the extended model developed here show better agreement with measured bio-signals, and these extensions provide an important first step towards estimating model parameters from patient data, using methods such as neural ordinary differential equations. The approach presented is generalizable to many other similar multi-compartmental models of the cardiovascular and respiratory systems.

An optimization framework for targeted spinal cord stimulation

Objective. Spinal cord stimulation (SCS) is a common neurostimulation therapy to manage chronic pain. Technological advances have produced new neurostimulation systems with expanded capabilities in an attempt to improve the clinical outcomes associated with SCS. However, these expanded capabilities have dramatically increased the number of possible stimulation parameters and made it intractable to efficiently explore this large parameter space within the context of standard clinical programming procedures. Therefore, in this study, we developed an optimization approach to define the optimal current amplitudes or fractions across individual contacts in an SCS electrode array(s). Approach. We developed an analytic method using the Lagrange multiplier method along with smoothing approximations. To test our optimization framework, we used a hybrid computational modeling approach that consisted of a finite element method model and multi-compartment models of axons and cells within the spinal cord. Moreover, we extended our approach to multi-objective optimization to explore the trade-off between activating regions of interest (ROIs) and regions of avoidance (ROAs). Main results. For simple ROIs, our framework suggested optimized configurations that resembled simple bipolar configurations. However, when we considered multi-objective optimization, our framework suggested nontrivial stimulation configurations that could be selected from Pareto fronts to target multiple ROIs or avoid ROAs. Significance. We developed an optimization framework for targeted SCS. Our method is analytic, which allows for the fast calculation of optimal solutions. For the first time, we provided a multi-objective approach for selective SCS. Through this approach, we were able to show that novel configurations can provide neural recruitment profiles that are not possible with conventional stimulation configurations (e.g. bipolar stimulation). Most importantly, once integrated with computational models that account for sources of interpatient variability (e.g. anatomy, electrode placement), our optimization framework can be utilized to provide stimulation settings tailored to the needs of individual patients.

Multi-compartment head modeling in EEG: Unstructured boundary-fitted tetra meshing with subcortical structures.

This paper introduces an automated approach for generating a finite element (FE) discretization of a multi-compartment human head model for electroencephalographic (EEG) source localization. We aim to provide an adaptable FE mesh generation tool for EEG studies. Our technique relies on recursive solid angle labeling of a surface segmentation coupled with smoothing, refinement, inflation, and optimization procedures to enhance the mesh quality. In this study, we performed numerical meshing experiments with the three-layer Ary sphere and a magnetic resonance imaging (MRI)-based multi-compartment head segmentation which incorporates a comprehensive set of subcortical brain structures. These experiments are motivated, on one hand, by the sensitivity of non-invasive subcortical source localization to modeling errors and, on the other hand, by the present lack of open EEG software pipelines to discretize all these structures. Our approach was found to successfully produce an unstructured and boundary-fitted tetrahedral mesh with a sub-one-millimeter fitting error, providing the desired accuracy for the three-dimensional anatomical details, EEG lead field matrix, and source localization. The mesh generator applied in this study has been implemented in the open MATLAB-based Zeffiro Interface toolbox for forward and inverse processing in EEG and it allows for graphics processing unit acceleration.

A GPU-based computational framework that bridges neuron simulation and artificial intelligence

Biophysically detailed multi-compartment models are powerful tools to explore computational principles of the brain and also serve as a theoretical framework to generate algorithms for artificial intelligence (AI) systems. However, the expensive computational cost severely limits the applications in both the neuroscience and AI fields. The major bottleneck during simulating detailed compartment models is the ability of a simulator to solve large systems of linear equations. Here, we present a novel Dendritic Hierarchical Scheduling (DHS) method to markedly accelerate such a process. We theoretically prove that the DHS implementation is computationally optimal and accurate. This GPU-based method performs with 2-3 orders of magnitude higher speed than that of the classic serial Hines method in the conventional CPU platform. We build a DeepDendrite framework, which integrates the DHS method and the GPU computing engine of the NEURON simulator and demonstrate applications of DeepDendrite in neuroscience tasks. We investigate how spatial patterns of spine inputs affect neuronal excitability in a detailed human pyramidal neuron model with 25,000 spines. Furthermore, we provide a brief discussion on the potential of DeepDendrite for AI, specifically highlighting its ability to enable the efficient training of biophysically detailed models in typical image classification tasks.

Gallation and B-Ring Dihydroxylation Increase Green Tea Catechin Residence Time in Plasma by Differentially Affecting Tissue-Specific Trafficking: Compartmental Model of Catechin Kinetics in Healthy Adults.

Catechins in green tea extract (GTE) (epigallocatechin gallate (EGCG), epigallocatechin (EGC), epicatechin (EC), epicatechin gallate (ECG)) vary in bioactivity. We developed a physiologically relevant mathematical model of catechin metabolism to test the hypothesis that fractional catabolic rates of catechins would be differentially affected by their structural attributes. Pharmacokinetic data of plasma and urine catechin concentrations were used from healthy adults (n = 19) who ingested confections containing 0.5 g GTE (290 mg EGCG, 87 mg EGC, 39 mg EC, 28 mg ECG). A 7-compartmental model of catechin metabolism comprised of the gastrointestinal tract (stomach, small and large intestine), liver, plasma, extravascular tissues, and kidneys was developed using a mean fraction dose of EGCG, ECG, EGC, and EC. Fitting was by iterative least squares regression analysis, and goodness of fit was ascertained by the estimated variability of parameters (FSD < 0.5). The interaction of gallation and B-ring dihydroxylation most greatly extended plasma residence time such that EGC > EC = EGCG > EGC. The interaction between gallation and B-ring dihydroxylation accelerated the transfer from the upper gastrointestinal tract to the small intestine but delayed subsequent transfers from the small intestine through the liver to plasma and from kidneys to urine. Gallation and B-ring dihydroxylation independently delayed the transfer from plasma to extravascular tissues, except the uptake to kidneys, which was slowed by gallation only. This multi-compartment model, to be validated in a future study, suggests that gallation and B-ring dihydroxylation affect catechin catabolism in a tissue-specific manner and thus their potential bioactivity.

Parametrizing analog multi-compartment neurons with genetic algorithms

Background: Finding appropriate model parameters for multi-compartmental neuron models can be challenging. Parameters such as the leak and axial conductance are not always directly derivable from neuron observations but are crucial for replicating desired observations. The objective of this study is to replicate the attenuation behavior of an excitatory postsynaptic potential (EPSP) traveling along a linear chain of compartments on the analog BrainScaleS-2 neuromorphic hardware platform. Methods: In the present publication we use genetic algorithms to find suitable model parameters. They promise parameterization without domain knowledge of the neuromorphic substrate or underlying neuron model. To validate the results of the genetic algorithms, a comprehensive grid search was conducted. Trial-to-trial variations in the analog system are counteracted utilizing spike-triggered averaging. Results and conclusions: The algorithm successfully replicated the desired EPSP attenuation behavior in both single and multi-objective searches illustrating the applicability of genetic algorithms to parameterize analog neuromorphic hardware.

Investigating the electrode-electrolyte interface modelling in cochlear implants

Objective. Proposing a good electrode-electrolyte interface (EEI) model and properly identifying relevant parameters may help designing safer and more optimized auditory nerve fiber stimulation and recording in cochlear implants (CI). However, in literature, EEI model parameter values exhibit large variability. We aim to explain some root causes of this variability using the Cole model and its simpler form, the Basic RC model. Approach. We use temporal and spectral methods and fit the models to stimulation pulse voltage response (SPVR) and electrochemical impedance spectroscopy (EIS) data. Main Results. Temporal fittings show that there are multiple sets of model parameters that provide a good fit to the SPVR data. Therefore, small methodological differences in literature may result in different model fits. While these models share similar characteristics at high frequencies >500 Hz, the SPVR fitting is blind to low frequencies, thus it cannot correctly estimate the Faradaic resistor. Similarly, the polarization capacitor and its fractional order are not estimated robustly (capacitor variations in the nano- to micro-farad range) due to limited observation of mid-range frequencies. EIS provides a good model fit down to ∼3Hz, and thus robust estimation for the polarization capacitor. At lower frequencies charge mechanisms may modify the EEI, requiring multi-compartment Cole model fitting to EIS to improve the estimation of Faradaic characteristics. Our EIS data measurements down to 0.05Hz show that a two-compartment Cole model is sufficient to explain the data. Significance. Our study describes the scope and limitation of SPVR and EIS fitting methods, by which literature variability is explained among CI EEI models. The estimation of mid-to-low-frequency characteristics of the CI EEI is not in the scope of the SPVR method. EIS provides a better fit; however, its results should not be extrapolated to unobserved frequencies where new charge transfer mechanisms may emerge at the EEI.

Novel modelling for a steam boiler under fast load dynamics with implications to control

This paper describes a new nonlinear dynamic model for a natural circulation boiler. The model is based on physical principles, i.e. mass, energy and momentum balances. A systematic approach is followed leading to new insights into the physics of drum water level and downcomer mass flow. The model captures fast dynamic responses that are necessary to describe the operation of a boiler under highly variable load conditions. New features of the model include (i) a multi-compartment model for the risers, (ii) a new model for drum water level, and (iii) a new dynamic model for the flow of water in the downcomers. Implications of the model for control system design are explored in detail. Finally, the suggested improvements are validated in a sugar mill boiler (50 [MW] Babcock & Wilcox water-tube boiler).

Cell anatomy and network input explain differences within but not between leech touch cells at two different locations.

Mechanosensory cells in the leech share several common features with mechanoreceptors in the human glabrous skin. Previous studies showed that the six T (touch) cells in each body segment of the leech are highly variable in their responses to somatic current injection and change their excitability over time. Here, we investigate three potential reasons for this variability in excitability by comparing the responses of T cells at two soma locations (T2 and T3): (1) Differential effects of time-dependent changes in excitability, (2) divergent synaptic input from the network, and (3) different anatomical structures. These hypotheses were explored with a combination of electrophysiological double recordings, 3D reconstruction of neurobiotin-filled cells, and compartmental model simulations. Current injection triggered significantly more spikes with shorter latency and larger amplitudes in cells at soma location T2 than at T3. During longer recordings, cells at both locations increased their excitability over time in the same way. T2 and T3 cells received the same amount of synaptic input from the unstimulated network, and the polysynaptic connections between both T cells were mutually symmetric. However, we found a striking anatomical difference: While in our data set all T2 cells innervated two roots connecting the ganglion with the skin, 50% of the T3 cells had only one root process. The sub-sample of T3 cells with one root process was significantly less excitable than the T3 cells with two root processes and the T2 cells. To test if the additional root process causes higher excitability, we simulated the responses of 3D reconstructed cells of both anatomies with detailed multi-compartment models. The anatomical subtypes do not differ in excitability when identical biophysical parameters and a homogeneous channel distribution are assumed. Hence, all three hypotheses may contribute to the highly variable T cell responses, but none of them is the only factor accounting for the observed systematic difference in excitability between cells at T2 vs. T3 soma location. Therefore, future patch clamp and modeling studies are needed to analyze how biophysical properties and spatial distribution of ion channels on the cell surface contribute to the variability and systematic differences of electrophysiological phenotypes.

A multi-compartment VRP model for the health care waste transportation problem

Health-care waste (HCW) is a special category of refuse generated when providing health care and can comprise materials with infectious and/or toxic characteristics. The disposal of this waste involves its transport from health-care facilities to treatment centers. This step can be threatening to humans and the environment if not managed with the utmost care. To ensure the safe management of HCW, this article proposes a new model for HCW transportation that separates hazardous from non-hazardous waste along the transportation route. The proposed model is based on the multi-compartment vehicle routing problem, where each type of waste is carried in a dedicated compartment, and the objective is to minimize the total route cost. Considering the NP-hardness of the problem, an adaptive genetic algorithm is proposed based on the results of statistical pre-post testing of the population. The experimental study, conducted on benchmark instances and real data instances, show the efficiency of the proposed approach against state-of-the-art algorithms.

Evaluation of a Rapid Four-Compartment Model and Stand-Alone Methods in Hispanic Adults

BackgroundA rapid 4-compartment (4C) model integrates dual-energy x-ray absorptiometry (DXA) and multi-frequency bioimpedance analysis (MFBIA), which may be useful for clinical and research settings seeking to employ a multi-compartment model. ObjectivesThis study aimed to determine the added benefit of a rapid 4C model over stand-alone DXA and MFBIA when estimating body composition. MethodsOne hundred and thirty participants (n = 60 male; n = 70 female) of Hispanic descent were included in the present analysis. A criterion 4C model that employed air displacement plethysmography (body volume), deuterium oxide (total body water), and DXA (bone mineral) was used to measure fat mass (FM), fat-free mass (FFM), and body fat percent (%BF). A rapid 4C model (DXA-derived body volume and bone mineral; MFBIA-derived total body water) and stand-alone DXA (GE Lunar Prodigy) and MFBIA (InBody 570) assessments were compared against the criterion 4C model. ResultsLin’s concordance correlation coefficient values were >0.90 for all comparisons. The standard error of the estimates ranged from 1.3 to 2.0 kg, 1.6 to 2.2 kg, and 2.1 to 2.7% for FM, FFM, and %BF, respectively. The 95% limits of agreement ranged from ±3.0 to 4.2 kg, ±3.1 to 4.2 kg, and ±4.9 to 5.2% for FM, FFM, and %BF, respectively. ConclusionsResults revealed that all 3 methods provided acceptable body composition results. The MFBIA device used in the current study may be a more economically friendly option than DXA or when there is a need to minimize radiation exposure. Nonetheless, clinics and laboratories that already have a DXA device in place or that value having the lowest individual error when conducting a test may consider continuing to use the machine. Lastly, a rapid 4C model may be useful for assessing body composition measures observed in the current study and those provided by a multi-compartment model (e.g., protein).

Valoración de resultados tras 112 radioembolizaciones con 90Y-microesferas

AimTo determine the results of radioembolization transarterial (TARE), in the treatment of liver tumors, a retrospective evaluation was performed after 112 TARE with 90Y-microspheres administered in 82 patients in a single hospital, analyzing efficacy and safety, after a follow-up greater than or equal to 1 year post-TARE in all patients, and evaluating the possible relationship between treatment response and patient survival. Material and methodsWe have administered 57 single TARE and 55 multiple TARE in patients with hepatocellular carcinoma (53), liver metastases (25) and cholangiocarcinoma (4), with prior multidisciplinary evaluation, clinical, angiographic and gammagraphic (planar/SPECT/SPECT-CT with 99mTc-MAA), multicompartment model (MIRD equations), post-TARE screening (planar/SPECT/SPECT-CT), clinical and radiological follow-up, tumor response evaluation (mRECIST criteria) and Kaplan–Meier analysis to determine progression-free survival (PFS) and overall survival (OS). ResultsTherapeutic intention was palliative (82%) and as bridge to liver transplantation/surgical resection (17%). We obtained response (R), complete or partial, in 65.9% of cases. One year after TARE 34.7% of patients with R and 19.2% of non-R were progression-free (p: 0.003), with OS of 80% for R and 37.5% for non-R (p: 0.001). Survival analysis showed median OS of 18 months (95% CI 15.7–20.3) for R and 9 months (95% CI 6.1–11.8) for non-R (p: 0.03). We found mild (27.6%) and severe (5.3%) side effects, all of them resolved, without higher incidence after multiple TARE. ConclusionTARE with 90Y-microspheres, in appropriately selected patients with liver tumors, provides therapeutic efficacy and low rate of toxicity, with higher PFS and OS in patients with TARE response compared to those who did not respond.

Glutamate spillover drives robust all-or-none dendritic plateau potentials-an in silico investigation using models of striatal projection neurons.

Plateau potentials are a critical feature of neuronal excitability, but their all-or-none behavior is not easily captured in modeling. In this study, we investigated models of plateau potentials in multi-compartment neuron models and found that including glutamate spillover provides robust all-or-none behavior. This result arises due to the prolonged duration of extrasynaptic glutamate. When glutamate spillover is not included, the all-or-none behavior is very sensitive to the steepness of the Mg2+ block. These results suggest a potentially significant role of glutamate spillover in plateau potential generation, providing a mechanism for robust all-or-none behavior across a wide range of slopes of the Mg2+ block curve. We also illustrate the importance of the all-or-none plateau potential behavior for nonlinear computation with regard to the nonlinear feature binding problem.

Comparison of Whole-Body Dosimetry Measurements in 131I Therapy Using Ceiling-Mounted Geiger-Müller Detectors and Gamma Camera Scans: An Agreement Analysis.

Background: In 131I therapies internal dosimetry is crucial for determining the mean absorbed dose to organs at risk, particularly the bone marrow, which has a dose constraint of 2 Gy. Traditionally, multicompartmental models have been used for bone marrow dosimetry, necessitating whole-body absorbed-dose assessments. However, noninvasive techniques, such as gamma camera scans or ceiling-mounted Geiger-Müller (GM) counters, can estimate the aforementioned. This study was aimed to evaluate the agreement between whole-body mean absorbed dose using gamma camera scans and ceiling-mounted GM in patients with thyroid carcinoma undergoing 131I therapy. Methods: This study included 31 patients with thyroid cancer who were treated with 131I. The whole-body time-integrated activity (TIA) and mean absorbed dose were estimated using the elimination curves obtained with gamma camera scans and ceiling-mounted GM. In addition, statistical analysis was performed on the data to determine the Coefficient Correlation Coefficient and the Bland-Altman limits of agreement for both parameters, as well as for the elimination curves' effective half-life. Results: The study revealed correlations of 0.562 and 0.586 between whole-body TIA and mean absorbed dose, respectively. The Bland-Altman limits of agreement were found to be below -3.75% and within 12.75% of the bone marrow dose constraint of 2 Gy. The nonparametric evaluation revealed that whole-body TIA and mean absorbed dose medians from GM were lower than those from gamma camera scans (p < 0.001). Effective half-life estimation mean was significantly lower in the GM than in the gamma camera of 13 and 23 h. Conclusions: Although GM calculates the whole-body absorbed dose with margins of error within clinical acceptance, underestimation of the effective half-life makes it an unacceptable substitute method for gamma cameras in clinical practice. Further research should be conducted to evaluate single-point GM measurement substitutions in time-activity curves.

Dissipation, residue distribution, and risk assessment of ethiprole and its metabolites in rice under various open field conditions.

Ethiprole has been registered to control planthoppers in rice fields for many years in Asia. However, its dissipation and residues in rice under natural field conditions and health hazards are largely unclear. Herein, a modified QuEChERS and high-performance liquid chromatography-tandem mass spectrometry method was established to detect ethiprole and its metabolites, ethiprole amide and ethiprole sulfone, in brown rice, rice husks, and rice straw. The field experiments were implemented in 12 representative provinces of China under Good Agricultural Practices to study the fate and terminal residues of ethiprole and its metabolites in rice. Finally, the dietary risk of ethiprole was evaluated. The average recoveries of these analytes in all matrices were 86.4%-99.0% with a repeatability of 0.575%-9.38%. The limits of quantification for each compound were 0.01 mg kg-1 . Dissipation of ethiprole followed the single first-order, first + first-order, and first-order multi-compartment kinetic models with a half-life of 2.68-8.99 days in rice husks. The dissipation half-life of ethiprole combining all metabolites was 5.20-16.2 days in rice husks. The terminal residues of ethiprole and its metabolites at preharvest intervals of 21 days were < 0.011, 0.25, and 0.20 mg kg-1 in brown rice, rice husks, and rice straw, respectively. Ethiprole amide was undetectable in all matrices, and the risk quotient of ethiprole was far less than 100%. Ethiprole rapidly converted to ethiprole sulfone in rice, and ethiprole and ethiprole sulfone mainly remained in rice husks and straws. The dietary risk of ethiprole was acceptable for Chinese consumers. This article is protected by copyright. All rights reserved.

SNS-Toolbox: An Open Source Tool for Designing Synthetic Nervous Systems and Interfacing Them with Cyber-Physical Systems.

One developing approach for robotic control is the use of networks of dynamic neurons connected with conductance-based synapses, also known as Synthetic Nervous Systems (SNS). These networks are often developed using cyclic topologies and heterogeneous mixtures of spiking and non-spiking neurons, which is a difficult proposition for existing neural simulation software. Most solutions apply to either one of two extremes, the detailed multi-compartment neural models in small networks, and the large-scale networks of greatly simplified neural models. In this work, we present our open-source Python package SNS-Toolbox, which is capable of simulating hundreds to thousands of spiking and non-spiking neurons in real-time or faster on consumer-grade computer hardware. We describe the neural and synaptic models supported by SNS-Toolbox, and provide performance on multiple software and hardware backends, including GPUs and embedded computing platforms. We also showcase two examples using the software, one for controlling a simulated limb with muscles in the physics simulator Mujoco, and another for a mobile robot using ROS. We hope that the availability of this software will reduce the barrier to entry when designing SNS networks, and will increase the prevalence of SNS networks in the field of robotic control.

A perceptron-based learning method for solving the inverse problem of the brain model via poroelastodynamics

Computer simulations and in silico models are currently the best tools for understanding complex biological processes. However, the complexity of biological tissues, with multiple cellular mechanisms in response to changing physical and chemical external stimuli, makes the corresponding mathematical models highly nonlinear with numerous parameters. These parameters are crucial to the models but are often fitted for specific conditions, making the conclusions drawn difficult to generalize. Moreover, some of these parameters will be hard to obtain through either clinical measurements or experiments. Hence, in this study, we introduced a perceptron-based method to determine unknown parameters of water transfer coefficients in the cerebral multi-compartmental poroelasticity model. Based on the nature and conditions of the available data, we designed a straightforward and functional model to solve a steady-state inverse problem. Moreover, we added an analytical solution to restrict the learning tendency of the model. It is to be noted that we only evaluated the unknown parameters without fitting the solution of PDEs. We believe that this study presents a functional perceptron-based approach for investigating and demonstrating unknown parameters using the cerebral multi-compartmental poroelasticity model. Besides, the algorithm was fully presented since we believed that our scheme has the ability to utilise in various field for those who need to estimate unknown parameters in PDEs. Furthermore, we tested the efficiency and effectiveness of the proposed method and demonstrated how the framework can help estimate the parameters rapidly. Finally, we discussed the unmet needs and forecasted future tasks of this framework.