Nonlinear Model Research Articles

A matrixed nonlinear grey Bernoulli model for interval number prediction of power generation in East China

A matrixed nonlinear grey Bernoulli model for interval number prediction of power generation in East China

Construction of a Compound Model to Enhance the Accuracy of Hepatic Fat Fraction Estimation with Quantitative Ultrasound

Background: we evaluated regression models based on quantitative ultrasound (QUS) parameters and compared them with a vendor-provided method for calculating the ultrasound fat fraction (USFF) in metabolic dysfunction-associated steatotic liver disease (MASLD). Methods: We measured the attenuation coefficient (AC) and the backscatter-distribution coefficient (BSC-D) and determined the USFF during a liver ultrasound and calculated the magnetic resonance imaging proton-density fat fraction (MRI-PDFF) and steatosis grade (S0–S4) in a combined retrospective–prospective cohort. We trained multiple models using single or various QUS parameters as independent variables to forecast MRI-PDFF. Linear and nonlinear models were trained during five-time repeated three-fold cross-validation in a retrospectively collected dataset of 60 MASLD cases. We calculated the models’ Pearson correlation (r) and the intraclass correlation coefficient (ICC) in a prospectively collected test set of 57 MASLD cases. Results: The linear multivariable model (r = 0.602, ICC = 0.529) and USFF (r = 0.576, ICC = 0.54) were more reliable in S0- and S1-grade steatosis than the nonlinear multivariable model (r = 0.492, ICC = 0.461). In S2 and S3 grades, the nonlinear multivariable (r = 0.377, ICC = 0.32) and AC-only (r = 0.375, ICC = 0.313) models’ approximated correlation and agreement surpassed that of the multivariable linear model (r = 0.394, ICC = 0.265). We searched a QUS parameter grid to find the optimal thresholds (AC ≥ 0.84 dB/cm/MHz, BSC-D ≥ 105), above which switching from a linear (r = 0.752, ICC = 0.715) to a nonlinear multivariable (r = 0.719, ICC = 0.641) model could improve the overall fit (r = 0.775, ICC = 0.718). Conclusions: The USFF and linear multivariable models are robust in diagnosing low-grade steatosis. Switching to a nonlinear model could enhance the fit to MRI-PDFF in advanced steatosis.

Reducing Structural Nonidentifiabilities in Upstream Bioprocess Models Using Profile-Likelihood.

Process models are increasingly used to support upstream process development in the biopharmaceutical industry for process optimization, scale-up and to reduce experimental effort. Parametric unstructured models based on biological mechanisms are highly promising, since they do not require large amounts of data. The critical part in the application is the certainty of the parameter estimates, since uncertainty of the parameter estimates propagates to model predictions and can increase the risk associated with those predictions. Currently Fisher-Information-Matrix based approximations or Monte-Carlo approaches are used to estimate parameter confidence intervals and regularization approaches to decrease parameter uncertainty. Here we apply profile likelihood to determine parameter identifiability of a recent upstream process model. We have investigated the effect of data amount on identifiability and found out that addition of data reduces non-identifiability. The likelihood profiles of nonidentifiable parameters were then used to uncover structural model changes. These changes effectively alleviate the remaining non-identifiabilities except for a single parameter out of 21 total parameters. We present the first application of profile likelihood to a complete upstream process model. Profile likelihood is a highly suitable method to determine parameter confidence intervals in upstream process models and provides reliable estimates even with nonlinear models and limited data.

Neural Network-Based Parameter Estimation and Compensation Control for Time-Delay Servo System of Aeroengine

Servo systems are important actuators of aeroengines. The repetitive, reciprocating motion of the servo system leads to significant changes in its time delay and gain characteristics, and degradation increases the uncertainty of these changes. These characteristic variations may have an adverse effect on the dynamic performance of the aeroengine. Therefore, a neural network-based parameter estimation and a multi-loop neural network-based predictive control (ML-NNPC) method for aeroengine inlet guide vane (IGV) servo systems (SVS) were proposed. In this study, the time delay estimation of the servo system was treated as a classification problem, and an SE (squeeze-and-excitation)-GRU (gated recurrent unit) network was proposed to estimate the time delay by using the selected dynamic data of the servo system. The estimated delay was embedded into an online sequential extreme learning machine, and a nonlinear model predictive controller was designed to obtain an optimal control sequence. The compensation control loop was designed to reduce the impact of the model and delay mismatch problems of the control system. The proposed method was applied to the IGV SVS control of a turboshaft engine. The simulation results demonstrate that the time delay is estimated accurately and compensated effectively. Compared to the existing PI and PI with Smith predictor methods, the ML-NNPC method achieves better control performance in the control of both the SVS and the engine rotor speed system. The stability and robustness of the ML-NNPC also show superiority. The results verify the effectiveness of the proposed time delay estimation method and the ML-NNPC method.

Grocery Shopping Under Simplified Marginal Value Theorem Predictions.

This study examined whether supermarkets can be considered patches in the marginal value theorem (MVT) sense despite their particular features and whether they are models of human food foraging in resource-dense conditions. On the basis of the MVT, the quantitative relationship between gains in the Euro and patch residence time was modeled as an exponential growth function toward an upper asymptote, allowing the choice of an optimal strategy under diminishing returns.N = 61 participants were interviewed about their current shopping trip and contextual variables at a German supermarket and provided data to estimate relevant model parameters. A nonlinear model of the patch residence time and resulting gain based on an exponential function was fitted via nonlinear orthogonal distance regression.The results generally revealed the relationships predicted by the model, with some uncertainty regarding the estimation of the upper asymptote due to a lack of data from participants with long residence times. Despite this limitation, the data support the applicability of the MVT-based model.The results show that approaches from optimal foraging theory, such as the MVT, can be used successfully to model human shopping behavior even when participants' verbal reports are used.

Population pharmacokinetic model of tranexamic acid in patients who undergo cardiac surgery with cardiopulmonary bypass.

Tranexamic acid (TXA) is widely used as an antifibrinolytic drug. However, studies to determine the optimal blood concentration of TXA have produced inconsistent results. During cardiac surgery, cardiopulmonary bypass (CPB) has serious effects on drug distribution, elimination, and plasma concentration. Therefore, we aimed to establish a population pharmacokinetics model of TXA in patients undergoing cardiac surgery with CPB that considers renal function as a covariate, thereby facilitating personalized treatment. In total, 453 TXA plasma samples were prospectively collected from 77 patients who underwent cardiac surgery with CPB. Plasma concentrations were determined by ultra-performance liquid chromatography-tandem mass spectrometry. The population pharmacokinetic model of TXA was analyzed using nonlinear mixed-effects modeling. The two-compartment-based model with combined errors was determined as the best. The final model included the effect of bodyweight and CLcr may be summarized as V1 (L) = 12.77 × (bodyweight / 61.4)0.911, V2 (L) = 6.857, CL1 (L/h) = 3.263 × [CLcr (L/h) / 61.0]0.752, CL2 (L/h) = 2.859. Patients who undergo cardiac surgery with CPB may require an adjusted dose of TXA tailored to CPB due to lower CL1 and increased V1. Our TXA population pharmacokinetic model may be useful for developing individualized dosing designs for TXA in patients who undergo cardiac surgery with CPB.

Comparison of the Performance of Nonlinear Time-Dependent Constitutive Models Calibrated with Minimal Test Data Applied to an Epoxy Resin

Epoxy resins are extensively employed as adhesives and matrices in fibre-reinforced composites. As polymers, they possess a viscoelastic nature and are prone to creep and stress relaxation even at room temperature. This phenomenon is also responsible for time-dependent failure or creep fracture due to cumulative strain. Several constitutive equations have been used to describe the mechanical time-dependent response of polymers. These models have been proposed over the past six decades, with minimal direct and practical confrontation. Each model is associated with a specific application or research group. This work assesses the predictive performance of four distinct time-dependent constitutive models based on experimental data. The models were deemed sufficiently straightforward to be readily integrated into practical engineering analyses. A range of loading cases, encompassing constant strain rate, creep, and relaxation tests, were conducted on a commercial epoxy resin. Model parameter calibration was conducted with a minimum data set. The extrapolative predictive capacity of the models was evaluated for creep loading by extending the tests to five decades. The selected rheological models comprise two viscoelastic models based on Volterra-type integrals, as originally proposed by Schapery and Rabotnov; one viscoplastic model, as originally proposed by Norton and Bailey; and the Burger model, in which two springs and two dashpots are combined in a serial and parallel configuration. The number of model parameters does not correlate positively to superior performance, even if it is high. Overall, the models exhibited satisfactory predictive performance, displaying similar outcomes with some relevant differences during the unloading phases.

Remaining Useful Life Analysis for Two‐Stage Nonlinear Inverse Gaussian Process With Random Effects

ABSTRACTEvaluating the reliability of high‐reliability, long‐life products remains a central challenge in the realm of reliability engineering. The intricate internal structure of these products results in a performance degradation process that is both nonlinear and multi‐staged. Distinguished from the traditional single‐stage degradation model, the two‐stage degradation model requires consideration of the degradation state at a critical transition point. This complexity significantly complicates the task of modeling and predicting the remaining useful life (RUL) for products exhibiting two‐stage nonlinear degradation. To address this challenge, this paper introduces a novel two‐stage inverse Gaussian (IG) degradation process model. This model builds upon and extends the traditional nonlinear IG degradation process model, incorporating a two‐stage approach to account for the uncertainty at the degradation transition point and randomizing the drift coefficients to better reflect the stochastic nature of the degradation process. The analytical expressions of probability density functions (PDF) and reliability functions are derived based on the definition of first hitting time (FHT). The unknown parameters of the model are estimated using the Gibbs sampling method within the Markov Chain Monte Carlo (MCMC) framework. The applicability and effectiveness of our proposed method are substantiated through a simulation example and by analyzing real‐world degradation data from a cabin door lock mechanism.

Holding up the mirror: The role of teacher educators and syllabi in perpetuating or disrupting inequity.

This blended pilot-empirical and theoretical manuscript documents a reflective journey undertaken by a group of early childhood teacher educators located across different regions of the United States as they examined their course design, materials, and syllabi construction. Grounded in reflective practice, intersectionality, and critical pedagogy, their collaborative endeavor necessitated profound self-examination and recognition of oppressive structures inherent within the field and reproduced throughout course syllabi, thereby perpetuating societal inequities inside and outside the classroom context. Their iterative, evolving effort resembled a reflective consultation group, marked by continuous self-reflection, challenging assumptions, and transforming actions, vividly portrayed in their vignettes. A nonlinear spiral model emerged as a visual representation of the multiple entry points into an ongoing process-highlighting access points that encourage curiosity and interrogation of academic syllabi and course content. The inclusive nature of this inquiry invites faculty members and practitioners to confront racism, ableism, and other systems of domination, amplify marginalized scholarship, and redefine early childhood education-related fields, including the Infant and Early Childhood Mental Health landscape. It also underscores the imperative of sustained introspection and collaborative action in nurturing equity.

Prediction and Comparison of Nonlinear Mathematical Models for the Biodegradation of Two Herbicides Under the Effect of Manure in Soils

Prediction and Comparison of Nonlinear Mathematical Models for the Biodegradation of Two Herbicides Under the Effect of Manure in Soils

Risk of hemorrhagic fever with renal syndrome associated with meteorological factors in diverse epidemic regions: a nationwide longitudinal study in China

BackgroundHemorrhagic fever with renal syndrome (HFRS) is a climate-sensitive zoonotic disease that poses a significant public health burden worldwide. While previous studies have established associations between meteorological factors and HFRS incidence, there remains a critical knowledge gap regarding the heterogeneity of these effects across diverse epidemic regions. Addressing this gap is essential for developing region-specific prevention and control strategies. This study conducted a national investigation to examine the associations between meteorological factors and HFRS in three distinct epidemic regions.MethodsWe collected daily meteorological data (temperature and relative humidity) and HFRS incidence cases of 285 cities in China from the Resource and Environment Science and Data Center and the Chinese National Notifiable Infectious Disease Reporting Information System from 2005–2022. Study locations were stratified into three distinct epidemic categories (Rattus-dominant, Apodemus-dominant, and mixed) based on the seasonality of peak incidence. The associations between meteorological variables and HFRS incidence were investigated using a time-stratified case-crossover design combined with distributed lag nonlinear modeling for each epidemic category.ResultsThe exposure-response relationships between meteorological factors and HFRS incidence revealed significant heterogeneity across epidemic regions, as evidenced by Cochran’s Q test for temperature (Q = 324.40, P < 0.01) and relative humidity (Q = 30.57, P < 0.01). The optimal daily average temperature for HFRS transmission in Rattus-dominant epidemic regions (− 6.6 °C), characterized by spring epidemics, was lower than that observed in Apodemus-dominant epidemic regions (13.7 °C), where primary cases occurred during autumn and winter months. Furthermore, the association between relative humidity and HFRS incidence exhibited as a monotonic negative correlation in Rattus-dominant regions, while Apodemus-dominant regions showed a nonlinear, inverted U-shaped association.ConclusionsThis study highlights the heterogeneous effects of meteorological factors on HFRS incidence across different epidemic regions. Targeted preventive measures should be taken during cold and dry spring days in Rattus-dominant regions, and during warm and moderately humid winter days in Apodemus-dominant regions. In mixed epidemic regions, both scenarios require attention. These findings provide novel scientific evidence for the formulation and implementation of region-specific HFRS prevention policies.Graphical

Universal Construction of Electrical Insulation and High-Thermal-Conductivity Composites Based on the In Situ Exfoliation of Boron Nitride-Graphene Hybrid Filler.

Hexagonal boron nitride (h-BN), with excellent thermal conductivity and insulation capability, has garnered significant attention in the field of electronic thermal management. However, the thermal conductivity of the h-BN-enhanced polymer composite material is far from that expected because of the insurmountable interfacial thermal resistance. In order to realize the high thermal conductivity of polymer composite thermal interface materials, herein, an in situ exfoliation method has been employed to prepare a boron nitride nanosheet-graphene (BNNS-Gr) hybrid filler. After being incorporated into a poly(ethylene glycol) (PEG) matrix, the thermal conductivity of composites is significantly improved on the premise of electrical insulation. Furthermore, a three-dimensional (3D) thermally conductive framework using this hybrid filler as the raw material has also been constructed. After incorporating poly(ethylene glycol) (PEG) through a vacuum impregnation method, this ordered structure effectively resolves the leakage issue in phase-change composites during actual working conditions and showcases enhanced thermal conductivity of 2.45 W m-1 K-1 at 10 vol %, along with excellent electrical insulation, shape stability, and cyclic stability. The modified Hashin-Shtrikman model and the Foygel nonlinear model prove that compounding graphene with BN reduces the interfacial thermal resistance of polymer composites for both disordered and ordered systems. This indicates that the in situ exfoliation strategy is an effective method to fabricate the nanofiller for reducing the interfacial thermal resistance of composites.

Hybridizing evolutionary algorithms and multiple non-linear regression technique for stream temperature modeling

Abstract The present study hybridizes the new-generation evolutionary algorithms and the nonlinear regression technique for stream temperature modeling and compares this approach with conventional gray and black box approaches under natural flow conditions, providing a comprehensive assessment. The nonlinear equation for water temperature modeling was optimized using biogeography-based optimization (BBO) and invasive weed optimization (IWO), simulated annealing algorithm (SA) and particle swarm optimization (PSO). Two black box approaches, a feedforward neural network (FNN) and a long short-term memory (LSTM) network, were also employed for comparison. Additionally, an adaptive neuro-fuzzy inference system (ANFIS) served as a gray box model for river thermal regimes. The models were evaluated based on accuracy, complexity, generality and interpretability. Performance metrics, such as the Nash–Sutcliffe efficiency (NSE), showed that the LSTM model achieved the highest accuracy (NSE = 0.96) but required significant computational resources. In contrast, evolutionary algorithm-based models offered acceptable performance while reducing the computational complexities of LSTM, with all models achieving NSE values above 0.5. Considering interpretability, accuracy and complexity, evolutionary-based nonlinear models are recommended for general applications, such as assessing thermal river habitats. For tasks requiring very high accuracy, the LSTM model is preferred, while ANFIS provides a balanced trade-off between accuracy and interpretability, making it suitable for engineers and ecologists. While all models demonstrate similar generality, this model is developed for a specific location. For other locations, independent models with a similar architecture would need to be developed. Ultimately, the choice of model depends on specific objectives and available resources.

Adhesive nonlinearity analysis of longitudinal guided wave using circumference pasted piezoelectric array based nonlinear shear stress lag model

The adhesive layer between an ultrasonic transducer and a circular tube can generate a nonlinear signal during guided wave excitation and reception, which is called adhesive nonlinearity (AN). It may override the damage-related signals and result in false detection if not adequately evaluated and mitigated. This study investigated the AN of the guided wave excitation model composed of a piezoelectric array in a circular tube structure. The classical shear stress lag model was extended to the circumference pasted piezoelectric array-based nonlinear shear stress lag model to investigate the coupling properties of the AN and to evaluate the AN by comparing it with other nonlinear factors within a circular tube structure. On this basis, the nonlinear shear stress was combined with the normal mode expansion to establish a frequency tuning model for the AN, which allowed the effect of the AN to be minimized by adjusting the half-wavelength of the guided wave to match the length of the actuator. Finite-element analyses and experiments validated the tuning characteristics of the AN mentioned above. This work was used to mitigate the effect of the AN on the nonlinear guided wave during thermal damage evaluation.

Experimental investigation of the second-mode internal solitary wave in continuous pycnocline and the applicability of weakly nonlinear theoretical models

The research on the propagation and evolution of the second-mode internal solitary waves(ISWs) is receiving more and more attention. In this study, second-mode internal solitary waves in continuous stratification are physically simulated in a laboratory-stratified fluid flume. Meanwhile, the second-mode ISWs and their induced flow field in the same stratification environment are solved based on the eigenvalue problem of the TG equation (Taylor-Goldstein), combined with the weakly non-linear ISW theoretical models. The experimental and theoretical results show that the symmetry of the second-mode ISW wave-flow field can be improved as the thickness ratio of the upper fluid layer and lower one approaches 1. The ISW speed and horizontal and vertical velocity range values in the continuous pycnocline are positively correlated with the changing ISW amplitude, while only the wavelength is negatively correlated with the iSW amplitude. The waveflow fields of the second-mode ISWs calculated by Korteweg-de Vries (KdV) and extended KdV (eKdV) models in the large amplitude cases are more consistent with the experimental results than those in the small amplitude cases. The two theoretical models used to describe second-mode ISWs can be significantly improved when the thickness ratio of the upper and lower fluid layers approaches 1. In this case, the eKdV model is more applicable than the KdV model.

Analysis and prediction of atmospheric ozone concentrations using machine learning

Atmospheric ozone chemistry involves various substances and reactions, which makes it a complex system. We analyzed data recorded by Switzerland's National Air Pollution Monitoring Network (NABEL) to showcase the capabilities of machine learning (ML) for the prediction of ozone concentrations (daily averages) and to document a general approach that can be followed by anyone facing similar problems. We evaluated various artificial neural networks and compared them to linear as well as non-linear models deduced with ML. The main analyses and the training of the models were performed on atmospheric air data recorded from 2016 to 2023 at the NABEL station Lugano-Università in Lugano, TI, Switzerland. As a first step, we used techniques like best subset selection to determine the measurement parameters that might be relevant for the prediction of ozone concentrations; in general, the parameters identified by these methods agree with atmospheric ozone chemistry. Based on these results, we constructed various models and used them to predict ozone concentrations in Lugano for the period between January 1, 2024, and March 31, 2024; then, we compared the output of our models to the actual measurements and repeated this procedure for two NABEL stations situated in northern Switzerland (Dübendorf-Empa and Zürich-Kaserne). For these stations, predictions were made for the aforementioned period and the period between January 1, 2023, and December 31, 2023. In most of the cases, the lowest mean absolute errors (MAE) were provided by a non-linear model with 12 components (different powers and linear combinations of NO2, NOX, SO2, non-methane volatile organic compounds, temperature and radiation); the MAE of predicted ozone concentrations in Lugano was as low as 9 μgm−3. For the stations in Zürich and Dübendorf, the lowest MAEs were around 11 μgm−3 and 13 μgm−3, respectively. For the tested periods, the accuracy of the best models was approximately 1 μgm−3. Since the aforementioned values are all lower than the standard deviations of the observations we conclude that using ML for complex data analyses can be very helpful and that artificial neural networks do not necessarily outperform simpler models.

Evaluating the interaction between human and paediatric robotic lower-limb exoskeleton: a model-based method

Abstract Lower-limb exoskeletons (LLEs) have shown potential in improving motor function in patients for both clinical rehabilitation and daily life. Despite this, the development and control of pediatric exoskeletons remain notably underserved. This study focuses on a unique pediatric robotic lower limb exoskeleton (PRLLE), tailored particularly for children aged 8–12. Each leg of the robot has 5 Degrees of Freedom (DOFs)—three at the hip and one each at the knee and ankle. The interaction between the child user and the PRLLE is intricate, necessitating adherence to essential requirements of comfort, safety, and adaptability. Testing numerous prototype variations against diverse user profiles, particularly for children with neurological disorders where each child differs, is impractical. Model-based methods offer a virtual testbed that is useful in the design stage. This study uses MATLAB® to simulate and evaluate the interaction between users and PRLLE after deriving the nonlinear dynamic model of the PRLLE, which is simplified through multiple layers. To verify the accuracy of the derived dynamic model, a Computed Torque Control method is employed. The study provides detailed outcomes for children aged 8, 10, and 12 years, for passive and active users along with variations in PRLLE assistance levels. The study shows significant reductions in human joint torques, up to 56%, alongside substantial actuator powers, reaching up to 98W, for a 10-year-old child user. Furthermore, examining 8 and 12-year-old child users revealed variations in interaction forces, with changes up to 29.5%. Consequently, meticulous consideration of the human user’s limitations is crucial during the PRLLE’s design and conceptualization phases, particularly for PRLLEs.

Assessment of tissue-air ratios in epoxy resin and PMMA phantoms for radiation dosimetry: findings from experimental measurements and Monte Carlo simulations.

This study assesses radiation doses in multi-slice computed tomography (CT) using epoxy resin and PMMA phantoms, focusing on the relationship between TAR (tissue air ratio) and kilovoltage peak (kVp). The research was conducted using a Hitachi Supria 16-slice CT scanner. An epoxy resin phantom was fabricated from commercially available materials, to simulate human tissue. The phantom contained four peripheral inserts and one central insert for dose measurement, with optically stimulated luminescent dosimeters positioned at various depths (2 to 10cm). Monte Carlo simulations were executed using the Geant4 Application for Tomographic Emission toolkit (GATE) to model photon transport, with the x-ray spectrum generated using SpekPy software. A non-linear fitting model was developed to describe the TAR-kVp relationship across different depths for epoxy resin and PMMA. Results indicated that TAR values were higher at low depths (2cm) and decreased with increasing depth, reflecting the x-ray beam's attenuation. For instance, at 80 kVp and 2cm depth, the experimental TAR for PMMA was 1.102 ± 0.011, closely matching the MC simulation value of 1.110 ± 0.036, resulting in a small difference of 0.7%. At a depth of 10cm, the experimental TAR for PMMA decreased to 0.245 ± 0.006, while the MC TAR was 0.248 ± 0.016, with a relative difference of 1.2%. Similar trends were observed for epoxy resin, where the experimental TAR ranged from 1.070 ± 0.014 at 2cm to 0.235 ± 0.009 at 10cm, while MC simulation values ranged from 1.080 ± 0.038 to 0.238 ± 0.017. Bland-Altman analysis confirmed these results, with mean differences of 0.008 for PMMA and 0.006 for epoxy resin, indicating high agreement between the experimental and simulated TAR values. This study highlights the importance of phantom material selection in dose assessment and the implications of TAR in dose correction within the context of diagnostic radiology.

Influence of remifentanil on the pharmacokinetics and pharmacodynamics of remimazolam in healthy volunteers

Background: Synergistic effects between opioids and remimazolam on bispectral index (BIS) and modified observer’s assessment of alertness and/or sedation (MOAAS) score were previously described. This study aimed to characterize the influence of remifentanil on the sedative properties of remimazolam as measured by MOAAS, BIS, tolerance to laryngoscopy or tetanic stimulation (TOL or TOTS) and to determine target concentrations that maximize MOAAS 2/3. Methods: A three-period, cross-over, dose-ranging clinical trial was performed in 24 healthy volunteers. In all periods, remimazolam was administered using a step-up and step-down TCI protocol (50 – 2000 ng/mL). Stable remifentanil target concentrations of 0.5 ng/mL and 0.1 to 4.0 ng/mL were maintained in periods 2 and 3, respectively. Remifentanil, remimazolam and CNS7054 (metabolite) concentrations and, MOAAS, BIS, TOL and TOTS were collected in each step of the TCI protocol. Data were analyzed using non-linear mixed-effects models, where p≤0.01 was considered significant. Results: Remifentanil reduced the apparent clearance of CNS7054 with a half-maximum inhibition at 8.0 ng/mL (95% CI 5.5 to 13.4 ng/mL). A pharmacodynamic interaction was detected on all endpoints. Simulations indicate that the probability of observing a MOAAS 2/3 is highest at remimazolam target concentration of 275, 250 or 200 ng/mL combined with 0, 0.1, or 0.5 ng/mL remifentanil resulting in probabilities of 45%, 45% and 44%, respectively. Additionally, simulations indicate that the highest probability of observing TOTS and TOL was 93.3% and 85.5%, respectively, at the highest studied target concentrations. Conclusions: A pharmacokinetic and pharmacodynamic drug-drug interaction between remimazolam and remifentanil was quantified in this clinical trial. Appropriate target concentrations for MOAAS and BIS could be estimated, but, for TOL and TOTS, the trial design did not allow to fully characterize the exposure-response relationship.

Distinct Behavioral Profiles and Neuronal Correlates of Heroin Vulnerability Versus Resiliency in a Multi-Symptomatic Model of Heroin Use Disorder in Rats.

The behavioral and diagnostic heterogeneity within the opioid use disorder (OUD) diagnosis is not readily captured in current animal models, limiting the translational relevance of the mechanistic research that is conducted in experimental animals. The authors hypothesized that a nonlinear clustering of OUD-like behavioral traits would capture population heterogeneity and yield subpopulations of OUD vulnerable rats with distinct behavioral and neurocircuit profiles. Over 900 male and female heterogeneous stock rats, a line capturing genetic and behavioral heterogeneity present in humans, were assessed for several measures of heroin use and rewarded and non-rewarded seeking behaviors. A nonlinear stochastic block model clustering analysis was used to assign rats to OUD vulnerable, intermediate, and resilient clusters. Additional behavioral tests and circuit analyses using c-fos protein activation were conducted on the vulnerable and resilient subpopulations. OUD vulnerable rats exhibited greater heroin taking and seeking behaviors relative to those in the intermediate and resilient clusters. Akin to human OUD diagnosis, further vulnerable rat subclustering revealed subpopulations with different combinations of behavioral traits, including sex differences. Lastly, heroin cue-induced neuronal patterns of circuit activation differed between resilient and vulnerable phenotypes. Behavioral sex differences were recapitulated in patterns of circuitry activation, including preferential engagement of extended amygdala stress circuitry in males and cortico-striatal drug cue-seeking circuitry in females. Using a nonlinear clustering approach in rats, the analysis captured behavioral diagnostic heterogeneity reflective of human OUD diagnosis. OUD vulnerability and resiliency were associated with distinct neuronal activation patterns, posing this approach as a translational tool in assessing neurobiological mechanisms underpinning OUD.