Model-based Predictions Research Articles

Molecular detection and characterization of foodborne bacteria: Recent progresses and remaining challenges.

The global food demand is expected to increase in the coming years, along with challenges around climate change and food security. Concomitantly, food safety risks, particularly those related to bacterial pathogens, may also increase. Thus, the food sector needs to innovate to rise to this challenge. Here, we discuss recent advancements in molecular techniques that can be deployed within various foodborne bacteria surveillance systems across food settings. To start with, we provide updates on nucleic acid-based detection, with a focus on polymerase chain reaction (PCR)-based technologies and loop-mediated isothermal amplification (LAMP). These include descriptions of novel genetic markers for several foodborne bacteria and progresses in multiplex PCR and droplet digital PCR. The next section provides an overview of the development of clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins systems, such as CRISPR-Cas9, CRISPR-Cas12a, and CRISPR-Cas13a, as tools for enhanced sensitive and specific detection of foodborne pathogens. The final section describes utilizations of whole genome sequencing for accurate characterization of foodborne bacteria, ranging from epidemiological surveillance to model-based predictions of bacterial phenotypic traits through genome-wide association studies or machine learning.

A physiologically-based kinetic (PBK) model for work-related diisocyanate exposure: Relevance for the design and reporting of biomonitoring studies

Diisocyanates are highly reactive substances and known causes of occupational asthma. Exposure occurs mainly in the occupational setting and can be assessed through biomonitoring which accounts for inhalation and dermal exposure and potential effects of protective equipment. However the interpretation of biomonitoring data can be challenging for chemicals with complex kinetic behavior and multiple exposure routes, as is the case for diisocyanates. To better understand the relation between external exposure and urinary concentrations of metabolites of diisocyanates, we developed a physiologically based kinetic (PBK) model for methylene bisphenyl isocyanate (MDI) and toluene di-isocyanate (TDI). The PBK model covers both inhalation and dermal exposure, and can be used to estimate biomarker levels after either single or chronic exposures. Key parameters such as absorption and elimination rates of diisocyanates were based on results from human controlled exposure studies. A global sensitivity analysis was performed on model predictions after assigning distributions reflecting a mixture of parameter uncertainty and population variability.Although model-based predictions of urinary concentrations of the degradation products of MDI and TDI for longer-term exposure scenarios compared relatively well to empirical results for a limited set of biomonitoring studies in the peer-reviewed literature, validation of model predictions was difficult because of the many uncertainties regarding the precise exposure scenarios that were used. Sensitivity analyses indicated that parameters with a relatively large impact on model estimates included the fraction of diisocyanates absorbed and the binding rate of diisocyanates to albumin relative to other macro molecules.We additionally investigated the effects of timing of exposure and intermittent urination, and found that both had a considerable impact on estimated urinary biomarker levels. This suggests that these factors should be taken into account when interpreting biomonitoring data and included in the standard reporting of isocyanate biomonitoring studies.

Chamoli flash floods of 7th February 2021 and recent deformation: A PSInSAR and deep learning neural network (DLNN) based perspective

The February 7, 2021, Joshimath flood scenario was one such event that caused widespread damage and led to complete washout of the many hydroelectric power projects located on the course of Dhauliganga River. The physical monitoring and mapping of such events is a difficult task that often involves deployment of labour force in inhospitable terrains. Therefore, remote sensing techniques are used for the mapping and machine learning model-based predictions for future scenarios. Synthetic Aperture RADAR (SAR) remote sensing has been widely used over the years for accurate estimation of many natural and anthropogenic disaster events. This study utilizes Persistent Scatterer SAR interferometry (PSInSAR) technique to map the surface displacement of the 2021 flood scenario and make predictions for future displacement using a Deep Learning Neural Network (DLNN) model. 16 images of both ascending and descending pass were taken for the estimation of Line of Sight (LOS) displacement velocity mapping between January 2020 and April 2021 for Tapovan area. Further, 36 images from January 2020 to December 2022, in ascending and descending passes were used for prediction and validation of future LOS surface displacement using a DLNN model for Joshimath town to see the possible impact of February 7, 2021 event. The PSInSAR LOS displacements were found to be −1.2 ​cm–1.2 ​cm between January 1, 2020 and April 14, 2021, for Tapovan region where the floods had occurred on February 7, 2021. The predicted LOS displacement was observed to be −10 cm–10 ​cm for December 2022 for Joshimath town. These observations clearly indicate the impact of the event to Joshimath town and as one of the causative factors of recent observations of widespread cracks in the buildings in the region.

A population pharmacokinetic model to guide clozapine dose selection, based on age, sex, ethnicity, body weight and smoking status.

Guidance on clozapine dosing in treatment-resistant schizophrenia is based largely on data from White young adult males. This study aimed to investigate the pharmacokinetic profiles of clozapine and N-desmethylclozapine (norclozapine) across the age range, accounting for sex, ethnicity, smoking status and body weight. A population pharmacokinetic model, implemented in Monolix, that linked plasma clozapine and norclozapine via a metabolic rate constant, was used to analyse data from a clozapine therapeutic drug monitoring service, 1993-2017. There were 17 787 measurements from 5960 patients (4315 male) aged 18-86 years. The estimated clozapine plasma clearance was reduced from 20.2 to 12.0L h-1 between 20 and 80 years. Model-based dose predictions to attain a predose plasma clozapine concentration of 0.35 mg L-1 was 275 (90% prediction interval 125, 625) mg day-1 in nonsmoking, White males weighing 70 kg and aged 40 years. The corresponding predicted dose was increased by 30% in smokers, decreased by 18% in females, and was 10% higher and 14% lower in otherwise analogous Afro-Caribbean and Asian patients, respectively. Overall, the predicted dose decreased by 56% between 20 and 80 years. The large sample size and wide age range of the patients studied allowed precise estimation of dose requirements to attain predose clozapine concentration of 0.35 mg L-1 . The analysis was, however, limited by the absence of data on clinical outcome and future studies are required to determine optimal predose concentrations specifically in those aged over 65 years.

Stimulus salience conflicts and colludes with endogenous goals during urgent choices

Selecting where to look next depends on both the salience of objects and current goals (what we are looking for), but discerning their relative contributions over the time frame of typical visuomotor decisions (200-250ms) has been difficult. Here we investigate this problem using an urgent choice task with which the two contributions can be dissociated and tracked moment by moment. Behavioral data from three monkeys corresponded with model-based predictions: when salience favored the target, perceptual performance evolved rapidly and steadily toward an asymptotic level; when salience favored the distracter, many rapid errors were produced and the rise in performance took more time-effects analogous to oculomotor and attentional capture. The results show that salience has a brief (∼50ms) but inexorable impact that leads to exogenous, involuntary capture, and this can either help or hinder performance, depending on the alignment between salience and ongoing internal goals.

Peak knee joint moments accurately predict medial and lateral knee contact forces in patients with valgus malalignment

Compressive knee joint contact force during walking is thought to be related to initiation and progression of knee osteoarthritis. However, joint loading is often evaluated with surrogate measures, like the external knee adduction moment, due to the complexity of computing joint contact forces. Statistical models have shown promising correlations between medial knee joint contact forces and knee adduction moments in particularly in individuals with knee osteoarthritis or after total knee replacements (R2 = 0.44–0.60). The purpose of this study was to evaluate how accurately model-based predictions of peak medial and lateral knee joint contact forces during walking could be estimated by linear mixed-effects models including joint moments for children and adolescents with and without valgus malalignment. Peak knee joint moments were strongly correlated (R2 > 0.85, p < 0.001) with both peak medial and lateral knee joint contact forces. The knee flexion and adduction moments were significant covariates in the models, strengthening the understanding of the statistical relationship between both moments and medial and lateral knee joint contact forces. In the future, these models could be used to evaluate peak knee joint contact forces from musculoskeletal simulations using peak joint moments from motion capture software, obviating the need for time-consuming musculoskeletal simulations.

Mechanistic modeling of the bioconcentration of (super)hydrophobic compounds in Hyalella azteca

Bioconcentration tests using the freshwater amphipod Hyalella azteca as an alternative to conventional fish tests have recently received much attention. An appropriate computational model of H. azteca could help in understanding the mechanisms behind bioconcentration, in comparison to the fish as test organism. We here present the first mechanistic model for H. azteca that considers the single diffusive processes in the gills and gut. The model matches with the experimental data from the literature quite well when appropriate physiological information is used. The implementation of facilitated transport was essential for modeling. Application of the model for superhydrophobic compounds revealed binding to organic matter and the resulting decrease in bioavailable fraction as the main reason for the observed counterintuitive decrease in uptake rate constants with increasing octanol/water partition coefficient. Furthermore, estimations of the time needed to reach steady state indicated that durations of more than a month could be needed for compounds with a log Kow > 8, limiting the experimental applicability of the test. In those cases, model-based bioconcentration predictions could be a preferable approach, which could be combined with in vitro biotransformation measurements. However, our sensitivity analysis showed that the uncertainty in determining the octanol/water partition coefficients is a strong source of error for superhydrophobic compounds.

Beyond the Michaelis-Menten: Accurate Prediction of Drug Interactions Through Cytochrome P450 3A4 Induction.

The US Food and Drug Administration (FDA) guidance has recommended several model-based predictions to determine potential drug-drug interactions (DDIs) mediated by cytochrome P450 (CYP) induction. In particular, the ratio of substrate area under the plasma concentration-time curve (AUCR) under and not under the effect of inducers is predicted by the Michaelis-Menten (MM) model, where the MM constant ( ) of a drug is implicitly assumed to be sufficiently higher than the concentration of CYP enzymes that metabolize the drug ( ) in both the liver and small intestine. Furthermore, the fraction absorbed from gut lumen ( ) is also assumed to be one because is usually unknown. Here, we found that such assumptions lead to serious errors in predictions of AUCR. To resolve this, we propose a new framework to predict AUCR. Specifically, was re-estimated from experimental permeability values rather than assuming it to be one. Importantly, we used the total quasi-steady-state approximation to derive a new equation, which is valid regardless of the relationship between and , unlike the MM model. Thus, our framework becomes much more accurate than the original FDA equation, especially for drugs with high affinities, such as midazolam or strong inducers, such as rifampicin, so that the ratio between and becomes low (i.e., the MM model is invalid). Our work greatly improves the prediction of clinical DDIs, which is critical to preventing drug toxicity and failure.

A Novel Model-Assisted Decentralized Multi-Agent Reinforcement Learning for Joint Optimization of Hybrid Beamforming in Massive MIMO mmWave Systems

To meet the explosive increasing demands for high data rates, hybrid beamforming has become a promising paradigm for massive MIMO millimeter wave (mmWave) systems. In this paper, we study the joint optimization of hybrid beamforming for maximizing the achievable sum rate in networked dynamic massive MIMO mmWave systems. To address this problem, we propose a novel Model-Assisted Decentralized Multi-Agent Reinforcement Learning (MAD-MARL) algorithm, which incorporates the following three designs: 1) a model-based prediction approach, which can improve the policy learning speed greatly and also predict each agent's potential future so as to alleviate the impact of delayed information sharing in a networked system, 2) an attentional prediction sharing approach, which is to effectively reduce the non-stationarity of the environment by combining the proposed model-based prediction and self-attention strategy for reaching global consensus and cooperative inter-agent coordination in a distributed manner, and 3) a decentralized model-free learning approach, which trains the agents with the assistance of model-based predictions. Extensive simulations are conducted and the numerical results demonstrate that MAD-MARL can significantly increase the learning speed and also greatly improve the overall performance compared with existing work.

Measurement of the 86Kr(α,n)89Sr cross section at energies relevant for the weak r-process

The r-process has been shown to be robust in reproducing the abundance distributions of heavy elements, such as europium, seen in ultra-metal poor stars. In contrast, observations of elements 26 &lt; Z &lt; 47 display overabundances relative to r-process model predictions. A proposed additional source of early nucleosynthesis is the weak r-process in neutrino-driven winds of core-collapse supernovae. It has been shown that in this site (α,n) reactions are both crucial to nucleosynthesis and the main source of uncertainty in model-based abundance predictions. Aiming to improve the certainty of nucleosynthesis predictions, the cross section of the important reaction 86Kr(α,n)89Sr has been measured at an energy relevant to the weak r-process. This experiment was conducted in inverse kinematics at TRIUMF with the EMMA recoil mass spectrometer and the TIGRESS gamma-ray spectrometer. A novel type of solid helium target was used.

Statistical constraints on climate model parameters using a scalable cloud-based inference framework

Abstract Atmospheric aerosols influence the Earth’s climate, primarily by affecting cloud formation and scattering visible radiation. However, aerosol-related physical processes in climate simulations are highly uncertain. Constraining these processes could help improve model-based climate predictions. We propose a scalable statistical framework for constraining the parameters of expensive climate models by comparing model outputs with observations. Using the C3.AI Suite, a cloud computing platform, we use a perturbed parameter ensemble of the UKESM1 climate model to efficiently train a surrogate model. A method for estimating a data-driven model discrepancy term is described. The strict bounds method is applied to quantify parametric uncertainty in a principled way. We demonstrate the scalability of this framework with 2 weeks’ worth of simulated aerosol optical depth data over the South Atlantic and Central African region, written from the model every 3 hr and matched in time to twice-daily MODIS satellite observations. When constraining the model using real satellite observations, we establish constraints on combinations of two model parameters using much higher time-resolution outputs from the climate model than previous studies. This result suggests that within the limits imposed by an imperfect climate model, potentially very powerful constraints may be achieved when our framework is scaled to the analysis of more observations and for longer time periods.

On the role of tail in stability and energetic cost of bird flapping flight

Migratory birds travel over impressively long distances. Consequently, they have to adopt flight regimes being both efficient—in order to spare their metabolic resources—and robust to perturbations. This paper investigates the relationship between both aspects, i.e., energetic performance and stability, in flapping flight of migratory birds. Relying on a poly-articulated wing morphing model and a tail-like surface, several families of steady flight regime have been identified and analysed. These families differ by their wing kinematics and tail opening. A systematic parametric search analysis has been carried out, in order to evaluate power consumption and cost of transport. A framework tailored for assessing limit cycles, namely Floquet theory, is used to numerically study flight stability. Our results show that under certain conditions, an inherent passive stability of steady and level flight can be achieved. In particular, we find that progressively opening the tail leads to passively stable flight regimes. Within these passively stable regimes, the tail can produce either upward or downward lift. However, these configurations entail an increase of cost of transport at high velocities penalizing fast forward flight regimes. Our model-based predictions suggest that long range flights require a furled tail configuration, as confirmed by field observations, and consequently need to rely on alternative mechanisms to stabilize the flight.

Wildfire prediction using zero-inflated negative binomial mixed models: Application to Spain

Wildfires have changed in recent decades. The catastrophic wildfires make it necessary to have accurate predictive models on a country scale to organize firefighting resources. In Mediterranean countries, the number of wildfires is quite high but they are mainly concentrated around summer months. Because of seasonality, there are territories where the number of fires is zero in some months and is overdispersed in others. Zero-inflated negative binomial mixed models are adapted to this type of data because they can describe patterns that explain both number of fires and their non-occurrence and also provide useful prediction tools. In addition to model-based predictions, a parametric bootstrap method is applied for estimating mean squared errors and constructing prediction intervals. The statistical methodology and developed software are applied to model and to predict number of wildfires in Spain between 2002 and 2015 by provinces and months.

Multilevel time-series models for small area estimation at different frequencies and domain levels

A small area estimation method is developed for repeatedly conducted multipurpose surveys. A multilevel time-series model is proposed that uses direct estimates for the most detailed domains observed at the highest frequency of the repeated survey. A consistent set of estimates at different aggregation levels is then derived by aggregation of the model-based predictions obtained for the most detailed domains observed at the highest frequency. The model borrows strength over time and space via smooth and local level trends at different aggregation levels. The model also borrows information from auxiliary series available from registers with coefficients that can vary over both domains and time. Regional domain random effects are allowed to vary smoothly over space according to a spatial autoregressive process. To account for the diversity of domains and for more volatile time-dependence, nonnormally distributed random effects and trend innovations are used via so-called global-local shrinkage priors. A Bayesian approach is taken, and the model is estimated by MCMC simulation. The method is illustrated with an application to the Dutch Labour Force Survey to produce monthly provincial and quarterly municipal unemployment figures.

Comparison of long-term field-measured and RUSLE-based modelled soil loss in Switzerland

Long-term field measurements to asses model-based soil erosion predictions by water are rare. We have compared field measurements based on erosion assessment surveys from a 10-year monitoring process with spatial-explicit model predictions with the Revised Universal Soil Loss Equation (RUSLE). Robust input data were available for both the mapped and the modelled parameters for 203 arable fields covering an area of 258 ha in the Swiss Midlands. The 1639 mapped erosion forms were digitized and converted to raster format with a 2 m resolution. A digital terrain model using 2 m resolution and a multiple flow direction algorithm for the calculation of the topographic factors and the support practice factor was available for modelling with the RUSLE. The other input data for the RUSLE were determined for each field. The comparison of mapped and modelled soil loss values revealed a substantially higher estimation of soil loss values from modelling by a factor of 8, with a mean mapped soil loss of 0.77 t/ha/yr vs. modelled soil loss of 6.20 t/ha/yr. However, high mapped soil losses of >4 t/ha/yr were reproduced quite reliably by the model, while the model predicted drastically higher erosion values for mapped losses of <4 t/ha/yr. Our study shows the value of long-term field data based on erosion assessment surveys for model evaluation. RUSLE-type model results should be compared with erosion assessment surveys at the field to landscape scale in order to improve the calibration of the model. Further factors related to land management like headlands, traffic lanes and potato furrows need to be included before they may be used for policy advice.

Developing Deep Learning Models for System Remaining Useful Life Predictions: Application to Aircraft Engines

Prognostics and health management (PHM) is an important part of ensuring reliable operations of complex safety- critical systems. System-level remaining useful life (RUL) estimation is a much more complex problem than making estimations at the component level, and system-level RUL methodologies remain sparse in the literature. Model-based approaches have traditionally worked in the past for components such as capacitors, MOSFETs, batteries, or hard-drives (to name a few examples), but developing high fidelity dynamics models of cyber physical systems that can be used to study the effects of multiple degrading components in the system remains a challenging task. Some initial work on model-based System RUL predictions was demonstrated in Khorasgani, et al [1], but, to generalize the system-level prognostics problem, we have to resort to pure data driven and hybrid approaches. In this work, we propose an end-to-end data- driven framework for developing deep learning models to predict remaining useful life of cyber physical systems operating under unknown faulty conditions. The raw data is organized with a data schema that improves the model development process anddown stream data analysis tasks. Due to the unknown faulty conditions, the raw sensor data is transformed into signals that expose the underlying degradation processes, which are then used for model development. Bayesian Optimization is used to tune the model parameters prior to training and validation. We show that this approach results in accurate predictions within 3 cycles to end of life (EOL). We demonstrate the effectiveness of our approach by applying it to the N-CMAPSS turbofan engine dataset recently released by NASA, which includes high fidelity degradation modeling, real world operating conditions, and a large set of fault operating modes.

Comprehensive analysis and prediction of long-term durability of factor IX activity following etranacogene dezaparvovec gene therapy in the treatment of hemophilia B

Objective Congenital hemophilia B is a rare bleeding disorder caused by defects in the gene encoding factor IX (FIX) leading to coagulation deficiency. Recurrent bleeds may cause chronic pain, disability, and reduced quality of life. Phase 2 b and 3 single-arm, open-label, single-dose trials assessing etranacogene dezaparvovec gene therapy for hemophilia B have demonstrated sustained FIX activity levels over observed periods, but long-term durability of the treatment effect has not been established. Using statistical modeling, we estimate long-term durability of FIX activity levels after receiving etranacogene dezaparvovec. Methods Participants from Phase 2 b (N = 3; NCT03489291) and 3 studies (N = 52; NCT03569891) were included. Two participants who did not respond to treatment were excluded. FIX activity was assessed by one-stage activated partial thromboplastin time assay. FIX activity levels at Month 6 post-treatment were considered baseline. Bayesian and Frequentist linear mixed models predicted FIX activity levels up to 25.5 years at an individual and population level with pre-treatment adeno-associated virus 5 (AAV5) neutralizing antibody (NAb) status as primary covariate. Results Bayesian and Frequentist linear mixed models predicted no more than 6/55 (10.91%) observed participants would have FIX activity levels <2% up to 25.5 years post-infusion. Bayesian model-based predictions of future participants suggest >80% would be free from prophylactic FIX replacement products 25.5 years post-infusion. Both models predicted FIX activity levels were not significantly influenced by pre-treatment AAV5 NAb status. Conclusions People with hemophilia B receiving etranacogene dezaparvovec would likely achieve durable FIX activity levels and remain free of prophylactic FIX replacement products for up to 25.5 years following single administration. The long-term factor IX durability predictions are based on statistical methods and results in vivo may differ.

Icebergs Melting

Iceberg calving accounts for half of the mass discharge from the Greenland and Antarctic ice sheets, which has increased dramatically over the last two decades. Through their displacement and progressive melt, icebergs can impact both the regional and large-scale ocean circulation and marine ecosystems by affecting their stratification and nutrient and carbon cycling. Freshwater input due to iceberg melt has the potential to impact regional sea ice distribution and the global overturning circulation. Notwithstanding their importance, our understanding of where and how icebergs melt is limited and their representation in ocean and climate models is oversimplistic, in part because they are informed by only a handful of observations. As a result, model-based predictions of iceberg melt rates, of the fate of the meltwater, and of its impact on the ocean are highly uncertain. New observational, modeling, and experimental studies are needed to improve our understanding of iceberg melting and hence, the forecasting power of climate models.

Bayesian Hierarchical Modelling for Uncertainty Quantification in Operational Thermal Resistance of LED Systems

Remaining useful life (RUL) prediction is central to prognostics and reliability assessment of light-emitting diode (LED) systems. Their unknown long-term service life remaining when subject to specific operating conditions is affected by various sources of uncertainty stemming from production of individual system components, application of the whole system, measurement and operation. To enhance the reliability of model-based predictions, it is essential to account for all of these uncertainties in a systematic manner. This paper proposes a Bayesian hierarchical modelling framework for inverse uncertainty quantification (UQ) in LED operation under thermal loading. The main focus is on the LED systems’ operational thermal resistances, which are subject to system and application variability. Posterior inference is based on a Markov chain Monte Carlo (MCMC) sampling scheme using the Metropolis–Hastings (MH) algorithm. Performance of the method is investigated for simulated data, which allow to focus on different UQ aspects in applications. Findings from an application scenario in which the impact of disregarded uncertainty on RUL prediction is discussed highlight the need for a comprehensive UQ to allow for reliable predictions.

A natural history and copula-based joint model for regional and distant breast cancer metastasis.

The few existing statistical models of breast cancer recurrence and progression to distant metastasis are predominantly based on multi-state modelling. While useful for summarising the risk of recurrence, these provide limited insight into the underlying biological mechanisms and have limited use for understanding the implications of population-level interventions. We develop an alternative, novel, and parsimonious approach for modelling latent tumour growth and spread to local and distant metastasis, based on a natural history model with biologically inspired components. We include marginal sub-models for local and distant breast cancer metastasis, jointly modelled using a copula function. Different formulations (and correlation shapes) are allowed, thus we can incorporate and directly model the correlation between local and distant metastasis flexibly and efficiently. Submodels for the latent cancer growth, the detection process, and screening sensitivity, together with random effects to account for between-patients heterogeneity, are included. Although relying on several parametric assumptions, the joint copula model can be useful for understanding - potentially latent - disease dynamics, obtaining patient-specific, model-based predictions, and studying interventions at a population level, for example, using microsimulation. We illustrate this approach using data from a Swedish population-based case-control study of postmenopausal breast cancer, including examples of useful model-based predictions.