Mechanistic Model Research Articles

Characterization and applicability of the Naima–Tiaret–Algeria illite-montmorillonite clay for methylene blue dye removal: adsorption kinetic, isotherm and thermodynamic studies

Abstract An illite-montmorillonite clay from Naima, IMN (Algeria) was treated via physical and chemical treatment (TIMN) and investigated for the removal of methylene blue. IMN and TIMN clays were characterized by XRD, XRF, SEM-EDS, DSC-GA, FTIR and DC electrical conductivity methods. To analyze the sorption behavior of MB on the clays, a mechanistic model for interpreting the sorption data was developed. IMN clay revealed high sorption capacity (1.925 × 10−2 kg kg−1) for MB in 60 min. The pseudo-second-order model had a very good agreement to describe the MB adsorption process. The adsorption capacities, qe,exp, of 4.327 × 10−2 and 4.914 × 10−2 kg kg−1 for IMN and TIMN, respectively, were obtained. The free energy from the D–R model from adsorbing MB using IMN and TIMN ranged from 1.581 to 0.745 × 10−3J mol−1, respectively, suggesting that the process is physisorption. Besides, the sorption process was more sensible to temperatures that increase was beyond 40 °C causing a decrease in adsorption capacity, indicating that the adsorption reaction of MB onto IMN was exothermic. The adsorption mechanism of I/M clay to remove MB was likely based on hydrogen bonding, electrostatic attraction, cation exchange and n–π interaction. These results proved that TIMN was a promising adsorbent for removing MB from its simulated wastewater.

Shared and divergent mental health characteristics of ADNP-, CHD8- and DYRK1A-related neurodevelopmental conditions.

Neurodevelopmental conditions such as intellectual disability (ID) and autism spectrum disorder (ASD) can stem from a broad array of inherited and de novo genetic differences, with marked physiological and behavioral impacts. We currently know little about the psychiatric phenotypes of rare genetic variants associated with ASD, despite heightened risk of psychiatric concerns in ASD more broadly. Understanding behavioral features of these variants can identify shared versus specific phenotypes across gene groups, facilitate mechanistic models, and provide prognostic insights to inform clinical practice. In this paper, we evaluate behavioral features within three gene groups associated with ID and ASD - ADNP, CHD8, and DYRK1A - with two aims: (1) characterize phenotypes across behavioral domains of anxiety, depression, ADHD, and challenging behavior; and (2) understand whether age and early developmental milestones are associated with later mental health outcomes. Phenotypic data were obtained for youth with disruptive variants in ADNP, CHD8, or DYRK1A (N = 65, mean age = 8.7 years, 40% female) within a long-running, genetics-first study. Standardized caregiver-report measures of mental health features (anxiety, depression, attention-deficit/hyperactivity, oppositional behavior) and developmental history were extracted and analyzed for effects of gene group, age, and early developmental milestones on mental health features. Patterns of mental health features varied by group, with anxiety most prominent for CHD8, oppositional features overrepresented among ADNP, and attentional and depressive features most prominent for DYRK1A. For the full sample, age was positively associated with anxiety features, such that elevations in anxiety relative to same-age and same-sex peers may worsen with increasing age. Predictive utility of early developmental milestones was limited, with evidence of early language delays predicting greater difficulties across behavioral domains only for the CHD8 group. Despite shared associations with autism and intellectual disability, disruptive variants in ADNP, CHD8, and DYRK1A may yield variable psychiatric phenotypes among children and adolescents. With replication in larger samples over time, efforts such as these may contribute to improved clinical care for affected children and adolescents, allow for earlier identification of emerging mental health difficulties, and promote early intervention to alleviate concerns and improve quality of life.

Mechanistic regulation of planarian shape during growth and degrowth.

Adult planarians can grow when fed and degrow (shrink) when starved while maintaining their whole-body shape. It is unknown how the morphogens patterning the planarian axes are coordinated during feeding and starvation or how they modulate the necessary differential tissue growth or degrowth. Here we investigate the dynamics of planarian shape together with a theoretical study of the mechanisms regulating whole-body proportions and shape. We found that the planarian body proportions scale isometrically following similar linear rates during growth and degrowth, but that fed worms are significantly wider than starved worms. By combining a descriptive model of planarian shape and size with a mechanistic model of anterior-posterior and medio-lateral signaling calibrated with a novel parameter optimization methodology, we theoretically demonstrate that the feedback loop between these positional information signals and the shape they control can regulate the planarian whole-body shape during growth. Furthermore, the computational model produced the correct shape and size dynamics during degrowth due to a predicted increase in apoptosis rate and pole signal during starvation. These results offer mechanistic insights into the dynamic regulation of whole-body morphologies.

Digital twins for cardiac electrophysiology: state of the art and future challenges.

Cardiac arrhythmias remain amajor cause of death and disability. Current antiarrhythmic therapies are effective to only a limited extent, likely in large part due to their mechanism-independent approach. Precision cardiology aims to deliver targeted therapy for an individual patient to maximize efficacy and minimize adverse effects. In-silico digital twins have emerged as apromising strategy to realize the vision of precision cardiology. While there is no uniform definition of adigital twin, it typically employs digital tools, including simulations of mechanistic computer models, based on patient-specific clinical data to understand arrhythmia mechanisms and/or make clinically relevant predictions. Digital twins have become part of routine clinical practice in the setting of interventional cardiology, where commercially available services use digital twins to non-invasively determine the severity of stenosis (computed tomography-based fractional flow reserve). Although routine clinical application has not been achieved for cardiac arrhythmia management, significant progress towards digital twins for cardiac electrophysiology has been made in recent years. At the same time, significant technical and clinical challenges remain. This article provides ashort overview of the history of digital twins for cardiac electrophysiology, including recent applications for the prediction of sudden cardiac death risk and the tailoring of rhythm control in atrial fibrillation. The authors highlight the current challenges for routine clinical application and discuss how overcoming these challenges may allow digital twins to enable asignificant precision medicine-based advancement in cardiac arrhythmia management.

Quality by design of solid‐phase peptide/protein coupling reaction via mechanistic reaction kinetics modeling approach

AbstractThe peptide coupling reaction is one of the most critical steps in the solid phase synthesis of therapeutic peptides/proteins. Improper reaction conditions can result in several common impurities such as single amino acid deletions, additions, N‐terminus modifications, and D‐isomers, all while potentially impacting the active pharmaceutical ingredient critical quality attributes. In this work, we developed a first‐principle mechanistic reaction kinetics model for the solid‐phase peptide/protein coupling reaction based on well‐established reaction mechanisms and experimental data from literature. Utilizing the reaction kinetics model, we present a systematic, quality by design approach for the coupling reaction control strategy. Critical process parameters are identified via univariate analysis and the design space is designated via multivariate risk assessment. The presented approach provides a novel solution for designing solid‐phase peptide/protein synthesis control strategies and identifying normal operating ranges for each process parameter, as well as the associated design space.

Mechanistically derived macroscopic root water uptake functions: The α and ω of root water uptake functions

AbstractWater uptake by plant roots is an important component of the soil water balance. Predicting to what extent potential transpiration from the canopy, that is, transpiration demand, can be met by supply of water from the soil through the root system is crucial to simulate the actual transpiration and assess vegetation water stress. In models that simulate the dynamics of vertical soil water content profiles as a function of water fluxes and soil water potential gradients, the root water uptake (RWU) distribution is represented by macroscopic sink terms. We present RWU functions that calculate sink terms based on a mechanistic model of water flow in the soil–root system. Based on soil–root hydraulics, we define α‐supply functions representing the maximal uptake by the root system from a certain soil depth when the root collar water potential equals the wilting point, ω‐supply factors representing the maximal supply from the entire root system, and a critical ωc factor representing the potential transpiration demand. These functions and factors are subsequently used to calculate RWU distributions directly from potential transpiration or demand and the soil water potentials. Unlike currently used approaches, which define α‐stress functions and ω factors representing ratios of actual uptake to uptake demand, the supply‐based formulations can be derived directly from soil and root hydraulic properties and can represent processes like root hydraulic redistribution and hydraulic lift.

Monitoring ESBL-Escherichia coli in Swiss wastewater between November 2021 and November 2022: insights into population carriage.

Antimicrobial resistance (AMR) is a global health threat and is commonly monitored in clinical settings, given its association with the risk of antimicrobial-resistant infections. Nevertheless, tracking AMR within a community proves challenging due to the substantial sample size required for a representative population, along with high associated costs and privacy concerns. By investigating high resolution temporal and geographic trends in extended-spectrum beta-lactamase producing Escherichia coli in wastewater, we provide an alternative approach to monitor AMR dynamics, distinct from the conventional clinical settings focus. Through this approach, we develop a mechanistic model, shedding light on the relationship between wastewater indicators and AMR carriage in the population. This perspective contributes valuable insights into trends of AMR carriage, emphasizing the importance of wastewater surveillance in informing effective public health interventions.

Improving a nitrogen mineralization model for predicting unfertilized corn yield

AbstractCrop N decision support tools are typically based on either empirical relationships that lack mechanistic underpinnings or simulation models that are too complex to use on farms with limited input data. We developed an N mineralization model for corn that lies between these endpoints; it includes a mechanistic model structure reflecting microbial and texture controls on N mineralization but requires just a few simple inputs: soil texture soil C and N concentration and cover crop N content and carbon to nitgrogen ratio (C/N). We evaluated a previous version of the model with an independent dataset to determine the accuracy in predictions of unfertilized corn (Zea mays L.) yield across a wider range of soil texture, cover crop, and growing season precipitation conditions. We tested three assumptions used in the original model: (1) soil C/N is equal to 10, (2) yield does not need to be adjusted for growing season precipitation, and (3) sand content controls humification efficiency (ε). The best new model used measured values for soil C/N, had a summertime precipitation adjustment, and included both sand and clay content as predictors of ε (root mean square error [RMSE] = 1.43 Mg ha−1; r2 = 0.69). In the new model, clay has a stronger influence than sand on ε, corresponding to lower predicted mineralization rates on fine‐textured soils. The new model had a reasonable validation fit (RMSE = 1.71 Mg ha−1; r2 = 0.56) using an independent dataset. Our results indicate the new model is an improvement over the previous version because it predicts unfertilized corn yield for a wider range of conditions.

The PR-10 protein Pru p 1 is an endonuclease that preferentially cleaves single-stranded RNA.

Pathogenesis-related class 10 (PR-10) proteins play a crucial role in plant defense by acting as ribonucleases. The specific mechanism of action and substrate specificity of these proteins have remained largely unexplored so far. In this study, we elucidate the enzymatic activity of Pru p 1, a PR-10 protein from peach. We demonstrate that this protein catalyzes the endonucleolytic backbone cleavage of RNA substrates intoshort oligonucleotides.Initial cleavage products, identified through kinetic analysis, can bind again, priming them for further degradation.NMR binding site mapping reveals thatthe largeinternal cavity of Pru p 1, which is characteristic for PR-10 proteins, serves as an anchoring site for single-stranded ribonucleotide chains. We propose a structure-based mechanistic model that accounts for the observed cleavage patterns and the inhibitory effect of zeatin, a nucleoside analog, on the ribonuclease activity of Pru p 1.

Mathematical models of drug-resistant tuberculosis lack bacterial heterogeneity: A systematic review.

Drug-resistant tuberculosis (DR-TB) threatens progress in the control of TB. Mathematical models are increasingly being used to guide public health decisions on managing both antimicrobial resistance (AMR) and TB. It is important to consider bacterial heterogeneity in models as it can have consequences for predictions of resistance prevalence, which may affect decision-making. We conducted a systematic review of published mathematical models to determine the modelling landscape and to explore methods for including bacterial heterogeneity. Our first objective was to identify and analyse the general characteristics of mathematical models of DR-mycobacteria, including M. tuberculosis. The second objective was to analyse methods of including bacterial heterogeneity in these models. We had different definitions of heterogeneity depending on the model level. For between-host models of mycobacterium, heterogeneity was defined as any model where bacteria of the same resistance level were further differentiated. For bacterial population models, heterogeneity was defined as having multiple distinct resistant populations. The search was conducted following PRISMA guidelines in five databases, with studies included if they were mechanistic or simulation models of DR-mycobacteria. We identified 195 studies modelling DR-mycobacteria, with most being dynamic transmission models of non-treatment intervention impact in M. tuberculosis (n = 58). Studies were set in a limited number of specific countries, and 44% of models (n = 85) included only a single level of "multidrug-resistance (MDR)". Only 23 models (8 between-host) included any bacterial heterogeneity. Most of these also captured multiple antibiotic-resistant classes (n = 17), but six models included heterogeneity in bacterial populations resistant to a single antibiotic. Heterogeneity was usually represented by different fitness values for bacteria resistant to the same antibiotic (61%, n = 14). A large and growing body of mathematical models of DR-mycobacterium is being used to explore intervention impact to support policy as well as theoretical explorations of resistance dynamics. However, the majority lack bacterial heterogeneity, suggesting that important evolutionary effects may be missed.

Putting theory to the test: An integrated computational/experimental chemostat model of the tragedy of the commons.

Cooperation via shared public goods is ubiquitous in nature, however, noncontributing social cheaters can exploit the public goods provided by cooperating individuals to gain a fitness advantage. Theory predicts that this dynamic can cause a Tragedy of the Commons, and in particular, a 'Collapsing' Tragedy defined as the extinction of the entire population if the public good is essential. However, there is little empirical evidence of the Collapsing Tragedy in evolutionary biology. Here, we experimentally demonstrate this outcome in a microbial model system, the public good-producing bacterium Pseudomonas aeruginosa grown in a continuous-culture chemostat. In a growth medium that requires extracellular protein digestion, we find that P. aeruginosa populations maintain a high density when entirely composed of cooperating, protease-producing cells but completely collapse when non-producing cheater cells are introduced. We formulate a mechanistic mathematical model that recapitulates experimental observations and suggests key parameters, such as the dilution rate and the cost of public good production, that define the stability of cooperative behavior. We combine model prediction with experimental validation to explain striking differences in the long-term cheater trajectories of replicate cocultures through mutational events that increase cheater fitness. Taken together, our integrated empirical and theoretical approach validates and parametrizes the Collapsing Tragedy in a microbial population, and provides a quantitative, mechanistic framework for generating testable predictions of social behavior.

Ensemble learning methods of inference for spatially stratified infectious disease systems.

Individual level models are a class of mechanistic models that are widely used to infer infectious disease transmission dynamics. These models incorporate individual level covariate information accounting for population heterogeneity and are generally fitted in a Bayesian Markov chain Monte Carlo (MCMC) framework. However, Bayesian MCMC methods of inference are computationally expensive for large data sets. This issue becomes more severe when applied to infectious disease data collected from spatially heterogeneous populations, as the number of covariates increases. In addition, summary statistics over the global population may not capture the true spatio-temporal dynamics of disease transmission. In this study we propose to use ensemble learning methods to predict epidemic generating models instead of time consuming Bayesian MCMC method. We apply these methods to infer disease transmission dynamics over spatially clustered populations, considering the clusters as natural strata instead of a global population. We compare the performance of two tree-based ensemble learning techniques: random forest and gradient boosting. These methods are applied to the 2001 foot-and-mouth disease epidemic in the U.K. and evaluated using simulated data from a clustered population. It is shown that the spatially clustered data can help to predict epidemic generating models more accurately than the global data.

Sedimentological characterization of earthquake-generated turbidites in fault-proximal glacial lakes: a case study from Jenny Lake, Teton range, Wyoming

Lakes in seismically active regions preserve valuable sedimentary archives of paleoseismic activity within their catchment and beyond. A series of glacially-excavated lakes positioned directly along the surface trace of the Teton normal fault at the base of the Teton Range, WY, are ideally situated to record past fault activity since their formation approximately 15,000 years ago. Here, we focus on the sediment fill contained in Jenny Lake (5 km2; approximately 73 m max depth) located at the bottom of Cascade Canyon, in the central Tetons, where postglacial slip rates are greatest. Past earthquakes that generated slope failures in and around Jenny Lake are expressed stratigraphically as coseismic turbidite deposits. These deposits were previously identified and dated in sub-bottom profiles and in sediment cores taken from multiple locations around the basin. In this study, we focus on the six thickest turbidites (ranging from 6 to 34 cm thick) present in multiple cores recovered from the central depositional basin and analyze them at sub-centimeter resolution for changes in physical, biological, and geochemical parameters, including sediment density, magnetic susceptibility, grain size distributions, organic content, and elemental composition. Results reveal each deposit contains a well-defined, three-component sedimentary sequence composed of a relatively homogenous and thick basal sandy unit with a sharp bottom contact, a similarly thick silt-rich middle unit, and a thin top unit of very fine-grained sediments. The characteristics of these components and their similarity between individual deposits suggest consistent sediment sources and transport pathways during successive earthquake events. Based on our analyses and the unique local geomorphic setting, we create a mechanistic model of coseismic turbidite formation in Jenny Lake, which may provide an improved framework for identifying, characterizing, and correlating earthquake-generated disturbance deposits in other Teton lakes and lakes in similar tectonic-geomorphological settings, for example, in the Basin and Range Province, USA or beyond.

Patient-Specific, Mechanistic Models of Tumor Growth Incorporating Artificial Intelligence and Big Data.

Despite the remarkable advances in cancer diagnosis, treatment, and management over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires the integration of patient-specific information with an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous yet practical mathematical theory of tumor initiation, development, invasion, and response to therapy. We begin this review with an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on big data and artificial intelligence. We then present illustrative examples of mathematical models manifesting their utility and discuss the limitations of stand-alone mechanistic and data-driven models. We then discuss the potential of mechanistic models for not only predicting but also optimizing response to therapy on a patient-specific basis. We describe current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models.

Activated carbon modification for real diesel adsorptive deep desulfurization: experiments and modeling

This study examined the desulfurization capacity of a high specific surface area activated carbon (AC) and its modifications for real diesel fuel of different sulfur concentrations. Adsorbent’s evaluation strategy included desulfurization experiments under batch and continuous conditions. Batch experiments were conducted by a homogeneous stirring of adsorbent and diesel fuel, while for the continuous tests, a fixed bed reactor was used to generate breakthrough desulfurization curves. In the fixed bed reactor’s tests, the sulfur removal – considering an initial concentration of about 6 parts per million weight (ppmw) – was substantial for the first 30 ml fuel per gram of adsorbent. For this specific quantity, the total sulfur content of the cumulatively processed fuel was less than 2 ppmw. The batch experimental results are elaborated using a mechanistic kinetic model for batch adsorption considering the total sulfur mass balance. Mass transfer coefficients are derived this way for all the conditions and adsorbents tested. The adsorption equilibrium data from batch experiments are utilized to model the fixed bed experimental results. In this way, mass transfer coefficients for the fixed bed are derived. In any case, the modeling results indicate that sulfur is not the only adsorbate of the process, but other adsorbates act competitively to sulfur, thereby complicating the process.

A survey of chromosomal instability measures across mechanistic models

Chromosomal instability (CIN) is the persistent reshuffling of cancer karyotypes via chromosome mis-segregation during cell division. In cancer, CIN exists at varying levels that have differential effects on tumor progression. However, mis-segregation rates remain challenging to assess in human cancer despite an array of available measures. We evaluated measures of CIN by comparing quantitative methods using specific, inducible phenotypic CIN models of chromosome bridges, pseudobipolar spindles, multipolar spindles, and polar chromosomes. For each, we measured CIN fixed and timelapse fluorescence microscopy, chromosome spreads, six-centromere FISH, bulk transcriptomics, and single-cell DNA sequencing (scDNAseq). As expected, microscopy of tumor cells in live and fixed samples significantly correlated (R = 0.72; P < 0.001) and sensitively detect CIN. Cytogenetics approaches include chromosome spreads and 6-centromere FISH, which also significantly correlate (R = 0.76; P < 0.001) but had limited sensitivity for lower rates of CIN. Bulk genomic DNA signatures and bulk transcriptomic scores, CIN70 and HET70, did not detect CIN. By contrast, scDNAseq detects CIN with high sensitivity, and significantly correlates with imaging methods (R = 0.82; P < 0.001). In summary, single-cell methods such as imaging, cytogenetics, and scDNAseq can measure CIN, with the latter being the most comprehensive method accessible to clinical samples. To facilitate the comparison of CIN rates between phenotypes and methods, we propose a standardized unit of CIN: Mis-segregations per Diploid Division. This systematic analysis of common CIN measures highlights the superiority of single-cell methods and provides guidance for measuring CIN in the clinical setting.

Microclimate, an important part of ecology and biogeography

AbstractBrief introduction: What are microclimates and why are they important?Microclimate science has developed into a global discipline. Microclimate science is increasingly used to understand and mitigate climate and biodiversity shifts. Here, we provide an overview of the current status of microclimate ecology and biogeography in terrestrial ecosystems, and where this field is heading next.Microclimate investigations in ecology and biogeographyWe highlight the latest research on interactions between microclimates and organisms, including how microclimates influence individuals, and through them populations, communities and entire ecosystems and their processes. We also briefly discuss recent research on how organisms shape microclimates from the tropics to the poles.Microclimate applications in ecosystem managementMicroclimates are also important in ecosystem management under climate change. We showcase new research in microclimate management with examples from biodiversity conservation, forestry and urban ecology. We discuss the importance of microrefugia in conservation and how to promote microclimate heterogeneity.Methods for microclimate scienceWe showcase the recent advances in data acquisition, such as novel field sensors and remote sensing methods. We discuss microclimate modelling, mapping and data processing, including accessibility of modelling tools, advantages of mechanistic and statistical modelling and solutions for computational challenges that have pushed the state‐of‐the‐art of the field.What's next?We identify major knowledge gaps that need to be filled for further advancing microclimate investigations, applications and methods. These gaps include spatiotemporal scaling of microclimate data, mismatches between macroclimate and microclimate in predicting responses of organisms to climate change, and the need for more evidence on the outcomes of microclimate management.

The role of memory-based movements in the formation of animal home ranges.

Most animals live in spatially-constrained home ranges. The prevalence of this space-use pattern in nature suggests that general biological mechanisms are likely to be responsible for their occurrence. Individual-based models of animal movement in both theoretical and empirical settings have demonstrated that the revisitation of familiar areas through memory can lead to the formation of stable home ranges. Here, we formulate a deterministic, mechanistic home range model that includes the interplay between a bi-component memory and resource preference, and evaluate resulting patterns of space-use. We show that a bi-component memory process can lead to the formation of stable home ranges and control its size, with greater spatial memory capabilities being associated with larger home range size. The interplay between memory and resource preferences gives rise to a continuum of space-use patterns-from spatially-restricted movements into a home range that is influenced by local resource heterogeneity, to diffusive-like movements dependent on larger-scale resource distributions, such as in nomadism. Future work could take advantage of this model formulation to evaluate the role of memory in shaping individual performance in response to varying spatio-temporal resource patterns.

Integrating UAV hyperspectral data and radiative transfer model simulation to quantitatively estimate maize leaf and canopy nitrogen content

Crop nitrogen (N) content reflects crop nutrient status and plays an important role in precision nutrient management. Accurate crop N content estimation from remote sensing has been well documented. However, the robustness (i.e., the ability of a model to perform consistently across various conditions) of these methods under varied soil conditions or different growth stages has rarely been considered. We proposed a hybrid method that integrates in-situ measurements and the data simulated by a mechanistic model to improve the estimation of maize N content. In-situ data included hyperspectral images collected by Unmanned Aerial Vehicle (UAV), and leaf and canopy N content (LNC and CNC). A mechanistic radiative transfer model (PROSAIL-PRO) was used to generate simulated data, i.e., canopy reflectance paired with target crop traits (i.e., LNC, CNC). We compared the performance from the hybrid method with a machine learning method (Gaussian Process Regression) and six different vegetation indices (VIs) on four in-situ datasets collected at three study sites from 2021 to 2022. Results show that the hybrid method consistently performed the best for LNC estimation across four testing datasets (RRMSE ranging from 10.08% to 10.84%). For CNC estimation, the hybrid method had the best estimation results on two out of the four testing datasets and performed comparably to the best method on the other two datasets (RRMSE ranging from 13.89% to 25.21%). Next, we assessed the estimation robustness of the hybrid method, the machine learning, and the best-VI by comparing the mean (µ) and standard deviation (σ) of RRMSE across diverse water and N treatments (condition #1) and different growth stages (condition #2). Among 16 total cases (two crop traits by four study sites by two conditions), the hybrid method had 11 cases of smallest µ and seven cases of smallest σ, outperforming the machine learning (0/16 for µ, 4/16 for σ) and the best-VI (5/16 for µ, 5/16 for σ). These results underscore the greater robustness of the hybrid method. This study highlights the potential of integrating in-situ measurements and simulated data to improve estimation accuracy and robustness for maize LNC and CNC. The promising performance of the hybrid method suggests its applicability to a broader range of crops and various crop traits.

Online monitoring of protein refolding in inclusion body processing using intrinsic fluorescence.

Inclusion bodies (IBs) are protein aggregates formed as a result of overexpression of recombinant protein in E. coli. The formation of IBs is a valuable strategy of recombinant protein production despite the need for additional processing steps, i.e., isolation, solubilization and refolding. Industrial process development of protein refolding is a labor-intensive task based largely on empirical approaches rather than knowledge-driven strategies. A prerequisite for knowledge-driven process development is a reliable monitoring strategy. This work explores the potential of intrinsic tryptophan and tyrosine fluorescence for real-time and in situ monitoring of protein refolding. In contrast to commonly established process analytical technology (PAT), this technique showed high sensitivity with reproducible measurements for protein concentrations down to 0.01gL . The change of protein conformation during refolding is reflected as a shift in the position of the maxima of the tryptophan and tyrosine fluorescence spectra as well as change in the signal intensity. The shift in the peak position, expressed as average emission wavelength of a spectrum, was correlated to the amount of folding intermediates whereas the intensity integral correlates to the extent of aggregation. These correlations were implemented as an observation function into a mechanistic model. The versatility and transferability of the technique were demonstrated on the refolding of three different proteins with varying structural complexity. The technique was also successfully applied to detect the effect of additives and process mode on the refolding process efficiency. Thus, the methodology presented poses a generic and reliable PAT tool enabling real-time process monitoring of protein refolding.