Mechanistic Model Research Articles

Genome editing outcomes reveal mycobacterial NucS participates in a short-patch repair of DNA mismatches.

In the canonical DNA mismatch repair (MMR) mechanism in bacteria, if a nucleotide is incorrectly mis-paired with the template strand during replication, the resulting repair of this mis-pair can result in the degradation and re-synthesis of hundreds or thousands of nucleotides on the newly-replicated strand (long-patch repair). While mycobacteria, which include important pathogens such as Mycobacterium tuberculosis, lack the otherwise highly-conserved enzymes required for the canonical MMR reaction, it was found that disruption of a mycobacterial mismatch-sensitive endonuclease NucS results in a hyper-mutative phenotype, leading to the idea that NucS might be involved in a cryptic, independently-evolved DNA MMR mechanism, perhaps mediated by homologous recombination (HR) with a sister chromatid. Using oligonucleotide recombination, which allows us to introduce mismatches specifically into the genomes of a model for M. tuberculosis, Mycobacterium smegmatis, we find that NucS participates in a direct repair of DNA mismatches where the patch of excised nucleotides is largely confined to within ∼5-6 bp of the mis-paired nucleotides, which is inconsistent with mechanistic models of canonical mycobacterial HR or other double-strand break (DSB) repair reactions. The results presented provide evidence of a novel NucS-associated mycobacterial MMR mechanism occurring in vivo to regulate genetic mutations in mycobacteria.

Filled Elastomers: Mechanistic and Physics-Driven Modeling and Applications as Smart Materials

Elastomers are made of chain-like molecules to form networks that can sustain large deformation. Rubbers are thermosetting elastomers that are obtained from irreversible curing reactions. Curing reactions create permanent bonds between the molecular chains. On the other hand, thermoplastic elastomers do not need curing reactions. Incorporation of appropriated filler particles, as has been practiced for decades, can significantly enhance mechanical properties of elastomers. However, there are fundamental questions about polymer matrix composites (PMCs) that still elude complete understanding. This is because the macroscopic properties of PMCs depend not only on the overall volume fraction (ϕ) of the filler particles, but also on their spatial distribution (i.e., primary, secondary, and tertiary structure). This work aims at reviewing how the mechanical properties of PMCs are related to the microstructure of filler particles and to the interaction between filler particles and polymer matrices. Overall, soft rubbery matrices dictate the elasticity/hyperelasticity of the PMCs while the reinforcement involves polymer–particle interactions that can significantly influence the mechanical properties of the polymer matrix interface. For ϕ values higher than a threshold, percolation of the filler particles can lead to significant reinforcement. While viscoelastic behavior may be attributed to the soft rubbery component, inelastic behaviors like the Mullins and Payne effects are highly correlated to the microstructures of the polymer matrix and the filler particles, as well as that of the polymer–particle interface. Additionally, the incorporation of specific filler particles within intelligently designed polymer systems has been shown to yield a variety of functional and responsive materials, commonly termed smart materials. We review three types of smart PMCs, i.e., magnetoelastic (M-), shape-memory (SM-), and self-healing (SH-) PMCs, and discuss the constitutive models for these smart materials.

Theoretical strategies for an embodied cognitive neuroscience: Mechanistic explanations of brain-body-environment systems

ABSTRACT Cognitive neuroscience seeks to explain mind, brain, and behavior. But how do we generate explanations? In this integrative theoretical paper, we review the commitments of the ‘New Mechanist’ movement within the philosophy of science, focusing specifically on the role of mechanistic models in scientific explanation. We highlight how this approach differs from other explanatory approaches within the field, showing its unique contributions to the efforts of scientific explanation. We then argue that the commitments of the Embodied Cognition framework converge with the commitments of the New Mechanist movement in a way that provides a necessary explanatory strategy available to cognitive neuroscience. We then discuss a number of consequences of this convergence, including issues related to the inadequacy of statistical prediction, neuroscientific reduction, the autonomy of psychology from neuroscience, and psychological and neuroscientific ontology. We hope that our integrative thesis provides researchers with a theoretical strategy for an embodied cognitive neuroscience.

Prediction of the First-Pass Metabolism of a Drug After Oral Intake Based on Structural Parameters and Physicochemical Properties.

The oral first-pass metabolism is a crucial factor that plays a key role in a drug's pharmacokinetic profile. Prediction of the oral first-pass metabolism based on chemical structural parameters can be useful in the drug-design process. Developing an orally administered drug with an acceptable pharmacokinetic profile is necessary to reduce the cost and time associated with evaluating the extent of the first-pass metabolism of a candidate compound in preclinical studies. The aim of this study is to estimate the first-pass metabolism of an orally administered drug. A set of compounds with reported first-pass metabolism data were collected. Moreover, human intestinal absorption percentage and oral bioavailability data were extracted from the literature to propose a classification system that split the drugs up based on their first-pass metabolism extents. Various structural parameters were calculated for each compound. The relations of the structural and physicochemical values of each compound to the class the compound belongs to were obtained using logistic regression. Initial analysis showed that compounds with logD7.4>1 or a rugosity factor of > 1.5 are more likely to have high first-pass metabolism. Four different models that can predict the oral first-pass metabolism with acceptable error were introduced. The overall accuracies of the models were in the range of 72% (for models with simple descriptors) to 78% (for models with complex descriptors). Although the models with simple descriptors have lower accuracies compared to complex models, they are more interpretable and easier for researchers to utilize. A novel classification of drugs based on the extent of the oral first-pass metabolism was introduced, and mechanistic models were developedto assign candidate compounds to the appropriate proposed classes.

Mechanistic microclimate models and plant pest risk modelling

AbstractClimatic conditions are key determining factors of whether plant pests flourish. Models of pest response to temperature are integral to pest risk assessment and management, helping to inform surveillance and control measures. The widespread use of meteorological data as predictors in these models compromises their reliability as these measurements are not thermally coupled to the conditions experienced by pest organisms or their body temperatures. Here, we present how mechanistic microclimate models can be used to estimate the conditions experienced by pest organisms to provide significant benefits to pest risk modelling. These well-established physical models capture how landscape, vegetation and climate interact to determine the conditions to which pests are exposed. Assessments of pest risk derived from microclimate conditions are likely to significantly diverge from those derived from weather station measurements. The magnitude of this divergence will vary across a landscape, over time and according to pest habitats and behaviour due to the complex mechanisms that determine microclimate conditions and their effect on pest biology. Whereas the application of microclimate models was once restricted to relatively homogeneous habitats, these models can now be applied readily to generate hourly time series across extensive and varied landscapes. We outline the benefits and challenges of more routine application of microclimate models to pest risk modelling. Mechanistic microclimate models provide a heuristic tool that helps discriminate between physical, mathematical and biological causes of model failure. Their use can also help understand how pest ecology, behaviour and physiology mediate the relationship between climate and pest response.

Dimerization of Fe(III) Ion in an Aqueous Medium: Mechanistic Modelling and Effects of Ligands.

Aqueous iron solutions generally undergo spontaneous hydrolysis followed by aggregation resulting in the precipitation of nanocrystalline oxyhydroxide minerals. The mechanism of nucleation of such multinuclear oxyhydroxide clusters are unclear due to limited experimental evidence. Here, we investigate the mechanistic pathway of dimerization of Fe(III) ions using density functional theory (DFT) in aqueous medium considering effects of other ligands. Two hydrolyzed monomeric Fe(III) ions in aqueous medium may react to form two closely related binuclear products, the m-oxo and the dihydroxo Fe2 dimer. Our studies indicate that the water molecules in the second coordination sphere and those co-ordinated to the Fe(III) ion, both participate in the dimerization process. The proposed mechanism effectively explains the formation of dihydroxo and μ-oxo Fe2 dimers with interconversion possibilities, for the first time. Results show, with only water molecules present in the second co-ordination sphere, dihydroxo Fe2 dimer is the thermodynamically and kinetically favored product with a low activation free energy. We calculated the step-wise reaction free energies of dimerization in the presence of nitrate ions in the first and second coordination sphere of Fe(III) ion separately, which shows that with nitrate ions in the second co-ordination sphere, the μ-oxo Fe2 dimer formation iskinetically favored.

A mechanistic model to predict headspace concentration profile of carvacrol for controlled release packaging

The efficacy of an antimicrobial agent in food packaging depends largely on its concentration profile in the package headspace. In this study, a mechanistic mathematical model was developed to predict the headspace concentration profile of carvacrol, a prominent antimicrobial agent, using the physical principles that govern the movement of carvacrol molecules in a controlled release packaging system. The model and its estimated parameter values were validated using three different sets of gas chromatography measurements—this rigorous process confirms the accuracy and reliability of the model. Advancing beyond existing models, our model enhances the understanding of the dynamics between the concentration profile and its interacting processes including evaporation, adsorption, and gas transmission. It provides packaging developers with a superior alternative to the trial-and-error approach to tailor concentration profiles for optimizing antimicrobial efficacy. It leads to more efficient and better design of controlled release packaging.

Indian Ocean temperature anomalies predict long-term global dengue trends

Despite identifying El Niño events as a factor in dengue dynamics, predicting the oscillation of global dengue epidemics remains challenging. Here, we investigate climate indicators and worldwide dengue incidence from 1990 to 2019 using climate-driven mechanistic models. We identify a distinct indicator, the Indian Ocean basin-wide (IOBW) index, as representing the regional average of sea surface temperature anomalies in the tropical Indian Ocean. IOBW is closely associated with dengue epidemics for both the Northern and Southern hemispheres. The ability of IOBW to predict dengue incidence likely arises as a result of its effect on local temperature anomalies through teleconnections. These findings indicate that the IOBW index can potentially enhance the lead time for dengue forecasts, leading to better-planned and more impactful outbreak responses.

Comparing the effectiveness of adulticide application interventions on mitigating local transmission of dengue virus

AbstractThe southern US has a large presence of mosquito vector species for dengue virus (DENV) and experiences thousands of DENV importations every year, which have led to several local outbreaks. Adulticide spraying targeting active mosquitoes is one of the most common insecticide strategies used as a response to an outbreak. The aim of this study is to evaluate the effectiveness of adulticide spraying conducted at different times of the day to curb DENV transmission. Based on unique dataset of Aedes aegypti diel activity patterns in Miami-Dade County, Florida, and Brownsville, Texas, we developed a mechanistic model of DENV transmission, which simulates adulticide spraying interventions. We estimated that spraying adulticide for 14 consecutive days at 7am or 8 pm was highly effective in reducing DENV outbreak probability from 10% in the absence of interventions to 0.1% for Miami-Dade County, and from 7.8 to 0.1% for Brownsville. Moreover, in case of a local outbreak in Miami-Dade County, we estimated the median number of symptomatic infections after the identification of a local outbreak to be reduced from 67.0 (IQR: 25.5–103.0) in the absence of interventions to 1.0 (IQR: 0.0–2.0) when spraying adulticide for 14 consecutive days at 8 pm. In Brownsville, the same intervention is estimated to lead to a decrease from 15.0 (IQR: 7.0–33.0) cases to 1.0 (IQR: 0.0–2.0). Our study highlights the importance of considering diel activity patterns of vector mosquito species in arbovirus preparedness and response planning and provide quantitative evidence to guide the decision-making of mosquito control authorities.

Semi-empirical model for timber truck speed profile and fuel consumption

ABSTRACT This paper presents a semi-empirical model for driving speed simulation and estimation of energy use in timber trucks in Sweden. The model is composed of two parts; the first part takes as input road properties along a route and applies a kinematic model to simulate a driving pattern. The kinematic model contains parameters that are found statistically from large-scale recordings of CAN bus data from 21 timber trucks in 1 year. The second part applies a mechanistic model on the simulated speed profile to compute fuel use. The driving pattern simulator can predict the driving time and energy use with roughly the same variance as can be observed between different trucks and drivers following the same route. An average rolling resistance coefficient of 0.0068 was found from a coast-down study, but with substantial variation between vehicles.

The adsorption of U(VI) on chlorite: batch, modeling and XPS study

Abstract A mechanistic modelling of the adsorption processes onto individual minerals presenting in the near- and far-fields can greatly enhance the credibility of long-term safety assessments of granite-based geological repositories. In this study, the titration and U(VI) adsorption characteristics of chlorite, one of the major minerals of rock fractures, have been studied. Potentiometric titration curves at two ionic strengths (0.1 and 0.4 mol/L NaCl) are successfully interpreted by considering protonation/deprotonation reactions on generic edge sites (≡SOH) in the framework of a non-electrostatic surface complexation model (SCM). The adsorption of U(VI) on chlorite was reached after 24 h, the adsorption kinetics can be described by a pseudo-second-order model. A non-electrostatic SCM with three surface complexes (≡SOUO2 +, ≡SO(UO2)3(OH)5 and ≡SO(UO2)3(OH)7 2−) was set up based on pH edges of U(VI) at adsorption equilibrium in the absence of CO2. Additional, experimental data measured as a function of U(VI) concentration, solid-to-liquid ratio and carbonate concentration were well reproduced by the proposed model. Finally, parallel experiments were conducted using X-ray photoelectron spectroscopy (XPS) to analyze the variation of U(VI) surface species speciation at different pH values. The good agreement between SCM prediction and XPS analysis demonstrates the reliability of the model in predicting and quantifying the radionuclides retention by chlorite.

Revealing Decision-Making Strategies of Americans in Taking COVID-19 Vaccination.

Efficient coverage for newly developed vaccines requires knowing which groups of individuals will accept the vaccine immediately and which will take longer to accept or never accept. Of those who may eventually accept the vaccine, there are two main types: success-based learners, basing their decisions on others' satisfaction, and myopic rationalists, attending to their own immediate perceived benefit. We used COVID-19 vaccination data to fit a mechanistic model capturing the distinct effects of the two types on the vaccination progress. We proved the identifiability of the population proportions of each type and estimated that of Americans behaved as myopic rationalists with a high variation across the jurisdictions, from in Mississippi to in Vermont. The proportion was correlated with the vaccination coverage, proportion of votes in favor of Democrats in 2020 presidential election, and education score.

Experimental research on influence of curing environment on mechanical properties of coal gangue cementation

Abstract To investigate the impact of curing environments on the mechanical properties of coal gangue cementation (CGC), various curing methods were established, including standard bag curing, standard curing, natural sealing curing, natural curing, water curing, and varying curing ages. By examining the uniaxial compressive strength (UCS) and stress–strain relationship of CGC by applying axial loads, the influence mechanism was analyzed in terms of both physical and chemical reactions. Furthermore, a mechanistic structural model was established to illustrate the impact of the curing environment on the mechanical properties of CGC. The primary substances and reasons affecting the mechanical properties of CGC were analyzed through the use of scanning electron microscopy and X-ray diffraction techniques. Evaluation of influence factors on CGC mechanical properties by grey correlation degree. The findings indicate that curing temperature, humidity, and carbonization are the principal factors influencing the UCS. Maintaining constant temperature and humidity while isolating CO2 is conducive to improving the UCS. The hydration products, such as needle-like ettringite and white fibrous calcium silicate hydrogel, fill the internal voids of CGC and are the primary substances affecting UCS. The hydration products formed during standard curing and natural curing of CGC can undergo carbonation with CO2 to form CaCO3, which interacts with ettringite and hydrated calcium silicate to provide strength support for CGC. However, beyond a certain age, CO2 will progressively diminish the UCS; the larger the contact area and the longer the exposure time to the gel materials in CGC, the faster the UCS decreases.

Unveiling the potential of specific growth rate control in fed-batch fermentation: bridging the gap between product quantity and quality.

During the epoch of sustainable development, leveraging cellular systems for production of diverse chemicals via fermentation has garnered attention. Industrial fermentation, extending beyond strain efficiency and optimal conditions, necessitates a profound understanding of microorganism growth characteristics. Specific growth rate (SGR) is designated as a key variable due to its influence on cellular physiology, product synthesis rates and end-product quality. Despite its significance, the lack of real-time measurements and robust control systems hampers SGR control strategy implementation. The narrative in this contribution delves into the challenges associated with the SGR control and presents perspectives on various control strategies, integration of soft-sensors for real-time measurement and control of SGR. The discussion highlights practical and simple SGR control schemes, suggesting their seamless integration into industrial fermenters. Recommendations provided aim to propose new algorithms accommodating mechanistic and data-driven modelling for enhanced progress in industrial fermentation in the context of sustainable bioprocessing.

Revisiting the metal sites of nitrous oxide reductase in a low-dose structure from Marinobacter nauticus.

Copper-containing nitrous oxide reductase catalyzes a 2-electron reduction of the green-house gas N2O to yield N2. It contains two metal centers, the binuclear electron transfer site CuA, and the unique, tetranuclear CuZ center that is the site of substrate binding. Different forms of the enzyme were described previously, representing variations in oxidation state and composition of the metal sites. Hypothesizing that many reported discrepancies in the structural data may be due to radiation damage during data collection, we determined the structure of anoxically isolated Marinobacter nauticus N2OR from diffraction data obtained with low-intensity X-rays from an in-house rotating anode generator and an image plate detector. The data set was of exceptional quality and yielded a structure at 1.5Å resolution in a new crystal form. The CuA site of the enzyme shows two distinct conformations with potential relevance for intramolecular electron transfer, and the CuZ cluster is present in a [4Cu:2S] configuration. In addition, the structure contains three additional types of ions, and an analysis of anomalous scattering contributions confirms them to be Ca2+, K+, and Cl-. The uniformity of the present structure supports the hypothesis that many earlier analyses showed inhomogeneities due to radiation effects. Adding to the earlier description of the same enzyme with a [4Cu:S] CuZ site, a mechanistic model is presented, with a structurally flexible CuZ center that does not require the complete dissociation of a sulfide prior to N2O binding.

Predicting fatigue crack growth rate of cement-based and asphalt paving materials based on indirect tensile cyclic loading tests

Fatigue crack growth rate (FCGR) is a critical indicator that reflects the rate at which material deterioration occurs under cyclic loads. However, there is currently a lack of a mechanistic quantitative analysis and prediction of the FCGR of paving materials under external indirect tensile (IDT) cyclic loads. To address this issue, this study aims to develop a mechanistic model for quantitative analysis and prediction of the FCGR in paving materials subjected to external IDT cyclic loads based on the J-integral based Paris’ law. Two types of paving materials, namely cement-treated aggregates and asphalt mixtures, were selected to demonstrate the results and model of the FCGR. The results indicate that the derived J-integral serves as the driving force for crack growth and is influenced by horizontal tensile stress, initial resilient modulus, damage density, specimen size, and surface energy of the paving materials. The fatigue life of cement-treated aggregates is primarily influenced by the crack initiation stage, while for asphalt mixtures, the crack growth stage plays a more significant role. The application of Paris’ law is found to be effective in predicting the FCGR at stable crack growth stage. The coefficients of Paris’ law exhibit minimal variation across different stress levels and loading frequencies, and can be utilized to evaluate the fatigue crack resistance of different paving materials.

A mechanistic model of gossip, reputations, and cooperation

Social reputations facilitate cooperation: those who help others gain a good reputation, making them more likely to receive help themselves. But when people hold private views of one another, this cycle of indirect reciprocity breaks down, as disagreements lead to the perception of unjustified behavior that ultimately undermines cooperation. Theoretical studies often assume population-wide agreement about reputations, invoking rapid gossip as an endogenous mechanism for reaching consensus. However, the theory of indirect reciprocity lacks a mechanistic description of how gossip actually generates consensus. Here, we develop a mechanistic model of gossip-based indirect reciprocity that incorporates two alternative forms of gossip: exchanging information with randomly selected peers or consulting a single gossip source. We show that these two forms of gossip are mathematically equivalent under an appropriate transformation of parameters. We derive an analytical expression for the minimum amount of gossip required to reach sufficient consensus and stabilize cooperation. We analyze how the amount of gossip necessary for cooperation depends on the benefits and costs of cooperation, the assessment rule (social norm), and errors in reputation assessment, strategy execution, and gossip transmission. Finally, we show that biased gossip can either facilitate or hinder cooperation, depending on the direction and magnitude of the bias. Our results contribute to the growing literature on cooperation facilitated by communication, and they highlight the need to study strategic interactions coupled with the spread of social information.

Battery degradation diagnosis under normal usage without requiring regular calibration data

Lithium-ion batteries undergo capacity loss and power fade over time. Despite indicating degradation, these changes lack internal insights. Degradation modes group various mechanisms but are challenging to quantify due to aging complexity. In this paper, a noninvasive and comprehensive diagnostic framework is proposed for the accurate estimation of the state of health (SOH) and degradation modes. A large amount of synthetic data is generated from the mechanistic model to cover many typical aging paths without using redundant experimental data. A data-driven multistep diagnosis method is developed by using incremental capacity sequences. This method analyses unique sensitivities to voltage responses from different degradation modes and identifies them sequentially to simplify complex interactions. In addition, overpotential correction is incorporated to estimate battery degradation modes under normal usage. This framework is validated through experimental data under various operating conditions, affirming the 2 % accuracy of SOH estimation and the effective automatic quantification of degradation modes. Furthermore, the method is applicable to common state-of-charge (SOC) windows ranging from 25% to 85 % (3.6 V–4.1 V for the voltage window) and a high current rate up to C/4. The diagnostic framework does not rely on any regular calibration data during model training and thus has high potential for practical application.

Surrogate-model-based optimization of a nonideal polymerization reactor for producing tailored molecular weight distribution

The optimization of polymerization reactors with nonideal mixing has been greatly hindered by a tremendous computation burden. In this study, a global optimization framework to integrate full MWD calculations and detailed reactor dynamics with nonideal mixing behaviors was developed by reducing the considerable computation time required for implementing full MWD. A combination of surrogate-model-based optimization and a computational fluid dynamics (CFD)-compartment reactor model was applied to produce the desired MWD in a nonideal reactor. A neural network (NN) surrogate model was trained using 3,000 MWD data produced from the mechanistic reactor model. The results showed that the model successfully described asymmetric MWD with bimodality resulting from the nonideal mixing behaviors in the reactor. The surrogate model was used to construct operation maps for the asymmetry and bimodality factors, with SHapley Additive exPlanations (SHAP) values showing that the flow rate of the chain transfer agent had the largest impact on the asymmetry factor, whereas temperature significantly influenced the bimodality factor. Finally, the optimization strategy based on the asymmetric and bimodality factors successfully determined operating conditions to produce four tailored MWDs, reported in actual polymerization reactors with R scores of 0.988–0.997.

Mechanistic Basis of the Cu(OAc)2 Catalyzed Azide-Ynamine (3 + 2) Cycloaddition Reaction.

The Cu-catalyzed azide-alkyne cycloaddition (CuAAC) reaction is used as a ligation tool throughout chemical and biological sciences. Despite the pervasiveness of CuAAC, there is a need to develop more efficient methods to form 1,4-triazole ligated products with low loadings of Cu. In this paper, we disclose a mechanistic model for the ynamine-azide (3 + 2) cycloadditions catalyzed by copper(II) acetate. Using multinuclear nuclear magnetic resonance spectroscopy, electron paramagnetic resonance spectroscopy, and high-performance liquid chromatography analyses, a dual catalytic cycle is identified. First, the formation of a diyne species via Glaser-Hay coupling of a terminal ynamine forms a Cu(I) species competent to catalyze an ynamine-azide (3 + 2) cycloaddition. Second, the benzimidazole unit of the ynamine structure has multiple roles: assisting C-H activation, Cu coordination, and the formation of a postreaction resting state Cu complex after completion of the (3 + 2) cycloaddition. Finally, reactivation of the Cu resting state complex is shown by the addition of isotopically labeled ynamine and azide substrates to form a labeled 1,4-triazole product. This work provides a mechanistic basis for the use of mixed valency binuclear catalytic Cu species in conjunction with Cu-coordinating alkynes to afford superior reactivity in CuAAC reactions. Additionally, these data show how the CuAAC reaction kinetics can be modulated by changes to the alkyne substrate, which then has a predictable effect on the reaction mechanism.