History-dependent attachment ofPseudomonas aeruginosato solid-liquid interfaces and the dependence of the bacterial surface density on the residence time distribution.

This study investigates how the recent history of bacteria affects their attachment to a solid-liquid interface. We compare the attachment from a flowing suspension of the bacterium,Pseudomonas aeruginosaPAO1, after one of two histories: (a) passage through a tube packed with glass beads or (b) passage through an empty tube. The glass beads were designed to increase the rate of bacterial interactions with solid-liquid surfaces prior to observation in a flow cell. Analysis of time-lapse microscopy of the bacteria in the flow cells shows that the residence time distribution and surface density of bacteria differ for these two histories. In particular, bacteria exiting the bead-filled tube, in contrast to those bacteria exiting the empty tube, are less likely to attach to the subsequent flow cell window and begin surface growth. In contrast, when we compared two histories defined by different lengths of tubing, there was no difference in either the mean residence time or the surface density. In order to provide a framework for understanding these results, we present a phenomenological model in which the rate of bacterial surface density growth,dN(t)/dt, depends on two terms. One term models the initial attachment of bacteria to a surface, and is proportional to the nonprocessive cumulative residence time distribution for bacteria that attach and detach from the surface without cell division. The second term for the rate is proportional to the bacterial surface density and models surface cell division. The model is in surprisingly good agreement with the data even though the surface growth process is a complex interplay between attachment/detachment at the solid-liquid interface and cell division on the surface.

Comparative Investigation of Phenomenological Modeling for Hysteresis Responses of Magnetorheological Elastomer Devices.

Magnetorheological elastomer (MRE) is a type of magnetic soft material consisting of ferromagnetic particles embedded in a polymeric matrix. MRE-based devices have characteristics of adjustable stiffness and damping properties, and highly nonlinear and hysteretic force-displacement responses that are dependent on external excitations and applied magnetic fields. To effectively implement the devices in mitigating the hazard vibrations of structures, numerically traceable and computationally efficient models should be firstly developed to accurately present the unique behaviors of MREs, including the typical Payne effect and strain stiffening of rubbers etc. In this study, the up-to-date phenomenological models for describing hysteresis response of MRE devices are experimentally investigated. A prototype of MRE isolator is dynamically tested using a shaking table in the laboratory, and the tests are conducted based on displacement control using harmonic inputs with various loading frequencies, amplitudes and applied current levels. Then, the test results are used to identify the parameters of different phenomenological models for model performance evaluation. The procedure of model identification can be considered as solving a global minimization optimization problem, in which the fitness function is the root mean square error between the experimental data and the model prediction. The genetic algorithm (GA) is employed to solve the optimization problem for optimal model parameters due to its advantages of easy coding and fast convergence. Finally, several evaluation indices are adopted to compare the performances of different models, and the result shows that the improved LuGre friction model outperforms other models and has optimal accuracy in predicting the hysteresis response of the MRE device.

The effects of player grip on the dynamic behaviour of a tennis racket.

The aim of this article is to characterise the extent to which the dynamic behaviour of a tennis racket is dependent on its mechanical characteristics and the modulation of the player's grip force. This problem is addressed through steps involving both experiment and modelling. The first step was a free boundary condition modal analysis on five commercial rackets. Operational modal analyses were carried out under "slight", "medium" and "strong" grip force conditions. Modal frequencies and damping factors were then obtained using a high-resolution method. Results indicated that the dynamic behaviour of a racket is not only determined by its mechanical characteristics, but is also highly dependent on the player's grip force. Depending on the grip force intensity, the first two bending modes and the first torsional mode frequencies respectively decreased and increased while damping factors increased. The second step considered the design of a phenomenological hand-gripped racket model. This model is fruitful in that it easily predicts the potential variations in a racket's dynamic behaviour according to the player's grip force. These results provide a new perspective on the player/racket interaction optimisation by revealing how grip force can drive racket dynamic behaviour, and hence underlining the necessity of taking the player into account in the racket design process.

Grain size-dependent crystal plasticity constitutive model for polycrystal materials.

Consideration of a core and mantle configuration for individual grains is a prominent method to capture the grain size-dependence in the constitutive models for polycrystal material. The mantle represents a region of the grain volume near the grain boundary where mechanical deformation is influenced by the grain boundaries, while the core represents the inner region of the grain volume. The grain size-dependence is then realized by assigning a set of values for the mechanical properties in the mantle that are different from those of the core region. However, these values for the mechanical properties of the mantle region are typically chosen arbitrarily, guided solely by the quality of the agreement between a model's predicted stress-strain behavior with that obtained experimentally. In the present study, a physics-based method to develop the grain size-dependent crystal plasticity constitutive model on the core and mantle configuration for polycrystal materials is presented. The method is based on the assumption that any resistance to dislocation nucleation and motion in a material manifests as an increase in yield strength and a decrease in strain-hardening modulus, and the mutual relationship between yield strength and strain-hardening is an inherent material property that determines the plasticity of that specific material. Accordingly, the same single crystal plasticity constitutive model that describes the behavior of the material under loading can be used to capture the increased resistance to dislocation nucleation and motion in the grain boundary influence region. The physics-based modeling is facilitated by introducing a shear flow strain distribution in the phenomenological formulation and a pile-up of dislocation density distribution in the dislocation based formulation, such that, the resulting variations in the yield strength and the strain-hardening modulus are identical to that produced by the increased resistance in the grain boundary influence region. Thus, the increase in strength and the decrease in the strain-hardening modulus, determined as spatially varying local material properties in the mantle, are mutually related through the grain size-independent inherent plastic properties specific to the material. A simplified model that considers the grain boundary effect averaged over the grain volume is also developed under this general framework. Implementation of this simplified model is demonstrated by considering the case of a power law flow rule and a hyperbolic-secant hardening rule for the phenomenological formulation, and Taylor strength relation for the dislocation based formulation. Finally, the grain size-dependent constitutive model is validated by comparing the predicted stress-strain behavior of polycrystal copper samples under uniaxial loading with experimental results.

Emergence of robust growth laws from optimal regulation of ribosome synthesis.

Bacteria must constantly adapt their growth to changes in nutrient availability; yet despite large-scale changes in protein expression associated with sensing, adaptation, and processing different environmental nutrients, simple growth laws connect the ribosome abundance and the growth rate. Here, we investigate the origin of these growth laws by analyzing the features of ribosomal regulation that coordinate proteome-wide expression changes with cell growth in a variety of nutrient conditions in the model organism Escherichia coli. We identify supply-driven feedforward activation of ribosomal protein synthesis as the key regulatory motif maximizing amino acid flux, and autonomously guiding a cell to achieve optimal growth in different environments. The growth laws emerge naturally from the robust regulatory strategy underlying growth rate control, irrespective of the details of the molecular implementation. The study highlights the interplay between phenomenological modeling and molecular mechanisms in uncovering fundamental operating constraints, with implications for endogenous and synthetic design of microorganisms.

The equivalence of different types of electric pulses for electrochemotherapy with cisplatin - an in vitro study.

BACKGROUND: Electrochemotherapy (ECT) is a treatment involving the administration of chemotherapeutics drugs followed by the application of 8 square monopolar pulses of 100 µs duration at a repetition frequency of 1 Hz or 5000 Hz. However, there is increasing interest in using alternative types of pulses for ECT. The use of high-frequency short bipolar pulses has been shown to mitigate pain and muscle contractions. Conversely, the use of millisecond pulses is interesting when combining ECT with gene electrotransfer for the uptake of DNA-encoding proteins that stimulate the immune response with the aim of converting ECT from a local to systemic treatment. Therefore, the aim of this study was to investigate how alternative types of pulses affect the efficiency of the ECT. MATERIALS AND METHODS: We performed in vitro experiments, exposing Chinese hamster ovary (CHO) cells to conventional ECT pulses, high-frequency bipolar pulses, and millisecond pulses in the presence of different concentrations of cisplatin. We determined cisplatin uptake by inductively coupled plasma mass spectrometry and cisplatin cytotoxicity by the clonogenic assay. RESULTS: We observed that the three tested types of pulses potentiate the uptake and cytotoxicity of cisplatin in an equivalent manner, provided that the electric field is properly adjusted for each pulse type. Furthermore, we quantified that the number of cisplatin molecules, resulting in the eradication of most cells, was 2-7 × 107 per cell. CONCLUSIONS: High-frequency bipolar pulses and millisecond pulses can potentially be used in ECT to reduce pain and muscle contraction and increase the effect of the immune response in combination with gene electrotransfer, respectively.

Predicting plant-pollinator interactions: concepts, methods, and challenges.

Plant-pollinator interactions are ecologically and economically important, and, as a result, their prediction is a crucial theoretical and applied goal for ecologists. Although various analytical methods are available, we still have a limited ability to predict plant-pollinator interactions. The predictive ability of different plant-pollinator interaction models depends on the specific definitions used to conceptualize and quantify species attributes (e.g., morphological traits), sampling effects (e.g., detection probabilities), and data resolution and availability. Progress in the study of plant-pollinator interactions requires conceptual and methodological advances concerning the mechanisms and species attributes governing interactions as well as improved modeling approaches to predict interactions. Current methods to predict plant-pollinator interactions present ample opportunities for improvement and spark new horizons for basic and applied research.

Strain-controlled electrophysiological wave propagation alters in silico scar-based substrate for ventricular tachycardia.

Introduction: Assessing a patient's risk of scar-based ventricular tachycardia (VT) after myocardial infarction is a challenging task. It can take months to years after infarction for VT to occur. Also, if selected for ablation therapy, success rates are low. Methods: Computational ventricular models have been presented previously to support VT risk assessment and to provide ablation guidance. In this study, an extension to such virtual-heart models is proposed to phenomenologically incorporate tissue remodeling driven by mechanical load. Strain amplitudes in the heart muscle are obtained from simulations of mechanics and are used to adjust the electrical conductivity. Results: The mechanics-driven adaptation of electrophysiology resulted in a more heterogeneous distribution of propagation velocities than that of standard models, which adapt electrophysiology in the structural substrate from medical images only. Moreover, conduction slowing was not only present in such a structural substrate, but extended in the adjacent functional border zone with impaired mechanics. This enlarged the volumes with high repolarization time gradients (≥10 ms/mm). However, maximum gradient values were not significantly affected. The enlarged volumes were localized along the structural substrate border, which lengthened the line of conduction block. The prolonged reentry pathways together with conduction slowing in functional regions increased VT cycle time, such that VT was easier to induce, and the number of recommended ablation sites increased from 3 to 5 locations. Discussion: Sensitivity testing showed an accurate model of strain-dependency to be critical for low ranges of conductivity. The model extension with mechanics-driven tissue remodeling is a potential approach to capture the evolution of the functional substrate and may offer insight into the progression of VT risk over time.

A phenomenological model of non-genomic variability of luminescent bacterial cells.

The light emitted by a luminescent bacterium serves as a unique native channel of information regarding the intracellular processes within the individual cell. In the presence of highly sensitive equipment, it is possible to obtain the distribution of bacterial culture cells by the intensity of light emission, which correlates with the amount of luciferase in the cells. When growing on rich media, the luminescence intensity of individual cells of brightly luminous strains of the luminescent bacteria Photobacterium leiognathi and Ph. phosporeum reaches 104-105 quanta/s. The signal of such intensity can be registered using sensitive photometric equipment. All experiments were carried out with bacterial clones (genetically homogeneous populations). A typical dynamics of luminous bacterial cells distributions with respect to intensity of light emission at various stages of batch culture growth in a liquid medium was obtained. To describe experimental distributions, a phenomenological model that links the light of a bacterial cell with the history of events at the molecular level was constructed. The proposed phenomenological model with a minimum number of fitting parameters (1.5) provides a satisfactory description of the complex process of formation of cell distributions by luminescence intensity at different stages of bacterial culture growth. This may be an indication that the structure of the model describes some essential processes of the real system. Since in the process of division all cells go through the stage of release of all regulatory molecules from the DNA molecule, the resulting distributions can be attributed not only to luciferase, but also to other proteins of constitutive (and not only) synthesis.

Exploring the Influence of pH on the Dynamics of Acetone-Butanol-Ethanol Fermentation.

Clostridium acetobutylicum is an anaerobic bacterium that is extensively studied for its ability to produce butanol. Over the past two decades, various genetic and metabolic engineering approaches have been used to investigate the physiology and regulation system of the biphasic metabolic pathway in this organism. However, there has been a relatively limited amount of research focused on the fermentation dynamics of C. acetobutylicum. In this study, we developed a pH-based phenomenological model to predict the fermentative production of butanol from glucose using C. acetobutylicum in a batch system. The model describes the relationship between the dynamics of growth and the production of desired metabolites and the extracellular pH of the media. Our model was found to be successful in predicting the fermentation dynamics of C. acetobutylicum, and the simulations were validated using experimental fermentation data. Furthermore, the proposed model has the potential to be extended to represent the dynamics of butanol production in other fermentation systems, such as fed-batch or continuous fermentation using single and multi-sugars.

Review and Assessment of Fatigue Delamination Damage of Laminated Composite Structures.

Fatigue delamination damage is one of the most important fatigue failure modes for laminated composite structures. However, there are still many challenging problems in the development of the theoretical framework, mathematical/physical models, and numerical simulation of fatigue delamination. What is more, it is essential to establish a systematic classification of these methods and models. This article reviews the experimental phenomena of delamination onset and propagation under fatigue loading. The authors reviewed the commonly used phenomenological models for laminated composite structures. The research methods, general modeling formulas, and development prospects of phenomenological models were presented in detail. Based on the analysis of finite element models (FEMs) for laminated composite structures, several simulation methods for fatigue delamination damage models (FDDMs) were carefully classified. Then, the whole procedure, range of applications, capability assessment, and advantages and limitations of the models, which were based on four types of theoretical frameworks, were also discussed in detail. The theoretical frameworks include the strength theory model (SM), fracture mechanics model (FM), damage mechanics model (DM), and hybrid model (HM). To the best of the authors' knowledge, the FDDM based on the modified Paris law within the framework of hybrid fracture and damage mechanics is the most effective method so far. However, it is difficult for the traditional FDDM to solve the problem of the spatial delamination of complex structures. In addition, the balance between the cost of acquiring the model and the computational efficiency of the model is also critical. Therefore, several potential research directions, such as the extended finite element method (XFEM), isogeometric analysis (IGA), phase-field model (PFM), artificial intelligence algorithm, and higher-order deformation theory (HODT), have been presented in the conclusions. Through validation by investigators, these research directions have the ability to overcome the challenging technical issues in the fatigue delamination prediction of laminated composite structures.

A phenomenological neural network powered by the National Wastewater Surveillance System for estimation of silent COVID-19 infections.

Although wastewater-based epidemiology (WBE) has emerged as an inexpensive and non-intrusive method in contrast to clinical testing to track public health at community levels, there is a lack of structured interpretative criteria to translate the SARS-CoV-2 concentrations in wastewater to COVID-19 infection cases. The difficulties lie in the uncertainties of the amount of virus shed by an infected individual to wastewater as documented in clinical studies. This situation is even worse considering the existence of a population of silent infections and many other confounding factors. In this research, a quantitative framework of a phenomenological neural network (PNN) was developed to compute silent infections. The PNN was trained using the WBE data from the National Wastewater Surveillance System (NWSS) - a program launched by the CDC of the United States in 2020. It is found that the PNN excelled with superior interpretability and reduced overfitting. A big-data perspective on virus shedding by an infected population revealed more deterministic virus-shedding dynamics compared to the clinical studies perspective on virus shedding by an infected individual. With such characteristics employed as the theoretical basis for the estimation of the silent infections, a ratio of silent to reported infections was found to be 5.7 as the national median during the studied period. The study also noted the influence of temperature, sewershed population, and per-capita flow rates on the computation of silent infections. It is expected that the proposed framework in this work would facilitate public health actions guided by the SARS-CoV-2 concentrations in wastewater. In case of a new wave emergence or a new virus disease outbreak like COVID-19, the PNN powered by the NWSS would outline consolidated and systematic information that would enable rapid deployment of public health actions.

A reduced complexity ECG imaging model for regularized inversion optimization.

The resolution of the inverse problem of electrocardiography represents a major interest in the diagnosis and catheter-based therapy of cardiac arrhythmia. In this context, the ability to simulate several cardiac electrical behaviors was crucial for evaluating and comparing the performance of inversion methods. For this application, existing models are either too complex or do not produce realistic cardiac patterns. In this work, a low-resolution heart-torso model generating realistic whole heart cardiac mappings and electrocardiograms in healthy and pathological cases is designed. This model was built upon a simplified heart-torso geometry and implements the monodomain formalism by using the finite element method. In addition, a model reduction step through a sensitivity analysis was proposed where parameters were identified using an evolutionary optimization approach. Finally, the study illustrates the usefulness of the proposed model by comparing the performance of different variants of Tikhonov-based inversion methods for the determination of the regularization parameter in healthy, ischemic and ventricular tachycardia scenarios. First, results of the sensitivity analysis show that among 58 parameters only 25 are influent. Note also that the level of influence of the parameters depends on the heart region. Besides, the synthesized electrocardiograms globally present the same characteristic shape compared to the reference once with a correlation value that reaches 88%. Regarding inverse problem, results highlight that only Robust Generalized Cross Validation and Discrepancy Principle provide best performance, with a quasi-perfect success rate for both, and a respective relative error, between the generated electrocardiograms to the reference one, of 0.75 and 0.62.

Radioresistance and radiosensitivity: a biophysical approach on bacterial cells robustness.

The study of radiosensitivity and radioresistance of organisms exposed to ionizing radiation has acquired additional relevance since a new bio-concept, coined as The primacy of Proteome over Genome, was proposed and demonstrated elsewhere a few years ago. According to that finding, genome integrity would require an actively functioning Proteome. However, when exposure to radiation takes place, Reactive Oxygen Species (ROS) from water radiolysis induce protein carbonylation (PC), an irreversible oxidative Proteome damage. The bio-models used in that study were the radiosensitive Escherichia coli and the extraordinarily robust Deinococcus radiodurans. The production of ROS induces protective reactions rendering them non-reactive forms. Protective entities present in the cytosol, moieties smaller than 3 kDa, shield the Proteome against ROS, yielding protection against carbonylation. Shown in the present study is the fact that the fate of proteins functionality is determined by the magnitude of the Protein Carbonylation Yield (YPC), a quantity here analytically defined using published YPC numerical results. Analytical YPC expressions for E. coli and D. radiodurans were the input for a phenomenological approach, where the radiobiological magnitudes PP and PN, the probabilities for production of protein damage and ROS neutralization, respectively, were also analytically deduced. These highly relevant magnitudes, associated with key radiosensitivity and radioresistance issues, are addressed and discussed in this study. Among the plethora of information and conclusions derived from the present study, those endowed with higher conceptual degree, vis-à-vis the "Primacy of Proteome over Genome" concept, are as follows: (1) the ROS neutralization process in D. radiodurans reaches a maximum at a dose interval corresponding to the repairing shoulder. Therefore, it is a signature of the higher efficiency of the PC neutralization process. (2) ROS neutralization in D. radiodurans is nearly one order of magnitude higher than in E. coli, thus accounting for its extraordinary radioresistance. (3) Both physical (ROS-induced carbonyl radicals) and biological (protein modifications) processes are imbedded in the Protein Carbonylation Yield. The amalgamation of these two processes was accomplished by means of a statistical formalism.

Influence of Alginate Properties and Calcium Chloride Concentration on Alginate Bead Reticulation and Size: A Phenomenological Approach.

Two types of alginates, AlgLF and AlgP, were used in this study to produce alginate beads by electro-vibratory extrusion. AlgLF and AlgP exhibited different Mannuronate/Guluronate (M/G) ratios and molecular weights as measured by NMR and SEC-MALS. The calcium chloride concentration was found to have the greatest effect on bead size. Higher concentrations resulted in smaller beads. AlgLF with a higher molecular weight and a lower proportion of G blocks showed smaller beads. For both alginates, the bead size was also influenced by the flow rate and vibration frequency. Alginate solution aging showed a minimal effect. Alginate reticulation was modeled using a mathematical equation. The study provides insights for the optimization of alginate-based materials in different applications by shedding light on the main factors influencing bead size. The importance of the molecular weight, M/G ratio and calcium ion concentration in the gelling process is highlighted, providing opportunities for the tailoring of alginate materials through a phenomenological model.

Modelling and validation of the non-linear elastic stress-strain behaviour of multi-layer silicone composites.

Multi-layer silicone composites are commonly used to mold deformable silicone vocal folds replicas. Nevertheless, so far the stress-strain characterisation of such composite specimens is limited to their effective Young's modulus (up to 40 kPa) characterising the elastic low-strain range, i.e. up to about 0.3. Therefore, in this work, the characterisation is extended to account for the non-linear strain range. Stress-strain curves on 6 single-layer and 34 multi-layer silicone specimens, with different layer stacking (serial, parallel, combined or arbitrary), are measured at room temperature using uni-axial tensile tests for strains up to 1.36, which amounts to about 4.5 times the extent of the linear low-strain range. Cubic polynomial and exponential two-parameter relationships are shown to provide accurate continuous fits (coefficient of determination R2≥99%) of the measured stress-strain data. It is then shown that the parameters can be a priori modelled as a constant or as a linear function of the effective low-strain Young's modulus for strains up to 1.55, i.e. 5 times the low-strain range. These a priori modelled parameter are confirmed by approximations of the best fit parameters for all assessed specimens as a function of the low-strain Young's modulus. Thus, the continuous stress-strain behaviour up to 1.55 can be predicted analytically from the effective low-strain Young's modulus either using the modelled parameters (R2≥85%) or the approximations of the best fit parameter sets (R2≥94%). Accurate stress-strain predictions are particularly useful for the design of composites with different composition and stacking. In addition, analytical expressions of the linear high-strain Young's modulus and the linear high-strain onset, again as a function of the effective low-strain Young's modulus, are formulated as well.

A conceptual framework for the dynamic modeling of time-resolved phenotypes for sets of genotype-environment-management combinations: a model library.

Introduction: Dynamic crop growth models are an important tool to predict complex traits, like crop yield, for modern and future genotypes in their current and evolving environments, as those occurring under climate change. Phenotypic traits are the result of interactions between genetic, environmental, and management factors, and dynamic models are designed to generate the interactions producing phenotypic changes over the growing season. Crop phenotype data are becoming increasingly available at various levels of granularity, both spatially (landscape) and temporally (longitudinal, time-series) from proximal and remote sensing technologies. Methods: Here we propose four phenomenological process models of limited complexity based on differential equations for a coarse description of focal crop traits and environmental conditions during the growing season. Each of these models defines interactions between environmental drivers and crop growth (logistic growth, with implicit growth restriction, or explicit restriction by irradiance, temperature, or water availability) as a minimal set of constraints without resorting to strongly mechanistic interpretations of the parameters. Differences between individual genotypes are conceptualized as differences in crop growth parameter values. Results: We demonstrate the utility of such low-complexity models with few parameters by fitting them to longitudinal datasets from the simulation platform APSIM-Wheat involving in silico biomass development of 199 genotypes and data of environmental variables over the course of the growing season at four Australian locations over 31 years. While each of the four models fits well to particular combinations of genotype and trial, none of them provides the best fit across the full set of genotypes by trials because different environmental drivers will limit crop growth in different trials and genotypes in any specific trial will not necessarily experience the same environmental limitation. Discussion: A combination of low-complexity phenomenological models covering a small set of major limiting environmental factors may be a useful forecasting tool for crop growth under genotypic and environmental variation.

Angular dependence of spin-flop transition in triangular lattice antiferromagnet Cu2(OH)3Br.

We report angular dependence of spin-flop transition in triangular lattice antiferromagnet Cu2(OH)3Br by angle-dependent magnetization and ESR measurements. The results show that the antiferromagnetic easy magnetization axis is the diagonal direction (θ= 45°) of theac*plane, i.e., the orientation of Cu1 spins based on the magnetic structure (2020Phys. Rev. Lett.125037204), whereas the spin-flop axis is thebaxis. A phenomenological model is proposed to describe the angle-dependent spin-flop transitions. Based on this model, Cu1 spins are sensitive to external magnetic field, while Cu2 spins are robust against to the field, showing partial decoupling. The model is expected to be used in other uniaxial antiferromagnets with a more general easy axis and complex spin-flop transitions.

A Review on Modeling Cure Kinetics and Mechanisms of Photopolymerization.

Photopolymerizations, in which the initiation of a chemical-physical reaction occurs by the exposure of photosensitive monomers to a high-intensity light source, have become a well-accepted technology for manufacturing polymers. Providing significant advantages over thermal-initiated polymerizations, including fast and controllable reaction rates, as well as spatial and temporal control over the formation of material, this technology has found a large variety of industrial applications. The reaction mechanisms and kinetics are quite complex as the system moves quickly from a liquid monomer mixture to a solid polymer. Therefore, the study of curing kinetics is of utmost importance for industrial applications, providing both the understanding of the process development and the improvement of the quality of parts manufactured via photopolymerization. Consequently, this review aims at presenting the materials and curing chemistry of such ultrafast crosslinking polymerization reactions as well as the research efforts on theoretical models to reproduce cure kinetics and mechanisms for free-radical and cationic photopolymerizations including diffusion-controlled phenomena and oxygen inhibition reactions in free-radical systems.

A Phenomenological Mouse Circadian Pacemaker Model.

Mathematical models have been used extensively in chronobiology to explore characteristics of biological clocks. In particular, for human circadian studies, the Kronauer model has been modified multiple times to describe rhythm production and responses to sensory input. This phenomenological model comprises a single set of parameters which can simulate circadian responses in humans under a variety of environmental conditions. However, corresponding models for nocturnal rodents commonly used in circadian rhythm studies are not available and may require new parameter values for different species and even strains. Moreover, due to a considerable variation in experimental data collected from mice of the same strain, within and across laboratories, a range of valid parameters is essential. This study develops a Kronauer-like model for mice by re-fitting relevant parameters to published phase response curve and period data using total least squares. Local parameter sensitivity analysis and parameter distributions determine the parameter ranges that give a near-identical model and data distribution of periods. However, the model required further parameter adjustments to match characteristics of other mouse strains, implying that the model itself detects changes in the core processes of rhythm generation and control. The model is a useful tool to understand and interpret future mouse circadian clock experiments.