Mechanistic Model Research Articles

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.

Computer-Aided Neuro-fuzzy Algorithm for Prediction of Ginger(Zingiber officinale) Rhizome Drying

Neuro-fuzzy modeling technique has been used for the development of computer-based adaptive process control systems for most postharvest operations instead of convectional mechanistic modeling technique owing to its good prediction without prior knowledge of process operation. Therefore, adaptive neuro-fuzzy inference system (ANFIS) modeling and prediction of ginger (Zingiber officinale) rhizome moisture content in a forced convective tray dryer was investigated in this study. Computer codes for an exhaustive search and grid partitioning were written in the Matlab software environment and used for the analysis of experimental data obtained from the ginger rhizome drying experiment. The data were trained and checked using the grid-partitioning ANFIS method for the development of neuro-fuzzy network for ginger drying prediction. The prediction accuracy of the network was evaluated by varying input and output Membership Function (MF) types. The exhaustive search analysis shows that drying time (DT) has the highest effect on the moisture content, followed by air drying temperature (ADT), air relative humidity (ARH), and air flow velocity (AFV). Moreover, combination of DT and ADT as two interactive variables; and combination DT, ADT and ARH as three interactive variables show highest effect on ginger moisture content removal during the process. Generalized bell (gbell) MF with two MF for each input variable and constant output MF 2 type were obtained as the best ginger drying ANFIS network with corresponding R and MSE of 0.9667 and 0.0971, respectively. Therefore, this study shows the reliability of the ANFIS model for the prediction of ginger rhizome drying in a tray dryer.

Computer-Aided Neuro-fuzzy Algorithm for Prediction of Ginger(Zingiber officinale) Rhizome Drying

Neuro-fuzzy modeling technique has been used for the development of computer-based adaptive process control systems for most postharvest operations instead of convectional mechanistic modeling technique owing to its good prediction without prior knowledge of process operation. Therefore, adaptive neuro-fuzzy inference system (ANFIS) modeling and prediction of ginger (Zingiber officinale) rhizome moisture content in a forced convective tray dryer was investigated in this study. Computer codes for an exhaustive search and grid partitioning were written in the Matlab software environment and used for the analysis of experimental data obtained from the ginger rhizome drying experiment. The data were trained and checked using the grid-partitioning ANFIS method for the development of neuro-fuzzy network for ginger drying prediction. The prediction accuracy of the network was evaluated by varying input and output Membership Function (MF) types. The exhaustive search analysis shows that drying time (DT) has the highest effect on the moisture content, followed by air drying temperature (ADT), air relative humidity (ARH), and air flow velocity (AFV). Moreover, combination of DT and ADT as two interactive variables; and combination DT, ADT and ARH as three interactive variables show highest effect on ginger moisture content removal during the process. Generalized bell (gbell) MF with two MF for each input variable and constant output MF 2 type were obtained as the best ginger drying ANFIS network with corresponding R and MSE of 0.9667 and 0.0971, respectively. Therefore, this study shows the reliability of the ANFIS model for the prediction of ginger rhizome drying in a tray dryer.

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 0.01 g L-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}. 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.Graphical abstractReal-time intrinsic fluorescence monitoring in protein refolding

Mechanistic Modeling of In Vitro Biopharmaceutic Data for a Weak Acid Drug: A Pathway Towards Deriving Fundamental Parameters for Physiologically Based Biopharmaceutic Modeling.

Mechanistic modeling of in vitro experiments using metabolic enzyme systems enables the extrapolation of metabolic clearance for in vitro-in vivo predictions. This is particularly important for successful clearance predictions using physiologically based pharmacokinetic (PBPK) modeling. The concept of mechanistic modeling can also be extended to biopharmaceutics, where in vitro data is used to predict the in vivo pharmacokinetic profile of the drug. This approach further allows for the identification of parameters that are critical for oral drug absorption in vivo. However, the routine use of this analysis approach has been hindered by the lack of an integrated analysis workflow. The objective of this tutorial is to (1) review processes and parameters contributing to oral drug absorption in increasing levels of complexity, (2) outline a general physiologically based biopharmaceutic modeling workflow for weak acids, and (3) illustrate the outlined concepts via an ibuprofen (i.e., a weak, poorly soluble acid) case example in order to provide practical guidance on how to integrate biopharmaceutic and physiological data to better understand oral drug absorption. In the future, we plan to explore the usefulness of this tutorial/roadmap to inform the development of PBPK models for BCS 2 weak bases, by expanding the stepwise modeling approach to accommodate more intricate scenarios, including the presence of diprotic basic compounds and acidifying agents within the formulation.

A method to partition boiling heat transfer mechanisms using synchronous through-substrate high-speed visual and infrared measurements

Heat transfer during boiling involves a variety of transport mechanisms. Available experimental techniques cannot yet fully delineate these mechanisms which has contributed to a long-standing, persistent challenge of constructing accurate mechanistic models for boiling. In this work, we develop a method to identify and distinguish between the individual heat transfer mechanisms that occur during boiling using synchronous, through-substrate, high-speed visual and infrared measurements. Local heat fluxes are deduced from temperature measurements and a synchronized set of binarized phase maps are obtained from processing high-speed visual measurements. Experimental pool boiling investigation of HFE-7100 fluid on an indium tin oxide surface revealed four distinct heat transfer signatures in the heat flux maps corresponding to liquid convection, contact line evaporation, vapor convection, and local microconvection due to rewetting during boiling. To classify these regions, pixel-wise binary and morphological operations are performed on the phase maps of the entire surface. In contrast with prior partitioning techniques which use standalone heat flux measurements, our synchronous high-resolution visualization enables the region classification around fine bubble footprints that otherwise would not be detected with heat flux maps alone. The heat fluxes and superheats are then partitioned into their underlying mechanisms using classified regions from the phase maps. Analysis of the nucleate boiling regime data shows that different mechanisms contribute in varying degrees to the overall heat transfer at different points along the boiling curve: for the surface-fluid combination studied, single-phase liquid heat transfer is the primary contributor at low heat fluxes while at high heat fluxes, contact line evaporation contributed the most. We further employ the experimental approach to partitioning dynamic processes resulting from step increases in heat input demonstrating its potential for investigating transient phenomena such as dryout. The experimental and post-processing method introduced in this study is the first to delineate partitioning between different heat transfer mechanism regions in the presence of multiple interacting bubbles over the entire surface throughout the boiling curve in both steady and transient operating conditions.

Robustness of carbon-ion radiotherapy against DNA damage repair associated radiosensitivity variation based on a biophysical model.

Interpatient variation of tumor radiosensitivity is rarely considered during the treatment planning process despite its known significance for the therapeutic outcome. To apply our mechanistic biophysical model to investigate the biological robustness of carbon ion radiotherapy (CIRT) against DNA damage repair interference (DDRi) associated patient-to-patient variability in radiosensitivity and its potential clinical advantages against conventional radiotherapy approaches. The "UNIfied and VERSatile bio response Engine" (UNIVERSE) was extended by carbon ions and its predictions were compared to a panel of in vitro and in vivo data including various endpoints and DDRi settings within clinically relevant dose and linear energy transfer (LET) ranges. The implications of UNIVERSE predictions were then assessed in a clinical patient scenario considering DDRi variance. UNIVERSE tests well against the applied benchmarks. While in vitro survival curves were predicted with an R2>0.92, deviations from in vivo RBE data were less than 5.6% The conducted paradigmatic patient plan study implies a markedly reduced significance of DDRi based radiosensitivity variability in CIRT (13% change of in target) compared to conventional radiotherapy (62%) and that boosting the LET within the target further amplifies this robustness of CIRT (8%). In the case of heightened tumor radiosensitivity, a dose de-escalation strategy for photons allows a reduction of the maximum effective dose within the normal tissue (NT) from a of 2.65 to 1.64Gy, which lies below the level found for CIRT ( =2.41Gy) for the analyzed plan and parameters. However, even after de-escalation, the integral effective dose in the NT is found to be substantially higher for conventional radiotherapy in comparison to CIRT ( of 0.75, 0.46, and 0.24Gy for the conventional plan, its de-escalation and CIRT, respectively). The framework offers adequate predictions of in vitro and in vivo radiation effects of CIRT while allowing the consideration of DRRi based solely on parameters derived from photon data. The results of the patient planning study underline the potential of CIRT to minimize important sources of interpatient divergence in therapy outcome, especially when combined with techniques that allow to maximize the LET within the tumor. Despite the potential of de-escalation strategies for conventional radiotherapy to reduce the maximum effective dose in the NT, CIRT appears to remain a more favorable option due to its ability to reduce the integral effective dose within the NT.

Mathematical Model-Driven Deep Learning Enables Personalized Adaptive Therapy.

Generation of interpretable and personalized adaptive treatment schedules using a deep reinforcement framework that interacts with a virtual patient model overcomes the limitations of standardized strategies caused by heterogeneous treatment responses.

Model-aided climate adaptation for future maize in the US

Abstract Over the next three decades rising population and changing dietary preferences are expected to increase food demand by 25%–75%. At the same time climate is also changing—with potentially drastic impacts on food production. Breeding new crop characteristics and adjusting management practices are critical avenues to mitigate yield loss and sustain yield stability under a changing climate. In this study, we use a mechanistic crop model (MAIZSIM) to identify high-performing trait and management combinations that maximize yield and yield stability for different agroclimate regions in the US under present and future climate conditions. We show that morphological traits such as total leaf area and phenological traits such as grain-filling start time and duration are key properties that impact yield and yield stability; different combinations of these properties can lead to multiple high-performing strategies under present-day climate conditions. We also demonstrate that high performance under present day climate does not guarantee high performance under future climate. Weakened trade-offs between canopy leaf area and reproductive start time under a warmer future climate led to shifts in high-performing strategies, allowing strategies with higher total leaf area and later grain-filling start time to better buffer yield loss and out-compete strategies with a smaller canopy leaf area and earlier reproduction. These results demonstrate that focused effort is needed to breed plant varieties to buffer yield loss under future climate conditions as these varieties may not currently exist, and showcase how information from process-based models can complement breeding efforts and targeted management to increase agriculture resilience.

A Multiscale Closed-Loop Neurotoxicity Model of Alzheimer's Disease Progression Explains Functional Connectivity Alterations.

The accumulation of amyloid-β (Aβ) and hyperphosphorylated-tau (hp-tau) are two classical histopathological biomarkers in Alzheimer's disease (AD). However, their detailed interactions with the electrophysiological changes at the meso- and macroscale are not yet fully understood. We developed a mechanistic multiscale model of AD progression, linking proteinopathy to its effects on neural activity and vice-versa. We integrated a heterodimer model of prion-like protein propagation and a brain network model of Jansen-Rit neural masses derived from human neuroimaging data whose parameters varied due to neurotoxicity. Results showed that changes in inhibition guided the electrophysiological alterations found in AD, and these changes were mainly attributed to Aβ effects. Additionally, we found a causal disconnection between cellular hyperactivity and interregional hypersynchrony contrary to previous beliefs. Finally, we demonstrated that early Aβ and hp-tau depositions' location determine the spatiotemporal profile of the proteinopathy. The presented model combines the molecular effects of both Aβ and hp-tau together with a mechanistic protein propagation model and network effects within a closed-loop model. This holds the potential to enlighten the interplay between AD mechanisms on various scales, aiming to develop and test novel hypotheses on the contribution of different AD-related variables to the disease evolution.

Kinetics of CO2 hydrogenation to methanol over CuO/ZnO/ZrO2 catalyst: Comparison of the differential and integral methods of kinetic analysis

AbstractThe experimental data over CuO/ZnO/ZrO2 catalyst for a wide range of operating conditions were used to develop the kinetics of the reaction CO2 hydrogenation to methanol. Three kinetic models such as the power law model, the Graaf kinetic model, and the Park kinetic model were tested against the experimental data. Both the mechanistic models have been developed based on the Langmuir‐Hinshelwood‐Hougen‐Watson approach and are specific only to the methanol synthesis from CO/CO2 hydrogenation. In an attempt to reduce the number of parameters in the two models, the abridged forms of these models were also tried. Overall, 25 kinetic rate equations were tested and the best‐fit kinetic rate expression with optimized parameters was worked out. Both the integral and differential methods of kinetic analysis were employed and their efficacy in finding the best‐fit expression was compared. The MATLAB built‐in function fminsearch was employed to perform the regression of the data. The Graaf model in its parent form, but with the new optimized values of the parameters, was found to be the best‐fit rate model. The Graaf kinetics, with re‐estimated parameters, could be helpful in designing and simulating a methanol synthesis reactor operating on CO2 and H2 feed and utilizing a CuO/ZnO/ZrO2 catalyst.

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 1.5 Å 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.Graphical The [4Cu:2S] CuZ site in M. nauticus N 2O reductase. The electron density map shown is contoured at the 5 σ level, highlighting the presence of two sulfide ligands. 705x677mm (72 x 72 DPI)