Mechanism-based Model Research Articles

A mechanism-based elasto-viscoplastic constitutive model for the creep deformation of martensitic heat-resistant steels

A mechanism-based elasto-viscoplastic constitutive model for the creep deformation of martensitic heat-resistant steels

Impact of obesity and roux-en-Y gastric bypass on the pharmacokinetics of (R)- and (S)-omeprazole and intragastric pH.

This study employed physiologically-based pharmacokinetic-pharmacodynamics (PBPK/PD) modeling to predict the effect of obesity and gastric bypass surgery on the pharmacokinetics and intragastric pH following omeprazole treatment. The simulated plasma concentrations closely matched the observed data from non-obese, morbidly obese, and post-gastric bypass populations. Obesity significantly reduces CYP3A4 and CYP2C19 activities, as reflected by the metabolic ratio [omeprazole sulphone]/[omeprazole] and [5-hydroxy-omeprazole]/[omeprazole]. The morbidly obese model accounted for the down-regulation of CYP2C19 and CYP3A4 to recapitulate the observed data. Sensitivity analysis showed that intestinal CYP3A4, gastric pH, small intestine bypass, and the delay in bile release do not have a major influence on omeprazole exposure. Hepatic CYP3A4 had a significant impact on the AUC of (S)-omeprazole, while hepatic CYP2C19 affected both (R)- and (S)-omeprazole AUC. After gastric bypass surgery, the activity of CYP3A4 and CYP2C19 is restored. The PBPK model was linked to a mechanism-based PD model to assess the effect of omeprazole on intragastric pH. Following 40 mg omeprazole, the mean intragastric pH was 4.3, 4.6, and 6.6 in non-obese, obese, and post-gastric bypass populations, and the daily time with pH >4 was 14.7, 16.4, and 24 h. Our PBPK/PD approach provides a comprehensive understating of the impact of obesity and weight loss on CYP3A4 and CYP2C19 activity and omeprazole pharmacokinetics. Given that simulated intragastric pH is relatively high in post-RYGB patients, irrespective of the dose of omeprazole, additional clinical outcomes are imperative to assess the effect of proton pump inhibitor in preventing marginal ulcers in this population.

Assessing the risk of E. coli contamination from manure application in Chinese farmland by integrating machine learning and Phydrus

This study aims to present a comprehensive study on the risks associated with the residual presence and transport of Escherichia coli (E. coli) in soil following the application of livestock manure in Chinese farmlands by integrating machine learning algorithms with mechanism-based models (Phydrus). We initially review 28 published papers to gather data on E. coli's die-off and attachment characteristics in soil. Machine learning models, including deep learning and gradient boosting machine, are employed to predict key parameters such as the die-off rate of E. coli and first-order attachment coefficient in soil. Then, Phydrus was used to simulate E. coli transport and survival in 23692 subregions in China. The model considered regional differences in E. coli residual risk and transport, influenced by soil properties, soil depths, precipitation, seasonal variations, and regional disparities. The findings indicate higher residual risks in regions such as the Northeast China, Eastern Qinghai-Tibet Plateau, and pronounced transport risks in the fringe of the Sichuan Basin fringe, the Loess Plateau, the North China Plain, the Northeast Plain, the Shigatse Basin, and the Shangri-La region. The study also demonstrates a significant reduction in both residual and transport risks one month after manure application, highlighting the importance of timing manure application and implementing region-specific standards. This research contributes to the broader understanding of pathogen behavior in agricultural soils and offers practical guidelines for managing the risks associated with manure use. This study's comprehensive method offers a potentially valuable tool for evaluating microbial contaminants in agricultural soils across the globe.

An Incentive Mechanism-Based Minimum Adjustment Consensus Model Under Dynamic Trust Relationship.

In traditional group decision making, the inconsistent experts are usually forced to make compromises toward the group opinion to increase the group consensus level. However, the strategy of reaching group consensus via an incentive mechanism encouraging adjustment of preferences is more effective than forcing, which is the aim of this article. Specifically, this article establishes a novel incentive mechanism to support group consensus under dynamic trust relationship. First, the supremum and infimum incentives-based rule driven by trust relationship is defined. Based on the assumption that if incentive conditions are met, then experts will be willing to adjust their preferences, the incentive behavior-driven minimum adjustment consensus model is developed to generate optimal incentive-based recommendation preferences. Thus, the proposed incentive mechanism can effectively reduce the preference adjustment cost and promote group consensus reaching. Third, the updated trust relationships between experts are shown to be strengthen by the proposed incentive-driven preference revision. Consequently, the optimization model based on trust interaction relationship is constructed to obtain the final group preference matrix. Finally, a supplier selection case of high-end medical equipment is provided to illustrate the proposed method and show the rationality and advantages of the proposed methodology with both a sensitivity analysis and a comparison analysis.

Fault Detection of Urban Wastewater Treatment Process Based on Combination of Deep Information and Transformer Network.

As one of the hot issues of concerns during modern social development, the wastewater treatment process is acknowledged to be a process with complex biochemical reactions and susceptible to an external environment, featuring strong nonlinear and time correlation characteristics, which are difficult for traditional mechanism-based models to tackle. For many classical data-driven fault detection methods, a complete retraining process is necessary to monitor every new fault, and most of the current neural network-based strategies rarely achieve satisfactory monitoring accuracy or robustness either. Giving full consideration to the aforementioned problems, this article takes advantage of position encoding, residual connection, and multihead attention mechanism embedded in the Transformer structure to establish an effective and efficient wastewater treatment process fault detection model, where offline modeling and online monitoring are performed successively to achieve accurate detection of the faults. In the experimental part, the advantages of the proposed method are strongly verified through the simulation monitoring of 27 faults on the benchmark simulation model 1 (BSM1), where the false alarm rate (FAR) and miss alarm rate (MAR) of the established method are proved to be significantly lower than those of the compared state-of-the-art methods.

Review of the creep constitutive models for rocks and the application of creep analysis in geomechanics

AbstractThe creep behavior of rocks has been broadly researched because of its extensive application in geomechanics. Since the time-dependent stability of underground constructions is a critical aspect of geotechnical engineering, a comprehensive understanding of the creep behavior of rocks plays a pivotal role in ensuring the safety of such structures. Various factors, including stress level, temperature, rock damage, water content, rock anisotropy, etc., can influence rocks’ creep characteristics. One of the main topics in the creep analysis of rocks is the constitutive models, which can be categorized into empirical, component, and mechanism-based models. In this research, the previously proposed creep models were reviewed, and their main characteristics were discussed. The effectiveness of the models in simulating the accelerated phase of rock creep was evaluated by comparing their performance with the creep test results of different types of rocks. The application of rock’s creep analysis in different engineering projects and adopting appropriate creep properties for rock mass were also examined. The primary limitation associated with empirical and classical component models lies in their challenges when it comes to modeling the tertiary phase of rock creep. The mechanism-based models have demonstrated success in effectively simulating the complete creep phases; nevertheless, additional validation is crucial to establish their broader applicability. However, further investigation is still required to develop creep models specific to rock mass. In this paper, we attempted to review and discuss the most recent studies in creep analysis of rocks that can be used by researchers conducting creep analysis in geomechanics.

A corrosion cracking mechanism-based model with molten salt corrosion damage coupling the effect of chloride impurity and plastic deformation

The corrosion kinetic models, incorporating the effects of chloride impurity and plastic deformation were proposed. Molten salt corrosion (MSC) damage was quantitatively described using the corrosion kinetic and the distance of materials points from the corrosion surface. By employing the elastic-viscoplastic theory with a hyperbolic-sine flow rule and the proposed damage model, a corrosion cracking mechanism-based model was developed and implemented using finite element (FE) analysis method. The corrosion-assisted cracking ahead of crack tips in accordance with the corrosion cracking mechanism was simulated. Furthermore, the predicted corrosion depth, corrosion morphologies, and multi-site corrosion cracking behaviors showed good agreement with the experimental results.

Applying Neural ODEs to Derive a Mechanism-Based Model for Characterizing Maturation-Related Serum Creatinine Dynamics in Preterm Newborns.

Serum creatinine in neonates follows complex dynamics due to maturation processes, most pronounced in the first few weeks of life. The development of a mechanism-based model describing complex dynamics requires high expertise in pharmacometric (PMX) modeling and substantial model development time. A recently published machine learning (ML) approach of low-dimensional neural ordinary differential equations (NODEs) is capable of modeling such data from newborns automatically. However, this efficient data-driven approach in itself does not result in a clinically interpretable model. In this work, an approach to deriving an interpretable model with reasonable PMX-type functions is presented. This "translation" was applied to derive a PMX model for serum creatinine in neonates considering maturation processes and covariates. The developed model was compared to a previously published mechanism-based PMX model whereas both models had similar mechanistic structures. The developed model was then utilized to simulate serum creatinine concentrations in the first few weeks of life considering different covariate values for gestational age and birth weight. The reference serum creatinine values derived from these simulations are consistent with observed serum creatinine values and previously published reference values. Thus, the presented NODE-based ML approach to model complex serum creatinine dynamics in newborns and derive interpretable, mathematical-statistical components similar to those in a conventional PMX model demonstrates a novel, viable approach to facilitate the modeling of complex dynamics in clinical settings and pediatric drug development.

Annual atrazine residue estimation in Chinese agricultural soils by integrated modeling of machine learning and mechanism-based models

This study presents a comprehensive approach to estimating annual atrazine residues in China's agricultural soils, integrating machine learning algorithms and mechanism-based models. First, machine learning was used to predict essential parameters influencing atrazine's adsorption, degradation, and dispersivity of solute transport. The results demonstrated that soil organic matter was the most important input variable for predicting adsorption and degradation; clay content was the primary variable for predicting dispersivity. The SHapley Additive exPlanations (SHAP) contribution of various soil properties on target variables were also analyzed to reveal whether each input variable has a positive, negative, or complex effect. Subsequently, these parameters inform the construction of a detailed model across 23,692 subregions of China, with a 20 km × 20 km resolution. The model considered regional variations and soil layer heterogeneity, including rainfall, soil depth-specific properties, and parameters for adsorption, degradation, and dispersivity. Utilizing the convection-dispersion equations and the Phydrus, the model simulated atrazine's transport and degradation patterns across diverse soil environments after applying 250 mL of atrazine (40%) per Chinese mu. The outcomes provided a spatially explicit distribution of atrazine residues, specifying that the arid areas have the highest residual risk, followed by the Northeast, Southwest, and Southeast. Atrazine levels may exceed national drinking water standards at 50 cm depth in Inner Mongolia, the Qinghai-Tibet Plateau, and the Jungar Basin. This study's integrative approach may also offer valuable insights and tools for evaluating residues of various pesticides and herbicides in agricultural soils.

Abstract PO5-07-03: Integrating mechanism-based and data-driven modeling to predict the response of triple negative breast cancer to therapy

Abstract Introduction Neoadjuvant chemotherapy (NAC) is the standard-of-care (SOC) for patients with locally advanced triple-negative breast cancer (TNBC) [1]. Unfortunately, only about half of TNBC patients achieve a pathological complete response (pCR) at the completion of NAC [2]. With the recent FDA approval of immunotherapy, the response rate improved by about 8% [3]. To try to further improve NAC response rates, we build upon methods that predict treatment response to optimize interventions and outcomes. Our previous studies have demonstrated that excellent predictive accuracy of TNBC response to NAC was achieved through calibrating mechanism-based models to both pre- and on- treatment imaging data [4]. However, by requiring on-treatment imaging data, therapy optimization can only occur after a patient begins NAC. Here, our goal is to relax the requirement of on-treatment imaging data such that we can predict, and potentially optimize, a patient’s response to NAC before initiating it. We will accomplish this through combining data-driven approaches with mechanism-based modeling to obtain spatiotemporal predictions of tumor response prior to NAC. More specifically, we integrate a reaction-diffusion equation based mathematical model with a U-Net based convolutional neural network (CNN). Methods The reaction-diffusion equation models the development of tumor cellularity over time as the sum of tumor cell diffusion, controlled by a fixed diffusivity, and tumor cell proliferation, controlled by a calibrated net proliferation rate. The net proliferation rate implicitly accounts for both proliferative and drug induced death effects. We calibrated the net proliferation rates in this model on a patient-specific basis for 128 patients from the ARTEMIS trial (NCT02276443) using three imaging timepoints – pre- (V1), post two cycles (V2), and post four cycles (V3) of A/C. We then employed a CNN to characterize the relationship between the calibrated net proliferation rates and the pre-treatment imaging data. The CNN takes pretreatment imaging inputs of the apparent diffusion coefficient map, percent enhancement maps from the dynamic contrast enhanced MRI timecourse, and precontrast T1 map. The CNN outputs an estimate of the calibrated net proliferation rate which can be used to run the mathematical model forward in time to predict cellularity maps at V2/3. In training the CNN, our loss considers both the concordance correlation coefficient (CCC) and the normalized root mean square error (NRMSE). We calculated loss on the net proliferation rate predictions as NRMSE with a CCC penalty to aid in characterizing the non-Gaussian distribution of the net proliferation rate. We calculated loss on the cellularity predictions as the CCC between predicted and measured voxel-wise cellularity. The CNN was trained using the Adam optimizer and a fivefold cross validation with a withheld test set of 26 patients. Results Using the CNN predictions to forward run the mechanism-based model in time to V3 we calculated change in total tumor cellularity and volume from V1 to V3. In the test cohort, we obtained CCC values between the predicted and measured changes of 0.95 and 0.91 for cellularity and volume, respectively. Discussion and summary Through integrating mechanistic modeling and deep learning, we can accurately predict the spatiotemporal development of TNBC response to NAC using only pretreatment imaging data. We will build upon these results by expanding our model to include separate proliferation and drug induced death terms to allow for therapy intervention and optimization. Additionally, we will incorporate other pretreatment data types including genetic data. [1] Liedtke et al., J of Clin Oncol, 2008. [2] Spring et al., Clin Cancer Res, 2020. [3] Schmid et al., NEJM, 2020. [4] Wu et al., Cancer Res, 2022. Citation Format: Casey Stowers, Chengyue Wu, Sidharth Kumar, Zhan Xu, Clinton Yam, Jong Bum Son, Jingfei Ma, Jonathan Tamir, Gaiane Rauch, Thomas Yankeelov. Integrating mechanism-based and data-driven modeling to predict the response of triple negative breast cancer to therapy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-07-03.

Abstract PO5-03-06: Optimizing therapeutic regimens via digital twins to improve triple negative breast cancer response to neoadjuvant therapy

Abstract Introduction: Neoadjuvant systemic therapy (NAT) has been the standard-of-care of stage II-III, locally advanced triple-negative breast cancer (TNBC). However, about 50% of TNBC patients achieve a pathological complete response (pCR) to conventional neoadjuvant chemotherapy (NAC)1. Recently approved combination of NAC with the immunotherapy pembrolizumab, has improved the pCR rate by 7.5%2, although with a 44% risk of immune-related adverse reaction3. Aside from the need to develop new therapies with higher efficacy and lower toxicity, a critical barrier to improving TNBC response is the lack of rigorous ways to personally tailor therapeutic regimens. We seek to address this challenge by employing digital twins (i.e., mathematical models that provide virtual representation of individual patients and predict the changes at future time points) to systematically evaluate individual TNBC patient’s response to different NAT regimens, thereby patient-specifically optimizing treatments. Methods: A TNBC cohort (n = 139) from the ARTEMIS trial (NCT02276433)4 was used for this study. All patients received 4 cycles of Adriamycin/Cytoxan (A/C) every 2 weeks, followed by 12 cycles of weekly Taxol or experimental therapy in Phase II trials. All patients had surgery after NAT and post-surgical pathology to assess response status. For each patient, longitudinal MRIs were collected before, during, and after A/C. We have developed digital twins to integrate the longitudinal MRIs with a mechanism-based model to accurately predict TNBC response5. The model was based on a reaction-diffusion equation that describes the change in tumor cellularity due to migration, proliferation, and drug-induced death. With parameters personalized using MRIs, the patient-specific model (i.e., digital twin) significantly improved the accuracy to predict pCR5. We investigated patient-specific treatment optimization on 37 (19 pCR, 18 non-pCR) chemo-sensitive patients (≥ 70% volume decrease after A/C) who received only NAC. We evaluated the effect of altering the A/C/Taxol schedules on patient response. Specifically, each patient’s digital twin was used to predict the patient’s response to 128 clinically reasonable schedules of A/C/Taxol; i.e., 8 candidate A/C schedules combining with 16 candidate Taxol schedules (Table 1). The predicted response (pCR or non-pCR) from each alternative schedule was compared to the patient response from the actual treatment. Results: Without changing the total dose, shortening the duration of A/C/Taxol administration increased the treatment efficacy. The effectiveness of altering the schedules varied substantially in different patients. In particular, 8 patients who had non-pCR responses to their actual treatment were predicted to achieve pCR with the dense-dose Taxol (i.e., 4 cycles Taxol, 2 weeks per cycle), indicating a 21.62% improvement of pCR rate in the cohort. Discussion and conclusion: The preliminary results with our digital twin approach provided a unique opportunity of improving TNBC response to NAT through patient-specific optimization of therapeutic schedules. The ongoing effort focuses on accounting for toxicity and investigating the effects of altering therapy types and doses in combination with the schedules on the patient response. [1] Spring et al., Clin Cancer Res, 2020. [2] Schmid et al., NEJM, 2022. [3] Shah et al., Clin Cancer Res, 2022. [4] Yam et al., Clin Cancer Res, 2021. [5] Wu et al., Cancer Res, 2022. Table 1. Candidate therapeutic schedules Citation Format: Chengyue Wu, Ernesto Lima, Casey Stowers, Zhan Xu, Clinton Yam, Jong Bum Son, Jingfei Ma, Gaiane Rauch, Thomas Yankeelov. Optimizing therapeutic regimens via digital twins to improve triple negative breast cancer response to neoadjuvant therapy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-03-06.

Development of a Mechanism-Based Kinetic Model and Process Optimization Incorporating Imidazole Autocatalysis in a 1,1′-Thiocarbonyldiimidazole-Mediated Thiocarbonyl Transfer

Development of a Mechanism-Based Kinetic Model and Process Optimization Incorporating Imidazole Autocatalysis in a 1,1′-Thiocarbonyldiimidazole-Mediated Thiocarbonyl Transfer

Irradiation Effects on Tensile Properties of Reduced Activation Ferritic/Martensitic Steel: A Micromechanical-Damage-Model-Based Numerical Investigation

The tensile properties of reduced activation ferritic/martensitic (RAFM) steels are significantly influenced by neutron irradiation. Here, a mechanism-based model taking account of the typical ductile damage process of void nucleation, growth, and coalescence was used to study the temperature and irradiation effects. The elastic–plastic response of RAFM steels irradiated up to 20 dpa was investigated by applying the GTN model coupled with different work hardening models. Through a numerical study of tensile curves, the GTN parameters were identified reasonably and satisfying simulation results were obtained. A combination of Swift law and Voce law was used to define the flow behavior of irradiated RAFM steels. The deformation localization could be adjusted effectively via setting the nucleation parameter εn close to the strain where necking occurs. Because εn changed with uniform elongation, εn decreased with the testing temperature and rose with an irradiation temperature above 300 °C. The nucleation parameter fn increased with the testing temperature for RAFM steels before irradiation. For irradiated RAFM steels, fn barely changed when the irradiation temperature was below 300 °C and then it rose at a higher irradiation temperature. Meanwhile, the ultimate strength of the simulated and experimental curves showed good agreement, indicating that this method can be applied to engineering design.

Two-Stage Neural Network Optimization for Robust Solar Photovoltaic Forecasting

Neural networks (NNs) have shown outstanding performance in solar photovoltaic (PV) power forecasting due to their ability to effectively learn unstable environmental variables and their complex interactions. However, NNs are limited in their practical industrial application in the energy sector because the optimization of the model structure or hyperparameters is a complex and time-consuming task. This paper proposes a two-stage NN optimization method for robust solar PV power forecasting. First, the solar PV power dataset is divided into training and test sets. In the training set, several NN models with different numbers of hidden layers are constructed, and Optuna is applied to select the optimal hyperparameter values for each model. Next, the optimized NN models for each layer are used to generate estimation and prediction values with fivefold cross-validation on the training and test sets, respectively. Finally, a random forest is used to learn the estimation values, and the prediction values from the test set are used as input to predict the final solar PV power. As a result of experiments in the Incheon area, the proposed method is not only easy to model but also outperforms several forecasting models. As a case in point, with the New-Incheon Sonae dataset—one of three from various Incheon locations—the proposed method achieved an average mean absolute error (MAE) of 149.53 kW and root mean squared error (RMSE) of 202.00 kW. These figures significantly outperform the benchmarks of attention mechanism-based deep learning models, with average scores of 169.87 kW for MAE and 232.55 kW for RMSE, signaling an advance that is expected to make a significant contribution to South Korea’s energy industry.

Exploring the power of pure attention mechanisms in blind room parameter estimation

Dynamic parameterization of acoustic environments has drawn widespread attention in the field of audio processing. Precise representation of local room acoustic characteristics is crucial when designing audio filters for various audio rendering applications. Key parameters in this context include reverberation time (RT60\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{60}$$\\end{document}) and geometric room volume. In recent years, neural networks have been extensively applied in the task of blind room parameter estimation. However, there remains a question of whether pure attention mechanisms can achieve superior performance in this task. To address this issue, this study employs blind room parameter estimation based on monaural noisy speech signals. Various model architectures are investigated, including a proposed attention-based model. This model is a convolution-free Audio Spectrogram Transformer, utilizing patch splitting, attention mechanisms, and cross-modality transfer learning from a pretrained Vision Transformer. Experimental results suggest that the proposed attention mechanism-based model, relying purely on attention mechanisms without using convolution, exhibits significantly improved performance across various room parameter estimation tasks, especially with the help of dedicated pretraining and data augmentation schemes. Additionally, the model demonstrates more advantageous adaptability and robustness when handling variable-length audio inputs compared to existing methods.

Kinetics and hydrothermal combustion characteristics of ethanol in supercritical water

Supercritical hydrothermal combustion is a novel, clean, and efficient combustion method. In this work, a mechanism-based kinetic model of ethanol appropriate for high-pressure hydrothermal environment was developed. Combined with theoretical analysis and continuous ignition experiments, the transient ignition process, ignition temperature, and extinction temperature were discussed. A turbulent combustion model coupling detailed kinetics was constructed to analyze the co-flow diffusion hydrothermal flame. It was found that the ethanol kinetic model can well predict the reaction process, critical ignition temperatures, extinction temperatures, and diffusion combustion process of hydrothermal combustion. The ignition temperatures of 2.40–5.72 wt% ethanol ranged between 500–390 °C, and OH was a significant ignition indicator. As an auxiliary fuel, ethanol is superior to methanol. During co-flow hydrothermal combustion, the interfacial reaction between fuel and oxidant in jet core area had an important influence, and the local high-temperature flame was mainly distributed near the downstream of fuel jet.

Spatial–Temporal Fusion Gated Transformer Network (STFGTN) for Traffic Flow Prediction

Traffic flow prediction is essential for smart city management and planning, aiding in optimizing traffic scheduling and improving overall traffic conditions. However, due to the correlation and heterogeneity of traffic data, effectively integrating the captured temporal and spatial features remains a significant challenge. This paper proposes a model spatial–temporal fusion gated transformer network (STFGTN), which is based on an attention mechanism that integrates temporal and spatial features. This paper proposes an attention mechanism-based model to address these issues and model complex spatial–temporal dependencies in road networks. The self-attention mechanism enables the model to achieve long-term dependency modeling and global representation of time series data. Regarding temporal features, we incorporate a time embedding layer and a time transformer to learn temporal dependencies. This capability contributes to a more comprehensive and accurate understanding of spatial–temporal dynamic patterns throughout the entire time series. As for spatial features, we utilize DGCN and spatial transformers to capture both global and local spatial dependencies, respectively. Additionally, we propose two fusion gate mechanisms to effectively accommodate to the complex correlation and heterogeneity of spatial–temporal information, resulting in a more accurate reflection of the actual traffic flow. Our experiments on three real-world datasets illustrate the superior performance of our approach.

Increasing risks of the explosive extratropical cyclones over the North Atlantic storm track: a perspective from their surface wind maxima

Extratropical cyclones play a crucial role in balancing the global momentum, energy, and moisture, and also, they shape the extreme weather events over the extra-tropics. As the strongest category of the extratropical cyclones, the explosive extratropical cyclones (EECs) frequently induce severe disasters through their strong surface winds. During the entire lifespan of an EEC, there is a maximum value of its surface wind (i.e. the maximum surface wind; MSW), which processes the greatest destructive power. After nearly a century of research on the EECs, key features about their MSWs still remain vague. In this study, we systematically investigate the EECs’ MSWs over the North Atlantic storm track (NAST) based on the ERA-Interim reanalysis. It is found that, the average intensity of EECs’ MSWs shows a significant increasing trend of ∼0.3 m s−1 per 10a. More importantly, for the last 20 years, even larger increasing rates of 1.5 m s−1 and 3.5 m s−1 per 10a are found in the average and maximum intensities of the EECs’ MSWs, respectively, implying the EECs’ risks increase notably for the NAST. We further clarify the physical mechanisms governing the production of EECs’ MSWs, and then establish a mechanism-based statistical model, which has the potential to predict the MSWs’ annual average intensity. In summary, our study fills a knowledge gap for the EECs’ MSWs, which would have broad implication of the economics and society.

Lipase-Catalyzed Synthesis of Cetyl Palmitate in the Presence of n-Hexane and Ethyl Ether

Cetyl palmitate was produced by the esterification of palmitic acid with cetyl alcohol using n-hexane and ethyl ether as solvents and a commercial lipase as a catalyst. The effect of solvents to reagents mass ratio (0.5 : 1–3 : 1), percentage of n-hexane in the solvent mixture (0–100%), and reaction temperature (25–55°C) on the reaction rate were evaluated for a fixed amount of enzyme (1 wt% related to the total mass of substrates) in an equimolar mixture of palmitic acid and cetyl alcohol. Temperature and n-hexane percentage in the solvent had positive effects on the reaction rate. The total solvent-to-reagent mass ratio showed a negative effect on the reaction rate when a solvent mixture rich in ethyl ether was used. The higher the concentration of n-hexane, the lower the effect of the mass ratio of solvents to reagents on the reaction rate. Although the amount of ethyl ether in the solvent mixture had a negative effect on the reaction rate, it had a positive effect on the solubility of the system, that is, the more ethyl ether in the mixture, the lower the solvent to reagents mass ratio required to ensure a homogeneous mixture. A ping-pong bi-bi mechanism-based model was proposed to represent the system kinetics and was well fitted to the experimental data.

Synergistic effects of inhaled aztreonam plus tobramycin on hypermutable cystic fibrosis Pseudomonas aeruginosa isolates in a dynamic biofilm model evaluated by mechanism-based modelling and whole genome sequencing

ObjectiveHypermutable Pseudomonas aeruginosa strains are highly prevalent in chronic lung infections of patients with cystic fibrosis (CF). Acute exacerbations of these infections have limited treatment options. This study aimed to investigate inhaled aztreonam and tobramycin against clinical hypermutable P. aeruginosa strains using the CDC dynamic in vitro biofilm reactor (CBR), mechanism-based mathematical modelling (MBM) and genomic studies. MethodsTwo CF multidrug-resistant strains were investigated in a 168 h CBR (n = 2 biological replicates). Regimens were inhaled aztreonam (75 mg 8-hourly) and tobramycin (300 mg 12-hourly) in monotherapies and combination. The simulated pharmacokinetic profiles of aztreonam and tobramycin (t1/2 = 3 h) were based on published lung fluid concentrations in patients with CF. Total viable and resistant counts were determined for planktonic and biofilm bacteria. MBM of total and resistant bacterial counts and whole genome sequencing were completed. ResultsBoth isolates showed reproducible bacterial regrowth and resistance amplification for the monotherapies by 168 h. The combination performed synergistically, with minimal resistant subpopulations compared to the respective monotherapies at 168 h. Mechanistic synergy appropriately described the antibacterial effects of the combination regimen in the MBM. Genomic analysis of colonies recovered from monotherapy regimens indicated noncanonical resistance mechanisms were likely responsible for treatment failure. ConclusionThe combination of aztreonam and tobramycin was required to suppress the regrowth and resistance of planktonic and biofilm bacteria in all biological replicates of both hypermutable multidrug-resistant P. aeruginosa CF isolates. The developed MBM could be utilised for future investigations of this promising inhaled combination.