Mechanistic Model Research Articles

Gas-liquid flow regimes in a novel rocking and rolling flow loop

Predicting two-phase flow pattern characteristics and flow transition is fundamental to address several industrial problems, e.g. for the oil and gas industry. In order to fulfil the upscaling from the laboratory to the industrial scale, resource-efficient flow testing facilities which closely replicate industrial flow characteristics are needed. To address this, we introduce a new experimental device named as the Rocking and Rolling Ring Flow Loop (3RFL), which is size, cost, and time-efficient. In the present work, we employ the 3RFL apparatus at atmospheric pressure and temperature, with air and water as working fluids. However, the ultimate goal of our work is to build an experimental set-up capable of capturing the under-pressure reactive multiphase flow (gas-liquid–solid) dynamics typical of flow assurance in oil production and transportation. At the moment, the 3RFL can induce different flow regimes by adjusting the system control parameters such as rocking angle, rocking rate, and liquid volume fraction, all without requiring a pump. We observe three flow regimes, and analyse the impact of control parameters on their emergence. Our findings reveal that flow regime transitions are influenced by the competition between gravitational and centrifugal forces, which arise due to the curvature of the tube. Among three employed modelling strategies, namely mechanistic modelling, total energy minimization and a combined approach, we find that the total energy minimization model best compares to the experimental liquid height.

Online optimization of petrochemical process via case-based reasoning and conditional mutual information

The strict mechanism model of petrochemical processes is often complex, resulting in long modeling times, low computational efficiency, and poor accuracy, which limits the application of mechanistic models in process optimization and advanced control. The advent of big data technology provides new solutions for this problem. Herein, a new method based on historical Case-Based Reasoning (CBR) is proposed to process online optimization and calculate variable attribute weights for case retrieval using conditional mutual information. Considering the process time-lag characteristics, a piecewise sequential CBR method for optimization is further proposed, which directly recognizes the pattern based on the industrial past data and amply utilized the process information. For case study and method validation, the approach is applied to an actual fluid catalytic cracking (FCC) process, converting low-quality heavy oils into valuable light transportation fuels and chemicals such as gasoline and propylene. As demonstrated by the case study results, the proposed method increases the average yield of gasoline and total liquid respectively by 2.5 % and 4.0 % while decreasing the average yield of coke by 1.3 %. The results further indicate that the overall optimization performance is comparable to many advanced intellectual optimization algorithms, allowing favorable operability and ensuring good robustness for different optimization targets. It could provide a solution with high accuracy and good adaptability for online process optimization in complex chemical processes.

Intelligent Soft Measurement of Catalyst Activity during a Polyethylene Reaction Based on Mechanistic Modeling and Deep Learning

Intelligent Soft Measurement of Catalyst Activity during a Polyethylene Reaction Based on Mechanistic Modeling and Deep Learning

Prototype Biodiversity Digital Twin: Disease Outbreaks

African swine fever is a transmissible virus impacting wild and domestic swine populations. In Europe, it is non-native and the recently introduced genotype affects wild boar populations with occasional outbreaks in domestic pigs. The ability to predict short-term spatial dynamics of this disease will greatly improve our ability to control and limit future spread of the virus. The BioDT disease outbreaks prototype, currently in development, implements an individual-based landscape-level mechanistic model as a digital twin aimed at providing informed support for management decisions in response to the spread of African swine fever in European wild boar populations.

Clinical narrative and the painful side of conscious experience

ABSTRACT This article explores a literary tradition situated at the intersection of scientific reports, memoirs, and creative writing, termed “clinical narrative.” This genre offers a profound approach to the painful aspects of conscious experience, particularly the phenomenological states associated with mental illness and brain disease, seen as unsettling landscapes of phenomenal experience. Through case studies providing multifaceted viewpoints – first-person, second-person, and third-person perspectives – we argue that clinical narratives are valuable resources for a transepistemic study of consciousness. By examining clinical conundrums such as somatic and nihilistic delusions, and anosognosia, we highlight the importance of detailed phenomenological, hermeneutic, and narrative accounts while acknowledging the significance of subpersonal, mechanistic models from cognitive and affective neuroscience. The tradition embodies the tension between the diverse perspectives in the field of mental health, including stories that directly challenge the medical discourse. However, the narrative arts can act as mediators or even peacemakers, by fostering an understanding between the opposing views. Stories are open to multiple interpretations, preserving the diversity of discourses on human meaning and avoiding the imposition of monolithic versions of our humanness.

Quantifying the effectiveness of brucellosis control strategies in northern China using a mechanistic and data-driven model

In recent years, with the frequent movement of livestock and related products on the Chinese mainland that have resulted in a rise in brucellosis cases in certain regions, the Chinese government has implemented “vaccination + culling” strategy to control the spread of brucellosis, but the epidemiological situation and the intensity of prevention and control measures vary from province to province, and much of the situation has not yet been reversed. Thus, it is crucial to understand and assess the combined effects of vaccination and culling on brucellosis transmission behind the data from multiple provinces and cities. In this paper, we combine an ordinary differential equation model and a deep neural network model to develop a hybrid model that not only characterizes the propagation mechanism of brucellosis, but also describes control strategies as time-varying parameters to track the dynamic evolution of brucellosis transmission under control measures with varying intensity. In addition, the real-time reproduction number is calculated and taken as an evaluation index. We then train and test the hybrid model using actual data from four provincial administrative regions in Northern China from 2010 to 2020. It is found that in Inner Mongolia, the sheep vaccination rate (θ(t)) and the culling rate of infected sheep (c(t)) have a weak effect on reducing the brucellosis transmission. In Gansu, the incomplete culling of infected sheep was the main reason for the increase in human brucellosis cases after the introduction of whole-flock vaccination in 2016. Culling and vaccination contributed to the decrease in human cases in both Xinjiang and Jilin. Prevention and control measures for human brucellosis are inadequate in Xinjiang. However, prevention and control measures for human brucellosis are inadequate, while indirect transmission of Brucella from the environment to humans and sheep poses a greater risk than direct contact with infected sheep in Jilin. The above analysis can provide theoretical foundation for decision-making on public health policies to control the pandemic.

Robust, fully quantifiable and scalable bioprocess utilizing spent sulfite liquor with Corynebacterium glutamicum

In this study, a bioprocessing strategy was designed to valorize ultra-filtered spent sulfite liquor (UF-SSL) without prior detoxification steps as well as using it purely as a carbon source supplement to defined or complex media. Hence, a minimal medium for the bioconversion of UF-SSL with Corynebacterium glutamicum was developed and process robustness and reproducibility were validated. Process quantifiability was ensured by development of a biomass measurement technique for matrices with high water-insoluble solids and verified using elemental balancing. Mechanistic modeling based on Monod equations was used to identify batch kinetics. In a final step, scale-up of the developed process was performed to showcase process maturity towards commercialisation.

Deciphering oxygen distribution and hypoxia profiles in the tumor microenvironment: a data-driven mechanistic modeling approach

Objective. The distribution of hypoxia within tissues plays a critical role in tumor diagnosis and prognosis. Recognizing the significance of tumor oxygenation and hypoxia gradients, we introduce mathematical frameworks grounded in mechanistic modeling approaches for their quantitative assessment within a tumor microenvironment. By utilizing known blood vasculature, we aim to predict hypoxia levels across different tumor types. Approach. Our approach offers a computational method to measure and predict hypoxia using known blood vasculature. By formulating a reaction-diffusion model for oxygen distribution, we derive the corresponding hypoxia profile. Main results. The framework successfully replicates observed inter- and intra-tumor heterogeneity in experimentally obtained hypoxia profiles across various tumor types (breast, ovarian, pancreatic). Additionally, we propose a data-driven method to deduce partial differential equation models with spatially dependent parameters, which allows us to comprehend the variability of hypoxia profiles within tissues. The versatility of our framework lies in capturing diverse and dynamic behaviors of tumor oxygenation, as well as categorizing states of vascularization based on the dynamics of oxygen molecules, as identified by the model parameters. Significance. The proposed data-informed mechanistic method quantitatively assesses hypoxia in the tumor microenvironment by integrating diverse histopathological data and making predictions across different types of data. The framework provides valuable insights from both modeling and biological perspectives, advancing our comprehension of spatio-temporal dynamics of tumor oxygenation.

Simultaneous dose and dose rate optimization via dose modifying factor modeling for FLASH effective dose.

Although the FLASH radiotherapy (FLASH) can improve the sparing of organs-at-risk (OAR) via the FLASH effect, it is generally a tradeoff between the physical dose coverage and the biological FLASH coverage, for which the concept of FLASH effective dose (FED) is needed to quantify the net improvement of FLASH, compared to the conventional radiotherapy (CONV). This work will develop the first-of-its-kind treatment planning method called simultaneous dose and dose rate optimization via dose modifying factor modeling (SDDRO-DMF) for proton FLASH that directly optimizes FED. SDDRO-DMF models and optimizes FED using FLASH dose modifying factor (DMF) models, which can be classified into two categories: (1) the phenomenological model of the FLASH effect, such as the FLASH effectiveness model (FEM); (2) the mechanistic model of the FLASH radiobiology, such as the radiolytic oxygen depletion (ROD) model. The general framework of SDDRO-DMF will be developed, with specific DMF models using FEM and ROD, as a demonstration of general applicability of SDDRO-DMF for proton FLASH via transmission beams (TB) or Bragg peaks (BP) with single-field or multi-field irradiation. The FLASH dose rate is modeled as pencil beam scanning dose rate. The solution algorithm for solving the inverse optimization problem of SDDRO-DMF is based on iterative convex relaxation method. SDDRO-DMF is validated in comparison with IMPT and a state-of-the-art method called SDDRO, with demonstrated efficacy and improvement for reducing the high dose and the high-dose volume for OAR in terms of FED. For example, in a SBRT lung case of the dose-limiting factor that the max dose of brachial plexus should be no more than 26Gy, only SDDRO-DMF met this max dose constraint; moreover, SDDRO-DMF completely eliminated the high-dose (V70%) volume to zero for CTV10mm (a high-dose region as a 10mm ring expansion of CTV). We have proposed a new proton FLASH optimization method called SDDRO-DMF that directly optimizes FED using phenomenological or mechanistic models of DMF, and have demonstrated the efficacy of SDDO-DMF in reducing the high-dose volume or/and the high-dose value for OAR, compared to IMPT and a state-of-the-art method SDDRO.

Influence of test paradigm on loading dynamics during proximal femur fracture tests simulating sideways falls

Fall-related hip fractures are a serious public health issue in older adults. As most mechanistic hip fracture risk prediction models incorporate tissue tolerance, test methods that can accurately characterize the fracture force of the femur (and factors that influence it) are imperative. While bone possesses viscoelastic properties, experimental characterization of rate-dependencies has been inconsistent in the whole-femur literature. The goal of this study was to investigate the influence of experimental paradigm on loading rate and fracture force (both means and variability) during mechanical tests simulating lateral fall loadings on the proximal femur. Six pairs of matched femurs were split randomly between two test paradigms: a ‘lower rate’ materials testing system (MTS) with a constant displacement rate of 60 mm/s, and a hip impact test system (HIT) comprised of a custom-built vertical drop tower utilizing an impact velocity of 4 m/s. The loading rate was 88-fold higher for the HIT (mean (SD) = 2465.49 (807.38) kN/s) compared to the MTS (27.78 (10.03) kN/s) paradigm. However, no difference in fracture force was observed between test paradigms (mean (SD) = 4096.4 (1272.6) N for HIT, and 3641.3 (1285.8) N for MTS). Within-paradigm variability was not significantly different across paradigms for either loading rate or fracture force (coefficients of variation ranging from 0.311 to 0.361). Within each test paradigm, significant positive relationships were observed between loading rate and fracture force (HIT adjusted R2 = 0.833, p = 0.007; MTS adjusted R2 = 0.983, p < 0.0001). Overall, this study provides evidence that energy-based impact simulators can be a valid method to measure femoral bone strength in the context of fall-related hip fractures. This study motivates future research to characterize potential non-linear relationships between loading rate and fracture threshold at both macro and microscales.

Risk of wind destruction to urban trees: Prediction workflow and relative importance of influencing factors

During typhoons, risk of wind destruction to trees is harder to predict for trees in urban areas than for those in plains, and the influencing factors are more intricate. This study integrated a simplified HWIND mechanistic model for trees, computational fluid dynamics simulations for airflow, and quantified tree morphology indicators to predict the risk of wind destruction to urban trees. The workflow was demonstrated using three typical streets in Guangzhou, China. The workflow was verified based on the observed damage states of case trees in the study areas after Typhoon Mangkhut. Original critical wind speed in plains (CWSP(10)) and urban regions (CWS(10)) at a height of 10 m were introduced to evaluate the wind resistance and destruction risk to urban trees. The relative influence of various factors on CWS(10) and CWSP(10) was evaluated through Relative Weight Analysis and Random Forest. For urban trees with uprooting as the primary destruction mode, wind resistance is mainly influenced by tree height and total root-soil length in terms of tree morphology indicators, while the decisive factor affecting wind destruction risk is the “built environment indicator,” defined as BEI=CWS(10)/CWSP(10).

Effectiveness of cooling interventions on heat-stressed dairy cows based on a mechanistic thermoregulatory model

Addressing heat stress in dairy farming is a substantial challenge, and there is an increasing need for efficient cooling systems, even in regions with moderate climates. Accurately predicting the efficacy of diverse cooling options under different climatic conditions is crucial for reducing heat stress in modern high-producing dairy cows, aligning with sustainability goals. This study assessed the effectiveness and feasibility of different cooling measures, including fans, sprinklers with fans, and evaporative air cooling, using a dynamic thermoregulatory model. This 3-node dynamic model was developed based on recent animal data simulating the processes of dairy cows' physiological regulation and heat dissipation under various environmental conditions. The cooling methods were based on two principles: enhancing heat loss from cows using fans with/without sprinklers; lowering the ambient temperature by evaporative air cooling. The predicted results were discussed and partly validated using the experimental data from the literature. The predictions indicated that fan cooling alone was effective in ambient temperatures below 26 °C, while higher temperatures required a combination of fans and sprinklers for effective heat stress alleviation. Consideration of individual cow characteristics and environmental factors, including fan speed and wetting area, is crucial for optimal cooling. In regions with high relative humidity, evaporative air cooling could be counterproductive to some extent. The model's predictions largely aligned with experimental data, demonstrating its capability to forecast cooling effects under various climatic conditions. Future model improvements included refining calculations for water holding capacity, wetted skin area, and dry time, depending on the influence of spraying time and rate.

In Situ Complexation of sgRNA and Cas12a Improves the Performance of a One-Pot RPA-CRISPR-Cas12 Assay.

Due to their ability to selectively target pathogen-specific nucleic acids, CRISPR-Cas systems are increasingly being employed as diagnostic tools. "One-pot" assays that combine nucleic acid amplification and CRISPR-Cas systems (NAAT-CRISPR-Cas) in a single step have emerged as one of the most popular CRISPR-Cas biosensing formats. However, operational simplicity comes at a cost, with one-pot assays typically being less sensitive than corresponding two-step NAAT-CRISPR-Cas assays and often failing to detect targets at low concentrations. It is thought that these performance reductions result from the competition between the two enzymatic processes driving the assay, namely, Cas-mediated cis-cleavage and polymerase-mediated amplification of the target DNA. Herein, we describe a novel one-pot RPA-Cas12a assay that circumvents this issue by leveraging in situ complexation of the target-specific sgRNA and Cas12a to purposefully limit the concentration of active Cas12a during the early stages of the assay. Using a clinically relevant assay against a DNA target for HPV-16, we show how this in situ format reduces competition between target cleavage and amplification and engenders significant improvements in detection limit when compared to the traditional one-pot assay format, even in patient-derived samples. Finally, to gain further insight into the assay, we use experimental data to formulate a mechanistic model describing the competition between the Cas enzyme and nucleic acid amplification. These findings suggest that purposefully limiting cis-cleavage rates of Cas proteins is a viable strategy for improving the performance of one-pot NAAT-CRISPR-Cas assays.

Transfer learning for cross-context prediction of protein expression from 5'UTR sequence.

Model-guided DNA sequence design can accelerate the reprogramming of living cells. It allows us to engineer more complex biological systems by removing the need to physically assemble and test each potential design. While mechanistic models of gene expression have seen some success in supporting this goal, data-centric, deep learning-based approaches often provide more accurate predictions. This accuracy, however, comes at a cost - a lack of generalization across genetic and experimental contexts that has limited their wider use outside the context in which they were trained. Here, we address this issue by demonstrating how a simple transfer learning procedure can effectively tune a pre-trained deep learning model to predict protein translation rate from 5' untranslated region (5'UTR) sequence for diverse contexts in Escherichia coli using a small number of new measurements. This allows for important model features learnt from expensive massively parallel reporter assays to be easily transferred to new settings. By releasing our trained deep learning model and complementary calibration procedure, this study acts as a starting point for continually refined model-based sequence design that builds on previous knowledge and future experimental efforts.

Recurrent Duplication and Diversification of a Vital DNA Repair Gene Family Across Drosophila.

Maintaining genome integrity is vital for organismal survival and reproduction. Essential, broadly conserved DNA repair pathways actively preserve genome integrity. However, many DNA repair proteins evolve adaptively. Ecological forces like UV exposure are classically cited drivers of DNA repair evolution. Intrinsic forces like repetitive DNA, which also imperil genome integrity, have received less attention. We recently reported that a Drosophila melanogaster-specific DNA satellite array triggered species-specific, adaptive evolution of a DNA repair protein called Spartan/MH. The Spartan family of proteases cleave hazardous, covalent crosslinks that form between DNA and proteins ("DNA-protein crosslink repair"). Appreciating that DNA satellites are both ubiquitous and universally fast-evolving, we hypothesized that satellite DNA turnover spurs adaptive evolution of DNA-protein crosslink repair beyond a single gene and beyond the D. melanogaster lineage. This hypothesis predicts pervasive Spartan gene family diversification across Drosophila species. To study the evolutionary history of the Drosophila Spartan gene family, we conducted population genetic, molecular evolution, phylogenomic, and tissue-specific expression analyses. We uncovered widespread signals of positive selection across multiple Spartan family genes and across multiple evolutionary timescales. We also detected recurrent Spartan family gene duplication, divergence, and gene loss. Finally, we found that ovary-enriched parent genes consistently birthed functionally diverged, testis-enriched daughter genes. To account for Spartan family diversification, we introduce a novel mechanistic model of antagonistic coevolution that links DNA satellite evolution and adaptive regulation of Spartan protease activity. This framework promises to accelerate our understanding of how DNA repeats drive recurrent evolutionary innovation to preserve genome integrity.

Advancing precision fermentation: Minimizing power demand of industrial scale bioreactors through mechanistic modelling

Minimizing power consumption in large-scale aerobic fermentation is essential for cost-effective operations. A mechanistic model of aerobic precision fermentation was developed integrating microbial growth parameters, thermodynamic data, and bioreactor properties. Results showed that agitation power dominated energy consumption at low oxygen transfer rates (OTR), shifting to aeration power (70 % of total) at high cell growth rates. In high OTRs, mixing time reduced to 60 s from an initial value of 211 s. Scale-up from 5 m³ to 100 m³ decreased total specific power by 88 %. Operating at elevated headspace pressure lowered agitation speed, reducing total power consumption at high OTR. Impeller to bioreactor diameter ratio impacted the required agitation speed without significantly altering total power demand. Experimental data in a 100 L case study indicated a 0.43 kW.m⁻³ average power requirement across a 96-hour fermentation period. Our model demonstrates effective strategies for minimization of power consumption in industrial-scale aerobic fermentations.

Comprehensive Evaluation of OATP‐ and BCRP‐Mediated Drug–Drug Interactions of Methotrexate Using Physiologically‐Based Pharmacokinetic Modeling

Methotrexate (MTX) is an antifolate agent widely used for treating conditions such as rheumatoid arthritis and hematologic cancer. This study aimed to quantitatively interpret the drug–drug interactions (DDIs) of MTX mediated by drug transporters using physiologically‐based pharmacokinetic (PBPK) modeling. An open‐label, randomized, 4‐treatment, 6‐sequence, 4‐period crossover study was conducted to investigate the effects of rifampicin (RFP), an inhibitor of organic anionic transporting peptides (OATP) 1B1/3, and febuxostat (FBX), an inhibitor of breast cancer resistance protein (BCRP), on the pharmacokinetics of MTX in healthy volunteers. PBPK models of MTX, RFP, and FBX were developed based on in vitro and in vivo data, and the performance of the simulation results for final PBPK models was validated in a clinical study. In the clinical study, when MTX was co‐administered with RFP or FBX, systemic exposure of MTX increased by 33% and 17%, respectively, compared with that when MTX was administered alone. When MTX was co‐administered with RFP and FBX, systemic exposure increased by 52% compared with that when MTX was administered alone. The final PBPK model showed a good prediction performance for the observed clinical data. The PBPK model of MTX was well developed in this study and can be used as a potential mechanistic model to predict and evaluate drug transporter‐mediated DDIs of MTX with other drugs.

High‐throughput in silico workflow for optimization and characterization of multimodal chromatographic processes

AbstractWhile high‐throughput (HT) experimentation and mechanistic modeling have long been employed in chromatographic process development, it remains unclear how these techniques should be used in concert within development workflows. In this work, a process development workflow based on HT experiments and mechanistic modeling was constructed. The integration of HT and modeling approaches offers improved workflow efficiency and speed. This high‐throughput in silico (HT‐IS) workflow was employed to develop a Capto MMC polishing step for mAb aggregate removal. High‐throughput batch isotherm data was first generated over a range of mobile phase conditions and a suite of analytics were employed. Parameters for the extended steric mass action (SMA) isotherm were regressed for the multicomponent system. Model validation was performed using the extended SMA isotherm in concert with the general rate model of chromatography using the CADET modeling software. Here, step elution profiles were predicted for eight RoboColumn runs across a range of ionic strength, pH, and load density. Optimized processes were generated through minimization of a complex objective function based on key process metrics. Processes were evaluated at lab‐scale using two feedstocks, differing in composition. The results confirmed that both processes obtained high monomer yield (&gt;85%) and removed of aggregate species. Column simulations were then carried out to determine sensitivity to a wide range of process inputs. Elution buffer pH was found to be the most critical process parameter, followed by resin ionic capacity. Overall, this study demonstrated the utility of the HT‐IS workflow for rapid process development and characterization.

Review of indentation size effect in crystalline materials: Progress, challenges and opportunities

Nanoindentation has been widely utilized for measuring mechanical properties at small scales over the past three decades. Indentation size effect (ISE) is a crucial phenomenon in nanoindentation testing of crystalline materials, which is usually manifested as an increase of hardness with a decrease of indent size. Tremendous efforts have been made to understand ISE with the aim of eliminating its influence on determining the intrinsic mechanical properties. More importantly, ISE is a key phenomenon for exploring the unique deformation mechanisms of crystalline materials at the nanoscale. We critically review the experimental observations of ISE in crystalline materials, focusing on the influences of the indenter geometry and crystal structure. In addition, the mechanistic models proposed for ISE as well as recent findings by numerical simulations are also examined in detail to explore the origins of ISE and identify the unsolved challenges.

Modeling Pressure Gradient of Gas–Oil–Water Three-Phase Flow in Horizontal Pipes Downstream of Restrictions

Gas–oil–water three-phase slug flows in pipes commonly exist in the oil and gas industry as oil fields are becoming mature and water production is becoming inevitable. Although studies on multiphase flows in pipes have been ongoing for decades, most previous research has focused on gas–liquid or oil–water two-phase flows, with limited studies on gas–liquid–liquid flows. This leads to limited modeling studies on gas–liquid–liquid flows. One factor contributing to the complexity of the gas–liquid–liquid flow is the mixing between the oil and water phases, which have closer fluid properties and low interfacial tension. Restrictions or piping components play a crucial role in altering phase mixing. Unfortunately, modeling studies that consider the effects of these restrictions are limited due to the scarcity of experimental research. To address this gap, we conducted experimental studies on a gas–liquid–liquid flow downstream of a restriction and developed a new mechanistic modeling approach to predict the pressure gradient. Our model focuses on the flow pattern where the oil and water phases are partially mixed. This work emphasizes the modeling approach. The model evaluation results show that the model outperforms other existing models, with an average absolute relative error of 6.71%. Additionally, the parametric study shows that the new modeling approach effectively captures the effects of restriction size, water cut, and gas and liquid flow rates on the three-phase slug flow pressure gradient in horizontal pipes. Most previous slug flow modeling work assumes either a stratified flow or fully dispersed flow between the oil and water phases. This work provides a novel perspective in modeling a three-phase slug flow in which the oil and water phases are partially mixed. In addition, this novel approach to modeling the restriction effects on the pressure gradient paves the way for future modeling for different types of piping components or restrictions.