Mechanistic Model Research Articles

Mechanistic modeling of twin screw wet granulation for pharmaceutical formulations: Calibration, sensitivity analysis, and model-driven workflow

Wet granulation, a particle size enlargement process, can significantly enhance the critical quality attributes of powders and improve the ability to form tablets in pharmaceutical manufacturing. In this study, a mechanistic-based population balance model is applied to twin screw wet granulation. This model incorporated a recently developed breakage kernel specifically designed for twin screw granulation, along with nucleation, layering, and consolidation. Calibration and validation were performed on Hydrochlorothiazide and Acetaminophen formulations, which exhibit different particle size and wettability characteristics. Utilizing a compartmental experimental dataset, a comprehensive global sensitivity analysis identified critical inputs impacting quality attributes. The study revealed that the nucleation rate process model, effectively represented particle size distributions for both formulations. Adjustments to nucleation and breakage rate parameters, influenced by material properties and screw configuration, improved the model’s accuracy. A model-driven workflow was proposed, offering step-by-step guidelines and facilitating PBM model usage, providing essential details for future active pharmaceutical ingredient (API) formulations.

New paradigm for watershed model development by coupling machine learning algorithm and mechanistic model

The development of watershed models faces double pressure of data requirements and physical interpretation. The simulation of nonpoint source (NPS) pollution is an important application of watershed model, and the best management practices (BMPs) have attracted wide attention as the main control approach of NPS pollution. In this study, a new paradigm was proposed based on the integration of data-driven and mechanistic methods, taking BMP evaluation as an example. Specifically, comprehensive databases were constructed for filter and retention BMPs by collecting, classifying and analyzing published data. Twelve machine learning algorithms were employed for regression analysis between BMPs efficiency and their influencing factors, while the constructed equations were coupled with physical-based models. A case study was performed in a typical catchment of Chaohu Lake Watershed, China. The results demonstrated total interception area, soil type, and vegetation biomass had significant impacts on BMPs performances, while the multilayer perceptron regression (MLPR), K nearest neighbor regression (KNRD), and extremely randomized tree (ETR) methods had the best performances of nutrients removal. The study generated over ten thousand datasets using mechanistic processes, resulting in more efficient and interpretable BMPs evaluator. Compare to traditional methods, this new paradigm has shown advantages in the model application at the watershed scale, enhancing the BMPs evaluator with a higher level of interpretability by coupling the approach with mechanistic models.

Approximate inference for longitudinal mechanistic HIV contact network

Network models are increasingly used to study infectious disease spread. Exponential Random Graph models have a history in this area, with scalable inference methods now available. An alternative approach uses mechanistic network models. Mechanistic network models directly capture individual behaviors, making them suitable for studying sexually transmitted diseases. Combining mechanistic models with Approximate Bayesian Computation allows flexible modeling using domain-specific interaction rules among agents, avoiding network model oversimplifications. These models are ideal for longitudinal settings as they explicitly incorporate network evolution over time. We implemented a discrete-time version of a previously published continuous-time model of evolving contact networks for men who have sex with men and proposed an ABC-based approximate inference scheme for it. As expected, we found that a two-wave longitudinal study design improves the accuracy of inference compared to a cross-sectional design. However, the gains in precision in collecting data twice, up to 18%, depend on the spacing of the two waves and are sensitive to the choice of summary statistics. In addition to methodological developments, our results inform the design of future longitudinal network studies in sexually transmitted diseases, specifically in terms of what data to collect from participants and when to do so.

Mechanistic Modeling of Hydraulic Energy Transmission Technology of an Improved Dental Patient Chair Design

This work focused on mechanistic modeling of hydraulic technology of an improved dental chair design. ISO 6385 standard for P(M)95 patient were basis for system parameters, and fathom masses between 60kg-93kg, corresponding to DINED 2003 mass of patients, produced total load on the pantograph. The force exerted by the lower limb of the dentist is an ergonomic stressor that was experimentally studied using Pascal’s law of the hydraulic system. Methodology adopted was mechanistic modeling technique. The results indicated that pedal force of 0.1375kN elevated trimmed chair (dead load) of 0.55kN, while pedal force of 211N to 277N were required to recline between 60kg-93kg masses on the system. It infers that the pump has a mechanical advantage of approximately 5, with displacement volume, 11.7825cm-2, that produced a platform displacement of 3. 75cm during a 15cm hydraulic pedal travel stroke. The experimental results corroborated Pascal’s laws and is has technological significance on carrier-long musculoskeletal disorders (MSDs) of lower limb among dental practitioners.

Kinetic modelling of glycolytic oscillations.

Glycolytic oscillations have been studied for well over 60 years, but aspects of their function, and mechanisms of regulation and synchronisation remain unclear. Glycolysis is amenable to mechanistic mathematical modelling, as its components have been well characterised, and the system can be studied at many organisational levels: in vitro reconstituted enzymes, cell free extracts, individual cells, and cell populations. In recent years, the emergence of individual cell analysis has opened new ways of studying this intriguing system.

Grid Cells in Cognition: Mechanisms and Function.

The activity patterns of grid cells form distinctively regular triangular lattices over the explored spatial environment and are largely invariant to visual stimuli, animal movement, and environment geometry. These neurons present numerous fascinating challenges to the curious (neuro)scientist: What are the circuit mechanisms responsible for creating spatially periodic activity patterns from the monotonic input-output responses of single neurons? How and why does the brain encode a local, nonperiodic variable-the allocentric position of the animal-with a periodic, nonlocal code? And, are grid cells truly specialized for spatial computations? Otherwise, what is their role in general cognition more broadly? We review efforts in uncovering the mechanisms and functional properties of grid cells, highlighting recent progress in the experimental validation of mechanistic grid cell models, and discuss the coding properties and functional advantages of the grid code as suggested by continuous attractor network models of grid cells.

Learning spatio-temporal patterns with Neural Cellular Automata.

Neural Cellular Automata (NCA) are a powerful combination of machine learning and mechanistic modelling. We train NCA to learn complex dynamics from time series of images and Partial Differential Equation (PDE) trajectories. Our method is designed to identify underlying local rules that govern large scale dynamic emergent behaviours. Previous work on NCA focuses on learning rules that give stationary emergent structures. We extend NCA to capture both transient and stable structures within the same system, as well as learning rules that capture the dynamics of Turing pattern formation in nonlinear PDEs. We demonstrate that NCA can generalise very well beyond their PDE training data, we show how to constrain NCA to respect given symmetries, and we explore the effects of associated hyperparameters on model performance and stability. Being able to learn arbitrary dynamics gives NCA great potential as a data driven modelling framework, especially for modelling biological pattern formation.

Development of coffee bean porosity and thermophysical properties during roasting

The development of coffee bean porosity during roasting was captured for a Kenyan Arabica coffee roasted under different constant inlet air temperature conditions in a pilot-scale spouted bed roaster. Coffee bean porosity was characterised using X-ray Micro Computed Tomography (Micro-CT). These data demonstrated a significant increase in coffee bean porosity of up to 60% during roasting. The coffee's intrinsic physical properties were also analysed to identify the relationship between porosity, thermophysical properties (density, thermal conductivity, specific heat capacity) and common process indicators (colour, mass, moisture, volume). Implications for heat transfer during roasting are also discussed. As Micro-CT, pycnometry and thermal properties analysers are not readily available tools, correlation of costly analytical methods with rapid discriminative tests are presented, allowing developers to utilise more accessible characterisation techniques to inform modulation of their processes and products. The data-driven approach to map coffee's physicochemical development during roasting provides comprehensive data that could be used to calibrate mechanistic or kinetic models. These physics-driven models could then be used as routine equations nested in heat and mass transfer simulations for developers to predict coffee's thermal evolution and subsequent physical transformation during roasting.

Novel Electrophysiological Signatures of Learning and Forgetting in Human Rapid Eye Movement Sleep.

Despite the known behavioral benefits of rapid eye movement (REM) sleep, discrete neural oscillatory events in human scalp electroencephalography (EEG) linked with behavior have not been discovered. This knowledge gap hinders mechanistic understanding of the function of sleep, as well as the development of biophysical models and REM-based causal interventions. We designed a detection algorithm to identify bursts of activity in high-density, scalp EEG within theta (4-8 Hz) and alpha (8-13 Hz) bands during REM sleep. Across 38 nights of sleep, we characterized the burst events (i.e., count, duration, density, peak frequency, amplitude) in healthy, young male and female human participants (38; 21F) and investigated burst activity in relation to sleep-dependent memory tasks: hippocampal-dependent episodic verbal memory and nonhippocampal visual perceptual learning. We found greater burst count during the more REM-intensive second half of the night (p < 0.05), longer burst duration during the first half of the night (p < 0.05), but no differences across the night in density or power (p > 0.05). Moreover, increased alpha burst power was associated with increased overnight forgetting for episodic memory (p < 0.05). Furthermore, we show that increased REM theta burst activity in retinotopically specific regions was associated with better visual perceptual performance. Our work provides a critical bridge between discrete REM sleep events in human scalp EEG that support cognitive processes and the identification of similar activity patterns in animal models that allow for further mechanistic characterization.

A Comparison of Markov and Mechanistic Models for Soil-Transmitted Helminth Prevalence Projections in the Context of Survey Design.

Globally, there are over 1 billion people infected with soil-transmitted helminths (STHs), mostly living in marginalized settings with inadequate sanitation in sub-Saharan Africa and Southeast Asia. The World Health Organization recommends an integrated approach to STH morbidity control through improved access to sanitation and hygiene education and the delivery of preventive chemotherapy (PC) to school-age children delivered through schools. Progress of STH control programs is currently estimated using a baseline (pre-PC) school-based prevalence survey and then monitored using periodical school-based prevalence surveys, known as Impact Assessment Surveys (IAS). We investigated whether integrating geostatistical methods with a Markov model or a mechanistic transmission model for projecting prevalence forward in time from baseline can improve IAS design strategies. To do this, we applied these 2 methods to prevalence data collected in Kenya, before evaluating and comparing their performance in accurately informing optimal survey design for a range of IAS sampling designs. We found that, although both approaches performed well, the mechanistic method more accurately projected prevalence over time and provided more accurate information for guiding survey design. Both methods performed less well in areas with persistent STH hotspots where prevalence did not decrease despite multiple rounds of PC. Our findings show that these methods can be useful tools for more efficient and accurate targeting of PC. The general framework built in this paper can also be used for projecting prevalence and informing survey design for other neglected tropical diseases.

Modeling temperature-dependent life-cycle toxicity of thiamethoxam in Chironomus riparius using a DEB-TKTD model

The neonicotinoid insecticide thiamethoxam (TMX) is widely used to protect crops against insect pests. Despite some desirable properties such as its low toxicity to birds and mammals, concerns have been raised about its toxicity to non-target arthropods, including freshwater insects like chironomids. Whereas multiple studies have investigated chronic effects of neonicotinoids in chironomid larvae at standardized laboratory conditions, a better understanding of their chronic toxicity under variable temperatures and exposure is needed for coherent extrapolation from the laboratory to the field. Here, we developed a quantitative mechanistic effect model for Chironomus riparius, to simulate the species’ life history under dynamic temperatures and exposure concentrations of TMX. Laboratory experiments at four different temperatures (12, 15, 20, 23 °C) and TMX concentrations between 4 and 51 µg/L were used to calibrate the model. Observed concentration-dependent effects of TMX in C. riparius included slower growth, later emergence, and higher mortality rates with increasing concentrations. Furthermore, besides a typical accelerating effect on the organisms’ growth and development, higher temperatures further increased the effects associated with TMX. With some data-informed modeling decisions, most prominently the inclusion of a size dependence that makes larger animals more sensitive to TMX, the model was parametrized to convincingly reproduce the data. Experiments at both a constant (20 °C) and a dynamically increasing temperature (15–23 °C) with pulsed exposure were used to validate the model. Finally, the model was used to simulate realistic exposure conditions using two reference exposure scenarios measured in Missouri and Nebraska, utilizing a moving time window (MTW) and either a constant temperature (20 °C) or the measured temperature profiles belonging to each respective scenario. Minimum exposure multiplication factors leading to a 10% effect (EP10) in the survival at pupation, i.e., the most sensitive endpoint found in this study, were 25.67 and 21.87 for the Missouri scenario and 38.58 and 44.64 for the Nebraska scenario, when using the respective temperature assumptions. While the results illustrate that the use of real temperature scenarios does not systematically modify the EPx in the same direction (making it either more or less conservative when used as a risk indicator), the advantage of this approach is that it increases the realism and thus reduces the uncertainty associated with the model predictions.

Gravity waves generated by the Hunga Tonga–Hunga Ha′apai volcanic eruption and their global propagation in the mesosphere/lower thermosphere observed by meteor radars and modeled with the High-Altitude general Mechanistic Circulation Model

Abstract. The Hunga Tonga–Hunga Ha′apai volcano erupted on 15 January 2022, launching Lamb waves and gravity waves into the atmosphere. In this study, we present results using 13 globally distributed meteor radars and identify the volcanogenic gravity waves in the mesospheric/lower thermospheric winds. Leveraging the High-Altitude Mechanistic general Circulation Model (HIAMCM), we compare the global propagation of these gravity waves. We observed an eastward-propagating gravity wave packet with an observed phase speed of 240 ± 5.7 m s−1 and a westward-propagating gravity wave with an observed phase speed of 166.5 ± 6.4 m s−1. We identified these waves in HIAMCM and obtained very good agreement of the observed phase speeds of 239.5 ± 4.3 and 162.2 ± 6.1 m s−1 for the eastward the westward waves, respectively. Considering that HIAMCM perturbations in the mesosphere/lower thermosphere were the result of the secondary waves generated by the dissipation of the primary gravity waves from the volcanic eruption, this affirms the importance of higher-order wave generation. Furthermore, based on meteor radar observations of the gravity wave propagation around the globe, we estimate the eruption time to be within 6 min of the nominal value of 15 January 2022 04:15 UTC, and we localized the volcanic eruption to be within 78 km relative to the World Geodetic System 84 coordinates of the volcano, confirming our estimates to be realistic.

Air-blast atomization of a liquid film

Air-blast atomizers are extensively used for a variety of purposes. Due to its complexity, the atomization mechanism has not been elucidated. In this study, a mechanistic model is proposed to predict the droplet diameter distribution based on the atomization process of a planar liquid film with co-current gas flows, and its validity is examined by comparing the estimated and measured droplet diameters using high-speed image analysis and laser measurement. As a result, using high-speed imaging, we clarified that the bag film rupture is caused not by the turbulence of the gas flow but by the impact of floating droplets on the liquid film of the expanding bag when the film is thin enough. The average thickness of the liquid film at the bag breakup is of the order of micrometres and varies greatly, resulting in a dispersed distribution of droplet diameters. After the film ruptures, the bag film shrinks towards its transversal and vertical rims due to surface tension, forming large-diameter ligaments. During the contraction process of the bag film, tiny droplets of the order of micrometers are formed at the edge of the perforation. Finally, the remaining ligaments with large diameters fragment into large droplets with submillimetre diameters. The good agreement between the measured and predicted droplet diameter distributions validated the mechanistic model.

Explanatory models in neuroscience, Part 1: Taking mechanistic abstraction seriously

Despite the recent success of neural network models in mimicking animal performance on various tasks, critics worry that these models fail to illuminate brain function. We take it that a central approach to explanation in systems neuroscience is that of mechanistic modeling, where understanding the system requires us to characterize its parts, organization, and activities, and how those give rise to behaviors of interest. However, it remains controversial what it takes for a model to be mechanistic, and whether computational models such as neural networks qualify as explanatory on this approach.We argue that certain kinds of neural network models are actually good examples of mechanistic models, when an appropriate notion of mechanistic mapping is deployed. Building on existing work on model-to-mechanism mapping (3M), we describe criteria delineating such a notion, which we call 3M++. These criteria require us, first, to identify an abstract level of description that is still detailed enough to be “runnable”, and then, to construct model-to-brain mappings using the same principles as those employed for brain-to-brain mapping across individuals.Perhaps surprisingly, the abstractions required are just those already in use in experimental neuroscience and deployed in the construction of more familiar computational models — just as the principles of inter-brain mappings are very much in the spirit of those already employed in the collection and analysis of data across animals.In a companion paper, we address the relationship between optimization and intelligibility, in the context of functional evolutionary explanations. Taken together, mechanistic interpretations of computational models and the dependencies between form and function illuminated by optimization processes can help us to understand why brain systems are built they way they are.

Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches.

Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer's disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer's disease.

Linking of Chemical Reactions and Silica Nanoparticle Contact Using Synthetic Helical Molecules

Comprehensive SummaryBiological cells exhibit diverse phenomena induced through linking of chemical reactions of molecules and solid surface contact. It is then a significant topic in the field of chemistry to study phenomena induced through this linking using synthetic systems, which can promote our understanding of biological phenomena and can be applied to the development of novel functions. Silica nanoparticles (SNPs), which are synthetic inorganic materials, are attractive for such purposes, because of their following characteristics: they can adsorb large amounts of molecules on their surfaces, they can aggregate through contact between SNPs as well as contact between molecules and SNPs, and the molecules can be easily removed from solutions by precipitation. The contact of SNP surfaces with molecules then affects chemical reactions of molecules and also behaviors of SNPs. This article describes systems derived from synthetic helical molecules and SNPs, which exhibit notable phenomena including selective adsorption and molecular recognition, equilibrium shift, step kinetics with induction period, precipitation with flow and sweeping, and disaggregation and desorption by sonication, in which the high affinity of helical molecules with SNP surfaces plays important roles. Mechanistic models that explain the phenomena are provided. Possible applications are also discussed, including the separation of molecules, capture of intermediates, the storage and release of molecules, equilibrium shift, clocking, and the translation of mechanical stimulations into chemical reactions. Key Scientists

Misapplied management makes matters worse: Spatially explicit control leverages biotic interactions to slow invasion.

A wide range of approaches has been used to manage the spread of invasive species, yet invaders continue to be a challenge to control. In some cases, management actions have no effect or may even inadvertently benefit the targeted invader. Here, we use the mid-20th century management of the Red Imported Fire Ant, Solenopsis invicta, in the US as a motivating case study to explore the conditions under which such wasted management effort may occur. Introduced in approximately 1940, the fire ant spread widely through the southeast USand became a problematic pest. Historically, fire ants were managed with broad-spectrum pesticides; unfortunately, these efforts were largely unsuccessful. One hypothesis suggests that, by also killing native ants, mass pesticide application reduced competitive burdens thereby enabling fire ants to invade more quickly than they would in the absence of management. We use a mechanistic competition model to demonstrate the landscape-level effects of such management. We explicitly model the extent and location of pesticide applications, showing that the same pesticide application can have a positive, neutral, or negative effect on the progress of an invasion, depending on where it is applied on the landscape with respect to the invasion front. When designing management, the target species is often considered alone; however, this work suggests that leveraging existing biotic interactions, specifically competition with native species, can increase the efficacy of management. Our model not only highlights the potential unintended consequences of ignoring biotic interactions, but also provides a framework for developing spatially explicit management strategies that take advantage of these biotic interactions to work smarter, not harder.

Estimating the effects of temperature on transmission of the human malaria parasite, Plasmodium falciparum

Despite concern that climate change could increase the human risk to malaria in certain areas, the temperature dependency of malaria transmission is poorly characterized. Here, we use a mechanistic model fitted to experimental data to describe how Plasmodium falciparum infection of the African malaria vector, Anopheles gambiae, is modulated by temperature, including its influences on parasite establishment, conversion efficiency through parasite developmental stages, parasite development rate, and overall vector competence. We use these data, together with estimates of the survival of infected blood-fed mosquitoes, to explore the theoretical influence of temperature on transmission in four locations in Kenya, considering recent conditions and future climate change. Results provide insights into factors limiting transmission in cooler environments and indicate that increases in malaria transmission due to climate warming in areas like the Kenyan Highlands, might be less than previously predicted.

Dispersal potential does not predict recent range expansions of sub-Antarctic plant species

AbstractDispersal influences many key aspects of plant ecology at both fine- and broad scales. However, dispersal events are challenging to quantify as they are difficult to observe and measure accurately, despite the importance of understanding species’ dispersal capacity. In this study, we quantified dispersal estimates for the dominant vascular flora of sub-Antarctic Marion Island, using a mechanistic model to estimate dispersal potential via anemochory and standardized experiments that simulate dispersal events via zoochory, human activity, and thalassochory, to test if dispersal capacity correlates to contemporary range expansion of the island’s native and alien species. Our results demonstrate the broad range of dispersal potential in the island’s flora and represent the first quantification of the dispersal potential (via multiple vectors) of the dominant vascular flora in the sub-Antarctic. Dispersal potential was not related to range expansion rates of native or alien species, suggesting that other mechanisms are responsible for the variation observed in species range expansion rates. Although this finding contradicts expectations and evidence from some other studies, it is consistent with research conducted in alpine regions (which may be climatically and physiognomically similar to this sub-Antarctic study site) where factors such as demography and competition are more important predictors of species range expansion than their dispersal ability, dispersal syndromes, or dispersal-related traits.

Anhydrous volatile fatty acid extraction through omniphobic membranes by hydrophobic deep eutectic solvents: Mechanistic understanding and future perspective

Volatile fatty acids (VFAs) derived from arrested anaerobic digestion (AD) can be recovered as a valuable commodity for value-added synthesis. However, separating VFAs from digestate with complex constituents and a high-water content is an energy-prohibitive process. This study developed an innovative technology to overcome this barrier by integrating deep eutectic solvents (DESs) with an omniphobic membrane into a membrane contactor for efficient extraction of anhydrous VFAs with low energy consumption. A kinetic model was developed to elucidate the mechanistic differences between this novel omniphobic membrane-enabled DES extraction and the previous hydrophobic membrane-enabled NaOH extraction. Experimental results and mechanistic modeling suggested that VFA extraction by the DES is a reversible adsorption process facilitating subsequent VFA separation via anhydrous distillation. High vapor pressure of shorter-chain VFAs and low Nernst distribution coefficients of longer-chain VFAs contributed to DES-driven extraction, which could enable continuous and in-situ recovery and conversion of VFAs from AD streams.