Friction Coefficient Calibration of Sunflower Seeds for Discrete Element Modeling Simulation

Sunflower (Helianthus annuus L.) is one of the four major oil crops in the world and has high economic value. However, the lack of discrete element method (DEM) models and parameters for sunflower seeds hinders the application of DEM for computer simulation in the key working processes of sunflower seed sowing and harvesting. The present study was conducted on two varieties of sunflower, and the DEM model of sunflower seeds was established by using 3D scanning technology based on the distribution of triaxial dimensions and volumes of the geometric model of sunflower seeds. Similarly, the physical characteristics parameters of sunflower seeds were determined by physical tests and the simulation parameters were screened for significance based on the Plackett-Burman test. Our results show that the coefficient of static friction between sunflower seeds and the coefficient of rolling friction have significant effects on the repose angle of the simulation test. Furthermore, the optimal range of the significance parameters was further determined by the steepest climb test, and the second-order regression model of the significance parameters and the repose angle was obtained according to the Box-Behnken design test and Response Surface Methodology (RSM), with the repose angle measured by the physical test as the optimized target value to obtain the optimal parameter combination. Finally, a two-sample t-test for the repose angle of the physical test and the repose angle of the simulation test yielded P > 0.05. Our results confirms that the repose angle obtained from simulation is not significantly different from the physical test value, and the relative errors between the repose angle of the simulation test and the physical test are 1.43% and 0.40%, respectively, for the optimal combination of parameters. Based on these results it can be concluded that the optimal parameters obtained from the calibration can be used for DEM simulation experiments related to the sunflower seed sowing and harvesting process.

     

Optimization of Process Parameters for a Quasi-Continuous Tablet Coating System Using Design of Experiments

The aim of this study was to identify and optimize the critical process parameters of the newly developed Supercell quasi-continuous coater for optimal tablet coat quality. Design of experiments, aided by multivariate analysis techniques, was used to quantify the effects of various coating process conditions and their interactions on the quality of film-coated tablets. The process parameters varied included batch size, inlet temperature, atomizing pressure, plenum pressure, spray rate and coating level. An initial screening stage was carried out using a 2^6−1(IV) fractional factorial design. Following these preliminary experiments, optimization study was carried out using the Box–Behnken design. Main response variables measured included drug-loading efficiency, coat thickness variation, and the extent of tablet damage. Apparent optimum conditions were determined by using response surface plots. The process parameters exerted various effects on the different response variables. Hence, trade-offs between individual optima were necessary to obtain the best compromised set of conditions. The adequacy of the optimized process conditions in meeting the combined goals for all responses was indicated by the composite desirability value. By using response surface methodology and optimization, coating conditions which produced coated tablets of high drug-loading efficiency, low incidences of tablet damage and low coat thickness variation were defined. Optimal conditions were found to vary over a large spectrum when different responses were considered. Changes in processing parameters across the design space did not result in drastic changes to coat quality, thereby demonstrating robustness in the Supercell coating process.     

Influence of Process Parameters on Tablet Bed Microenvironmental Factors During Pan Coating

Recent studies have shown the importance of monitoring microenvironmental conditions (temperature, relative humidity) experienced by the tablet bed during a pan coating process, thereby necessitating the need to understand how various process parameters influence these microenvironmental conditions. The process parameters studied in this work include exhaust air temperature, spray rate, inlet airflow rate, gun-to-bed distance, coating suspension percent solids, and atomization and pattern air pressure. Each of these process parameters was found to have an impact on the tablet bed relative humidity (RH), as measured using PyroButton data logging devices. A higher tablet bed RH was obtained with an increase in spray rate and atomization air pressure and with a decrease in exhaust air temperature, inlet airflow rate, gun-to-bed distance, suspension percent solids, and pattern air pressure. Based on this work, it can be concluded that the tablet bed thermodynamic conditions are a cumulative effect of the various process conditions. A strong correlation between the tablet bed RH and the frequency of tablet coating defect (logo bridging) was established, with increasing RH resulting in a higher percent of logo bridging events.     

De-risking Pharmaceutical Tablet Manufacture Through Process Understanding,Latent Variable Modeling,and Optimization Technologies

In pharmaceutical tablet manufacturing processes, a major source of disturbance affecting drug product quality is the (lot-to-lot) variability of the incoming raw materials. A novel modeling and process optimization strategy that compensates for raw material variability is presented. The approach involves building partial least squares models that combine raw material attributes and tablet process parameters and relate these to final tablet attributes. The resulting models are used in an optimization framework to then find optimal process parameters which can satisfy all the desired requirements for the final tablet attributes, subject to the incoming raw material lots. In order to de-risk the potential (lot-to-lot) variability of raw materials on the drug product quality, the effect of raw material lot variability on the final tablet attributes was investigated using a raw material database containing a large number of lots. In this way, the raw material variability, optimal process parameter space and tablet attributes are correlated with each other and offer the opportunity of simulating a variety of changes in silico without actually performing experiments. The connectivity obtained between the three sources of variability (materials, parameters, attributes) can be considered a design space consistent with Quality by Design principles, which is defined by the ICH-Q8 guidance (USDA 2006). The effectiveness of the methodologies is illustrated through a common industrial tablet manufacturing case study.     

The Heritable Activation of Cryptic Suppressor-Mutator Elements by an Active Element

A weakly active maize Suppressor-mutator (Spm-omega) element is able to heritably activate cryptic Spm elements in the maize genome. The spontaneous activation frequency, which is 1-5 x 10(-5) in the present genetic background, increases by about 100-fold in the presence of an Spm-omega and remains an order of magnitude above the background level a generation after removal of the activating Spm-omega. Sectorial somatic reactivation of cryptic elements can be detected phenotypically in kernels. Selection of such kernels constitutes an efficient selection for plants with reactivated Spm elements. Analysis of the reactivation process reveals that it is gradual and proceeds through genetically metastable intermediates that exhibit different patterns of element expression during plant development. Newly reactivated elements tend to return to an inactive form. However, the probability that an element will remain in a heritably active state increases when the element is maintained in the presence of an active Spm element for several generations.     

Application of Physicochemical Properties and Process Parameters in the Development of a Neural Network Model for Prediction of Tablet Characteristics

The importance of in silico modeling in the pharmaceutical industry is continuously increasing. The aim of the present study was the development of a neural network model for prediction of the postcompressional properties of scored tablets based on the application of existing data sets from our previous studies. Some important process parameters and physicochemical characteristics of the powder mixtures were used as training factors to achieve the best applicability in a wide range of possible compositions. The results demonstrated that, after some pre-processing of the factors, an appropriate prediction performance could be achieved. However, because of the poor extrapolation capacity, broadening of the training data range appears necessary.     

Development and Application of a Process Window for Achieving High-Quality Coating in a Fluidized Bed Coating Process

Next to the coating formulation, process conditions play important roles in determining coating quality. This study aims to develop an operational window that separates layering from agglomeration regimes and, furthermore, the one that leads to the best coating quality in a fluidized bed coater. The bed relative humidity and the droplet size of the coating aerosol were predicted using a set of engineering models. The coating quality was characterized using a quantitative image analysis method, which measures the coating thickness distribution, the total porosity, and the pore size in the coating. The layering regime can be achieved by performing the coating process at a certain excess of the viscous Stokes number (ΔSt _v). This excess is dependent on the given bed relative humidity and droplet size. The higher the bed relative humidity, the higher is the ΔSt _v required to keep the process in the layering regime. Further, it is shown that using bed relative humidity and droplet size alone is not enough to obtain constant coating quality. The changes in bed relative humidity and droplet size have been identified to correlate to the fractional area of particles sprayed per unit of time. This parameter can effectively serve as an additional parameter to be considered for a better control on the coating quality. High coating quality is shown to be achieved by performing the process close to saturation and spraying droplets small enough to obtain high spraying rate, but not too small to cause incomplete coverage of the core particles.     

Rapid Assessment of Tablet Film Coating Quality by Multispectral UV Imaging

Chemical imaging techniques are beneficial for control of tablet coating layer quality as they provide spectral and spatial information and allow characterization of various types of coating defects. The purpose of this study was to assess the applicability of multispectral UV imaging for assessment of the coating layer quality of tablets. UV images were used to detect, characterize, and localize coating layer defects such as chipped parts, inhomogeneities, and cracks, as well as to evaluate the coating surface texture. Acetylsalicylic acid tablets were prepared on a rotary tablet press and coated with a polyvinyl alcohol-polyethylene glycol graft copolymer using a pan coater. It was demonstrated that the coating intactness can be assessed accurately and fast by UV imaging. The different types of coating defects could be differentiated and localized based on multivariate image analysis and Soft Independent Modeling by Class Analogy applied to the UV images. Tablets with inhomogeneous texture of the coating could be identified and distinguished from those with a homogeneous surface texture. Consequently, UV imaging was shown to be well-suited for monitoring of the tablet coating layer quality. UV imaging is a promising technique for fast quality control of the tablet coating because of the high data acquisition speed and its nondestructive analytical nature.     

Angular Circulation Speed of Tablets in a Vibratory Tablet Coating Pan

In this work, a single tablet model and a discrete element method (DEM) computer simulation are developed to obtain the angular circulation speed of tablets in a vibratory tablet coating pan for range of vibration frequencies and amplitudes. The models identify three important dimensionless parameters that influence the speed of the tablets: the dimensionless amplitude ratio (a/R), the Froude number (aω ²/g), and the tablet–wall friction coefficient, where a is the peak vibration amplitude at the drum center, ω is the vibration angular frequency, R is the drum radius, and g is the acceleration due to gravity. The models predict that the angular circulation speed of tablets increases with an increase in each of these parameters. The rate of increase in the angular circulation speed is observed to decrease for larger values of a/R. The angular circulation speed reaches an asymptote beyond a tablet–wall friction coefficient value of about 0.4. Furthermore, it is found that the Froude number should be greater than one for the tablets to start circulating. The angular circulation speed increases as Froude number increases but then does not change significantly at larger values of the Froude number. Period doubling, where the motion of the bed is repeated every two cycles, occurs at a Froude number larger than five. The single tablet model, although much simpler than the DEM model, is able to predict the maximum circulation speed (the limiting case for a large value of tablet–wall friction coefficient) as well as the transition to period doubling.     

Discrete Modeling of Dynamics of Zooplankton Community at the Different Stages of an Antropogeneous Eutrophication

Mathematical modeling is a convenient way for characterization of complex ecosystems. This approach was applied to study the dynamics of zooplankton in Lake Sevan (Armenia) at different stages of anthropogenic eutrophication with the use of a novel method called discrete modeling of dynamical systems with feedback (DMDS). Simulation demonstrated that the application of this method helps in characterization of inter- and intra-component relationships in a natural ecosystem. This method describes all possible pairwise inter-component relationships like “plus–plus,” “minus–minus,” “plus–minus,” “plus–zero,” “minus–zero,” and “zero–zero” that occur in most ecosystems. Based on the results, a working hypothesis was formulated. It was found that the sensitivity to weak external influence in zooplanktons was the greatest during the mid period of eutrophication in Lake Sevan, whereas in the final stages of eutrophication, an outbreak in the biomass production of cyanobacteria was evident. To support this approach, a weak external disturbance in the form of magnetic storm was used to see its effect on species Daphnia longispina sevanica. A statistically significant correlation between the frequency of magnetic storms and the number of this species was revealed and an increase in the number of toxic cyanobacteria species as a consequence of eutrophication. This paper, for the first time, suggests a DMDS method, to diagnose impact of anthropogenic eutrophication on environment.     

A Dynamic Analysis of IRS-PKR Signaling in Liver Cells: A Discrete Modeling Approach

A major challenge in systems biology is to develop a detailed dynamic understanding of the functions and behaviors in a particular cellular system, which depends on the elements and their inter-relationships in a specific network. Computational modeling plays an integral part in the study of network dynamics and uncovering the underlying mechanisms. Here we proposed a systematic approach that incorporates discrete dynamic modeling and experimental data to reconstruct a phenotype-specific network of cell signaling. A dynamic analysis of the insulin signaling system in liver cells provides a proof-of-concept application of the proposed methodology. Our group recently identified that double-stranded RNA-dependent protein kinase (PKR) plays an important role in the insulin signaling network. The dynamic behavior of the insulin signaling network is tuned by a variety of feedback pathways, many of which have the potential to cross talk with PKR. Given the complexity of insulin signaling, it is inefficient to experimentally test all possible interactions in the network to determine which pathways are functioning in our cell system. Our discrete dynamic model provides an in silico model framework that integrates potential interactions and assesses the contributions of the various interactions on the dynamic behavior of the signaling network. Simulations with the model generated testable hypothesis on the response of the network upon perturbation, which were experimentally evaluated to identify the pathways that function in our particular liver cell system. The modeling in combination with the experimental results enhanced our understanding of the insulin signaling dynamics and aided in generating a context-specific signaling network.     

Discrete Element Framework for Modelling Extracellular Matrix,Deformable Cells and Subcellular Components

This paper presents a framework for modelling biological tissues based on discrete particles. Cell components (e.g. cell membranes, cell cytoskeleton, cell nucleus) and extracellular matrix (e.g. collagen) are represented using collections of particles. Simple particle to particle interaction laws are used to simulate and control complex physical interaction types (e.g. cell-cell adhesion via cadherins, integrin basement membrane attachment, cytoskeletal mechanical properties). Particles may be given the capacity to change their properties and behaviours in response to changes in the cellular microenvironment (e.g., in response to cell-cell signalling or mechanical loadings). Each particle is in effect an ‘agent’, meaning that the agent can sense local environmental information and respond according to pre-determined or stochastic events. The behaviour of the proposed framework is exemplified through several biological problems of ongoing interest. These examples illustrate how the modelling framework allows enormous flexibility for representing the mechanical behaviour of different tissues, and we argue this is a more intuitive approach than perhaps offered by traditional continuum methods. Because of this flexibility, we believe the discrete modelling framework provides an avenue for biologists and bioengineers to explore the behaviour of tissue systems in a computational laboratory.     

Finite Element Modeling of a Beetle Wing

Numerical simulation is a very important method for understanding the behaviors of insect flight. In this study, a method of building a finite element model is proposed on the basis of a real beetle wing, which is 50 mm long in the spanwise direction and 20 mm long in the chordwise direction. We scanned a real beetle wing using a scanner to get the 2D image. The scanned 2D image was used to produce CAD data of the outer lines of the membranes and veins. Then the lines were used to build the finite element model. The model was divided into 48 regions so that the variation in the thickness of the membranes and veins could be taken into account. The effect of the cross section of the veins on the exactness of the finite element model was investigated. The finite element model was used to simulate the bending test of a real beetle wing, and the analysis results are in agreement with the experimental results.     

Combining the Finite Element Method with Structural Connectome-based Analysis for Modeling Neurotrauma: Connectome Neurotrauma Mechanics

This article presents the integration of brain injury biomechanics and graph theoretical analysis of neuronal connections, or connectomics, to form a neurocomputational model that captures spatiotemporal characteristics of trauma. We relate localized mechanical brain damage predicted from biofidelic finite element simulations of the human head subjected to impact with degradation in the structural connectome for a single individual. The finite element model incorporates various length scales into the full head simulations by including anisotropic constitutive laws informed by diffusion tensor imaging. Coupling between the finite element analysis and network-based tools is established through experimentally-based cellular injury thresholds for white matter regions. Once edges are degraded, graph theoretical measures are computed on the "damaged" network. For a frontal impact, the simulations predict that the temporal and occipital regions undergo the most axonal strain and strain rate at short times (less than 24 hrs), which leads to cellular death initiation, which results in damage that shows dependence on angle of impact and underlying microstructure of brain tissue. The monotonic cellular death relationships predict a spatiotemporal change of structural damage. Interestingly, at 96 hrs post-impact, computations predict no network nodes were completely disconnected from the network, despite significant damage to network edges. At early times ([Formula: see text]) network measures of global and local efficiency were degraded little; however, as time increased to 96 hrs the network properties were significantly reduced. In the future, this computational framework could help inform functional networks from physics-based structural brain biomechanics to obtain not only a biomechanics-based understanding of injury, but also neurophysiological insight.     

The Global Conformation of an Active Hammerhead RNA during the Process of Self-cleavage

The RNA “hammerhead” domain is a small element of secondary structure found in the genomes of certain plant pathogens. It possesses a core of conserved sequence at the conjunction of three helix stems, and is capable of undergoing self-cleavage in the presence of divalent cations. Both crystallographic and solution studies suggest that the domain is highly structured, with the three stems assuming a Y-shaped global conformation; however, such studies have employed either RNA analogues that were catalytically inactive, or conditions of temperature and pH for which rates of self-cleavage are slow. Thus, it was unknown whether such species represented the principal conformers during the cleavage process itself. In order to address this issue, a series of time- resolved, transient electric birefringence measurements was conducted in an effort to define the global conformation of an RNA hammerhead in real time throughout the process of self-cleavage. The current study demonstrates that the angular relationship between the two helices that flank the cleavage center is essentially unchanged between the pre-cleavage and post-cleavage forms. Moreover, despite the fact that at least one kinetic intermediate is formed during the self-cleavage reaction, there is no evidence for the existence of a significant population of intermediates with altered global conformation during cleavage. Thus, any conformational isomerism that may occur is likely to be relatively localized to the active center. Finally, it was observed that sequence elements lying outside of the conserved region, at the base of stem I, influence interhelix geometry. The current results are consistent with a structural model in which the active center possesses similar conformations pre-cleavage and post-cleavage. Such a model would help to explain the significant rate of reversal of the cleavage reaction (self-ligation).     

Modeling the Population Level Effects of an HIV-1 Vaccine in an Era of Highly Active Antiretroviral Therapy

First generation HIV vaccines may have limited ability to prevent infection. Instead, they may delay the onset of AIDS or reduce the infectiousness of vaccinated individuals who become infected. To assess the population level effects of such a vaccine, we formulate a deterministic model for the spread of HIV in a homosexual population in which the use of highly active antiretroviral therapy (HAART) to treat HIV infection is incorporated. The basic reproduction number R ₀ is obtained under this model. We then expand the model to include the potential effects of a prophylactic HIV vaccine. The reproduction number R _f is derived for a population in which a fraction f of susceptible individuals is vaccinated and continues to benefit from vaccination. We define f ^* as the minimum vaccination fraction for which R _f ≤1 and describe situations in which it equals the critical vaccination fraction necessary to eliminate disease. When R ₀ is large or an HIV vaccine is only partially effective, the critical vaccination fraction may exceed one. HIV vaccination, however, may still reduce the prevalence of disease if the reduction in infectiousness is at least as great as the reduction in the rate of disease progression. In particular, a vaccine that reduces infectiousness during acute infection may have an important public health impact especially if coupled with counseling to reduce risky behavior.     

Prediction of Tablet Film-coating Thickness Using a Rotating Plate Coating System and NIR Spectroscopy

The purpose of this research was to create a calibration model based on near-infrared (NIR) spectroscopy data obtained during a small-scale coating process to predict in-line the coating layer thickness of tablets coated in a side-vented drum coater. The developed setup for the small-scale coating process consisted of a rotating plate with 20 tablets molds that pass a spraying unit, a heating unit, and an in-line NIR spectroscopy probe during one rotation. High-density polyethylene (HDPE) was compressed to flat-faced tablets, and these were coated with a sustained release coating suspension containing Kollicoat IR and Kollicoat SR 30D. The film thickness of these tablets was determined for each tablet individually with a digital micrometer. A calibration model of predicted film thickness versus real-film thickness using PLS regression was developed. This model was tested against in-line NIR data obtained from a coating drum process, in which biconvex HDPE tablets were film-coated with the same film-coating suspension. The model predicted a final coating thickness of 240 μm, while the measured average thickness (n = 100 tablets) was 210 μm. Taking into account the use of a different setup and differently shaped tablets, it was possible to predict the coating thickness with accuracy comparable to the one of the digital micrometer. Thus, the small-scale rotating plate system was found to be an efficient means of preparing calibration model for a tablet-coating drum process.