A one-dimensional biofilm model was developed based on the basic principle of conservation of mass. Three simple, generic processes were combined in the model which includes microbial growth, diffusive and convective mass transport. The final model could generate a quantitative description of the relationship between the microbial growth and the consumption of substrate (oxygen) within the fixed biofilm thickness. Mass transfer resistance contributes large influence on the substrates and microbial concentration across the biofilm thickness due to the effect of biofilm structure.
The mixed Lagrange time-delay estimation autoregressive (MLTDEAR) model is proposed as a solution to estimate image noise variance. The only information available to the proposed estimator is a corrupted image and the nature of additive white noise. The image autocorrelation function is calculated and used to obtain the MLTDEAR model coefficients; the relationship between the MLTDEAR and linear prediction models is utilized to estimate the model coefficients. The forward-backward prediction is then used to obtain the predictor coefficients; the MLTDEAR model coefficients and prior samples of zero-offset autocorrelation values are next used to predict the power of the noise-free image. Furthermore, the fundamental performance limit of the signal and noise estimation, as derived from the Cramer-Rao inequality, is presented.
People typically associate health with only physical health. However, health is also interconnected to mental and emotional health. People who are emotionally healthy are in control of their behaviors and experience better quality of life. Hence, understanding human behavior is very important in ensuring the complete understanding of one's holistic health. In this paper, we attempt to map human behavior state (HBS) profiles onto recalibrated speech affective space model (rSASM). Such an approach is derived from hypotheses that: 1) Behavior is influenced by emotion, 2) Emotion can be quantified through speech, 3) Emotion is dynamic and changes over time and 4) the emotion conveyance is conditioned by culture. Empirical results illustrated that the proposed approach can complement other types of behavior analysis in such a way that it offers more explanatory components from the perspective of emotion primitives (valence and arousal). Four different driving HBS; namely: distracted, laughing, sleepy and normal are profiled onto the rSASM to visualize the correlation between HBS and emotion. This approach can be incorporated in the future behavior analysis to envisage better performance.
The recurrent patterns in the commonness and rarity of species in ecological communities--the relative species abundance--have puzzled ecologists for more than half a century. Here we show that the framework of the current neutral theory in ecology can easily be generalized to incorporate symmetric density dependence. We can calculate precisely the strength of the rare-species advantage that is needed to explain a given RSA distribution. Previously, we demonstrated that a mechanism of dispersal limitation also fits RSA data well. Here we compare fits of the dispersal and density-dependence mechanisms for empirical RSA data on tree species in six New and Old World tropical forests and show that both mechanisms offer sufficient and independent explanations. We suggest that RSA data cannot by themselves be used to discriminate among these explanations of RSA patterns--empirical studies will be required to determine whether RSA patterns are due to one or the other mechanism, or to some combination of both.
Breast cancer is the most common cancer among women globally, and the number of young women diagnosed with this disease is gradually increasing over the years. Mammography is the current gold-standard technique although it is known to be less sensitive in detecting tumors in woman with dense breast tissue. Detecting an early-stage tumor in young women is very crucial for better survival chance and treatment. The thermography technique has the capability to provide an additional functional information on physiological changes to mammography by describing thermal and vascular properties of the tissues. Studies on breast thermography have been carried out to improve the accuracy level of the thermography technique in various perspectives. However, the limitation of gathering women affected by cancer in different age groups had necessitated this comprehensive study which is aimed to investigate the effect of different density levels on the surface temperature distribution profile of the breast models. These models, namely extremely dense (ED), heterogeneously dense (HD), scattered fibroglandular (SF), and predominantly fatty (PF), with embedded tumors were developed using the finite element method. A conventional Pennes' bioheat model was used to perform the numerical simulation on different case studies, and the results obtained were then compared using a hypothesis statistical analysis method to the reference breast model developed previously. The results obtained show that ED, SF, and PF breast models had significant mean differences in surface temperature profile with a p value <0.025, while HD breast model data pair agreed with the null hypothesis formulated due to the comparable tissue composition percentage to the reference model. The findings suggested that various breast density levels should be considered as a contributing factor to the surface thermal distribution profile alteration in both breast cancer detection and analysis when using the thermography technique.
The prevalence of prosthodontic treatment has been well recognized, and the need is continuously increasing with the ageing population. While the oral mucosa plays a critical role in the treatment outcome, the associated biomechanics is not yet fully understood. Using the literature available, this paper provides a critical review on four aspects of mucosal biomechanics, including static, dynamic, volumetric and interactive responses, which are interpreted by its elasticity, viscosity/permeability, apparent Poisson's ratio and friction coefficient, respectively. Both empirical studies and numerical models are analysed and compared to gain anatomical and physiological insights. Furthermore, the clinical applications of such biomechanical knowledge on the mucosa are explored to address some critical concerns, including stimuli for tissue remodelling (interstitial hydrostatic pressure), pressure-pain thresholds, tissue displaceability and residual bone resorption. Through this review, the state of the art in mucosal biomechanics and their clinical implications are discussed for future research interests, including clinical applications, computational modelling, design optimization and prosthetic fabrication.
Renewable energy from lignocellulosic biomass has been deemed an alternative to depleting fossil fuels. In order to improve this technology, we aim to develop robust mathematical models for the enzymatic lignocellulose degradation process. By analyzing 96 groups of previously published and newly obtained lignocellulose saccharification results and fitting them to Weibull distribution, we discovered Weibull statistics can accurately predict lignocellulose saccharification data, regardless of the type of substrates, enzymes and saccharification conditions. A mathematical model for enzymatic lignocellulose degradation was subsequently constructed based on Weibull statistics. Further analysis of the mathematical structure of the model and experimental saccharification data showed the significance of the two parameters in this model. In particular, the λ value, defined the characteristic time, represents the overall performance of the saccharification system. This suggestion was further supported by statistical analysis of experimental saccharification data and analysis of the glucose production levels when λ and n values change. In conclusion, the constructed Weibull statistics-based model can accurately predict lignocellulose hydrolysis behavior and we can use the λ parameter to assess the overall performance of enzymatic lignocellulose degradation. Advantages and potential applications of the model and the λ value in saccharification performance assessment were discussed.
A sequential algorithm is developed for the non-linear dual-sorption model developed by Chandrasekaran et al. [1,2] which monitors pharmacokinetic profiles in percutaneous drug absorption. In the experimental study of percutaneous absorption, it is often observed that the lag-time decreases with the increase in the donor concentration when two or more donor concentrations of the same compound are used. The dual-sorption model has sometimes been employed to explain such experimental results. In this paper, it is shown that another feature observed after vehicle removal may also characterize the dual-sorption model. Soon after vehicle removal, the plots of the drug flux versus time become straight lines on a semilogarithmic scale as in the linear model, but the half-life is prolonged thereafter when the dual-sorption model prevails. The initial half-life after vehicle removal with a low donor concentration is longer than that with a higher donor concentration. These features, if observed in experiments, may be used as evidence to confirm that the dual-sorption model gives an explanation to the non-linear kinetic behaviour of a permeant.
The development of methods that can identify athlete-specific optimum sports techniques-arguably the holy grail of sports biomechanics-is one of the greatest challenges for researchers in the field. This 'perspectives article' critically examines, from a dynamical systems theoretical standpoint, the claim that athlete-specific optimum sports techniques can be identified through biomechanical optimisation modelling. To identify athlete-specific optimum sports techniques, dynamical systems theory suggests that a representative set of organismic constraints, along with their non-linear characteristics, needs to be identified and incorporated into the mathematical model of the athlete. However, whether the athlete will be able to adopt, and reliably reproduce, his/her predicted optimum technique will largely be dependent on his/her intrinsic dynamics. If the attractor valley corresponding to the existing technique is deep, or if the attractor valleys corresponding to the existing technique and the predicted optimum technique are in different topographical regions of the dynamic landscape, technical modifications may be challenging or impossible to reliably implement even after extended practice. The attractor layout defining the intrinsic dynamics of the athlete, therefore, needs to be determined to establish the likelihood of the predicted optimum technique being reliably attainable by the athlete. Given the limited set of organismic constraints typically used in mathematical models of athletes, combined with the methodological challenges associated with mapping the attractor layout of an athlete, it seems unlikely that athlete-specific optimum sports techniques will be identifiable through biomechanical optimisation modelling for the majority of sports skills in the near future.
Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions. This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in small scale systems. In addition, the results of this study can be used to estimate kinetic parameter values in the stage of model selection for different experimental conditions.
A model of glucose regulation system was combined with a model of insulin-signaling pathways in this study. A feedback loop was added to link the transportation of glucose into cells (by GLUT4 in the insulin-signaling pathways) and the insulin-dependent glucose uptake in the glucose regulation model using the Michaelis-Menten kinetic model. A value of K(m) for GLUT4 was estimated using Genetic Algorithm. The estimated value was found to be 25.3 mM, which was in the range of K(m) values found experimentally from in vivo and in vitro human studies. Based on the results of this study, the combined model enables us to understand the overall dynamics of glucose at the systemic level, monitor the time profile of components in the insulin-signaling pathways at the cellular level and gives a good estimate of the K(m) value of glucose transportation by GLUT4. In conclusion, metabolic modeling such as displayed in this study provides a good predictive method to study the step-by-step reactions in an organism at different levels and should be used in combination with experimental approach to increase our understanding of metabolic disorders such as type 2 diabetes.
We introduce a novel framework for conceptualising, quantifying and unifying discordant patterns of species richness along geographical gradients. While not itself explicitly mechanistic, this approach offers a path towards understanding mechanisms. In this study, we focused on the diverse patterns of species richness on mountainsides. We conjectured that elevational range midpoints of species may be drawn towards a single midpoint attractor - a unimodal gradient of environmental favourability. The midpoint attractor interacts with geometric constraints imposed by sea level and the mountaintop to produce taxon-specific patterns of species richness. We developed a Bayesian simulation model to estimate the location and strength of the midpoint attractor from species occurrence data sampled along mountainsides. We also constructed midpoint predictor models to test whether environmental variables could directly account for the observed patterns of species range midpoints. We challenged these models with 16 elevational data sets, comprising 4500 species of insects, vertebrates and plants. The midpoint predictor models generally failed to predict the pattern of species midpoints. In contrast, the midpoint attractor model closely reproduced empirical spatial patterns of species richness and range midpoints. Gradients of environmental favourability, subject to geometric constraints, may parsimoniously account for elevational and other patterns of species richness.
A frequent observation in plant-animal mutualistic networks is that abundant species tend to be more generalised, interacting with a broader range of interaction partners than rare species. Uncovering the causal relationship between abundance and generalisation has been hindered by a chicken-and-egg dilemma: is generalisation a by-product of being abundant, or does high abundance result from generalisation? Here, we analyse a database of plant-pollinator and plant-seed disperser networks, and provide strong evidence that the causal link between abundance and generalisation is uni-directional. Specifically, species appear to be generalists because they are more abundant, but the converse, that is that species become more abundant because they are generalists, is not supported by our analysis. Furthermore, null model analyses suggest that abundant species interact with many other species simply because they are more likely to encounter potential interaction partners.
Oral mucosa is a mucous membrane lining the oral cavity. Its main function is to protect the deeper structures against the external factors; thermal, chemical, mechanical and biological stimuli. Apart from that, it also plays a significant role during mastication, deglutition and speech. Some oral diseases or injuries to oral mucosa lead to impairment of the oral functions and aesthetics which eventually result in permanent defect of oral mucosa. In order to overcome this defect, different approaches for the development of reconstructed oral mucosa models have been employed including skin/autologous grafts, guided tissue replacement, vestibuloplasty etc. However, the finding of an acceptable source for the transplantations or autologous grafts seems a bit challenging. To overcome this problem, the development of oral mucosa using tissue engineering approach has been widely studied involving various cell lines from different sources. This paper aims to highlight various cell sources used in the development of tissueengineered oral mucosa models based on articles retrieved from PubMed and MEDLINE databases using the search terms "oral mucosa tissue engineering", regardless of time when published.
Dengue infection in China has increased dramatically in recent years. Guangdong province (main city Guangzhou) accounted for more than 94% of all dengue cases in the 2014 outbreak. Currently, there is no existing effective vaccine and most efforts of control are focused on the vector itself. This study aimed to evaluate different dengue management strategies in a region where this disease is emerging. This work was done by establishing a dengue simulation model for Guangzhou to enable the testing of control strategies aimed at vector control and vaccination. For that purpose, the computer-based dengue simulation model (DENSiM) together with the Container-Inhabiting Mosquito Simulation Model (CIMSiM) has been used to create a working dengue simulation model for the city of Guangzhou. In order to achieve the best model fit against historical surveillance data, virus introduction scenarios were run and then matched against the actual dengue surveillance data. The simulation model was able to predict retrospective outbreaks with a sensitivity of 0.18 and a specificity of 0.98. This new parameterisation can now be used to evaluate the potential impact of different control strategies on dengue transmission in Guangzhou. The knowledge generated from this research would provide useful information for authorities regarding the historic patterns of dengue outbreaks, as well as the effectiveness of different disease management strategies.
As a landscape becomes increasingly fragmented through habitat loss, the individual patches become smaller and more isolated and thus less likely to sustain a local population. Metapopulation theory is appropriate for analyzing fragmented landscapes because it combines empirical landscape features with species-specific information to produce direct information on population extinction risks. This approach contrasts with descriptions of habitat fragments, which provide only indirect information on risk. Combining a spatially explicit metapopulation model with empirical data on endemic species' ranges and maps of habitat cover, we calculated the metapopulation capacity-a measure of a landscape's ability to sustain a metapopulation. Mangroves provide an ideal model landscape because they are of conservation concern and their patch boundaries are easily delineated. For 2000-20015, we calculated global metapopulation capacity for 99 metapopulations of 32 different bird species endemic to mangroves. Northern Australia and Southeast Asia had the highest richness of mangrove endemic birds. The Caribbean, Pacific coast of Central America, Madagascar, Borneo, and isolated patches in Southeast Asia in Myanmar and Malaysia had the highest metapopulation losses. Regions with the highest loss of habitat area were not necessarily those with the highest loss of metapopulation capacity. Often, it was not a matter of how much, but how the habitat was lost. Our method can be used by managers to evaluate and prioritize a landscape for metapopulation persistence.
Finite element analysis (FEA) is a commonly used tool in musculoskeletal biomechanics and vertebrate paleontology. The accuracy and precision of finite element models (FEMs) are reliant on accurate data on bone geometry, muscle forces, boundary conditions and tissue material properties. Simplified modeling assumptions, due to lack of in vivo experimental data on material properties and muscle activation patterns, may introduce analytical errors in analyses where quantitative accuracy is critical for obtaining rigorous results. A subject-specific FEM of a rhesus macaque mandible was constructed, loaded and validated using in vivo data from the same animal. In developing the model, we assessed the impact on model behavior of variation in (i) material properties of the mandibular trabecular bone tissue and teeth; (ii) constraints at the temporomandibular joint and bite point; and (iii) the timing of the muscle activity used to estimate the external forces acting on the model. The best match between the FEA simulation and the in vivo experimental data resulted from modeling the trabecular tissue with an isotropic and homogeneous Young's modulus and Poisson's value of 10GPa and 0.3, respectively; constraining translations along X,Y, Z axes in the chewing (left) side temporomandibular joint, the premolars and the m1; constraining the balancing (right) side temporomandibular joint in the anterior-posterior and superior-inferior axes, and using the muscle force estimated at time of maximum strain magnitude in the lower lateral gauge. The relative strain magnitudes in this model were similar to those recorded in vivo for all strain locations. More detailed analyses of mandibular strain patterns during the power stroke at different times in the chewing cycle are needed.
The main failure factors of cortical bone are aging or osteoporosis, accident and high energy trauma or physiological activities. However, the mechanism of damage evolution coupled with yield criterion is considered as one of the unclear subjects in failure analysis of cortical bone materials. Therefore, this study attempts to assess the structural response and progressive failure process of cortical bone using a brittle damaged plasticity model. For this reason, several compressive tests are performed on cortical bone specimens made of bovine femur, in order to obtain the structural response and mechanical properties of the material. Complementary finite element (FE) model of the sample and test is prepared to simulate the elastic-to-damage behavior of the cortical bone using the brittle damaged plasticity model. The FE model is validated in a comparative method using the predicted and measured structural response as load-compressive displacement through simulation and experiment. FE results indicated that the compressive damage initiated and propagated at central region where maximum equivalent plastic strain is computed, which coincided with the degradation of structural compressive stiffness followed by a vast amount of strain energy dissipation. The parameter of compressive damage rate, which is a function dependent on damage parameter and the plastic strain is examined for different rates. Results show that considering a similar rate to the initial slope of the damage parameter in the experiment would give a better sense for prediction of compressive failure.