The use of three-dimensional model construction of virtual technology in orthopedic treatment

ObjectiveThe objective of this study is to explore the construction of a digital three-dimensional model of virtual technology that plays an auxiliary role in orthopedic treatment.MethodsThree fracture patients were selected, with no abnormality was observed in bone examination, no musculoskeletal disease in the past; and spiral CT scan of the spine and pelvis, upper limbs, and lower limbs was performed. The virtual technology was used to build a digital 3D model, mainly using the editing software Mimics10.0 software. In addition, the virtual three-dimensional model was verified by virtual surgery, data storage security, work efficiency of the model, model validity, three-dimensional characteristics of the model, the interaction mode of the model, and the data accuracy of the model were studied.ResultsThe digital 3D model was successfully established by Mimics10.0 software. The data fitting efficiency was very high. The data storage security of the 3D model was greatly improved compared with the 2D model, and the work efficiency was improved by at least 50%. There was also a significant change in the accuracy and interaction of data acquisition. Therefore, the detection of digital 3D model work through virtual surgery simulation fully demonstrated the positive auxiliary role of 3D model in orthopedic treatment.ConclusionThe digital 3D model based on Mimics10.0 software is efficient and accurate in obtaining data. It is very effective for subsequent adjuvant therapy in the field of orthopedics, reducing the probability of misdiagnosis by doctors, saving time and improving efficiency, reducing patient's physical pain and unnecessary economic expenses.     

ELM evaluation model of regional groundwater quality based on the crow search algorithm

According to the multi-parameter evaluation of groundwater quality, an evaluation model of groundwater quality based on the improved Extreme Learning Machine (ELM) was proposed to resolve fuzziness of the water quality evaluation and incompatibility of water parameters. A training sample set and testing sample set were randomly generated according to the classification standards of groundwater quality, then Crow Search Algorithm (CSA) was used to optimize the input weights and thresholds of hidden-layer neurons of the ELM; thus, the CSA-ELM evaluation model of groundwater quality was constructed based on optimization of the ELM by the CSA. Base on the training sample set and testing sample set, the CSA-ELM model was tested. The test results indicate that the evaluating precision and generalization ability of the CSA-ELM model reach a high level and can be used for comprehensive evaluations of groundwater quality. The Jiansanjiang Administration in Heilongjiang Province, China, was used as an example; the groundwater quality of 15 farms in this region was evaluated based on the CSA-ELM model. The groundwater quality in this region was generally good, and the groundwater quality appeared to have spatial distribution characteristics. Compared with the Nemerow Index Method (NIM), the CSA-ELM evaluation model of groundwater quality is more reasonable and can be used for the comprehensive evaluation of groundwater quality. The stability of the NIM, ELM model, back propagation (BP) model and CSA-ELM model was analyzed using the theory of serial number summation and Spearman's correlation coefficient. The stability of the NIM and BP model in groundwater quality evaluation was poor, while the stability of the ELM model and CSA-ELM model was relatively superior. The ranked results of stability are CSA-ELM model > ELM model > NIM > BP model. The reliability of the NIM, ELM model, BP model and CSA-ELM model was analyzed using the theory of distinction degree. The reliability of the NIM was not good, although its distinction degree was large; the distinction degrees of the ELM model, BP model and CSA-ELM model were close to each other. The ranked results of reliability are CSA-ELM model > ELM model > BP model. The CSA-ELM model can provide a stable and reliable evaluation method for the evaluation of related fields and thus has important practical applicability.     

Correcting observer effect bias in estimates of nesting success of a coastal bird,the White-fronted Plover Charadrius marginatus

Capsule A recently developed observer-effects model gives better estimates than the survival model.

Aims To compare the performance of the traditional survival model with that of an observer-effects model in estimating nesting success.

Methods We used survival data from 654 nests in a covariate-dependent extension of a maximum-likelihood estimator model to simultaneously estimate daily survival rate and observer effect on nesting success in four different sectors of a study site. Standard likelihood-ratio tests and Akaike's information criterion differences were used to compare model performances.

Results A significant (negative) observer effect was detected in one of four sectors of the study area, and was probably attributable to small mammal predators. Despite significantly higher predation risk during the laying period, there was a significant positive observer effect during the laying period, contrasting with a weak negative effect during the incubation period, suggesting that an observer's tracks around unattended nests reduced predation risk. Likelihood-ratio tests indicated a significantly better fit using the observer effects model versus the Mayfield survival model in four of six scenarios.

Conclusion The observer-effects model accounts for bias in estimates of nest survival rates, and is preferred over the survival model because the inclusion of observer effect as a variable does not over-parameterize the model.     

Validation of a Photosynthesis Model through the Use of the CO2Balance of a Greenhouse Tomato Canopy

Several leaf photosynthesis models were developed from wellcontrolled experiments in growth chambers. However, only a fewhave been validated under greenhouse conditions for their quantitativeand qualitative adequacy. In this paper, rates of net photosynthesisfor a tomato crop (Lycopersicon esculentum Mill) were measuredin a semi-commercial greenhouse (615 m³) for a significant timeperiod. Concomitant measurements of climatic conditions andLAI were used for simulation of net photosynthesis using theTOMGRO model which integrates Acock's model for photosynthesiscalculations. From simulations and from sensitivity analysis,the prediction of net photosynthesis appeared to be very sensitiveto the quantum use efficiency. The Acock model with originalparameters underestimated the net photosynthesis rate, but anincrease in the quantum use efficiency by 10% gave a good fit.In an effort to generalize the validity of the model, a residualanalysis was performed and showed a systematic bias relatedto light intensity intercepted by the canopy. The Marquardtalgorithm was used to adjust our data to the model but did noteliminate residual heterogeneity of variance with new parametervalues. On the basis of collected data, the criteria of goodnessof fit used showed that the photosynthesis model is inadequatein describing the CO₂-balance of the greenhouse agrosystem.However, it was determined that it could be used as a submodelwithin a more complex model for predicting growth and development.Copyright 1999 Annals of Botany Company Greenhouse, CO₂-balance, photosynthesis, TOMGRO model, Acock's model, residuals, tomato Lycopersicon esculentum Mill.     

Co-operative response of chemically excitable membrane III. Three-state model

The general three-state model is formulated first, which is the direct extension of the unified two-state model previously formulated (Kijima & Kijima, 1978). In this model, each protomer in a symmetrically interacting system (oligomers or lattices) can take three states, S, R and Q, where S and R states are the same as in the two-state model and Q state is another state either corresponding to a different open-state of ionophore from R open-state or corresponding to another closed state of ionophore. The model has no restriction on the value of Hill coefficient at the midpoint of the dose-response curves in contrast to two-state models. It is applied on GABA sensitive inhibitory synapse of crayfish muscle to account for anomalous behaviour of the membrane in I^? solution.The simplified versions of the above general three-state model are also formulated (simplified three-state model), in which it is assumed that R and Q state are equivalent in regard to the nearest neighbor interaction. By this assumption, R and Q state are collectively treated as state A and mathematical formula obtained on Ising model are applicable on this model. This model is applied on the insect sugar receptor which was shown to be incompatible with the two-state models (Kijima & Kijima, 1980). Further simplification of the above simplified model results in two convenient models: three-state KNF model and three-state MWC model, which have minimum parameters but sufficient to account for most experiments. They give plausible physico-chemical base on the “classical model” in which the existence of both inactive and active ligand-receptor complex is assumed.     

An improved dynamic model of photosynthesis for estimation of carbon gain in sunfleck light regimes

A dynamic model of leaf photosynthesis for C₃ plants has been developed for examination of the role of the dynamic properties of the photosynthetic apparatus in regulating CO₂ assimilation in variable light regimes. The model is modified from the Farquhar-von Caemmerer-Berry model by explicitly including metabolite pools and the effects of light activation and deactivation of Calvin cycle enzymes. It is coupled to a dynamic stomatal conductance model, with the assimilation rate at any time being determined by the joint effects of the dynamic biochemical model and the stomatal conductance model on the intercellular CO₂ pressure. When parametrized for each species, the model was shown to exhibit responses to step changes in photon flux density that agreed closely with the observed responses for both the understory plant Alocasia macrorrhiza and the crop plant Glycine max. Comparisons of measured and simulated photosynthesis under simulated light regimes having natural patterns of lightfleck frequencies and durations showed that the simulated total for Alocasia was within ±4% of the measured total assimilation, but that both were 12–50% less than the predictions from a steady–state solution of the model. Agreement was within ±10% for Glycine max, and only small differences were apparent between the dynamic and steady–state predictions. The model may therefore be parametrized for quite different species, and is shown to reflect more accurately the dynamics of photosynthesis than earlier dynamic models.     

A physics-based approach of coarse-graining the cytoplasm of Escherichia coli (CGCYTO)

We have investigated protein stability in an environment of Escherichia coli cytoplasm using coarse-grained computer simulations. To coarse-grain a small slide of E. coli's cytoplasm consisting of over 16 million atoms, we have developed a self-assembled clustering algorithm (CGCYTO). CGCYTO uses the shape parameter and asphericity as well as a parameter λ (ranging from 0 to 1) that measures the covolume of a test protein and a macromolecule against the covolume of a test protein and a sphere of equal volume as that of a macromolecule for the criteria of coarse-graining a cytoplasmic model. A cutoff λ_c = 0.8 was chosen based on the size of a test protein and computational resources and it determined the resolution of a coarse-grained cytoplasm. We compared the results from a polydisperse cytoplasmic model (PD model) produced by CGCYTO with two other coarse-grained hard-sphere cytoplasmic models: 1), F70 model, macromolecules in the cytoplasm were modeled by homogeneous hard spheres with a radius of 55 Å, the size of Ficoll70 and 2), HS model, each macromolecule in the cytoplasm was modeled by a hard sphere of equal volume. It was found that the folding temperature T_f of a test protein (apoazurin) in a PD model is ～5° greater than that in a F70 model. In addition, the deviation of T_f in a PD model is twice as much as that in a HS model when an apoazurin is randomly placed at different voids formed by particle fluctuations in PD models.     

A network model for the control of the movement of a redundant manipulator

In an earlier investigation (Cruse and Brüwer 1987) an algorithmic model was proposed which describes targeting movements of a human arm when restricted to a horizontal plane. As three joints at shoulder, elbow and wrist are allowed to move, the system is redundant. Two models are discussed here which replace this algorithmic model by a network model. Both networks solve the static problem, i.e. they provide the joint angles which the arm has to adopt in order to reach a given point in the workspace. In the first model the position of this point is given in the form ofx —y coordinates, the second model obtains this information by means of a retina-like input layer. The second model is expanded by a simple procedure to describe movements from a start to an end point. The results qualitatively correspond to those obtained from human subjects. The advantages of the network models in comparison to the algorithmic model are discussed.     

A Simple Phenological Model of Muskmelon Development

Utilizing information gathered in previous growth chamber andfield experiments, we developed a simple temperature-drivencrop phenology model of muskmelon (Cucumis melo L.) to helpcommercial growers time crop phenological events and predictharvest dates. The model quantifies vegetative development interms of main vine node numbers which allows the model to simulateeither a direct-seeded or a transplanted crop. The model operateson an hourly time-step but requires only daily weather dataand a few cultivar-specific parameters including plastochroninterval and thermal time requirements to reach six predefineddevelopmental stages. The model was tested against an independentdata set consisting of three muskmelon cultivars grown at fivetransplanting dates. Tests of the model indicate an averageability to predict main vine node numbers to within one to twonodes of observed values. Estimated harvest date predictionswere more variable than those for main vine node number butan average model accuracy of 1 to 3 d was obtained in modeltests with a data set used to construct the model. Proceduresfor calibrating the model for different cultivars, culturalpractices or environments are outlined. Copyright 0000 Cucumis melo L., cantaloupe, temperature, model, thermal time, plastochron interval, growth duration     

Parametric and non-parametric modeling of short-term synaptic plasticity. Part II: Experimental study

This paper presents a synergistic parametric and non-parametric modeling study of short-term plasticity (STP) in the Schaffer collateral to hippocampal CA1 pyramidal neuron (SC) synapse. Parametric models in the form of sets of differential and algebraic equations have been proposed on the basis of the current understanding of biological mechanisms active within the system. Non-parametric Poisson–Volterra models are obtained herein from broadband experimental input–output data. The non-parametric model is shown to provide better prediction of the experimental output than a parametric model with a single set of facilitation/depression (FD) process. The parametric model is then validated in terms of its input–output transformational properties using the non-parametric model since the latter constitutes a canonical and more complete representation of the synaptic nonlinear dynamics. Furthermore, discrepancies between the experimentally-derived non-parametric model and the equivalent non-parametric model of the parametric model suggest the presence of multiple FD processes in the SC synapses. Inclusion of an additional set of FD process in the parametric model makes it replicate better the characteristics of the experimentally-derived non-parametric model. This improved parametric model in turn provides the requisite biological interpretability that the non-parametric model lacks.     

Topographic components analysis of evoked potentials: estimation of model parameters and evaluation of parameter uniqueness

The purpose of this paper is to evaluate the practicability of the ‘topographic components model’ proposed by Möcks¹ for the spatio-temporal characterization of multi-channel evoked potentials (EP), and to present a complete and detailed algorithm for this method of analysis. Details of the algorithm are discussed along with various computational issues, especially with regard to contrasts with traditional principal components analysis. The algorithm is applied to multi-channel pattern-shift visual EP data obtained from normal subjects, and the model is demonstrated to provide data reduction of 71% with a relative mean-squared error (MSE) of 2%. Obvious features of the data are seen to be reflected in the estimated model parameters, lending support to the appropriateness of the model. The results also demonstrate that although the model parameters are uniquely identifiable in theory¹, care must be taken when fitting the model to insure that the MSE is not so insensitive to perturbations in the model parameters that they are rendered ‘non-unique’ for all practical purposes. The proper selection of model order is shown to play a critical role in avoiding this problem. Finally, a theoretical analysis is presented which evaluates the relationship between parameter ‘uniqueness’, model order, and the non-orthogonality of the model components.     

An evaluation of prior influence on the predictive ability of Bayesian model averaging

Model averaging is gaining popularity among ecologists for making inference and predictions. Methods for combining models include Bayesian model averaging (BMA) and Akaike’s Information Criterion (AIC) model averaging. BMA can be implemented with different prior model weights, including the Kullback–Leibler prior associated with AIC model averaging, but it is unclear how the prior model weight affects model results in a predictive context. Here, we implemented BMA using the Bayesian Information Criterion (BIC) approximation to Bayes factors for building predictive models of bird abundance and occurrence in the Chihuahuan Desert of New Mexico. We examined how model predictive ability differed across four prior model weights, and how averaged coefficient estimates, standard errors and coefficients’ posterior probabilities varied for 16 bird species. We also compared the predictive ability of BMA models to a best single-model approach. Overall, Occam’s prior of parsimony provided the best predictive models. In general, the Kullback–Leibler prior, however, favored complex models of lower predictive ability. BMA performed better than a best single-model approach independently of the prior model weight for 6 out of 16 species. For 6 other species, the choice of the prior model weight affected whether BMA was better than the best single-model approach. Our results demonstrate that parsimonious priors may be favorable over priors that favor complexity for making predictions. The approach we present has direct applications in ecology for better predicting patterns of species’ abundance and occurrence.     

Vital statistics,absolute abundance and preservation rate of Tyrannosaurus rex

I present a simulation model on vital statistics, absolute abundance (N, total number of individuals that ever lived) and preservation rate (p, minimum number of fossils known divided by N) of Tyrannosaurus rex. It is based on a published age-structured population model that assumes a reptile or bird-like reproduction for T. rex to estimate its age-specific survival rates. My model applies input variables and equations from a recently published model on N and p. This model yielded 2.5 billion T. rex individuals (N) and one fossil per 80 million individuals (p). The average N values calculated by my model were at minimum 27.6% and p values at maximum 361.5% that of a previous model and uncertainties in all output variables were always larger in my model. The equation on output variable ‘population density’ introduced the largest uncertainty to N and p. The output variable ‘generation time’ differed the most between models, but for N and p, the huge size of the input area modelled and geological longevity minimized this difference. Unlike my model, the generation time as well as life expectancies, gross reproduction rates, and reproductive values of individuals calculated from the previous model all strongly contradicted our current understanding of the biology of T. rex and of other theropods. Their values also disagreed with those of large extant reptiles, birds and mammals. All of these shortcomings of the previous model favour the assessment of individual and population characteristics of T. rex and of other extinct species using my model.     

A computer model of succession and fire response of the high-altitude Eucalyptus forest of the Brindabella Range,Australian Capital Territory

The BRIND model, a computer model of the high altitude forests in the Brindabella Range near Canberra (Australian Capital Territory), is documented and the results of a series of tests on the model are provided. The BRIND model simulates a 1/12 ha forest stand by computing the growth of each individual tree in the stand. It considers establishment and death of trees on a tree-by-tree basis using stochastic functions. The model also simulates the effects of prescribed fire and wildfire on the forests. The model presently is restricted to southeasterly facing slopes (moist, sheltered situations) above 850 m in altitude. The BRIND model is tested in four ways: (1) A single example simulation (for 500years) is inspected for agreement with stand dynamics in wet sclerophyll forests. (2) By varying wildfire frequency, the model is used to develop a succession diagram for forests in the alpine ash (E. delegatensis) zone of the Brindabella Range. This diagram is considered in terms of the successional patterns described for this ecological zone. (3) By subjecting the model to different climatic conditions and wildfire frequencies, a simulated altitude zonation is developed. This simulated pattern of forest types is compared with the extant forest types in the Brindabella Range. (4) The model is tested on its ability to duplicate basal area, stocking density, and average diameters for different age stands found in an independent data set. The model was found to simulate patterns of vegetation that resemble those of the forests of the Brindabella Range in both space and time. The successional pattern was found to be complex and to differ from classic theories of succession originating with Clements. Potential model applications are discussed     

The Application of the Grey Disaster Model to Forecast Epidemic Peaks of Typhoid and Paratyphoid Fever in China

Objective

The objectives of this study were to forecast epidemic peaks of typhoid and paratyphoid fever in China using the grey disaster model, to evaluate its feasibility of predicting the epidemic tendency of notifiable diseases.

Methods

According to epidemiological features, the GM(1,1) model and DGM model were used to build the grey disaster model based on the incidence data of typhoid and paratyphoid fever collected from the China Health Statistical Yearbook. Model fitting accuracy test was used to evaluate the performance of these two models. Then, the next catastrophe date was predicted by the better model.

Results

The simulation results showed that DGM model was better than GM(1,1) model in our data set. Using the DGM model, we predicted the next epidemic peak time will occur between 2023 to 2025.

Conclusion

The grey disaster model can predict the typhoid and paratyphoid fever epidemic time precisely, which may provide valuable information for disease prevention and control.     

The Cost of Simplifying Air Travel When Modeling Disease Spread

Background

Air travel plays a key role in the spread of many pathogens. Modeling the long distance spread of infectious disease in these cases requires an air travel model. Highly detailed air transportation models can be over determined and computationally problematic. We compared the predictions of a simplified air transport model with those of a model of all routes and assessed the impact of differences on models of infectious disease.

Methodology/Principal Findings

Using U.S. ticket data from 2007, we compared a simplified “pipe” model, in which individuals flow in and out of the air transport system based on the number of arrivals and departures from a given airport, to a fully saturated model where all routes are modeled individually. We also compared the pipe model to a “gravity” model where the probability of travel is scaled by physical distance; the gravity model did not differ significantly from the pipe model. The pipe model roughly approximated actual air travel, but tended to overestimate the number of trips between small airports and underestimate travel between major east and west coast airports. For most routes, the maximum number of false (or missed) introductions of disease is small (<1 per day) but for a few routes this rate is greatly underestimated by the pipe model.

Conclusions/Significance

If our interest is in large scale regional and national effects of disease, the simplified pipe model may be adequate. If we are interested in specific effects of interventions on particular air routes or the time for the disease to reach a particular location, a more complex point-to-point model will be more accurate. For many problems a hybrid model that independently models some frequently traveled routes may be the best choice. Regardless of the model used, the effect of simplifications and sensitivity to errors in parameter estimation should be analyzed.     

Effects of distance from models on the fitness of floral mimics

• Rewardless plants can attract pollinators by mimicking floral traits of rewarding heterospecific plants. This should result in the pollination success of floral mimics being dependent on the relative abundance of their models, as pollinator abundance and conditioning on model signals should be higher in the vicinity of the models. However, the attraction of pollinators to signals of the models may be partially innate, such that spatial isolation of mimics from model species may not strongly affect pollination success of mimics.
• We tested whether pollination rates and fruit set of the rewardless orchid Disa pulchra were influenced by proximity and abundance of its rewarding model species, Watsonia lepida.
• Pollination success of the orchid increased with proximity to the model species, while fruit set of the orchid increased with local abundance of the model species. Orchids that were experimentally translocated outside the model population experienced reduced pollinaria removal and increased pollinator‐mediated self‐pollination.
• These results confirm predictions that the pollination success of floral mimics should be dependent on the proximity and abundance of model taxa, and thus highlight the importance of ecological facilitation among species involved in mimicry systems.

     

Determination of Thermal Inactivation Kinetics of Hepatitis A Virus in Blue Mussel (Mytilus edulis) Homogenate

Hepatitis A virus (HAV) is a food-borne enteric virus responsible for outbreaks of hepatitis associated with shellfish consumption. The objectives of this study were to determine the thermal inactivation behavior of HAV in blue mussels, to compare the first-order and Weibull models to describe the data, to calculate Arrhenius activation energy for each model, and to evaluate model efficiency by using selected statistical criteria. The times required to reduce the population by 1 log cycle (D-values) calculated from the first-order model (50 to 72°C) ranged from 1.07 to 54.17 min for HAV. Using the Weibull model, the times required to destroy 1 log unit (t_{D = 1}) of HAV at the same temperatures were 1.57 to 37.91 min. At 72°C, the treatment times required to achieve a 6-log reduction were 7.49 min for the first-order model and 8.47 min for the Weibull model. The z-values (changes in temperature required for a 90% change in the log D-values) calculated for HAV were 15.88 ± 3.97°C (R², 0.94) with the Weibull model and 12.97 ± 0.59°C (R², 0.93) with the first-order model. The calculated activation energies for the first-order model and the Weibull model were 165 and 153 kJ/mol, respectively. The results revealed that the Weibull model was more appropriate for representing the thermal inactivation behavior of HAV in blue mussels. Correct understanding of the thermal inactivation behavior of HAV could allow precise determination of the thermal process conditions to prevent food-borne viral outbreaks associated with the consumption of contaminated mussels.