空间直观景观模型的验证方法

空间直观景观模型已是当前景观生态学研究的一大热点。空间景观模型模拟空间格局变化。其模拟结果包含非空间数据和空间数据。空间直观景观模型的验证除进行非空间数据的验证外,还需要进行空间数据的验证。本文回顾了空间直观模型发展历程,总结现有的空间直观模型验证方法。包括主观评价、图形比较、偏差分析、回归分析、假设检验、多尺度拟合度分析和景观指数分析,同时提出今后空间直观景观模型验证方法研究的重点方向。     

Virtual traumatology of pregnant women: The PRegnant car Occupant Model for Impact Simulations (PROMIS)

This study report documents the development of a finite element (FE) model for analyzing trauma in pregnant women involved in road accidents and help the design of a specific safety device. The model is representative of a 50th percentile pregnant woman at 26 weeks of pregnancy in sitting position. To achieve this, the HUMOS 2 model, which has been validated in a wide range of dynamic tests, was scaled to the morphology of a woman in the 50th percentile and coupled with a model of gravid uterus. During scaling, special attention was paid to the pelvic region which is known to differ considerably in morphological terms between men and women. The gravid uterus model includes a placenta, a fetus, uterosacral ligaments and the amniotic fluid by means of fluid structure interaction formulation. The uterus and the female model were coupled using an original method whereby the growth of an uterus was simulated to compress the abdominal organs in a realistic manner. The model was validated based on experimental tests described in the literature. Additional tests based on abdominal loadings with a seatbelt on Post Mortem Human Surrogates (PMHS) coupled to silicone uterus were also performed.     

Validation of daily surface water depth model of the Greater Everglades based on real-time stage monitoring and aerial ground elevation survey

As part of the Everglades Depth Estimation Network (EDEN) project, this paper describes validation of raster-based daily surface water depth models of the Greater Everglades in Florida developed using real-time stage data and elevation data obtained from a survey with an aerial height finder. Daily median stage data obtained at over 200 locations were interpolated using the multiquadric radial basis function. Surface water depth was obtained by subtracting a digital elevation model from the interpolated stage raster. The model was validated with 751 independent field measurements of surface water depth between 1999 and 2004. Correlations between prediction error and both density of the monitoring gages and distance from a major linear geographic feature, such as a canal, were weak, suggesting that the error does not depend on these features. South Florida has distinct dry and wet seasons and the study area is dominated by sawgrass and wet prairie. Seasonality and ground vegetation type significantly affect prediction error. Correlation between observed and predicted water depth was high for all combination of season and vegetation type (0.83–0.96). Model validation using an equivalence test provided evidence of equivalence between predicted and observed water depths in dry season prairie-dominated and wet season sawgrass-dominated areas with the strict test and in dry season sawgrass-dominated areas with the liberal test, but not in wet season prairie-dominated areas. Equivalence between observed and predicted water depth for both dry season sawgrass- and wet season prairie-dominated areas were confirmed with the strict test after further model calibrations using linear regressions.     

QSAR-based solubility model for drug-like compounds

Solubility plays a very important role in the selection of compounds for drug screening. In this context, a QSAR model was developed for predicting water solubility of drug-like compounds. First, a set of relevant parameters for establishing a drug-like chemical space was defined. The comparison of chemical structures from the FDAMDD and PHYSPROP databases allowed the selection of properties that were more efficient in discriminating drug-like compounds from other chemicals. These filters were later on applied to the PHYSPROP database and 1174 chemicals fulfilling these criteria and with experimental solubility information available at 25 °C were retained. Several QSAR solubility models were developed from this set of compounds, and the best one was selected based on the accuracy of correct classifications obtained for randomly chosen training and validation subsets. Further validation of the model was performed with a set of 102 drugs for which experimental solubility data have been recently reported. A good agreement between the predictions and the experimental values confirmed the reliability of the QSAR model.     

Human shoulder response to side impacts: a finite element study

This study aimed at developing a shoulder finite element (FE) model able to simulate the dynamic behaviour and to predict injuries in case of side impacts. This model is an updated version of the initial Human Model for Safety (HUMOS) FE model of the human body. Simulations performed with the model have been compared to experimental results of side impact tests conducted previously at INRETS. The shoulder model response under lateral impact appears to be in good agreement with experimental data such as impact force and shoulder deflections for different impact speeds and impact directions. These results seem promising for future applications such as shoulder injury prediction in simulated car crashes.     

Validation of a multi-criteria evaluation model for animal welfare

The aim of this paper was to validate an alternative multi-criteria evaluation system to assess animal welfare on farms based on the Welfare Quality® (WQ) project, using an example of welfare assessment of growing pigs. This alternative methodology aimed to be more transparent for stakeholders and more flexible than the methodology proposed by WQ. The WQ assessment protocol for growing pigs was implemented to collect data in different farms in Schleswig-Holstein, Germany. In total, 44 observations were carried out. The aggregation system proposed in the WQ protocol follows a three-step aggregation process. Measures are aggregated into criteria, criteria into principles and principles into an overall assessment. This study focussed on the first two steps of the aggregation. Multi-attribute utility theory (MAUT) was used to produce a value of welfare for each criterion and principle. The utility functions and the aggregation function were constructed in two separated steps. The MACBETH (Measuring Attractiveness by a Categorical-Based Evaluation Technique) method was used for utility function determination and the Choquet integral (CI) was used as an aggregation operator. The WQ decision-makers’ preferences were fitted in order to construct the utility functions and to determine the CI parameters. The validation of the MAUT model was divided into two steps, first, the results of the model were compared with the results of the WQ project at criteria and principle level, and second, a sensitivity analysis of our model was carried out to demonstrate the relative importance of welfare measures in the different steps of the multi-criteria aggregation process. Using the MAUT, similar results were obtained to those obtained when applying the WQ protocol aggregation methods, both at criteria and principle level. Thus, this model could be implemented to produce an overall assessment of animal welfare in the context of the WQ protocol for growing pigs. Furthermore, this methodology could also be used as a framework in order to produce an overall assessment of welfare for other livestock species. Two main findings are obtained from the sensitivity analysis, first, a limited number of measures had a strong influence on improving or worsening the level of welfare at criteria level and second, the MAUT model was not very sensitive to an improvement in or a worsening of single welfare measures at principle level. The use of weighted sums and the conversion of disease measures into ordinal scores should be reconsidered.     

Experimental validation of a finite element model of the proximal femur using digital image correlation and a composite bone model

Computational biomechanical models are useful tools for supporting orthopedic implant design and surgical decision making, but because they are a simplification of the clinical scenario they must be carefully validated to ensure that they are still representative. The goal of this study was to assess the validity of the generation process of a structural finite element model of the proximal femur employing the digital image correlation (DIC) strain measurement technique. A finite element analysis model of the proximal femur subjected to gait loading was generated from a CT scan of an analog composite femur, and its predicted mechanical behavior was compared with an experimental model. Whereas previous studies have employed strain gauging to obtain discreet point data for validation, in this study DIC was used for full field quantified comparison of the predicted and experimentally measured strains. The strain predicted by the computational model was in good agreement with experimental measurements, with R(2) correlation values from 0.83 to 0.92 between the simulation and the tests. The sensitivity and repeatability of the strain measurements were comparable to or better than values reported in the literature for other DIC tests on tissue specimens. The experimental-model correlation was in the same range as values obtained from strain gauging, but the DIC technique produced more detailed, full field data and is potentially easier to use. As such, the findings supported the validity of the model generation process, giving greater confidence in the model's predictions, and digital image correlation was demonstrated as a useful tool for the validation of biomechanical models.     

The influence of model parameters on model validation

The study examined the sensitivity of two musculoskeletal models to the parameters describing each model. Two different models were examined: a phenomenological model of human jumping with parameters based on live subject data, and the second a model of the First Dorsal Interosseous with parameters based on cadaveric measurements. Both models were sensitive to the model parameters, with the use of mean group data not producing model outputs reflective of either the performance of any group member or the mean group performance. These results highlight the value of subject specific model parameters, and the problems associated with model validation.     

A physiologically based in silico kinetic model predicting plasma cholesterol concentrations in humans

Increased plasma cholesterol concentration is associated with increased risk of cardiovascular disease. This study describes the development, validation, and analysis of a physiologically based kinetic (PBK) model for the prediction of plasma cholesterol concentrations in humans. This model was directly adapted from a PBK model for mice by incorporation of the reaction catalyzed by cholesterol ester transfer protein and contained 21 biochemical reactions and eight different cholesterol pools. The model was calibrated using published data for humans and validated by comparing model predictions on plasma cholesterol levels of subjects with 10 different genetic mutations (including familial hypercholesterolemia and Smith-Lemli-Opitz syndrome) with experimental data. Average model predictions on total cholesterol were accurate within 36% of the experimental data, which was within the experimental margin. Sensitivity analysis of the model indicated that the HDL cholesterol (HDL-C) concentration was mainly dependent on hepatic transport of cholesterol to HDL, cholesterol ester transfer from HDL to non-HDL, and hepatic uptake of cholesterol from non-HDL-C. Thus, the presented PBK model is a valid tool to predict the effect of genetic mutations on cholesterol concentrations, opening the way for future studies on the effect of different drugs on cholesterol levels in various subpopulations in silico.     

A spatially explicit computer model for immature distributions of Glyptotendipes paripes (Diptera: Chironomidae) in central Florida lakes

Density and spatial distribution data of immature Glyptotendipes paripes Edwards (Diptera: Chironomidae), in relation to selected water and sediment characteristics prevailing in three central Florida lakes, were used to develop and calibrate a qualitative model of G. paripes distributions with the purpose of developing more efficient sampling plans for research or population management. For the model, each lake habitat was defined by a two-dimensional matrix, while a three-dimensional matrix was used to simulate the life stage-structured population. A Lefkovitch population matrix was used to project survival and development of the population in each spatial unit. The model incorporated lake bathymetry, sediment dry weight, water level, food availability (as a function of Secchi disk transparency) and strength of density dependence as influences on immature survival and adult fecundity. Temperature-dependent development data for G. paripes were incorporated to estimate development time. A simple redistribution function was used to simulate the dispersal of adults over the simulated habitat for oviposition. Immatures and relevant environmental data taken for eight dates, at each of two other central Florida lakes were used to validate the model. The mean correct prediction rate of the model for field density distributions within 0.5 log(n+1) immatures/m² density in spatial habitat cells was 0.64 and 0.66 for the validation lakes. Presence/absence correct prediction rates were 0.61 and 0.76, while matching to a proposed sampling stratification based on a treatment threshold was 0.85 and 0.88. These values indicate that the model is efficient for preparing stratified immature G. paripes sampling plans for central Florida lakes.     

Fitting and validating the genomic evaluation model to Polish Holstein-Friesian cattle

The aim of the study was to fit the genomic evaluation model to Polish Holstein-Friesian dairy cattle. A training data set for the estimation of additive effects of single nucleotide polymorphisms (SNPs) consisted of 1227 Polish Holstein-Friesian bulls. Genotypes were obtained by the use of Illumina BovineSNP50 Genotyping BeadChip. Altogether 29 traits were considered: milk-, fat- and protein- yields, somatic cell score, four female fertility traits, and 21 traits describing conformation. The prediction of direct genomic values was based on a mixed model containing deregressed national proofs as a dependent variable and random SNP effects as independent variables. The correlations between direct genomic values and conventional estimated breeding values estimated for the whole data set were overall very high and varied between 0.98 for production traits and 0.78 for non return rates for cows. For the validation data set of 232 bulls the corresponding correlations were 0.38 for milk-, 0.37 for protein-, and 0.32 for fat yields, while the correlations between genomic enhanced breeding values and conventional estimated breeding values for the four traits were: 0.43, 0.44, 0.31, and 0.35. This model was able to pass the interbull validation criteria for genomic selection, which indicates that it is realistic to implement genomic selection in Polish Holstein-Friesian cattle.     

A novel formulation for blood trauma prediction by a modified power-law mathematical model

With the increasing use of artificial organs, blood damage has been raising ever more clinical concern. Blood trauma is in fact a major complication resulting from the implantation of medical devices and the use of life support apparatuses. Red blood cells damage predictive models furnish critical information on both the design and the evaluation of artificial organs, because their correct usage and implementation are thought to provide clear and rational guidance for the improvement of safety and efficacy. The currently adopted power-law shear-induced haemolysis prediction model lacks sensitivity with respect to the cumulative effect of previously applied stress magnitudes. An alternative model is proposed where a mechanical quantity was defined, able to describe the blood damage sustained by red cells under unsteady stress conditions, taking into account the load history. The proposed formulation predicted the same trend as the available experimental data. The obtained results have to be considered a preliminary validation of the basic hypothesis of this modified red blood cell damage prediction model. To date, the necessity to design further experiments to validate the proposed damage function clashes with the limitations inherent to current systems to get the time-varying shear stress completely under control.     

Numerical model (switchable/dual model) of the human head for rigid body and finite elements applications

In this paper, a methodology for the development and validation of a numerical model of the human head using generic procedures is presented. All steps required, starting with the model generation, model validation and applications will be discussed. The proposed model may be considered as a dual one due to its capabilities to switch from deformable to a rigid body according to the application's requirements. The first step is to generate the numerical model of the human head using geometry files or medical images. The required stiffness and damping for the elastic connection used for the rigid body model are identified by performing a natural frequency analysis. The presented applications for model validation are related to impact analysis. The first case is related to Nahum's (Nahum and Smith 1970) experiments pressure data being evaluated and a pressure map generated using the results from discrete elements. For the second case, the relative displacement between the brain and the skull is evaluated according to Hardy's (Hardy WH, Foster CD, Mason, MJ, Yang KH, King A, Tashman S. 2001.Investigation of head injury mechanisms using neutral density technology and high-speed biplanar X-ray. Stapp Car Crash J. 45:337–368, SAE Paper 2001-22-0016) experiments. The main objective is to validate the rigid model as a quick and versatile tool for acquiring the input data for specific brain analyses.     

A measurement error model for time-series studies of air pollution and mortality

One barrier to interpreting the observational evidence concerning the adverse health effects of air pollution for public policy purposes is the measurement error inherent in estimates of exposure based on ambient pollutant monitors. Exposure assessment studies have shown that data from monitors at central sites may not adequately represent personal exposure. Thus, the exposure error resulting from using centrally measured data as a surrogate for personal exposure can potentially lead to a bias in estimates of the health effects of air pollution. This paper develops a multi-stage Poisson regression model for evaluating the effects of exposure measurement error on estimates of effects of particulate air pollution on mortality in time-series studies. To implement the model, we have used five validation data sets on personal exposure to PM10. Our goal is to combine data on the associations between ambient concentrations of particulate matter and mortality for a specific location, with the validation data on the association between ambient and personal concentrations of particulate matter at the locations where data have been collected. We use these data in a model to estimate the relative risk of mortality associated with estimated personal-exposure concentrations and make a comparison with the risk of mortality estimated with measurements of ambient concentration alone. We apply this method to data comprising daily mortality counts, ambient concentrations of PM10measured at a central site, and temperature for Baltimore, Maryland from 1987 to 1994. We have selected our home city of Baltimore to illustrate the method; the measurement error correction model is general and can be applied to other appropriate locations.Our approach uses a combination of: (1) a generalized additive model with log link and Poisson error for the mortality-personal-exposure association; (2) a multi-stage linear model to estimate the variability across the five validation data sets in the personal-ambient-exposure association; (3) data augmentation methods to address the uncertainty resulting from the missing personal exposure time series in Baltimore. In the Poisson regression model, we account for smooth seasonal and annual trends in mortality using smoothing splines. Taking into account the heterogeneity across locations in the personal-ambient-exposure relationship, we quantify the degree to which the exposure measurement error biases the results toward the null hypothesis of no effect, and estimate the loss of precision in the estimated health effects due to indirectly estimating personal exposures from ambient measurements.     

Cunninghamella--a microbial model for drug metabolism studies--a review

Drug metabolism studies constitute an important and necessary step in the evaluation of drug efficacy and safety. In vivo drug metabolism studies suffer from many disadvantages. Hence there is a rise in validation of in vitro microbial models. This review describes the transformation studies of drugs by the fungus, Cunninghamella and correlating them with the metabolism/biotransformation in animal systems and providing technical methods to develop microbial models. Emphasis is laid on the potential of Cunninghamella fungus to mimic mammalian drug biotransformations and to use as in vitro model for drug metabolism studies and for further toxicological and pharmacological studies of metabolites.     

Context-oriented model validation of individual-based models in ecology: A hierarchically structured approach to validate qualitative,compositional and quantitative characteristics

Validation constitutes a vital process in model development and application, as it ensures the applicability of a model for the intended purposes and trustworthy results within the range of model assumptions. Commonly, independent empirical data sets are statistically compared with the generated model results, which is an adequate approach for models which operate on a single hierarchical level, such as most equation-based models. Individual-based models (IBM) can operate on different organisational levels synchronously and have an inherent complex and variable interaction structure for many applications. Thus a plain comparison of data congruity on the result levels might leave too many questions unanswered. However, a more comprehensive assessment of model validity can require additional investigations which encompass also qualitative and structural relationships.Here we describe a hierarchically structured validation which is oriented towards the investigated context of the model and allows organising the validation process in close relation to the different hierarchical levels which are covered in the model. The context oriented organisation protocol for validation includes the following steps: (1) assessing the different model levels separately, then, (2) applying a set of different techniques such as visual inspection, statistical comparison, involvement of experts, aggregation of data on higher integration levels and experimental validation.The context oriented approach accounts for the specificity of individual-based models – i.e., the dynamic self-organisation of model outcomes from biologically underpinned individual interactions without an inherent determination of properties on higher hierarchical levels – and extends the potential of the validation process qualitatively, as it allows to assess complex structural and causal relations and multi-level feedback processes of the developed models.