Development and validation of an atlas-based finite element brain model

Traumatic brain injury is a leading cause of disability and injury-related death. To enhance our ability to prevent such injuries, brain response can be studied using validated finite element (FE) models. In the current study, a high-resolution, anatomically accurate FE model was developed from the International Consortium for Brain Mapping brain atlas. Due to wide variation in published brain material parameters, optimal brain properties were identified using a technique called Latin hypercube sampling, which optimized material properties against three experimental cadaver tests to achieve ideal biomechanics. Additionally, falx pretension and thickness were varied in a lateral impact variation. The atlas-based brain model (ABM) was subjected to the boundary conditions from three high-rate experimental cadaver tests with different material parameter combinations. Local displacements, determined experimentally using neutral density targets, were compared to displacements predicted by the ABM at the same locations. Error between the observed and predicted displacements was quantified using CORrelation and Analysis (CORA), an objective signal rating method that evaluates the correlation of two curves. An average CORA score was computed for each variation and maximized to identify the optimal combination of parameters. The strongest relationships between CORA and material parameters were observed for the shear parameters. Using properties obtained through the described multiobjective optimization, the ABM was validated in three impact configurations and shows good agreement with experimental data. The final model developed in this study consists of optimized brain material properties and was validated in three cadaver impacts against local brain displacement data.     

A minimum area discrepancy method (MADM) for force displacement response correlation

With the increasing use of Computer Aided Engineering, it has become vital to be able to evaluate the accuracy of numerical models. Specific methods such as CORA were developed to objectively evaluate the correlation between a physical test and a numerical simulation results in terms of parameter vs time. However, no metric has so far been developed for Force Vs Deflection (FvD) signals often used in crashworthiness and biomechanics. A unique method called the Minimum Area Discrepancy Method, or MADM, is proposed to address this deficiency. This new method initially calculates a parameter ‘R’ which represents the area between numerical model and the average physical test response and then divides it by the average area generated by the upper and lower test corridors, based on the same standard deviation. The parameter ‘R’ is then normalized between 0 (no correlation) and 1 (perfect correlation) to become the MADM correlation rating. The MADM method was then validated by comparing a one dimensional Finite Element (FE) model of a chest model, under 2 impact velocities, against reference Post Mortem Human Subject (PMHS) data. The MADM method was further used to improve the correlation of this thorax model, by varying model parameters and generating 81 model variations. Based on the MADM ratings, a set parameter values leading to the best fit was identified. The best fit exhibits a response significantly better than the original chest model. MADM is novel, unique, easy to use and fulfills an important gap in objectively evaluating FVD correlation responses. Abbreviations MADM Correlation rating value (Minimum Area Discrepancy Method)

MADM_n,m MADM correlation rating using a specific scaling value of ‘n’ and power rating ‘m’

FvD Force versus Displacement

FvT Force versus Time

DvT Displacement versus Time

NM Numerical model

PE Physical Experiment

A_model Area under the average signal and the Numerical Model

A_upper Area under the average signal +1 standard deviation

A_lower Area under the average signal -1 standard deviation

R Ratio between A_model and the average of A_upper and A_lower

     

江西千烟洲地区散射辐射的模拟与验证

散射辐射的准确估算对于评价其对陆地生态系统碳交换的影响具有重要意义.基于我国中亚热带江西千烟洲气象观测场2012年3月1日—2013年2月28日散射辐射实际观测数据对目前常用的5个散射辐射分解模型（Reindl-1、Reindl-2、Reindl-3、Boland、BRL）的模拟结果进行验证.结果表明： 在30 min尺度上,虽然5个模型在总体上都可以较好地模拟该地区的散射辐射,但模型模拟效果随着晴空指数（k_t）的升高而显著降低.特别是当k_t>0.75时,各模型已无法模拟该地区散射辐射.从散射辐射季节变化的模拟来看,5个模型能够很好地模拟大多数月份的散射辐射.5个模型年尺度散射辐射模拟值与观测值的相对偏差最高为7.1%（BRL）,最低为0.04%（Reindl-1）,平均为3.6%.在全年辐射最强、温度最高和降水偏少的夏季,5个模型的模拟值均出现了过高估计.以7月为例,散射辐射被高估14.5%～28.2%,平均高估21.2%.这可能与高k_t条件下散射辐射的估算方法有关,这种不确定性需要在模型应用中做进一步深入评价.根据验证结果并考虑模拟精度和输入变量的易获取性,5个模型的模拟效果依次为BRL>Reindl-3>Reindl-2>Reindl-1>Boland.     

Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models

Predictive performance is important to many applications of species distribution models (SDMs). The SDM ‘ensemble’ approach, which combines predictions across different modelling methods, is believed to improve predictive performance, and is used in many recent SDM studies. Here, we aim to compare the predictive performance of ensemble species distribution models to that of individual models, using a large presence–absence dataset of eucalypt tree species. To test model performance, we divided our dataset into calibration and evaluation folds using two spatial blocking strategies (checkerboard-pattern and latitudinal slicing). We calibrated and cross-validated all models within the calibration folds, using both repeated random division of data (a common approach) and spatial blocking. Ensembles were built using the software package ‘biomod2’, with standard (‘untuned’) settings. Boosted regression tree (BRT) models were also fitted to the same data, tuned according to published procedures. We then used evaluation folds to compare ensembles against both their component untuned individual models, and against the BRTs. We used area under the receiver-operating characteristic curve (AUC) and log-likelihood for assessing model performance. In all our tests, ensemble models performed well, but not consistently better than their component untuned individual models or tuned BRTs across all tests. Moreover, choosing untuned individual models with best cross-validation performance also yielded good external performance, with blocked cross-validation proving better suited for this choice, in this study, than repeated random cross-validation. The latitudinal slice test was only possible for four species; this showed some individual models, and particularly the tuned one, performing better than ensembles. This study shows no particular benefit to using ensembles over individual tuned models. It also suggests that further robust testing of performance is required for situations where models are used to predict to distant places or environments.     

Derivation of the first clinical diagnostic models for dehydration severity in patients over five years with acute diarrhea

Diarrheal diseases lead to an estimated 1.3 million deaths each year, with the majority of those deaths occurring in patients over five years of age. As the severity of diarrheal disease can vary widely, accurately assessing dehydration status remains the most critical step in acute diarrhea management. The objective of this study is to empirically derive clinical diagnostic models for assessing dehydration severity in patients over five years with acute diarrhea in low resource settings. We enrolled a random sample of patients over five years with acute diarrhea presenting to the icddr,b Dhaka Hospital. Two blinded nurses independently assessed patients for symptoms/signs of dehydration on arrival. Afterward, consecutive weights were obtained to determine the percent weight change with rehydration, our criterion standard for dehydration severity. Full and simplified ordinal logistic regression models were derived to predict the outcome of none (<3%), some (3–9%), or severe (>9%) dehydration. The reliability and accuracy of each model were assessed. Bootstrapping was used to correct for over-optimism and compare each model’s performance to the current World Health Organization (WHO) algorithm. 2,172 patients were enrolled, of which 2,139 (98.5%) had complete data for analysis. The Inter-Class Correlation Coefficient (reliability) was 0.90 (95% CI = 0.87, 0.91) for the full model and 0.82 (95% CI = 0.77, 0.86) for the simplified model. The area under the Receiver-Operator Characteristic curve (accuracy) for severe dehydration was 0.79 (95% CI: 0.76–0.82) for the full model and 0.73 (95% CI: 0.70, 0.76) for the simplified model. The accuracy for both the full and simplified models were significantly better than the WHO algorithm (p<0.001). This is the first study to empirically derive clinical diagnostic models for dehydration severity in patients over five years. Once prospectively validated, the models may improve management of patients with acute diarrhea in low resource settings.     

Comparison of approaches to combine species distribution models based on different sets of predictors

Distribution models should take into account the different limiting factors that simultaneously influence species ranges. Species distribution models built with different explanatory variables can be combined into more comprehensive ones, but the resulting models should maximize complementarity and avoid redundancy. Our aim was to compare the different methods available for combining species distribution models. We modelled 19 threatened vertebrate species in mainland Spain, producing models according to three individual explanatory factors: spatial constraints, topography and climate, and human influence. We used five approaches for model combination: Bayesian inference, Akaike weight averaging, stepwise variable selection, updating, and fuzzy logic. We compared the performance of these approaches by assessing different aspects of their classification and discrimination capacity. We demonstrated that different approaches to model combination give rise to disparities in the model outputs. Bayesian integration was systematically affected by an error in the equations that are habitually used in distribution modelling. Akaike weights produced models that were driven by the best single factor and therefore failed at combining the models effectively. The updating and the stepwise approaches shared recalibration as the basic concept for model combination, were very similar in their performance, and showed the highest sensitivity and discrimination capacity. The fuzzy‐logic approach yielded models with the highest classification capacity according to Cohen's kappa. In conclusion: 1) Bayesian integration, employing the currently used equation, and the Akaike weight procedure should be avoided; 2) the updating and stepwise approaches can be considered minor variants of the same recalibrating approach; and 3) there is a trade‐off between this recalibrating approach, which has the highest sensitivity, and fuzzy logic, which has the highest overall classification capacity. Recalibration is better if unfavourable conditions in one environmental factor may be counterbalanced with favourable conditions in a different factor, otherwise fuzzy logic is better.     

气候变化对马尾松潜在分布影响预估的多模型比较

物种分布模型被广泛应用于评估气候变化对物种分布的影响。随着计算机和统计学的发展, 模拟物种分布的模型层出不穷, 但对这些模型的相对表现知之甚少, 因此需要对其进行对比分析, 以便更可靠地评估气候变化的影响。该文采用3个比较新颖的组合集成学习(ensemble learning)模型(随机森林(random forest, RF)、广义助推法和NeuralEnsembles)、3个常规模型(广义线性模型、广义加法模型和分类回归树)、3个大气环流模型(global circulation model, GCM) (MIROC32_medres, JP; CCCMA_CGCM3, CA; BCCR-BCM2.0, NW)和一个气体排放情景(SRES_A2), 模拟分析了马尾松(Pinus massoniana)历史基准气候(1961-1990)和未来3个不同时期(2010-2039, 2020s; 2040-2069, 2050s; 2070-2099, 2080s)的潜在分布。基于环境阈值方法选择物种不发生区, 依据ClimateChina软件进行当前和未来气候数据的降尺度处理, 采用接收机工作特征曲线(receiver operator characteristic, ROC)下的面积(area under the curve, AUC)、Kappa值和真实技巧统计法(true skill statistic, TSS)以及马尾松种子区划范围来评价模型的预测精度。结果表明: 6个物种分布模型都具有较高的预测精度, 但组合集成学习模型的预测精度稍高于其他常规模型, 其中RF的预测精度最高。3个GCM和6个模型模拟条件下, 马尾松对气候变化的响应格局既有一致性也有异同性。一致性表现在: 随着时间的推移, 马尾松分布区将逐渐向北迁移, 未来潜在分布区的面积将逐渐增加; 异同性表现在: 在不同模型和不同气候情景下, 马尾松潜在分布区的迁移距离和面积变化幅度不同, 其中NW模式下预测的变化幅度小于CA和JP模式; RF模型预测的分布区迁移距离和面积变化幅度最大。随着时间的推移, 未来马尾松的18个潜在分布空间预测图(6个模型 × 3 GCM)之间的差异也逐渐增大, 其中空间不一致性地区主要集中发生在马尾松潜在分布区的北部和西部边缘地带。模型本身不同的构建原理以及GCM之间的差异是导致预测结果存在差异的主要原因。     

Estimating linear temporal trends from aggregated environmental monitoring data

Trend estimates are often used as part of environmental monitoring programs. These trends inform managers (e.g., are desired species increasing or undesired species decreasing?). Data collected from environmental monitoring programs is often aggregated (i.e., averaged), which confounds sampling and process variation. State-space models allow sampling variation and process variations to be separated. We used simulated time-series to compare linear trend estimations from three state-space models, a simple linear regression model, and an auto-regressive model. We also compared the performance of these five models to estimate trends from a long term monitoring program. We specifically estimated trends for two species of fish and four species of aquatic vegetation from the Upper Mississippi River system. We found that the simple linear regression had the best performance of all the given models because it was best able to recover parameters and had consistent numerical convergence. Conversely, the simple linear regression did the worst job estimating populations in a given year. The state-space models did not estimate trends well, but estimated population sizes best when the models converged. We found that a simple linear regression performed better than more complex autoregression and state-space models when used to analyze aggregated environmental monitoring data.     

Evaluation of sampling tools for environmental sampling of bacterial endospores from porous and nonporous surfaces

Aims: Having and executing a well-defined and validated sampling protocol is critical following a purposeful release of a biological agent for response and recovery activities, for clinical and epidemiological analysis and for forensic purposes. The objective of this study was to address the need for validated sampling and analysis methods called out by the General Accounting Office and others to systematically compare the collection efficiency of various swabs and wipes for collection of bacterial endospores from five different surfaces, both porous and nonporous. This study was also designed to test the collection and extraction solutions used for endospore recovery from swabs and wipes. Methods and Results: Eight collection tools, five swabs and three wipes, were used. Three collection/preservation solutions were evaluated: an ink jet aerosol generator was used to apply Bacillus subtilis endospores to five porous and nonporous surfaces. The collection efficiencies of the swabs and wipes were compared using a statistical multiple comparison analysis. Conclusions: The ScottPure^® wipe had the highest collection efficiency and phosphate-buffered saline (PBST) with 0·3% Tween was the best collection solution of those tested. Significance and Impact of the Study: Validated sampling for potential biological warfare is of significant importance and this study answered some relevant questions.     

稻田甲烷排放模型研究——模型的验证

模型的有效性检验是模型应用于估计区域尺度稻田甲烷排放量的基本前提 ,尤其是针对多种不同的土壤、气候以及农业管理方式等可能影响稻田甲烷排放的环境条件下的模型检验。利用覆盖全国主要水稻产区的 94个甲烷排放观测案例对稻田甲烷排放模型 (CH4 MOD)进行了验证。这些观测区域分布范围北至北京 (4 0°30′N,116°2 5′E) ,南至广州 (2 3°0 8′N,113°2 0′E) ,东起杭州 (30°19′N,12 0°12′E) ,西到四川的土主 (2 9°4 0′N,10 3°5 0′E)。既有双季稻 ,也有单季稻 ,稻田灌溉及施肥方式也多种多样 ,对我国水稻生产具有较广泛的代表性。观测获得的稻田甲烷排放季节总量从 3.1kg C/hm2到 76 1.7kg C/hm2 ,平均值为199.4 (± 187.3) kg C/hm2 ;相应的模拟值分别为 13.9、82 4 .3和 2 2 4 .6 (± 187.0 ) kg C/hm2。模拟值与实测值的线性相关系数(r2 )为 0 .84 (n=94 ,p<0 .0 0 1)。CH4 MOD模型能够通过较少的输入参数有效地模拟我国主要农作方式下的稻田甲烷排放     

A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area

To investigate the comparative abilities of six different bioclimatic models in an independent area, utilizing the distribution of eight different species available at a global scale and in Australia. Global scale and Australia. We tested a variety of bioclimatic models for eight different plant species employing five discriminatory correlative species distribution models (SDMs) including Generalized Linear Model (GLM), MaxEnt, Random Forest (RF), Boosted Regression Tree (BRT), Bioclim, together with CLIMEX (CL) as a mechanistic niche model. These models were fitted using a training dataset of available global data, but with the exclusion of Australian locations. The capabilities of these techniques in projecting suitable climate, based on independent records for these species in Australia, were compared. Thus, Australia is not used to calibrate the models and therefore it is as an independent area regarding geographic locations. To assess and compare performance, we utilized the area under the receiver operating characteristic (ROC) curves (AUC), true skill statistic (TSS), and fractional predicted areas for all SDMs. In addition, we assessed satisfactory agreements between the outputs of the six different bioclimatic models, for all eight species in Australia. The modeling method impacted on potential distribution predictions under current climate. However, the utilization of sensitivity and the fractional predicted areas showed that GLM, MaxEnt, Bioclim, and CL had the highest sensitivity for Australian climate conditions. Bioclim calculated the highest fractional predicted area of an independent area, while RF and BRT were poor. For many applications, it is difficult to decide which bioclimatic model to use. This research shows that variable results are obtained using different SDMs in an independent area. This research also shows that the SDMs produce different results for different species; for example, Bioclim may not be good for one species but works better for other species. Also, when projecting a “large” number of species into novel environments or in an independent area, the selection of the “best” model/technique is often less reliable than an ensemble modeling approach. In addition, it is vital to understand the accuracy of SDMs' predictions. Further, while TSS, together with fractional predicted areas, are appropriate tools for the measurement of accuracy between model results, particularly when undertaking projections on an independent area, AUC has been proved not to be. Our study highlights that each one of these models (CL, Bioclim, GLM, MaxEnt, BRT, and RF) provides slightly different results on projections and that it may be safer to use an ensemble of models.     

Prediction of one-hour running performance using constant duration tests

Critical velocity (CV) represents, theoretically, the highest velocity that can be sustained without fatigue. The aim of this study was to compare CV computed from 5 mathematical models in order to determine which CV estimate is better correlated with 1-hour performance and which model provides the most accurate prediction of performance. Twelve trained middle- and long-distance male runners (29 +/- 5 years) performed 3 randomly ordered constant duration tests (6, 9, and 12 minutes), a maximal running velocity test for the estimation of CV, and a 1-hour track test (actual performance). Two linear, 2 nonlinear, and 1 exponential mathematical models were used to estimate CV and to predict the highest velocity that could be sustained during 1 hour (predicted performance). Although all CV estimates were correlated with performance (0.80 < r < 0.93, p < 0.01), it appeared that CV estimated from the exponential model was more closely associated with performance than all other models (r = 0.93; p < 0.01). Analysis of the bias +/- 95% interval of confidence between actual and predicted performance revealed that none of the models provided an accurate prediction of the 1-hour performance velocity. In conclusion, the estimation of CV allows us to rank middle- and long-distance runners with regard to their ability to perform well in long-distance running. However, no models provide an accurate prediction of performance that could be used as a reference for coaches or athletes.     

A temperature-precipitation based leafing model and its application in Northeast China

Plant phenology models, especially leafing models, play critical roles in evaluating the impact of climate change on the primary production of temperate plants. Existing models based on temperature alone could not accurately simulate plant leafing in arid and semi-arid regions. The objective of the present study was to test the suitability of the existing temperature-based leafing models in arid and semi-arid regions, and to develop a temperature-precipitation based leafing model (TP), based on the long-term (i.e., 12-27 years) ground leafing observation data and meteorological data in Northeast China. The better simulation of leafing for all the plant species in Northeast China was given by TP with the fixed starting date (TPn) than with the parameterized starting date (TPm), which gave the smallest average root mean square error (RMSE) of 4.21 days. Tree leafing models were validated with independent data, and the coefficient of determination (R(2)) was greater than 0.60 in 75% of the estimates by TP and the spring warming model (SW) with the fixed starting date. The average RMSE of herb leafing simulated by TPn was 5.03 days, much lower than other models (>9.51 days), while the average R(2) of TPn and TPm were 0.68 and 0.57, respectively, much higher than the other models (<0.22). It indicates that TPn is a universal model and more suitable for simulating leafing of trees and herbs than the prior models. Furthermore, water is an important factor determining herb leafing in arid and semi-arid temperate regions.     

Comparative Study of SSVEP- and P300-Based Models for the Telepresence Control of Humanoid Robots

In this paper, we evaluate the control performance of SSVEP (steady-state visual evoked potential)- and P300-based models using Cerebot—a mind-controlled humanoid robot platform. Seven subjects with diverse experience participated in experiments concerning the open-loop and closed-loop control of a humanoid robot via brain signals. The visual stimuli of both the SSVEP- and P300- based models were implemented on a LCD computer monitor with a refresh frequency of 60 Hz. Considering the operation safety, we set the classification accuracy of a model over 90.0% as the most important mandatory for the telepresence control of the humanoid robot. The open-loop experiments demonstrated that the SSVEP model with at most four stimulus targets achieved the average accurate rate about 90%, whereas the P300 model with the six or more stimulus targets under five repetitions per trial was able to achieve the accurate rates over 90.0%. Therefore, the four SSVEP stimuli were used to control four types of robot behavior; while the six P300 stimuli were chosen to control six types of robot behavior. Both of the 4-class SSVEP and 6-class P300 models achieved the average success rates of 90.3% and 91.3%, the average response times of 3.65 s and 6.6 s, and the average information transfer rates (ITR) of 24.7 bits/min 18.8 bits/min, respectively. The closed-loop experiments addressed the telepresence control of the robot; the objective was to cause the robot to walk along a white lane marked in an office environment using live video feedback. Comparative studies reveal that the SSVEP model yielded faster response to the subject’s mental activity with less reliance on channel selection, whereas the P300 model was found to be suitable for more classifiable targets and required less training. To conclude, we discuss the existing SSVEP and P300 models for the control of humanoid robots, including the models proposed in this paper.     

A finite element model of an anthropomorphic test device lower limb to assess risk of injuries during vertical accelerative loading

Improvised explosive devices (IEDs) were used extensively to target occupants of military vehicles during the conflicts in Iraq and Afghanistan (2003–2011). War fighters exposed to an IED attack were highly susceptible to lower limb injuries. To appropriately assess vehicle safety and make informed improvements to vehicle design, a novel Anthropomorphic Test Device (ATD), called the Warrior Injury Assessment Manikin (WIAMan), was designed for vertical loading. The main objective of this study was to develop and validate a Finite Element (FE) model of the WIAMan lower limb (WIAMan-LL). Appropriate materials and contacts were applied to realistically model the physical dummy. Validation of the model was conducted based on experiments performed on two different test rigs designed to simulate the vertical loading experienced during an under-vehicle explosion. Additionally, a preliminary evaluation of the WIAMan and Hybrid-III test devices was performed by comparing force responses to post-mortem human surrogate (PMHS) corridors. The knee axial force recorded by the WIAMan-LL when struck on the plantar surface of the foot (2 m/s) fell mostly within the PMHS corridor, but the corresponding data predicted by the Hybrid-III was almost 60% higher. Overall, good agreements were observed between the WIAMan-LL FE predictions and experiments at various pre-impact speeds ranging from 2 m/s up to 5.8 m/s. Results of the FE model were backed by mean objective rating scores of 0.67–0.76 which support its accuracy relative to the physical lower limb dummy. The observations and objective rating scores show the model is validated within the experimental loading conditions. These results indicate the model can be used in numerical studies related to possible dummy design improvements once additional PMHS data is available. The numerical lower limb is currently incorporated into a whole body model that will be used to evaluate the vehicle design for underbody blast protection.     

Sensitivity of predictive species distribution models to change in grain size

Predictive species distribution modelling (SDM) has become an essential tool in biodiversity conservation and management. The choice of grain size (resolution) of environmental layers used in modelling is one important factor that may affect predictions. We applied 10 distinct modelling techniques to presence-only data for 50 species in five different regions, to test whether: (1) a 10-fold coarsening of resolution affects predictive performance of SDMs, and (2) any observed effects are dependent on the type of region, modelling technique, or species considered. Results show that a 10 times change in grain size does not severely affect predictions from species distribution models. The overall trend is towards degradation of model performance, but improvement can also be observed. Changing grain size does not equally affect models across regions, techniques, and species types. The strongest effect is on regions and species types, with tree species in the data sets (regions) with highest locational accuracy being most affected. Changing grain size had little influence on the ranking of techniques: boosted regression trees remain best at both resolutions. The number of occurrences used for model training had an important effect, with larger sample sizes resulting in better models, which tended to be more sensitive to grain. Effect of grain change was only noticeable for models reaching sufficient performance and/or with initial data that have an intrinsic error smaller than the coarser grain size.     

Selecting discriminant function models for predicting the expected richness of aquatic macroinvertebrates

1. The predictive modelling approach to bioassessment estimates the macroinvertebrate assemblage expected at a stream site if it were in a minimally disturbed reference condition. The difference between expected and observed assemblages then measures the departure of the site from reference condition. 2. Most predictive models employ site classification, followed by discriminant function (DF) modelling, to predict the expected assemblage from a suite of environmental variables. Stepwise DF analysis is normally used to choose a single subset of DF predictor variables with a high accuracy for classifying sites. An alternative is to screen all possible combinations of predictor variables, in order to identify several ‘best’ subsets that yield good overall performance of the predictive model. 3. We applied best‐subsets DF analysis to assemblage and environmental data from 199 reference sites in Oregon, U.S.A. Two sets of 66 best DF models containing between one and 14 predictor variables (that is, having model orders from one to 14) were developed, for five‐group and 11‐group site classifications. 4. Resubstitution classification accuracy of the DF models increased consistently with model order, but cross‐validated classification accuracy did not improve beyond seventh or eighth‐order models, suggesting that the larger models were overfitted. 5. Overall predictive model performance at model training sites, measured by the root‐mean‐squared error of the observed/expected species richness ratio, also improved steadily with DF model order. But high‐order DF models usually performed poorly at an independent set of validation sites, another sign of model overfitting. 6. Models selected by stepwise DF analysis showed evidence of overfitting and were outperformed by several of the best‐subsets models. 7. The group separation strength of a DF model, as measured by Wilks’Λ, was more strongly correlated with overall predictive model performance at training sites than was DF classification accuracy. 8. Our results suggest improved strategies for developing reliable, parsimonious predictive models. We emphasise the value of independent validation data for obtaining a realistic picture of model performance. We also recommend assessing not just one or two, but several, candidate models based on their overall performance as well as the performance of their DF component. 9. We provide links to our free software for stepwise and best‐subsets DF analysis.     

A carcinoembryonic antigen (CEA) binding protein from ascites influences CEA uptake by macrophages

A variant of CEA which is less readily endocytosed by macrophages has been isolated from malignant ascites. In vivo, CEA is cleared more slowly by the liver (t1/2 = 15.1 minutes) than CEAs isolated from hepatic metastases (t1/2 = 3.1 minutes). In vitro, rat and human Kupffer cells and rat alveolar macrophages endocytose this CEA less effectively. This slow clearing form of CEA is associated with a smaller (45kD) acidic glycoprotein (CORA) with which it forms a stable complex. CORA can be visualized on reducing gels but not on non reducing gels or by HPLC run under non reducing conditions. This suggests a non-covalent complex between the two glycoproteins. Analysis of protein conformation by circular dichroism revealed changes in the ascites CEA consistent with binding of CORA to the molecule. Western blot showed that CORA crossreacts with antisera to alpha 1-acid glycoprotein and double immunodiffusion demonstrated cross-reactivity but not identify. Sequencing of CNBr peptides showed sequence homology with alpha 1-acid glycoprotein but areas of unique sequence were also found. It is suggested that binding of CORA to CEA blocks the macrophage receptor binding of CEA.