Finite element modelling of primary hip stem stability: the effect of interference fit

The most commonly reported complications related to cementless hip stems are loosening and thigh pain; both of these have been attributed to high levels of relative micromotion at the bone-implant interface due to insufficient primary fixation. Primary fixation is believed by many to rely on achieving a sufficient interference fit between the implant and the bone. However, attempting to achieve a high interference fit not infrequently leads to femoral canal fracture either intra-operatively or soon after. The appropriate range of diametrical interference fit that ensures primary stability without risking femoral fracture is not well understood. In this study, a finite element model was constructed to predict micromotion and, therefore, instability of femoral stems. The model was correlated with an in vitro micromotion experiment carried out on four cadaver femurs. It was confirmed that interference fit has a very significant effect on micromotion and ignoring this parameter in an analysis of primary stability is likely to underestimate the stability of the stem. Furthermore, it was predicted that the optimal level of interference fit is around 50 microm as this is sufficient to achieve good primary fixation while having a safety factor of 2 against femoral canal fracture. This result is of clinical relevance as it indicates a recommendation for the surgeon to err on the side of a low interference fit rather than risking femoral fracture.     

A validated model of passive skeletal muscle to predict force and intramuscular pressure

The passive properties of skeletal muscle are often overlooked in muscle studies, yet they play a key role in tissue function in vivo. Studies analyzing and modeling muscle passive properties, while not uncommon, have never investigated the role of fluid content within the tissue. Additionally, intramuscular pressure (IMP) has been shown to correlate with muscle force in vivo and could be used to predict muscle force in the clinic. In this study, a novel model of skeletal muscle was developed and validated to predict both muscle stress and IMP under passive conditions for the New Zealand White Rabbit tibialis anterior. This model is the first to include fluid content within the tissue and uses whole muscle geometry. A nonlinear optimization scheme was highly effective at fitting model stress output to experimental stress data (normalized mean square error or NMSE fit value of 0.993) and validation showed very good agreement to experimental data (NMSE fit values of 0.955 and 0.860 for IMP and stress, respectively). While future work to include muscle activation would broaden the physiological application of this model, the passive implementation could be used to guide surgeries where passive muscle is stretched.     

Design and analysis of accelerated degradation tests for the stability of biological standards II. A flexible computer program for data analysis

The accelerated degradation test is commonly used to predict the stability of a biological standard during long-term storage at low temperature. A flexible computer program is described which has been written to analyse degradation test results by the method of maximum likelihood. In addition to predicting the degradation rate at low temperature, the program furnishes estimates of statistical precision and it carried out a test of goodness of fit of the data to the assumed Arrhenius equation model.     

New instruments for preparation of the prosthesis socket and primary stability of the acetabular Press-fit cups]

The aim of the present study was to develop surgical instruments necessary to achieve a precisely reamed surface and stable initial fixation. The instruments used to prepare the socket were a gauge-drill guide, a liner for the spigot hole and two spigotted reamers of different design and indentation intended to achieve a precisely reamed surface while preserving subchondral bone. For each reamer we implanted in a synthetic hip model 10 uncemented cups with 2 mm press-fit and loaded at 2.4 kN in the physiological axis (Mod. 8501, Instron, Canton, MA, USA). The micromotion between implant and bone socket was measured using an inductive micrometric measuring system (MultiNCDT-500, Micro-Epsilon, Ortenburg, Germany) and compared with that seen after using conventional instruments. The use of the new reamer of elliptical design significantly reduced the standard deviations of the measured values (p < 0.01 at the ischium and pubis) and also reduced maximum movement (p < 0.01 at the ischium); at the same time, all the components showed overall limited movement (< 150 microns at the ischium, pubis and ilium) under maximum loading (2.4 kN). Manufacturing tolerances, the quality and wear of the instruments, acetabular bone stock and surgical technique all impact on the degree of press-fit obtainable at surgery. The results of our study show that press-fit and initial stability can be optimized by using adequate instruments to prepare the socket.     

Multidimensional screening and design of pharmaceuticals by using endocrine pharmacophores.

A novel computational technology derived from gene structure has been developed for screening, selecting, and designing pharmaceutical candidates. Pharmacophores, or three-dimensional molecular blueprints, were created by docking known active structures into specific sites in partially unwound DNA. The pharmacophores are composites of the van der Waals surfaces and hydrogen bonding functional groups of active molecules. Once created, molecules can be inserted into the pharmacophores and degree of fit quantitated by the volume of the molecule that fits within the composite surface and the magnitude of electrostatic interactions with charged atoms on the pharmacophore. Here, we describe endocrine pharmacophores and in particular the estrogen pharmacophore derived by docking active ligands into partially unwound DNA. Fit of candidate structures into the estrogen pharmacophore correlated with estrogenic (uterotropic) activity. For example, the super active estrogens moxestrol and 11beta-acetoxyestradiol fit better within the site than estradiol. Bisphenol A, a putative endocrine disrupter with suspected estrogenic activity, was a poor fit in the pharmacophore. Consistent with this prediction, bisphenol A was recently shown to lack uterotropic activity. The capacity of the endocrine pharmacophores to predict certain nontarget activities was demonstrated by using the antiandrogen cyproterone acetate that did not fit the estrogen or thyroid pharmacophores but fit partially into the progestin and glucocorticoid pharmacophores. Cyproterone acetate has been reported to have weak progestational and glucocorticoid activities. The pharmacophores provide for the first time a multidimensional computational method that can simultaneously predict multiple activities of diverse molecular structures.     

Fluid transport in a thick layer above an active ciliated surface.

The discrete cilia approach is employed to describe the flow field above an active ciliated surface in a layer of finite depth. The infinite-size cilia surface model predicts a uniform flow above the cilia surface. Modifications as a result of a finitely extending cilia surface inside a finite-size dish predict a backward parabolic profile above an active cilia surface. Experiments are described which demonstrate a good fit between theoretical predictions and observations. The results provide a sound physical basis for the proper interpretation of fluid-flow observations close to an active ciliary field.     

Non-linear effects of temperature and urea on the thermodynamics and kinetics of folding and unfolding of hisactophilin

Extensive measurements and analysis of thermodynamic stability and kinetics of urea-induced unfolding and folding of hisactophilin are reported for 5-50 degrees C, at pH 6.7. Under these conditions hisactophilin has moderate thermodynamic stability, and equilibrium and kinetic data are well fit by a two-state transition between the native and the denatured states. Equilibrium and kinetic m values decrease with increasing temperature, and decrease with increasing denaturant concentration. The betaF values at different temperatures and urea concentrations are quite constant, however, at about 0.7. This suggests that the transition state for hisactophilin unfolding is native-like and changes little with changing solution conditions, consistent with a narrow free energy profile for the transition state. The activation enthalpy and entropy of unfolding are unusually low for hisactophilin, as is also the case for the corresponding equilibrium parameters. Conventional Arrhenius and Eyring plots for both folding and unfolding are markedly non-linear, but these plots become linear for constant DeltaG/T contours. The Gibbs free energy changes for structural changes in hisactophilin have a non-linear denaturant dependence that is comparable to non-linearities observed for many other proteins. These non-linearities can be fit for many proteins using a variation of the Tanford model, incorporating empirical quadratic denaturant dependencies for Gibbs free energies of transfer of amino acid constituents from water to urea, and changes in fractional solvent accessible surface area of protein constituents based on the known protein structures. Noteworthy exceptions that are not well fit include amyloidogenic proteins and large proteins, which may form intermediates. The model is easily implemented and should be widely applicable to analysis of urea-induced structural transitions in proteins.     

Robust design for acetabular cup stability accounting for patient and surgical variability

The stability of cementless acetabular cups depends on a close fit between the components and reamed acetabular cavities to promote bone ingrowth. Cup performance and stability are affected by both design and environmental (patient-dependent and surgical) factors. This study used a statistically based metamodel to determine the relative influences of design and environmental factors on acetabular cup stability by incorporating a comprehensive set of patient-dependent and surgical variables. Cup designs with 2 mm or 3 mm intended equatorial bone-implant interferences appeared to perform the best, improving implant stability with smaller mean and variability in cup relative motions and greater mean and smaller variability in ingrowth areas. Cup eccentricity was found to have no effect on implant performance. Design variables did not contribute as much to the variation in performance measures compared to the environmental variables, except for potential ingrowth areas.     

An ensemble simulation approach for artificial neural network: An example from chlorophyll a simulation in Lake Poyang,China

Artificial neural network (ANN) models have been widely used in environmental modeling with considerable success. To improve the reliability of ANN models, ensemble simulations were applied in this study to develop four ANN ensemble models for chlorophyll a simulation in the largest freshwater lake (Lake Poyang) in China. Reliability (evaluated by model fit and stability) of these ANN ensemble models was compared with that of single ANN models from ensemble members. The model fit of these single ANN models varied significantly over repeated runs, indicating the unstable performance of the single ANN models. Comparing with the single ANN models, the ANN ensemble models showed a better model fit and stability, implying the potential of ensemble simulation in achieving a more reliable model. An ensemble size of 30 was adequate for the ANN ensemble models to achieve a good model fit, while an ensemble size of 50 was adequate to achieve good stability. This case study highlighted both the necessity and potential of the ensemble simulation approach to achieve a reliable ANN model with good model fit and stability.     

Liapunov functions: Geometry and stability

Summary Several geometrical interpretations of Liapunov functions for non-linear ecological models are examined and their limitations pointed out. In particular the geometrical nonuniqueness of Liapunov functions is illustrated by displaying explicitly and comparing four different Liapunov functions for the symmetric competition model for two species. The main point is that considerable care must be taken in using the geometrical properties of an arbitrary Liapunov function as a guide to stability under perturbations.     

Cryo‐EM model validation using independent map reconstructions

An increasing number of cryo‐electron microscopy (cryo‐EM) density maps are being generated with suitable resolution to trace the protein backbone and guide sidechain placement. Generating and evaluating atomic models based on such maps would be greatly facilitated by independent validation metrics for assessing the fit of the models to the data. We describe such a metric based on the fit of atomic models with independent test maps from single particle reconstructions not used in model refinement. The metric provides a means to determine the proper balance between the fit to the density and model energy and stereochemistry during refinement, and is likely to be useful in determining values of model building and refinement metaparameters quite generally.     

Optimized Null Model for Protein Structure Networks

Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs) as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model for RIGs, by comparing various RIG definitions against a series of network models.     

The contribution of the glenoid labrum to glenohumeral stability under physiological joint loading using finite element analysis

The labrum contributes to passive glenohumeral joint stability. Cadaveric studies have demonstrated that this has position and load dependency, which has not been quantified under physiological loads. This study aims to validate subject-specific finite element (FE) models against in vitro measurements of joint stability and to utilise the FE models to predict joint stability under physiological loads. The predicted stability values were within ± one standard deviation of experimental data and the FE models showed a reduction in stability of 10–15% with high, physiological, loads. The developed regression equations provide the first representation of passive glenohumeral stability and will aid surgical decision-making.     

3D-QSAR studies of farnesyltransferase inhibitors: a comparative molecular field analysis approach

3D-QSAR analysis has been performed on a series of previously synthesized benzonitrile derivatives, which were screened as farnesyltransferase inhibitors, using comparative molecular field analysis (CoMFA) with partial least-square fit to predict the steric and electrostatic molecular field interactions for the activity. The CoMFA study was carried out using a training set of 34 compounds. The predictive ability of the model developed was assessed using a test set of eight compounds (r(pred)(2) as high as 0.770). The analyzed 3D-QSAR CoMFA model has demonstrated a good fit, having r(2) value of 0.991 and cross-validated coefficient q(2) value as 0.619. The analysis of CoMFA contour maps provided insight into the possible modification of the molecules for better activity.     

Statistical evaluation of improvement in RNA secondary structure prediction

With discovery of diverse roles for RNA, its centrality in cellular functions has become increasingly apparent. A number of algorithms have been developed to predict RNA secondary structure. Their performance has been benchmarked by comparing structure predictions to reference secondary structures. Generally, algorithms are compared against each other and one is selected as best without statistical testing to determine whether the improvement is significant. In this work, it is demonstrated that the prediction accuracies of methods correlate with each other over sets of sequences. One possible reason for this correlation is that many algorithms use the same underlying principles. A set of benchmarks published previously for programs that predict a structure common to three or more sequences is statistically analyzed as an example to show that it can be rigorously evaluated using paired two-sample t-tests. Finally, a pipeline of statistical analyses is proposed to guide the choice of data set size and performance assessment for benchmarks of structure prediction. The pipeline is applied using 5S rRNA sequences as an example.     

Formulation of medium constituents by multiresponse analysis of central composite design to enhance chitinase production in Pantoea dispersa

In the present study, a high chitinase producing strain Pantoea dispersa was isolated from the sea dumps at Bhavnagar, India. Chitin, urea, CaCl2 and MgSO4 x 7H2O were variables used in central composite design for chitinase production. Chitinase, biomass and pH were the responses used in different models to evaluate individually fit ones. Quadratic model was found to be fit for chitinase response whereas in the case of biomass and pH, linear model was found to be fit without the effect of others. Chitinase production was optimized with respect to other responses such as biomass and pH in multiresponse analysis of response surface design by using desirability approach. In multiresponse analysis, following medium formulation (g/l), chitin, 15; urea, 0.32; CaCl2, 0.10 and MgSO4 x 7H2O, 0.08 was found to predict optimum chitinase production of 482.77 units/ml with overall highest desirability of 0.854 as compared to other formulations. The selection of model was done on the basis of high Adjusted R-squared value and lowered p-value for each model in individual analysis of each response. In multiresponse experiment, it was found that for response chitinase quadratic model and for responses pH and biomass linear models were well fit. Through desirability analysis, it was found that in the chitinase production, pH was essential as compared to biomass in P. dispersa. Endochitinase and chitobiase actvities were also studied.     

The prediction of stress-relaxation of ligaments and tendons using the quasi-linear viscoelastic model

Recent studies have questioned the ability of the quasi-linear viscoelastic (QLV) model to predict stresses and strains in response to loading conditions other than those used to fit the model. The objective of this study was to evaluate the ability of several models in the literature to predict the elastic stress response of ligament and tendon at strain levels higher than the levels used to fit the model. The constitutive models were then used to evaluate the ability of the QLV model to predict the overall stress response during stress relaxation. The models expressing stress as an exponential function of strain significantly overestimated stress when used at higher strain levels. The polynomial formulation of the Mooney–Rivlin model more accurately predicted the stress–strain behavior of ligament and tendon. The results demonstrate that the ability of the QLV model to accurately predict the stress-relaxation response is dependent in part on the accuracy of the function used to model the elastic response of the soft tissue.     

Mapping malaria risk among children in Côte d’Ivoire using Bayesian geo-statistical models

ABSTRACT: BACKGROUND: In Cote d'Ivoire, an estimated 767,000 disability-adjusted life years are due to malaria, placing the country at position number 14 with regard to the global burden of malaria. Risk maps are important to guide control interventions, and hence, the aim of this study was to predict the geographical distribution of malaria infection risk in children aged <16 years in Cote d'Ivoire at high spatial resolution. METHODS: Using different data sources, a systematic review was carried out to compile and georeference survey data on Plasmodium spp. infection prevalence in Cote d'Ivoire, focusing on children aged <16 years. The period from 1988 to 2007 was covered. A suite of Bayesian geo-statistical logistic regression models was fitted to analyse malaria risk. Non-spatial models with and without exchangeable random effect parameters were compared to stationary and non-stationary spatial models. Non-stationarity was modelled assuming that the underlying spatial process is a mixture of separate stationary processes in each ecological zone. The best fitting model based on the deviance information criterion was used to predict Plasmodium spp. infection risk for entire Cote d'Ivoire, including uncertainty. RESULTS: Overall, 235 data points at 170 unique survey locations with malaria prevalence data for individuals aged <16 years were extracted. Most data points (n = 182, 77.4%) were collected between 2000 and 2007. A Bayesian non-stationary regression model showed the best fit with annualized rainfall and maximum land surface temperature identified as significant environmental covariates. This model was used to predict malaria infection risk at nonsampled locations. High-risk areas were mainly found in the north-central and western area, while relatively low-risk areas were located in the north at the country border, in the northeast, in the south-east around Abidjan, and in the central-west between two high prevalence areas. CONCLUSION: The malaria risk map at high spatial resolution gives an important overview of the geographical distribution of the disease in Cote d'Ivoire. It is a useful tool for the national malaria control programme and can be utilized for spatial targeting of control interventions and rational resource allocation.     

Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization

The concentrations of glucose and total reducing sugars obtained by chemical hydrolysis of three different lignocellulosic feedstocks were maximized. Two response surface methodologies were applied to model the amount of sugars produced: (1) classical quadratic least-squares fit (QLS), and (2) artificial neural networks based on radial basis functions (RBF). The results obtained by applying RBF were more reliable and better statistical parameters were obtained. Depending on the type of biomass, different results were obtained. Improvements in fit between 35% and 55% were obtained when comparing the coefficients of determination (R²) computed for both QLS and RBF methods. Coupling the obtained RBF models with particle swarm optimization to calculate the global desirability function, allowed to perform multiple response optimization. The predicted optimal conditions were confirmed by carrying out independent experiments.