Development and experimental validation of a finite element model of total ankle replacement.

Total ankle replacement remains a less satisfactory solution compared to other joint replacements. The goal of this study was to develop and validate a finite element model of total ankle replacement, for future testing of hypotheses related to clinical issues. To validate the finite element model, an experimental setup was specifically developed and applied on 8 cadaveric tibias. A non-cemented press fit tibial component of a mobile bearing prosthesis was inserted into the tibias. Two extreme anterior and posterior positions of the mobile bearing insert were considered, as well as a centered one. An axial force of 2kN was applied for each insert position. Strains were measured on the bone surface using digital image correlation. Tibias were CT scanned before implantation, after implantation, and after mechanical tests and removal of the prosthesis. The finite element model replicated the experimental setup. The first CT was used to build the geometry and evaluate the mechanical properties of the tibias. The second CT was used to set the implant position. The third CT was used to assess the bone-implant interface conditions. The coefficient of determination (R-squared) between the measured and predicted strains was 0.91. Predicted bone strains were maximal around the implant keel, especially at the anterior and posterior ends. The finite element model presented here is validated for future tests using more physiological loading conditions.

Genetic dissection of azole resistance mechanisms in Candida albicans and their validation in a mouse model of disseminated infection.

Principal mechanisms of resistance to azole antifungals include the upregulation of multidrug transporters and the modification of the target enzyme, a cytochrome P450 (Erg11) involved in the 14alpha-demethylation of ergosterol. These mechanisms are often combined in azole-resistant Candida albicans isolates recovered from patients. However, the precise contributions of individual mechanisms to C. albicans resistance to specific azoles have been difficult to establish because of the technical difficulties in the genetic manipulation of this diploid species. Recent advances have made genetic manipulations easier, and we therefore undertook the genetic dissection of resistance mechanisms in an azole-resistant clinical isolate. This isolate (DSY296) upregulates the multidrug transporter genes CDR1 and CDR2 and has acquired a G464S substitution in both ERG11 alleles. In DSY296, inactivation of TAC1, a transcription factor containing a gain-of-function mutation, followed by sequential replacement of ERG11 mutant alleles with wild-type alleles, restored azole susceptibility to the levels measured for a parent azole-susceptible isolate (DSY294). These sequential genetic manipulations not only demonstrated that these two resistance mechanisms were those responsible for the development of resistance in DSY296 but also indicated that the quantitative level of resistance as measured in vitro by MIC determinations was a function of the number of genetic resistance mechanisms operating in any strain. The engineered strains were also tested for their responses to fluconazole treatment in a novel 3-day model of invasive C. albicans infection of mice. Fifty percent effective doses (ED(50)s) of fluconazole were highest for DSY296 and decreased proportionally with the sequential removal of each resistance mechanism. However, while the fold differences in ED(50) were proportional to the fold differences in MICs, their magnitude was lower than that measured in vitro and depended on the specific resistance mechanism operating.

Reverse transcription quantitative real-time polymerase chain reaction reference genes in the spared nerve injury model of neuropathic pain: validation and literature search.

BACKGROUND: The reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is a widely used, highly sensitive laboratory technique to rapidly and easily detect, identify and quantify gene expression. Reliable RT-qPCR data necessitates accurate normalization with validated control genes (reference genes) whose expression is constant in all studied conditions. This stability has to be demonstrated.We performed a literature search for studies using quantitative or semi-quantitative PCR in the rat spared nerve injury (SNI) model of neuropathic pain to verify whether any reference genes had previously been validated. We then analyzed the stability over time of 7 commonly used reference genes in the nervous system - specifically in the spinal cord dorsal horn and the dorsal root ganglion (DRG). These were: Actin beta (Actb), Glyceraldehyde-3-phosphate dehydrogenase (GAPDH), ribosomal proteins 18S (18S), L13a (RPL13a) and L29 (RPL29), hypoxanthine phosphoribosyltransferase 1 (HPRT1) and hydroxymethylbilane synthase (HMBS). We compared the candidate genes and established a stability ranking using the geNorm algorithm. Finally, we assessed the number of reference genes necessary for accurate normalization in this neuropathic pain model. RESULTS: We found GAPDH, HMBS, Actb, HPRT1 and 18S cited as reference genes in literature on studies using the SNI model. Only HPRT1 and 18S had been once previously demonstrated as stable in RT-qPCR arrays. All the genes tested in this study, using the geNorm algorithm, presented gene stability values (M-value) acceptable enough for them to qualify as potential reference genes in both DRG and spinal cord. Using the coefficient of variation, 18S failed the 50% cut-off with a value of 61% in the DRG. The two most stable genes in the dorsal horn were RPL29 and RPL13a; in the DRG they were HPRT1 and Actb. Using a 0.15 cut-off for pairwise variations we found that any pair of stable reference gene was sufficient for the normalization process. CONCLUSIONS: In the rat SNI model, we validated and ranked Actb, RPL29, RPL13a, HMBS, GAPDH, HPRT1 and 18S as good reference genes in the spinal cord. In the DRG, 18S did not fulfill stability criteria. The combination of any two stable reference genes was sufficient to provide an accurate normalization.

Extraction of soil water by plants: development and validation of a model

A quantitative model of water movement within the immediate vicinity of an individual root is developed and results of an experiment to validate the model are presented. The model is based on the assumption that the amount of water transpired by a plant in a certain period is replaced by an equal volume entering its root system during the same time. The model is based on the Darcy-Buckingham equation to calculate the soil water matric potential at any distance from a plant root as a function of parameters related to crop, soil and atmospheric conditions. The model output is compared against measurements of soil water depletion by rice roots monitored using γ-beam attenuation in a greenhouse of the Escola Superior de Agricultura "Luiz de Queiroz"/Universidade de São Paulo(ESALQ/USP) in Piracicaba, State of São Paulo, Brazil, in 1993. The experimental results are in agreement with the output from the model. Model simulations show that a single plant root is able to withdraw water from more than 0.1 m away within a few days. We therefore can assume that root distribution is a less important factor for soil water extraction efficiency.

An active contour-based atlas registration model applied to automatic subthalamic nucleus targeting on MRI: method and validation.

This paper presents a new non parametric atlas registration framework, derived from the optical flow model and the active contour theory, applied to automatic subthalamic nucleus (STN) targeting in deep brain stimulation (DBS) surgery. In a previous work, we demonstrated that the STN position can be predicted based on the position of surrounding visible structures, namely the lateral and third ventricles. A STN targeting process can thus be obtained by registering these structures of interest between a brain atlas and the patient image. Here we aim to improve the results of the state of the art targeting methods and at the same time to reduce the computational time. Our simultaneous segmentation and registration model shows mean STN localization errors statistically similar to the most performing registration algorithms tested so far and to the targeting expert's variability. Moreover, the computational time of our registration method is much lower, which is a worthwhile improvement from a clinical point of view.

Derivation and validation of a prognostic model for pulmonary embolism.

RATIONALE: An objective and simple prognostic model for patients with pulmonary embolism could be helpful in guiding initial intensity of treatment. OBJECTIVES: To develop a clinical prediction rule that accurately classifies patients with pulmonary embolism into categories of increasing risk of mortality and other adverse medical outcomes. METHODS: We randomly allocated 15,531 inpatient discharges with pulmonary embolism from 186 Pennsylvania hospitals to derivation (67%) and internal validation (33%) samples. We derived our prediction rule using logistic regression with 30-day mortality as the primary outcome, and patient demographic and clinical data routinely available at presentation as potential predictor variables. We externally validated the rule in 221 inpatients with pulmonary embolism from Switzerland and France. MEASUREMENTS: We compared mortality and nonfatal adverse medical outcomes across the derivation and two validation samples. MAIN RESULTS: The prediction rule is based on 11 simple patient characteristics that were independently associated with mortality and stratifies patients with pulmonary embolism into five severity classes, with 30-day mortality rates of 0-1.6% in class I, 1.7-3.5% in class II, 3.2-7.1% in class III, 4.0-11.4% in class IV, and 10.0-24.5% in class V across the derivation and validation samples. Inpatient death and nonfatal complications were <or= 1.1% among patients in class I and <or= 1.9% among patients in class II. CONCLUSIONS: Our rule accurately classifies patients with pulmonary embolism into classes of increasing risk of mortality and other adverse medical outcomes. Further validation of the rule is important before its implementation as a decision aid to guide the initial management of patients with pulmonary embolism.

Validation of a model to predict adverse outcomes in patients with pulmonary embolism.

AIMS: To validate a model for quantifying the prognosis of patients with pulmonary embolism (PE). The model was previously derived from 10 534 US patients. METHODS AND RESULTS: We validated the model in 367 patients prospectively diagnosed with PE at 117 European emergency departments. We used baseline data for the model's 11 prognostic variables to stratify patients into five risk classes (I-V). We compared 90-day mortality within each risk class and the area under the receiver operating characteristic curve between the validation and the original derivation samples. We also assessed the rate of recurrent venous thrombo-embolism and major bleeding within each risk class. Mortality was 0% in Risk Class I, 1.0% in Class II, 3.1% in Class III, 10.4% in Class IV, and 24.4% in Class V and did not differ between the validation and the original derivation samples. The area under the curve was larger in the validation sample (0.87 vs. 0.78, P=0.01). No patients in Classes I and II developed recurrent thrombo-embolism or major bleeding. CONCLUSION: The model accurately stratifies patients with PE into categories of increasing risk of mortality and other relevant complications. Patients in Risk Classes I and II are at low risk of adverse outcomes and are potential candidates for outpatient treatment.

The transtheoretical model in weight management: Validation of the Processes of Change Questionnaire

Objective: The processes of change implied in weight management remain unclear. The present study aimed to identify these processes by validating a questionnaire designed to assess processes of change (the P-Weight) in line with the transtheoretical model. The relationship of processes of change with stages of change and other external variables is also examined. Methods: Participants were 723 people from community and clinical settings in Barcelona. Their mean age was 32.07 (SD = 14.55) years; most of them were women (75.0%), and their mean BMI was 26.47 (SD = 8.52) kg/m2. They all completed the P-Weight and the stages of change questionnaire (SWeight), both applied to weight management, as well as two subscales from the Eating Disorders Inventory-2 and Eating Attitudes Test-40 questionnaires about the concern with dieting. Results: A 34-item version of the PWeight was obtained by means of a refinement process. The principal components analysis applied to half of the sample identified four processes of change. A confirmatory factor analysis was then carried out with the other half of the sample, revealing that the model of four freely correlated first-order factors showed the best fit (GFI = 0.988, AGFI = 0.986, NFI = 0.986, and SRMR = 0.0559). Corrected item-total correlations (0.322-0.865) and Cronbach"s alpha coefficients (0.781-0.960) were adequate. The relationship between the P-Weight and the S-Weight and the concern with dieting measures from other questionnaires supported the validity of the scale. Conclusion: The study identified processes of change involved in weight management and reports the adequate psychometric properties of the P-Weight. It also reveals the relationship between processes and stages of change and other external variables.

Validation of and comparison between a semidistributed rainfall-runoff hydrological model (PREVAH) and a spatially distributed snow-evolution model (SnowModel) for snow cover prediction in mountain ecosystems

Snow cover is an important control in mountain environments and a shift of the snow-free period triggered by climate warming can strongly impact ecosystem dynamics. Changing snow patterns can have severe effects on alpine plant distribution and diversity. It thus becomes urgent to provide spatially explicit assessments of snow cover changes that can be incorporated into correlative or empirical species distribution models (SDMs). Here, we provide for the first time a with a lower overestimation comparison of two physically based snow distribution models (PREVAH and SnowModel) to produce snow cover maps (SCMs) at a fine spatial resolution in a mountain landscape in Austria. SCMs have been evaluated with SPOT-HRVIR images and predictions of snow water equivalent from the two models with ground measurements. Finally, SCMs of the two models have been compared under a climate warming scenario for the end of the century. The predictive performances of PREVAH and SnowModel were similar when validated with the SPOT images. However, the tendency to overestimate snow cover was slightly lower with SnowModel during the accumulation period, whereas it was lower with PREVAH during the melting period. The rate of true positives during the melting period was two times higher on average with SnowModel with a lower overestimation of snow water equivalent. Our results allow for recommending the use of SnowModel in SDMs because it better captures persisting snow patches at the end of the snow season, which is important when modelling the response of species to long-lasting snow cover and evaluating whether they might survive under climate change.

Predicting recurrence after unprovoked venous thromboembolism: prospective validation of the updated Vienna Prediction Model.

The updated Vienna Prediction Model for estimating recurrence risk after an unprovoked venous thromboembolism (VTE) has been developed to identify individuals at low risk for VTE recurrence in whom anticoagulation (AC) therapy may be stopped after 3 months. We externally validated the accuracy of the model to predict recurrent VTE in a prospective multicenter cohort of 156 patients aged ≥65 years with acute symptomatic unprovoked VTE who had received 3 to 12 months of AC. Patients with a predicted 12-month risk within the lowest quartile based on the updated Vienna Prediction Model were classified as low risk. The risk of recurrent VTE did not differ between low- vs higher-risk patients at 12 months (13% vs 10%; P = .77) and 24 months (15% vs 17%; P = 1.0). The area under the receiver operating characteristic curve for predicting VTE recurrence was 0.39 (95% confidence interval [CI], 0.25-0.52) at 12 months and 0.43 (95% CI, 0.31-0.54) at 24 months. In conclusion, in elderly patients with unprovoked VTE who have stopped AC, the updated Vienna Prediction Model does not discriminate between patients who develop recurrent VTE and those who do not. This study was registered at www.clinicaltrials.gov as #NCT00973596.

Validation of plant dynamic model by online and laboratory measurements – a tool to predict online COD loads out of production of mechanical printing papers

COD discharges out of processes have increased in line with elevating brightness demands for mechanical pulp and papers. The share of lignin-like substances in COD discharges is on average 75%. In this thesis, a plant dynamic model was created and validated as a means to predict COD loading and discharges out of a mill. The assays were carried out in one paper mill integrate producing mechanical printing papers. The objective in the modeling of plant dynamics was to predict day averages of COD load and discharges out of mills. This means that online data, like 1) the level of large storage towers of pulp and white water 2) pulp dosages, 3) production rates and 4) internal white water flows and discharges were used to create transients into the balances of solids and white water, referred to as “plant dynamics”. A conversion coefficient was verified between TOC and COD. The conversion coefficient was used for predicting the flows from TOC to COD to the waste water treatment plant. The COD load was modeled with similar uncertainty as in reference TOC sampling. The water balance of waste water treatment was validated by the reference concentration of COD. The difference of COD predictions against references was within the same deviation of TOC-predictions. The modeled yield losses and retention values of TOC in pulping and bleaching processes and the modeled fixing of colloidal TOC to solids between the pulping plant and the aeration basin in the waste water treatment plant were similar to references presented in literature. The valid water balances of the waste water treatment plant and the reduction model of lignin-like substances produced a valid prediction of COD discharges out of the mill. A 30% increase in the release of lignin-like substances in the form of production problems was observed in pulping and bleaching processes. The same increase was observed in COD discharges out of waste water treatment. In the prediction of annual COD discharge, it was noticed that the reduction of lignin has a wide deviation from year to year and from one mill to another. This made it difficult to compare the parameters of COD discharges validated in plant dynamic simulation with another mill producing mechanical printing papers. However, a trend of moving from unbleached towards high-brightness TMP in COD discharges was valid.

APROS model validation in case of fast transients in a boiling water reactor

This Master´s thesis investigates the performance of the Olkiluoto 1 and 2 APROS model in case of fast transients. The thesis includes a general description of the Olkiluoto 1 and 2 nuclear power plants and of the most important safety systems. The theoretical background of the APROS code as well as the scope and the content of the Olkiluoto 1 and 2 APROS model are also described. The event sequences of the anticipated operation transients considered in the thesis are presented in detail as they will form the basis for the analysis of the APROS calculation results. The calculated fast operational transient situations comprise loss-of-load cases and two cases related to a inadvertent closure of one main steam isolation valve. As part of the thesis work, the inaccurate initial data values found in the original 1-D reactor core model were corrected. The input data needed for the creation of a more accurate 3-D core model were defined. The analysis of the APROS calculation results showed that while the main results were in good accordance with the measured plant data, also differences were detected. These differences were found to be caused by deficiencies and uncertainties related to the calculation model. According to the results the reactor core and the feedwater systems cause most of the differences between the calculated and measured values. Based on these findings, it will be possible to develop the APROS model further to make it a reliable and accurate tool for the analysis of the operational transients and possible plant modifications.

Analysis and validation of space averaged drag model for numerical simulations of gas-solid flows in fluidized beds

This thesis presents an approach for formulating and validating a space averaged drag model for coarse mesh simulations of gas-solid flows in fluidized beds using the two-fluid model. Proper modeling for fluid dynamics is central in understanding any industrial multiphase flow. The gas-solid flows in fluidized beds are heterogeneous and usually simulated with the Eulerian description of phases. Such a description requires the usage of fine meshes and small time steps for the proper prediction of its hydrodynamics. Such constraint on the mesh and time step size results in a large number of control volumes and long computational times which are unaffordable for simulations of large scale fluidized beds. If proper closure models are not included, coarse mesh simulations for fluidized beds do not give reasonable results. The coarse mesh simulation fails to resolve the mesoscale structures and results in uniform solids concentration profiles. For a circulating fluidized bed riser, such predicted profiles result in a higher drag force between the gas and solid phase and also overestimated solids mass flux at the outlet. Thus, there is a need to formulate the closure correlations which can accurately predict the hydrodynamics using coarse meshes. This thesis uses the space averaging modeling approach in the formulation of closure models for coarse mesh simulations of the gas-solid flow in fluidized beds using Geldart group B particles. In the analysis of formulating the closure correlation for space averaged drag model, the main parameters for the modeling were found to be the averaging size, solid volume fraction, and distance from the wall. The closure model for the gas-solid drag force was formulated and validated for coarse mesh simulations of the riser, which showed the verification of this modeling approach. Coarse mesh simulations using the corrected drag model resulted in lowered values of solids mass flux. Such an approach is a promising tool in the formulation of appropriate closure models which can be used in coarse mesh simulations of large scale fluidized beds.