Photographs of dental casts or digital models: rating dental arch relationships in bilateral cleft lip and palate

Yardsticks have been developed to measure dental arch relations in cleft lip and palate (CLP) patients as diagnostic proxies for the underlying skeletal relationship. Travelling with plaster casts to compare results between CLP centres is inefficient so the aim of this study was to investigate the reliability of using digital models or photographs of dental casts instead of plaster casts for rating dental arch relationships in children with complete bilateral cleft lip and palate (CBCLP). Dental casts of children with CBCLP (n=20) were included. Plaster casts, digital models and photographs of the plaster casts were available for all the children at 6, 9, and 12 years of age. All three record formats were scored using the bilateral cleft lip and palate (BCLP) yardstick by four observers in random order. No significant differences were found for the BCLP yardstick scores among the three formats. The interobserver weighted kappa scores were between 0.672 and 0.934. Comparison between the formats per observer resulted in weighted kappa scores between 0.692 and 0.885. It is concluded that digital models and photographs of dental casts can be used for rating dental arch relationships in patients with CBCLP. These formats are a reliable alternative for BCLP yardstick assessments on conventional plaster casts.

Sevoflurane remifentanil interaction: comparison of different response surface models

Various pharmacodynamic response surface models have been developed to quantitatively describe the relationship between two or more drug concentrations with their combined clinical effect. We examined the interaction of remifentanil and sevoflurane on the probability of tolerance to shake and shout, tetanic stimulation, laryngeal mask airway insertion, and laryngoscopy in patients to compare the performance of five different response surface models.

Secondary Collisions Revisited: Real-World Crash Characteristics and Relationship to Crash Test Criteria

Previous research conducted in the late 1980’s suggested that vehicle impacts following an initial barrier collision increase severe occupant injury risk. Now over twenty-five years old, the data used in the previous research is no longer representative of the currently installed barriers or US vehicle fleet. The purpose of this study is to provide a present-day assessment of secondary collisions and to determine if full-scale barrier crash testing criteria provide an indication of secondary collision risk for real-world barrier crashes. The analysis included 1,383 (596,331 weighted) real-world barrier midsection impacts selected from thirteen years (1997-2009) of in-depth crash data available through the National Automotive Sampling System (NASS) / Crashworthiness Data System (CDS). For each suitable case, the scene diagram and available scene photographs were used to determine roadside and barrier specific variables not available in NASS/CDS. Binary logistic regression models were developed for second event occurrence and resulting driver injury. Barrier lateral stiffness, post-impact vehicle trajectory, vehicle type, and pre-impact tracking conditions were found to be statistically significant contributors toward secondary event occurrence. The presence of a second event was found to increase the likelihood of a serious driver injury by a factor of seven compared to cases with no second event present. Twenty-four full-scale crash test reports were obtained for common non-proprietary US barriers, and the risk of secondary collisions was determined using recommended evaluation criteria from NCHRP Report 350. It was found that the NCHRP Report 350 exit angle criterion alone was not sufficient to predict second collision occurrence for real-world barrier crashes.

Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.

The chironomid-temperature relationship: expression in nature and palaeoenvironmental implications

Fossils of chironomid larvae (non-biting midges) preserved in lake sediments are well-established palaeotemperature indicators which, with the aid of numerical chironomid-based inference models (transfer functions), can provide quantitative estimates of past temperature change. This approach to temperature reconstruction relies on the strong relationship between air and lake surface water temperature and the distribution of individual chironomid taxa (species, species groups, genera) that has been observed in different climate regions (arctic, subarctic, temperate and tropical) in both the Northern and Southern hemisphere. A major complicating factor for the use of chironomids for palaeoclimate reconstruction which increases the uncertainty associated with chironomid-based temperature estimates is that the exact nature of the mechanism responsible for the strong relationship between temperature and chironomid assemblages in lakes remains uncertain. While a number of authors have provided state of the art overviews of fossil chironomid palaeoecology and the use of chironomids for temperature reconstruction, few have focused on examining the ecological basis for this approach. Here, we review the nature of the relationship between chironomids and temperature based on the available ecological evidence. After discussing many of the surveys describing the distribution of chironomid taxa in lake surface sediments in relation to temperature, we also examine evidence from laboratory and field studies exploring the effects of temperature on chironomid physiology, life cycles and behaviour. We show that, even though a direct influence of water temperature on chironomid development, growth and survival is well described, chironomid palaeoclimatology is presently faced with the paradoxical situation that the relationship between chironomid distribution and temperature seems strongest in relatively deep, thermally stratified lakes in temperate and subarctic regions in which the benthic chironomid fauna lives largely decoupled from the direct influence of air and surface water temperature. This finding suggests that indirect effects of temperature on physical and chemical characteristics of lakes play an important role in determining the distribution of lake-living chironomid larvae. However, we also demonstrate that no single indirect mechanism has been identified that can explain the strong relationship between chironomid distribution and temperature in all regions and datasets presently available. This observation contrasts with the previously published hypothesis that climatic effects on lake nutrient status and productivity may be largely responsible for the apparent correlation between chironomid assemblage distribution and temperature. We conclude our review by summarizing the implications of our findings for chironomid-based palaeoclimatology and by pointing towards further avenues of research necessary to improve our mechanistic understanding of the chironomid-temperature relationship.

Cellular and immunological basis of the host-parasite relationship during infection with Neospora caninum

Neospora caninum is an apicomplexan parasite that is closely related to Toxoplasma gondii, the causative agent of toxoplasmosis in humans and domestic animals. However, in contrast to T. gondii, N. caninum represents a major cause of abortion in cattle, pointing towards distinct differences in the biology of these two species. There are 3 distinct key features that represent potential targets for prevention of infection or intervention against disease caused by N. caninum. Firstly, tachyzoites are capable of infecting a large variety of host cells in vitro and in vivo. Secondly, the parasite exploits its ability to respond to alterations in living conditions by converting into another stage (tachyzoite-to-bradyzoite or vice versa). Thirdly, by analogy with T. gondii, this parasite has evolved mechanisms that modulate its host cells according to its own requirements, and these must, especially in the case of the bradyzoite stage, involve mechanisms that ensure long-term survival of not only the parasite but also of the host cell. In order to elucidate the molecular and cellular bases of these important features of N. caninum, cell culture-based approaches and laboratory animal models are being exploited. In this review, we will summarize the current achievements related to host cell and parasite cell biology, and will discuss potential applications for prevention of infection and/or disease by reviewing corresponding work performed in murine laboratory infection models and in cattle.

Markers Models in Survival Analysis and Applications to Issues Associated with AIDS

Jewell and Kalbfleisch (1992) consider the use of marker processes for applications related to estimation of the survival distribution of time to failure. Marker processes were assumed to be stochastic processes that, at a given point in time, provide information about the current hazard and consequently on the remaining time to failure. Particular attention was paid to calculations based on a simple additive model for the relationship between the hazard function at time t and the history of the marker process up until time t. Specific applications to the analysis of AIDS data included the use of markers as surrogate responses for onset of AIDS with censored data and as predictors of the time elapsed since infection in prevalent individuals. Here we review recent work on the use of marker data to tackle these kinds of problems with AIDS data. The Poisson marker process with an additive model, introduced in Jewell and Kalbfleisch (1992) may be a useful "test" example for comparison of various procedures.

Gauss-Seidel Estimation of Generalized Linear Mixed Models with Application to Poisson Modeling of Spatially Varying Disease Rates

Generalized linear mixed models (GLMMs) provide an elegant framework for the analysis of correlated data. Due to the non-closed form of the likelihood, GLMMs are often fit by computational procedures like penalized quasi-likelihood (PQL). Special cases of these models are generalized linear models (GLMs), which are often fit using algorithms like iterative weighted least squares (IWLS). High computational costs and memory space constraints often make it difficult to apply these iterative procedures to data sets with very large number of cases. This paper proposes a computationally efficient strategy based on the Gauss-Seidel algorithm that iteratively fits sub-models of the GLMM to subsetted versions of the data. Additional gains in efficiency are achieved for Poisson models, commonly used in disease mapping problems, because of their special collapsibility property which allows data reduction through summaries. Convergence of the proposed iterative procedure is guaranteed for canonical link functions. The strategy is applied to investigate the relationship between ischemic heart disease, socioeconomic status and age/gender category in New South Wales, Australia, based on outcome data consisting of approximately 33 million records. A simulation study demonstrates the algorithm's reliability in analyzing a data set with 12 million records for a (non-collapsible) logistic regression model.

Semiparametric Normal Transformation Models for Spatially Correlated Survival Data

There is an emerging interest in modeling spatially correlated survival data in biomedical and epidemiological studies. In this paper, we propose a new class of semiparametric normal transformation models for right censored spatially correlated survival data. This class of models assumes that survival outcomes marginally follow a Cox proportional hazard model with unspecified baseline hazard, and their joint distribution is obtained by transforming survival outcomes to normal random variables, whose joint distribution is assumed to be multivariate normal with a spatial correlation structure. A key feature of the class of semiparametric normal transformation models is that it provides a rich class of spatial survival models where regression coefficients have population average interpretation and the spatial dependence of survival times is conveniently modeled using the transformed variables by flexible normal random fields. We study the relationship of the spatial correlation structure of the transformed normal variables and the dependence measures of the original survival times. Direct nonparametric maximum likelihood estimation in such models is practically prohibited due to the high dimensional intractable integration of the likelihood function and the infinite dimensional nuisance baseline hazard parameter. We hence develop a class of spatial semiparametric estimating equations, which conveniently estimate the population-level regression coefficients and the dependence parameters simultaneously. We study the asymptotic properties of the proposed estimators, and show that they are consistent and asymptotically normal. The proposed method is illustrated with an analysis of data from the East Boston Ashma Study and its performance is evaluated using simulations.

LIKELIHOOD ESTIMATION OF CONJUGACY RELATIONSHIPS IN LINEAR MODELS WITH APPLICATIONS TO HIGH-THROUGHPUT GENOMICS

In the simultaneous estimation of a large number of related quantities, multilevel models provide a formal mechanism for efficiently making use of the ensemble of information for deriving individual estimates. In this article we investigate the ability of the likelihood to identify the relationship between signal and noise in multilevel linear mixed models. Specifically, we consider the ability of the likelihood to diagnose conjugacy or independence between the signals and noises. Our work was motivated by the analysis of data from high-throughput experiments in genomics. The proposed model leads to a more flexible family. However, we further demonstrate that adequately capitalizing on the benefits of a well fitting fully-specified likelihood in the terms of gene ranking is difficult.

Modeling multilevel sleep transitional data via Poisson log-linear multilevel models

This paper proposes Poisson log-linear multilevel models to investigate population variability in sleep state transition rates. We specifically propose a Bayesian Poisson regression model that is more flexible, scalable to larger studies, and easily fit than other attempts in the literature. We further use hierarchical random effects to account for pairings of individuals and repeated measures within those individuals, as comparing diseased to non-diseased subjects while minimizing bias is of epidemiologic importance. We estimate essentially non-parametric piecewise constant hazards and smooth them, and allow for time varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming piecewise constant hazards. This relationship allows us to synthesize two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed.

Perspectives on periodontal risk factors

The development of a clinical decision tree based on knowledge about risks and reported outcomes of therapy is a necessity for successful planning and outcome of periodontal therapy. This requires a well-founded knowledge of the disease entity and a broad knowledge of how different risk conditions attribute to periodontitis. The infectious etiology, a complex immune response, and influence from a large number of co-factors are challenging conditions in clinical periodontal risk assessment. The difficult relationship between independent and dependent risk conditions paired with limited information on periodontitis prevalence adds to difficulties in periodontal risk assessment. The current information on periodontitis risk attributed to smoking habits, socio-economic conditions, general health and subjects' self-perception of health, is not comprehensive, and this contributes to limited success in periodontal risk assessment. New models for risk analysis have been advocated. Their utility for the estimation of periodontal risk assessment and prognosis should be tested. The present review addresses several of these issues associated with periodontal risk assessment.

Primary perivascular epithelioid cell tumor of the liver not related to hepatic ligaments: hepatic PEComa as an emerging entity

Primary perivascular epithelioid cell tumor (PEComa) of the liver is a very rare example of an emerging family of hepatic PEC tumors. Only few cases have been described so far. We report the case of a large but benign hepatic PEComa in a 53-year-old man without signs of tuberous sclerosis. In contrast to recently described PEC-derived liver tumors in children and young adults, this neoplasm was not related to the hepatic ligaments but had developed deeply within the liver substance. The neoplastic cells displayed the complete phenotype typical for PEComas, i.e. reactivity for several melanoma markers and for smooth muscle actin. The unique relationship of myoid tumor cells to the adventitia of blood vessels prompted us, in comparison with published findings obtained with angiomyolipomas, to comment on the possible origin of the still enigmatic perivascular epithelioid cells.

High level of extracellular potassium and its correlates after severe head injury: relationship to high intracranial pressure

OBJECT: Disturbed ionic and neurotransmitter homeostasis are now recognized as probably the most important mechanisms contributing to the development of secondary brain swelling after traumatic brain injury (TBI). Evidence obtained in animal models indicates that posttraumatic neuronal excitation by excitatory amino acids leads to an increase in extracellular potassium, probably due to ion channel activation. The purpose of this study was therefore to measure dialysate potassium in severely head injured patients and to correlate these results with measurements of intracranial pressure (ICP), patient outcome, and levels of dialysate glutamate and lactate, and cerebral blood flow (CBF) to determine the role of ischemia in this posttraumatic ion dysfunction. METHODS: Eighty-five patients with severe TBI (Glasgow Coma Scale Score < 8) were treated according to an intensive ICP management-focused protocol. All patients underwent intracerebral microdialyis. Dialysate potassium levels were analyzed using flame photometry, and dialysate glutamate and dialysate lactate levels were measured using high-performance liquid chromatography and an enzyme-linked amperometric method in 72 and 84 patients, respectively. Cerebral blood flow studies (stable xenon computerized tomography scanning) were performed in 59 patients. In approximately 20% of the patients, dialysate potassium values were increased (dialysate potassium > 1.8 mM) for 3 hours or more. A mean amount of dialysate potassium greater than 2 mM throughout the entire monitoring period was associated with ICP above 30 mm Hg and fatal outcome, as were progressively rising levels of dialysate potassium. The presence of dialysate potassium correlated positively with dialysate glutamate (p < 0.0001) and lactate (p < 0.0001) levels. Dialysate potassium was significantly inversely correlated with reduced CBF (p = 0.019). CONCLUSIONS: Dialysate potassium was increased after TBI in 20% of measurements. High levels of dialysate potassium were associated with increased ICP and poor outcome. The simultaneous increase in dialysate potassium, together with dialysate glutamate and lactate, supports the concept that glutamate induces ionic flux and consequently increases ICP, which the authors speculate may be due to astrocytic swelling. Reduced CBF was also significantly correlated with increased levels of dialysate potassium. This may be due to either cell swelling or altered vasoreactivity in cerebral blood vessels caused by higher levels of potassium after trauma. Additional studies in which potassium-sensitive microelectrodes are used are needed to validate these ionic events more clearly.

High extracellular potassium and its correlates after severe head injury: relationship to high intracranial pressure

Disturbed ionic and neurotransmitter homeostasis are now recognized to be probably the most important mechanisms contributing to the development of secondary brain swelling after traumatic brian injury (TBI). Evidence obtained from animal models indicates that posttraumatic neuronal excitation via excitatory amino acids leads to an increase in extracellular potassium, probably due to ion channel activation. The purpose of this study was therefore to measure dialysate potassium in severely head injured patients and to correlate these results with intracranial pressure (ICP), outcome, and also with the levels of dialysate glutamate, lactate, and cerebral blood flow (CBF) so as to determine the role of ischemia in this posttraumatic ionic dysfunction. Eighty-five patients with severe TBI (Glasgow Coma Scale score < 8) were treated according to an intensive ICP management-focused protocol. All patients underwent intracerebral microdialyis. Dialysate potassium levels were analyzed by flame photometry, as were dialysate glutamate and dialysate lactate levels, which were measured using high-performance liquid chromatography and an enzyme-linked amperometric method in 72 and 84 patients respectively. Cerebral blood flow studies (stable Xenon--computerized tomography scanning) were performed in 59 patients. In approximately 20% of the patients, potassium values were increased (dialysate potassium > 1.8 mmol). Mean dialysate potassium (> 2 mmol) was associated with ICP above 30 mm Hg and fatal outcome. Dialysate potassium correlated positively with dialysate glutamate (p < 0.0001) and lactate levels (p < 0.0001). Dialysate potassium was significantly inversely correlated with reduced CBF (p = 0.019). Dialysate potassium was increased after TBI in 20% of measurements. High levels of dialysate potassium were associated with increased ICP and poor outcome. The simultaneous increase of potassium, together with dialysate glutamate and lactate, supports the hypothesis that glutamate induces ionic flux and consequently increases ICP due to astrocytic swelling. Reduced CBF was also significantly correlated with increased levels of dialysate potassium. This may be due to either cell swelling or altered potassium reactivity in cerebral blood vessels after trauma.