Bovine pericardium based non-cross linked collagen matrix for successful root coverage, a clinical study in human

Introduction The aim of this study was to clinically assess the capacity of a novel bovine pericardium based, non-cross linked collagen matrix in root coverage. Methods 62 gingival recessions of Miller class I or II were treated. The matrix was adapted underneath a coronal repositioned split thickness flap. Clinical values were assessed at baseline and after six months. Results The mean recession in each patient was 2.2 mm at baseline. 6 Months after surgery 86.7% of the exposed root surfaces were covered. On average 0,3 mm of recession remained. The clinical attachment level changed from 3.5 ± 1.3 mm to 1,8 ( ± 0,7) mm during the observational time period. No statistically significant difference was found in the difference of probing depth. An increase in the width of gingiva was significant. With a baseline value of 1.5 ± 0.9 mm an improvement of 2.4 ± 0.8 mm after six month could be observed. 40 out of 62 recessions were considered a thin biotype at baseline. After 6 months all 62 sites were assessed thick. Conclusions The results demonstrate the capacity of the bovine pericardium based non-cross linked collagen matrix for successful root coverage. This material was able to enhance gingival thickness and the width of keratinized gingiva. The percentage of root coverage achieved thereby is comparable to existing techniques. This method might contribute to an increase of patient's comfort and an enhanced aesthetical outcome.

Investigation of a unique short open reading frame within the 3' untranslated region of the canine distemper virus matrix messenger RNA

Increasing evidence suggest that the long "untranslated" region (UTR) between the matrix (M) and the fusion (F) proteins of morbilliviruses has a functional role. In canine distemper virus (CDV), the F 5' UTR was recently shown to code for a long F signal peptide (Fsp). Subsequently, it was reported that the M/F UTRs combined with the long Fsp were synergistically regulating the F mRNA and protein expression, thereby modulating virulence. Unique to CDV, a short putative open reading frame (ORF) has been identified within the wild-type CDV-M 3' UTR (termed M2). Here, we investigated whether M2 was expressed from the genome of the virulent and demyelinating A75/17-CDV strain. An expression plasmid encoding the M2 ORF tagged both at its N-terminal (HA) and C-terminal domains (RFP), was first constructed. Then, a recombinant virus with its putative M2 ORF replaced by HA-M2-RFP was successfully recovered from cDNA (termed recA75/17(green)-HA-M2-RFP). M2 expression in cells transfected or infected with these mutants was studied by immunoprecipitation, immunofluorescence, immunoblot and flow cytometry analyses. Although fluorescence was readily detected in HA-M2-RFP-transfected cells, absence of red fluorescence emission in several recA75/17(green)-HA-M2-RFP-infected cell types suggested lack of M2 biosynthesis, which was confirmed by the other techniques. Consistent with these data, no functional role of the short polypeptide was revealed by infecting various cell types with HA-M2-RFP over-expressing or M2-knockout recombinant viruses. Thus, in sharp contrast to the CDV-F 5' UTR reported to translate a long Fsp, our data provided evidence that the CDV-M 3' UTR does not express any polypeptides.

Prevalence and types of pitfall in the assessment of mitral isthmus linear conduction block

To identify and understand clinically encountered pitfalls in the assessment of transmitral conduction block using differential coronary sinus and left atrial appendage pacing techniques in patients with left mitral isthmus linear ablation.

Genuine cross-correlations: Which surrogate based measure reproduces analytical results best?

The analysis of short segments of noise-contaminated, multivariate real world data constitutes a challenge. In this paper we compare several techniques of analysis, which are supposed to correctly extract the amount of genuine cross-correlations from a multivariate data set. In order to test for the quality of their performance we derive time series from a linear test model, which allows the analytical derivation of genuine correlations. We compare the numerical estimates of the four measures with the analytical results for different correlation pattern. In the bivariate case all but one measure performs similarly well. However, in the multivariate case measures based on the eigenvalues of the equal-time cross-correlation matrix do not extract exclusively information about the amount of genuine correlations, but they rather reflect the spatial organization of the correlation pattern. This may lead to failures when interpreting the numerical results as illustrated by an application to three electroencephalographic recordings of three patients suffering from pharmacoresistent epilepsy.

A Functional-Based Distribution Diagnostic for a Linear Model with Correlated Outcomes: Technical Report

Despite the widespread popularity of linear models for correlated outcomes (e.g. linear mixed modesl and time series models), distribution diagnostic methodology remains relatively underdeveloped in this context. In this paper we present an easy-to-implement approach that lends itself to graphical displays of model fit. Our approach involves multiplying the estimated marginal residual vector by the Cholesky decomposition of the inverse of the estimated marginal variance matrix. Linear functions or the resulting "rotated" residuals are used to construct an empirical cumulative distribution function (ECDF), whose stochastic limit is characterized. We describe a resampling technique that serves as a computationally efficient parametric bootstrap for generating representatives of the stochastic limit of the ECDF. Through functionals, such representatives are used to construct global tests for the hypothesis of normal margional errors. In addition, we demonstrate that the ECDF of the predicted random effects, as described by Lange and Ryan (1989), can be formulated as a special case of our approach. Thus, our method supports both omnibus and directed tests. Our method works well in a variety of circumstances, including models having independent units of sampling (clustered data) and models for which all observations are correlated (e.g., a single time series).

Cholesky Residuals for Assessing Normal Errors in a Linear Model with Correlated Outcomes: Technical Report

Despite the widespread popularity of linear models for correlated outcomes (e.g. linear mixed models and time series models), distribution diagnostic methodology remains relatively underdeveloped in this context. In this paper we present an easy-to-implement approach that lends itself to graphical displays of model fit. Our approach involves multiplying the estimated margional residual vector by the Cholesky decomposition of the inverse of the estimated margional variance matrix. The resulting "rotated" residuals are used to construct an empirical cumulative distribution function and pointwise standard errors. The theoretical framework, including conditions and asymptotic properties, involves technical details that are motivated by Lange and Ryan (1989), Pierce (1982), and Randles (1982). Our method appears to work well in a variety of circumstances, including models having independent units of sampling (clustered data) and models for which all observations are correlated (e.g., a single time series). Our methods can produce satisfactory results even for models that do not satisfy all of the technical conditions stated in our theory.

Computational Techniques for Spatial Logistic Regression with Large Datasets

In epidemiological work, outcomes are frequently non-normal, sample sizes may be large, and effects are often small. To relate health outcomes to geographic risk factors, fast and powerful methods for fitting spatial models, particularly for non-normal data, are required. We focus on binary outcomes, with the risk surface a smooth function of space. We compare penalized likelihood models, including the penalized quasi-likelihood (PQL) approach, and Bayesian models based on fit, speed, and ease of implementation. A Bayesian model using a spectral basis representation of the spatial surface provides the best tradeoff of sensitivity and specificity in simulations, detecting real spatial features while limiting overfitting and being more efficient computationally than other Bayesian approaches. One of the contributions of this work is further development of this underused representation. The spectral basis model outperforms the penalized likelihood methods, which are prone to overfitting, but is slower to fit and not as easily implemented. Conclusions based on a real dataset of cancer cases in Taiwan are similar albeit less conclusive with respect to comparing the approaches. The success of the spectral basis with binary data and similar results with count data suggest that it may be generally useful in spatial models and more complicated hierarchical models.

On the Behaviour of Marginal and Conditional Akaike Information Criteria in Linear Mixed Models

In linear mixed models, model selection frequently includes the selection of random effects. Two versions of the Akaike information criterion (AIC) have been used, based either on the marginal or on the conditional distribution. We show that the marginal AIC is no longer an asymptotically unbiased estimator of the Akaike information, and in fact favours smaller models without random effects. For the conditional AIC, we show that ignoring estimation uncertainty in the random effects covariance matrix, as is common practice, induces a bias that leads to the selection of any random effect not predicted to be exactly zero. We derive an analytic representation of a corrected version of the conditional AIC, which avoids the high computational cost and imprecision of available numerical approximations. An implementation in an R package is provided. All theoretical results are illustrated in simulation studies, and their impact in practice is investigated in an analysis of childhood malnutrition in Zambia.

VMC++ versus BEAMnrc: a comparison of simulated linear accelerator heads for photon beams

BEAMnrc, a code for simulating medical linear accelerators based on EGSnrc, has been bench-marked and used extensively in the scientific literature and is therefore often considered to be the gold standard for Monte Carlo simulations for radiotherapy applications. However, its long computation times make it too slow for the clinical routine and often even for research purposes without a large investment in computing resources. VMC++ is a much faster code thanks to the intensive use of variance reduction techniques and a much faster implementation of the condensed history technique for charged particle transport. A research version of this code is also capable of simulating the full head of linear accelerators operated in photon mode (excluding multileaf collimators, hard and dynamic wedges). In this work, a validation of the full head simulation at 6 and 18 MV is performed, simulating with VMC++ and BEAMnrc the addition of one head component at a time and comparing the resulting phase space files. For the comparison, photon and electron fluence, photon energy fluence, mean energy, and photon spectra are considered. The largest absolute differences are found in the energy fluences. For all the simulations of the different head components, a very good agreement (differences in energy fluences between VMC++ and BEAMnrc <1%) is obtained. Only a particular case at 6 MV shows a somewhat larger energy fluence difference of 1.4%. Dosimetrically, these phase space differences imply an agreement between both codes at the <1% level, making VMC++ head module suitable for full head simulations with considerable gain in efficiency and without loss of accuracy.

Soft-decision equalization techniques for frequency selective MIMO channels

Multi-input multi-output (MIMO) technology is an emerging solution for high data rate wireless communications. We develop soft-decision based equalization techniques for frequency selective MIMO channels in the quest for low-complexity equalizers with BER performance competitive to that of ML sequence detection. We first propose soft decision equalization (SDE), and demonstrate that decision feedback equalization (DFE) based on soft-decisions, expressed via the posterior probabilities associated with feedback symbols, is able to outperform hard-decision DFE, with a low computational cost that is polynomial in the number of symbols to be recovered, and linear in the signal constellation size. Building upon the probabilistic data association (PDA) multiuser detector, we present two new MIMO equalization solutions to handle the distinctive channel memory. With their low complexity, simple implementations, and impressive near-optimum performance offered by iterative soft-decision processing, the proposed SDE methods are attractive candidates to deliver efficient reception solutions to practical high-capacity MIMO systems. Motivated by the need for low-complexity receiver processing, we further present an alternative low-complexity soft-decision equalization approach for frequency selective MIMO communication systems. With the help of iterative processing, two detection and estimation schemes based on second-order statistics are harmoniously put together to yield a two-part receiver structure: local multiuser detection (MUD) using soft-decision Probabilistic Data Association (PDA) detection, and dynamic noise-interference tracking using Kalman filtering. The proposed Kalman-PDA detector performs local MUD within a sub-block of the received data instead of over the entire data set, to reduce the computational load. At the same time, all the inter-ference affecting the local sub-block, including both multiple access and inter-symbol interference, is properly modeled as the state vector of a linear system, and dynamically tracked by Kalman filtering. Two types of Kalman filters are designed, both of which are able to track an finite impulse response (FIR) MIMO channel of any memory length. The overall algorithms enjoy low complexity that is only polynomial in the number of information-bearing bits to be detected, regardless of the data block size. Furthermore, we introduce two optional performance-enhancing techniques: cross- layer automatic repeat request (ARQ) for uncoded systems and code-aided method for coded systems. We take Kalman-PDA as an example, and show via simulations that both techniques can render error performance that is better than Kalman-PDA alone and competitive to sphere decoding. At last, we consider the case that channel state information (CSI) is not perfectly known to the receiver, and present an iterative channel estimation algorithm. Simulations show that the performance of SDE with channel estimation approaches that of SDE with perfect CSI.

Linear and Piecewise Functions

This morning Dr. Battle will review basic concepts of linear functions and piecewise functions and how they can be used as models for real-world applications. She will also introduce techniques for using a spreadsheet to analyze data.

Initial results of in vivo high-resolution morphological and biochemical cartilage imaging of patients after matrix-associated autologous chondrocyte transplantation (MACT) of the ankle

OBJECTIVE: The aim of this study was to use morphological as well as biochemical (T2 and T2* relaxation times and diffusion-weighted imaging (DWI)) magnetic resonance imaging (MRI) for the evaluation of healthy cartilage and cartilage repair tissue after matrix-associated autologous chondrocyte transplantation (MACT) of the ankle joint. MATERIALS AND METHODS: Ten healthy volunteers (mean age, 32.4 years) and 12 patients who underwent MACT of the ankle joint (mean age, 32.8 years) were included. In order to evaluate possible maturation effects, patients were separated into short-term (6-13 months) and long-term (20-54 months) follow-up cohorts. MRI was performed on a 3.0-T magnetic resonance (MR) scanner using a new dedicated eight-channel foot-and-ankle coil. Using high-resolution morphological MRI, the magnetic resonance observation of cartilage repair tissue (MOCART) score was assessed. For biochemical MRI, T2 mapping, T2* mapping, and DWI were obtained. Region-of-interest analysis was performed within native cartilage of the volunteers and control cartilage as well as cartilage repair tissue in the patients subsequent to MACT. RESULTS: The overall MOCART score in patients after MACT was 73.8. T2 relaxation times (approximately 50 ms), T2* relaxation times (approximately 16 ms), and the diffusion constant for DWI (approximately 1.3) were comparable for the healthy volunteers and the control cartilage in the patients after MACT. The cartilage repair tissue showed no significant difference in T2 and T2* relaxation times (p > or = 0.05) compared to the control cartilage; however, a significantly higher diffusivity (approximately 1.5; p < 0.05) was noted in the cartilage repair tissue. CONCLUSION: The obtained results suggest that besides morphological MRI and biochemical MR techniques, such as T2 and T2* mapping, DWI may also deliver additional information about the ultrastructure of cartilage and cartilage repair tissue in the ankle joint using high-field MRI, a dedicated multichannel coil, and sophisticated sequences.

Image quality of supine chest radiographs: intra-individual comparison of computed radiography and low-dose linear-slit digital radiography

The purpose of this retrospective study was to intra-individually compare the image quality of computed radiography (CR) and low-dose linear-slit digital radiography (LSDR) for supine chest radiographs. A total of 90 patients (28 female, 62 male; mean age, 55.1 years) imaged with CR and LSDR within a mean time interval of 2.8 days +/- 3.0 were included in this study. Two independent readers evaluated the image quality of CR and LSDR based on modified European Guidelines for Quality Criteria for chest X-ray. The Wilcoxon test was used to analyse differences between the techniques. The overall image quality of LSDR was significantly better than the quality of CR (9.75 vs 8.16 of a maximum score of 10; p < 0.001). LSDR performed significantly better than CR for delineation of anatomical structures in the mediastinum and the retrocardiac lung (p < 0.001). CR was superior to LSDR for visually sharp delineation of the lung vessels and the thin linear structures in the lungs. We conclude that LSDR yields better image quality and may be more suitable for excluding significant pathological features of the chest in areas with high attenuation compared with CR.

Trigger activity more than three years after left atrial linear ablation without pulmonary vein isolation in patients with atrial fibrillation.

OBJECTIVES The aim of this study was to analyze trigger activity in the long-term follow-up after left atrial (LA) linear ablation. BACKGROUND Interventional strategies for curative treatment of atrial fibrillation (AF) are targeted at the triggers and/or the maintaining substrate. After substrate modification using nonisolating linear lesions, the activity of triggers is unknown. METHODS With the LA linear lesion concept, 129 patients were treated using intraoperative ablation with minimal invasive surgical techniques. Contiguous radiofrequency energy-induced lesion lines involving the mitral annulus and the orifices of the pulmonary veins without isolation were placed under direct vision. RESULTS After a mean follow-up of 3.6 +/- 0.4 years, atrial ectopy, atrial runs, and reoccurrence of AF episodes were analyzed by digital 7-day electrocardiograms in 30 patients. Atrial ectopy was present in all patients. Atrial runs were present in 25 of 30 patients (83%), with a median number of 9 runs per patient/week (range 1 to 321) and a median duration of 1.2 s/run (range 0.7 to 25), without a significant difference in atrial ectopy and atrial runs between patients with former paroxysmal (n = 17) or persistent AF (n = 13). Overall, 87% of all patients were completely free from AF without antiarrhythmic drugs. CONCLUSIONS A detailed rhythm analysis late after specific LA linear lesion ablation shows that trigger activity remains relatively frequent but short and does not induce AF episodes in most patients. The long-term success rate of this concept is high in patients with paroxysmal or persistent AF.