Ventrales femoroacetabuläres Impingement nach geheilter Schenkelhalsfraktur

Zusammenfassung: Fragestellung.: Darstellung des ventralen femoroacetabulären Impingements (VFAI) als Ursache persistierender schmerzhafter Bewegungseinschränkungen und fortschreitender Gelenkschädigung nach geheilter Schenkelhalsfraktur sowie der Ergebnisse nach operativer Therapie des VFAI. Methodik.: Bei 11Patienten wurde ein VFAI mit bewegungs- und belastungsabhängigen Leistenschmerzen nach in Retrotorsion geheilter Schenkelhalsfraktur vermutet und nativröntgenologisch sowie mit radialer Arthro-MRT-Untersuchung bestätigt. Mit chirurgischer (Sub-)Luxation des Hüftgelenks wurde das Impingement offen überprüft und durch Wiederherstellung der Kontur des anterioren Übergangs zwischen Femurkopf und Schenkelhals beseitigt. Ergebnisse.: Bei sämtlichen Patienten zeigte sich eine Abflachung der Kontur des ventralen Kopf-Hals-Übergangs und ein dadurch hervorgerufenes Cam-Impingement mit konsekutiver Schädigung des pfannenrandnahen acetabulären Knorpels. Bei der Nachuntersuchung 5Jahre postoperativ fand sich eine deutliche Besserung der Symptomatik ohne Zunahme der Gelenkschädigung. Schlussfolgerung.: Bei chronischen Beschwerden nach geheilter Schenkelhalsfraktur ohne Kopfnekrose ist an die Möglichkeit eines VFAI durch Retrotorsion des Kopfes gegenüber dem Hals zu denken. Die durch VFAI hervorgerufene Symptomatik lässt sich durch chirurgische Optimierung des Kopf-Hals-Offset längerfristig verbessern. Ein bereits entstandener Gelenkschaden lässt sich allerdings kaum angehen. Eine Schenkelhalsfraktur sollte anatomisch reponiert werden, um der Arthroseentwicklung vorzubeuge

A study of blow-ups in the Keller-Segel model of chemotaxis

We study the Keller-Segel model of chemotaxis and develop a composite particle-grid numerical method with adaptive time stepping which allows us to accurately resolve singular solutions. The numerical findings (in two dimensions) are then compared with analytical predictions regarding formation and interaction of singularities obtained via analysis of the stochastic differential equations associated with the Keller-Segel model

Not All Children with Cystic Fibrosis Have Abnormal Esophageal Neutralization during Chemical Clearance of Acid Reflux.

PurposeAcid neutralization during chemical clearance is significantly prolonged in children with cystic fibrosis, compared to symptomatic children without cystic fibrosis. The absence of available reference values impeded identification of abnormal findings within individual patients with and without cystic fibrosis. The present study aimed to test the hypothesis that significantly more children with cystic fibrosis have acid neutralization durations during chemical clearance that fall outside the physiological range.MethodsPublished reference value for acid neutralization duration during chemical clearance (determined using combined impedance/pH monitoring) was used to assess esophageal acid neutralization efficiency during chemical clearance in 16 children with cystic fibrosis (3 to <18 years) and 16 age-matched children without cystic fibrosis.ResultsDuration of acid neutralization during chemical clearance exceeded the upper end of the physiological range in 9 of 16 (56.3%) children with and in 3 of 16 (18.8%) children without cystic fibrosis (p=0.0412). The likelihood ratio for duration indicated that children with cystic fibrosis are 2.1-times more likely to have abnormal acid neutralization during chemical clearance, and children with abnormal acid neutralization during chemical clearance are 1.5-times more likely to have cystic fibrosis.ConclusionSignificantly more (but not all) children with cystic fibrosis have abnormally prolonged esophageal clearance of acid. Children with cystic fibrosis are more likely to have abnormal acid neutralization during chemical clearance. Additional studies involving larger sample sizes are needed to address the importance of genotype, esophageal motility, composition and volume of saliva, and gastric acidity on acid neutralization efficiency in cystic fibrosis children

The development of a top-surface mounted technique for the measurement of moisture profiles in drying concrete slabs

Failure of surface coatings on concrete slabs can occur if there is excess moisture in the slab. There is a need for reliable standard measurement procedures to determine whether a concrete slab is dry enough to have a surface coating applied. Accordingly the Building Research Association of New Zealand commissioned this project to develop a top-surface mounted resistive technique for measuring moisture profiles in concrete flooring slabs. Geophysical vertical electric sounding (VES) techniques have been adapted to determine resistivity profiles in concrete slabs through mathematical inversion of apparent resistivity readings made at the surface. Relative-humidity profiles may then be extracted when the relationship between relative-humidity and resistivity has been determined. The project has involved the development and testing of: • 'wet' electrodes (ie. wooden electrodes wetted with a conducting solution) which are able to reduce and stabilise the otherwise high, variable and non-reproducible electrode-concrete interface resistance, • a VES instrument comprising an array of electrodes multiplexed to a computer controlled resistivity meter and operated through a graphical user interface and software able to 'invert' the apparent resistivity curves determined, • embedded electrode systems for independent measurement of resistivity profiles for use in evaluating the VES instrument and technique and determining the relationship between relative humidity and resistivity. Resistivity ρ and relative-humidity ψ profiles have been measured using a range of concrete samples and the relationship between them, away from the dry surface region, has been found to be described by the equation ψ = -aln(ρ) + b where a and b are coefficients that are functions of depth and the age of the concrete. The ability of the VES instrument to determine resistivity profiles from non-reinforced slabs is demonstrated in this report. However reinforcing at shallow depths (30 mm below the surface) does not allow profile recovery and makes commercialisation of the instrument unlikely. It is suggested that the embedded electrode systems developed here, provide a convenient and inexpensive method of directly measuring resistivity profiles from which relative-humidity profiles may be extracted with a high degree of precision

The Influence of Specimen Thickness on the High Temperature Corrosion Behavior of CMSX-4 during Thermal-Cycling Exposure

CMSX-4 is a single-crystalline Ni-base superalloy designed to be used at very high temperatures and high mechanical loadings. Its excellent corrosion resistance is due to external alumina-scale formation, which however can become less protective under thermal-cycling conditions. The metallic substrate in combination with its superficial oxide scale has to be considered as a composite suffering high stresses. Factors like different coefficients of thermal expansion between oxide and substrate during temperature changes or growing stresses affect the integrity of the oxide scale. This must also be strongly influenced by the thickness of the oxide scale and the substrate as well as the ability to relief such stresses, e.g., by creep deformation. In order to quantify these effects, thin-walled specimens of different thickness (t = 100500 lm) were prepared. Discontinuous measurements of their mass changes were carried out under thermal-cycling conditions at a hot dwell temperature of 1100 C up to 300 thermal cycles. Thin-walled specimens revealed a much lower oxide-spallation rate compared to thick-walled specimens, while thinwalled specimens might show a premature depletion of scale-forming elements. In order to determine which of these competetive factor is more detrimental in terms of a component’s lifetime, the degradation by internal precipitation was studied using scanning electron microscopy (SEM) in combination with energy-dispersive X-ray spectroscopy (EDS). Additionally, a recently developed statistical spallation model was applied to experimental data [D. Poquillon and D. Monceau, Oxidation of Metals, 59, 409–431 (2003)]. The model describes the overall mass change by oxide scale spallation during thermal cycling exposure and is a useful simulation tool for oxide scale spallation processes accounting for variations in the specimen geometry. The evolution of the net-mass change vs. the number of thermal cycles seems to be strongly dependent on the sample thickness

CLaSPS: a new methodology for Knowledge extraction from complex astronomical dataset

In this paper we present the Clustering-Labels-Score Patterns Spotter (CLaSPS), a new methodology for the determination of correlations among astronomical observables in complex datasets, based on the application of distinct unsupervised clustering techniques. The novelty in CLaSPS is the criterion used for the selection of the optimal clusterings, based on a quantitative measure of the degree of correlation between the cluster memberships and the distribution of a set of observables, the labels, not employed for the clustering. In this paper we discuss the applications of CLaSPS to two simple astronomical datasets, both composed of extragalactic sources with photometric observations at different wavelengths from large area surveys. The first dataset, CSC+, is composed of optical quasars spectroscopically selected in the SDSS data, observed in the X-rays by Chandra and with multi-wavelength observations in the near-infrared, optical and ultraviolet spectral intervals. One of the results of the application of CLaSPS to the CSC+ is the re-identification of a well-known correlation between the alphaOX parameter and the near ultraviolet color, in a subset of CSC+ sources with relatively small values of the near-ultraviolet colors. The other dataset consists of a sample of blazars for which photometric observations in the optical, mid and near infrared are available, complemented for a subset of the sources, by Fermi gamma-ray data. The main results of the application of CLaSPS to such datasets have been the discovery of a strong correlation between the multi-wavelength color distribution of blazars and their optical spectral classification in BL Lacs and Flat Spectrum Radio Quasars and a peculiar pattern followed by blazars in the WISE mid-infrared colors space. This pattern and its physical interpretation have been discussed in details in other papers by one of the authors.Comment: 18 pages, 9 figures, accepted for publication in Ap

Extreme State Aggregation Beyond MDPs

We consider a Reinforcement Learning setup where an agent interacts with an environment in observation-reward-action cycles without any (esp.\ MDP) assumptions on the environment. State aggregation and more generally feature reinforcement learning is concerned with mapping histories/raw-states to reduced/aggregated states. The idea behind both is that the resulting reduced process (approximately) forms a small stationary finite-state MDP, which can then be efficiently solved or learnt. We considerably generalize existing aggregation results by showing that even if the reduced process is not an MDP, the (q-)value functions and (optimal) policies of an associated MDP with same state-space size solve the original problem, as long as the solution can approximately be represented as a function of the reduced states. This implies an upper bound on the required state space size that holds uniformly for all RL problems. It may also explain why RL algorithms designed for MDPs sometimes perform well beyond MDPs.Comment: 28 LaTeX pages. 8 Theorem

Paradigm of tunable clustering using binarization of consensus partition matrices (Bi-CoPaM) for gene discovery

Copyright @ 2013 Abu-Jamous et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight clusters that focus on their cores or wide clusters that overlap and contain all possibly relevant genes. In this paper, a new clustering paradigm is proposed. In this paradigm, all three eventualities of a gene being exclusively assigned to a single cluster, being assigned to multiple clusters, and being not assigned to any cluster are possible. These possibilities are realised through the primary novelty of the introduction of tunable binarization techniques. Results from multiple clustering experiments are aggregated to generate one fuzzy consensus partition matrix (CoPaM), which is then binarized to obtain the final binary partitions. This is referred to as Binarization of Consensus Partition Matrices (Bi-CoPaM). The method has been tested with a set of synthetic datasets and a set of five real yeast cell-cycle datasets. The results demonstrate its validity in generating relevant tight, wide, and complementary clusters that can meet requirements of different gene discovery studies.National Institute for Health Researc

Kernel Spectral Clustering and applications

In this chapter we review the main literature related to kernel spectral clustering (KSC), an approach to clustering cast within a kernel-based optimization setting. KSC represents a least-squares support vector machine based formulation of spectral clustering described by a weighted kernel PCA objective. Just as in the classifier case, the binary clustering model is expressed by a hyperplane in a high dimensional space induced by a kernel. In addition, the multi-way clustering can be obtained by combining a set of binary decision functions via an Error Correcting Output Codes (ECOC) encoding scheme. Because of its model-based nature, the KSC method encompasses three main steps: training, validation, testing. In the validation stage model selection is performed to obtain tuning parameters, like the number of clusters present in the data. This is a major advantage compared to classical spectral clustering where the determination of the clustering parameters is unclear and relies on heuristics. Once a KSC model is trained on a small subset of the entire data, it is able to generalize well to unseen test points. Beyond the basic formulation, sparse KSC algorithms based on the Incomplete Cholesky Decomposition (ICD) and $L_0$, $L_1, L_0 + L_1$, Group Lasso regularization are reviewed. In that respect, we show how it is possible to handle large scale data. Also, two possible ways to perform hierarchical clustering and a soft clustering method are presented. Finally, real-world applications such as image segmentation, power load time-series clustering, document clustering and big data learning are considered.Comment: chapter contribution to the book "Unsupervised Learning Algorithms