Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Modeling

Finite element (FE) analysis is an important computational tool in biomechanics. However, its adoption into clinical practice has been hampered by its computational complexity and required high technical competences for clinicians. In this paper we propose a supervised learning approach to predict the outcome of the FE analysis. We demonstrate our approach on clinical CT and X-ray femur images for FE predictions ( FEP), with features extracted, respectively, from a statistical shape model and from 2D-based morphometric and density information. Using leave-one-out experiments and sensitivity analysis, comprising a database of 89 clinical cases, our method is capable of predicting the distribution of stress values for a walking loading condition with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.

Minimal Nielsen Root Classes and Roots of Liftings

Given a continuous map f : K -> M from a 2-dimensional CW complex into a closed surface, the Nielsen root number N(f) and the minimal number of roots mu(f) of f satisfy N(f) <= mu(f). But, there is a number mu(C)(f) associated to each Nielsen root class of f, and an important problem is to know when mu(f) = mu(C)(f)N(f). In addition to investigate this problem, we determine a relationship between mu(f) and mu((f) over tilde), when (f) over tilde f is a lifting of f through a covering space, and we find a connection between this problems, with which we answer several questions related to them when the range of the maps is the projective plane.

Stiffly accurate Runge-Kutta methods for stiff stochastic differential equations

In this paper we discuss implicit methods based on stiffly accurate Runge-Kutta methods and splitting techniques for solving Stratonovich stochastic differential equations (SDEs). Two splitting techniques: the balanced splitting technique and the deterministic splitting technique, are used in this paper. We construct a two-stage implicit Runge-Kutta method with strong order 1.0 which is corrected twice and no update is needed. The stability properties and numerical results show that this approach is suitable for solving stiff SDEs. (C) 2001 Elsevier Science B.V. All rights reserved.

Predictor-corrector methods of Runge-Kutta type for stochastic differential equations

In this paper we construct predictor-corrector (PC) methods based on the trivial predictor and stochastic implicit Runge-Kutta (RK) correctors for solving stochastic differential equations. Using the colored rooted tree theory and stochastic B-series, the order condition theorem is derived for constructing stochastic RK methods based on PC implementations. We also present detailed order conditions of the PC methods using stochastic implicit RK correctors with strong global order 1.0 and 1.5. A two-stage implicit RK method with strong global order 1.0 and a four-stage implicit RK method with strong global order 1.5 used as the correctors are constructed in this paper. The mean-square stability properties and numerical results of the PC methods based on these two implicit RK correctors are reported.

A four-stage index 2 Diagonally Implicit Runge-Kutta method

High index Differential Algebraic Equations (DAEs) force standard numerical methods to lower order. Implicit Runge-Kutta methods such as RADAU5 handle high index problems but their fully implicit structure creates significant overhead costs for large problems. Singly Diagonally Implicit Runge-Kutta (SDIRK) methods offer lower costs for integration. This paper derives a four-stage, index 2 Explicit Singly Diagonally Implicit Runge-Kutta (ESDIRK) method. By introducing an explicit first stage, the method achieves second order stage calculations. After deriving and solving appropriate order conditions., numerical examples are used to test the proposed method using fixed and variable step size implementations. (C) 2001 IMACS. Published by Elsevier Science B.V. All rights reserved.

De novo mutation in the KCNQ1 gene causal to Jervell and Lange-Nielsen syndrome.

Jervell and Lange-Nielsen syndrome (JLNS) is an autosomal recessive disorder, clinically characterized by severe cardiac arrhythmias [due to prolonged QTc interval in electrocardiogram (ECG)] and bilateral sensory neural deafness. Molecular defects causal to JLNS are either homozygous or compound heterozygous mutations, predominantly in the KCNQ1 gene and occasionally in the KCNE1 gene. As the molecular defect is bi-allelic, JLNS patients inherit one pathogenic mutation causal to the disorder from each parent. In this report, we show for the first time that such a disorder could also occur due to a spontaneous de novo mutation in the affected individual, not inherited from the parent, which makes this case unique unlike the previously reported JLNS cases.

Heterobathmia pseuderiocrania Kristensen & Nielsen (Lepidoptera, Heterobathmiidae): identificación basada en DNA-barcoding y notas morfológicas e historia de vida de los estados inmaduros

Heterobathmia pseuderiocrania Kristensen & Nielsen (Lepidoptera, Heterobathmiidae): identification based on DNA-barcoding and notes on the morphology and life history of the immature stages. The larva morphology of the species Heterobathmia pseuderiocrania (Lepidoptera, Heterobathmiidae), a Nothofagus obliqua leafminer in Chile, is described. The tissue-feeding first and last instars are described. Also, the number of larval stages, some aspects of the biology and life cycle of the species are provided.

Genotype-phenotype analysis of Jervell and Lange-Nielsen syndrome in six families from Saudi Arabia.

We sought to explore the genotype-phenotype of Jervell and Lange-Nielsen syndrome (JLNS) patients in Saudi Arabia. We have also assessed the plausible effect of consanguinity into the pathology of JLNS. Six families with at least one JLNS-affected member attended our clinic between 2011 and 2013. Retrospective and prospective clinical data were collected and genetic investigation was performed. Pathogenic mutations in the KCNQ1 gene were detected in all JLNS patients. The homozygous mutations detected were Leu273Phe, Asp202Asn, Ile567Thr, and c.1486_1487delCT and compound heterozygous mutations were c.820_ 830del and c.1251+1G>T. All living JLNS patients except one had a QTc of >500 ms and a history of recurrent syncope. β-Blockers abolished the cardiac-related events in all patients except two siblings with homozygous Ile567Thr mutation. Four of the six mutations were originally reported in autosomal dominant long QT syndrome (LQTS) patients. Eighty percent of the heterozygote mutation carriers showed prolongation of QTc, but majority of these reported no symptoms attributable to arrhythmias. Mutations detected in this study will be advantageous in tribe and region-specific cascade screening of LQTS in Saudi Arabia.

Aksel Wiin-Nielsen (1924–2010)

Aksel Christopher Wiin-Nielsen, professor emeritus of geophysics at the University of Copenhagen, Denmark, died on 26 April 2010 at the age of 85. He was an honorary member of the American Meteorological Society, the former European Geophysical Society, and the Royal Meteorological Society. He received several awards including the 1982 Buys Ballot Medal, given by the Royal Netherlands Academy of Arts and Sciences, and the 1983 Wihuri International Prize, given by the Wihuri Foundation for International Prizes. He will be remembered for his impressive and outstanding leadership in meteorology.