Role of ankle mobility in foot rollover during gait in individuals with diabetic neuropathy

Background: The purpose of this study was to investigate the ankle range of motion during neuropathic gait and its influence on plantar pressure distribution in two phases during stance: at heel-strike and at push-off. Methods: Thirty-one adults participated in this study (control group, n = 16; diabetic neuropathic group, n = 15). Dynamic ankle range of motion (electrogoniometer) and plantar pressures (PEDAR-X system) were acquired synchronously during walking. Plantar pressures were evaluated at rearfoot. midfoot and forefoot during the two phases of stance. General linear model repeated measures analysis of variance was applied to investigate relationships between groups, areas and stance phases. Findings: Diabetic neuropathy patients walked using a smaller ankle range of motion in stance phase and smaller ankle flexion at heel-strike (P = 0.0005). Peak pressure and pressure-time integral values were higher in the diabetic group in the midfoot at push-off phase when compared to heel-strike phase. On the other hand, the control group showed similar values of peak pressure in midfoot during both stance phases. Interpretation: The ankle mobility reduction observed could be associated to altered plantar pressure distribution observed in neuropathic subjects. Results demonstrated that midfoot and forefoot play a different role in subjects with neuropathy by receiving higher loads at push-off phase that are probably due to smaller ankle flexion at stance phase. This may explain the higher loads in anterior areas of the foot observed in diabetic neuropathy subjects and confirm an inadequate foot rollover associated to the smaller ankle range of motion at the heel-strike phase. (C) 2009 Elsevier Ltd. All rights reserved.

ELECTROMYOGRAPHIC AMPLITUDE RATIO OF SERRATUS ANTERIOR AND UPPER TRAPEZIUS MUSCLES DURING MODIFIED PUSH-UPS AND BENCH PRESS EXERCISES

Imbalance and weakness of the serratus anterior and upper trapezius force couple have been described in patients with shoulder dysfunction. There is interest in identifying exercises that selectively activate these muscles and including it in rehabilitation protocols. This study aims to verify the UT/SA electromyographic (EMG) amplitude ratio, performed in different upper limb exercises and on two bases of support. Twelve healthy men were tested (average age = 22.8 +/- 3.1 years), and surface EMG was recorded from the upper trapezius and serratus anterior using single differential surface electrodes. Volunteers performed isometric contractions over a stable base of support and on a Swiss ball during the wall push-up (WP), bench press (BP), and push-up (PU) exercises. All SEMG data are reported as a percentage of root mean square or integral of linear envelope from the maximal value obtained in one of three maximal voluntary contractions for each muscle studied. A linear mixed-effect model was performed to compare UT/SA ratio values. The WP, BP, and PU exercises showed UT/SA ratio mean +/- SD values of 0.69 +/- 0.72, 0.14 +/- 0.12, and 0.39 +/- 0.37 for stable surfaces, respectively, whereas for unstable surfaces, the values were 0.73 +/- 0.67, 0.43 +/- 0.39, and 0.32 +/- 0.30. The results demonstrate that UT/SA ratio was influenced by the exercises and by the upper limb base of support. The practical application is to show that BP on a stable surface is the exercise preferred over WP and PU on either surfaces for serratus anterior muscle training in patients with imbalance between the UT/SA force couple or serratus anterior weakness.

Ultrasonographic Evaluation of Achilles Tendon Repair After Percutaneous Sectioning for the Correction of Congenital Clubfoot Residual Equinus

Background: Most cases of congenital clubfoot treated with the Ponseti technique require percutaneous Achilles tenotomy to correct the residual equinus. Clinical evidence suggests that complete healing occurs between the cut tendon stumps, but there have not yet been any detailed studies investigating this reparative process. This study was performed to assess Achilles tendon repair after percutaneous section to correct the residual equinus of clubfoot treated with the Ponseti method. Method: A prospective study analyzed 37 tenotomies in 26 patients with congenital clubfoot treated with the Ponseti technique, with a minimum follow-up of 1 year after the section. The tenotomy was performed percutaneously with a large-bore needle bevel with patient sedation and local anesthesia. Ultrasonographic scanning was performed after section to ascertain that the tenotomy had been completed and to measure the stump separation. In the follow-up period, the reparative process was followed ultrasonographically and assessed at 3 weeks, 6 months, and 1 year posttenotomy. Results: The ultrasonography performed immediately after the procedure showed that in some cases, residual strands between the tendon ends persisted, and these were completely sectioned under ultrasound control. A mean retraction of 5.65 mm +/- 2.26 mm (range, 2.3 to 11.0 mm) between tendon stumps after section was observed. Unusual bleeding occurred in one case and was controlled by digital pressure, with no interference with the final treatment. After 3 weeks, ultrasonography showed tendon repair with the tendon gap filled with irregular hypoechoic tissue, and also with transmission of muscle motion to the heel. Six months after tenotomy, there was structural filling with a fibrillar aspect, mild or moderate hypoechogenicity, and tendon scar thickening when compared with a normal tendon. One year after tenotomy, ultrasound showed a fibrillar structure and echogenicity at the repair site that was similar to a normal tendon, but with persistent tendon scarring thickness. Conclusions: There is a fast reparative process after Achilles tendon percutaneous section that reestablishes continuity between stumps. The reparative tissue evolved to tendon tissue with a normal ultrasonographic appearance except for mild thickening, suggesting a predominantly intrinsic repair mechanism.

Detection of Auditory Cortex Activity by fMRI Using a Dependent Component Analysis

Functional MRI (fMRI) data often have low signal-to-noise-ratio (SNR) and are contaminated by strong interference from other physiological sources. A promising tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). BSS is based on the assumption that the detected signals are a mixture of a number of independent source signals that are linearly combined via an unknown mixing matrix. BSS seeks to determine the mixing matrix to recover the source signals based on principles of statistical independence. In most cases, extraction of all sources is unnecessary; instead, a priori information can be applied to extract only the signal of interest. Herein we propose an algorithm based on a variation of ICA, called Dependent Component Analysis (DCA), where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We applied such method to inspect functional Magnetic Resonance Imaging (fMRI) data, aiming to find the hemodynamic response that follows neuronal activation from an auditory stimulation, in human subjects. The method localized a significant signal modulation in cortical regions corresponding to the primary auditory cortex. The results obtained by DCA were also compared to those of the General Linear Model (GLM), which is the most widely used method to analyze fMRI datasets.

Genetic associations between age at first calving and heifer body weight and scrotal circumference in Nelore cattle

P>Age at first calving (AFC) measures the entry of heifers into the beef cattle production system. This trait can be used as a selection criterion for earlier reproductive performance. Using data from Nelore cattle participating in the `Program for Genetic Improvement of the Nelore Breed` (PMGRN-Nelore Brazil), bi-trait analyses were performed using the restricted maximum likelihood method, based on an AFC animal model and the following traits: female body weight adjusted to 365 (BW365) and 450 (BW450) days of age, and male scrotal circumference adjusted to 365 (SC365), 450 (SC450), 550 (SC550) and 730 (SC730) days of age. The heritability estimates for AFC ranged from 0.02 +/- 0.02 to 0.04 +/- 0.02. The estimates of additive direct heritabilities (with standard error) for BW365, BW450, SC365, SC450, SC550 and SC730 were 0.36 +/- 0.07, 0.38 +/- 0.07, 0.48 +/- 0.07, 0.65 +/- 0.07, 0.64 +/- 0.07 and 0.42 +/- 0.07, respectively, and the genetic correlations with AFC were -0.38, -0.33, 0.10, -0.13, -0.13 and 0.06, respectively. In the herds studied, selection for SC365, SC450, SC550 or SC730 should not cause genetic changes in AFC. Selection based on BW365 or BW450 would favor smaller AFC breeding values. However, the low magnitude of direct heritability estimates for AFC in these farms indicates that changes in phenotypical expression depend mostly on non-genetic factors.

The Stability of the Blood Oxygenation Level-Dependent Functional MRI Response to Motor Tasks Is Altered in Patients With Chronic Ischemic Stroke

Background and Purpose-Functional MRI is a powerful tool to investigate recovery of brain function in patients with stroke. An inherent assumption in functional MRI data analysis is that the blood oxygenation level-dependent (BOLD) signal is stable over the course of the examination. In this study, we evaluated the validity of such assumption in patients with chronic stroke. Methods-Fifteen patients performed a simple motor task with repeated epochs using the paretic and the unaffected hand in separate runs. The corresponding BOLD signal time courses were extracted from the primary and supplementary motor areas of both hemispheres. Statistical maps were obtained by the conventional General Linear Model and by a parametric General Linear Model. Results-Stable BOLD amplitude was observed when the task was executed with the unaffected hand. Conversely, the BOLD signal amplitude in both primary and supplementary motor areas was progressively attenuated in every patient when the task was executed with the paretic hand. The conventional General Linear Model analysis failed to detect brain activation during movement of the paretic hand. However, the proposed parametric General Linear Model corrected the misdetection problem and showed robust activation in both primary and supplementary motor areas. Conclusions-The use of data analysis tools that are built on the premise of a stable BOLD signal may lead to misdetection of functional regions and underestimation of brain activity in patients with stroke. The present data urge the use of caution when relying on the BOLD response as a marker of brain reorganization in patients with stroke. (Stroke. 2010; 41:1921-1926.)

A zero-augmented gamma mixed model for longitudinal data with many zeros

In many occupational safety interventions, the objective is to reduce the injury incidence as well as the mean claims cost once injury has occurred. The claims cost data within a period typically contain a large proportion of zero observations (no claim). The distribution thus comprises a point mass at 0 mixed with a non-degenerate parametric component. Essentially, the likelihood function can be factorized into two orthogonal components. These two components relate respectively to the effect of covariates on the incidence of claims and the magnitude of claims, given that claims are made. Furthermore, the longitudinal nature of the intervention inherently imposes some correlation among the observations. This paper introduces a zero-augmented gamma random effects model for analysing longitudinal data with many zeros. Adopting the generalized linear mixed model (GLMM) approach reduces the original problem to the fitting of two independent GLMMs. The method is applied to evaluate the effectiveness of a workplace risk assessment teams program, trialled within the cleaning services of a Western Australian public hospital.

Bridging the gap between different statistical approaches: An integrated framework for modelling

This paper proposes a template for modelling complex datasets that integrates traditional statistical modelling approaches with more recent advances in statistics and modelling through an exploratory framework. Our approach builds on the well-known and long standing traditional idea of 'good practice in statistics' by establishing a comprehensive framework for modelling that focuses on exploration, prediction, interpretation and reliability assessment, a relatively new idea that allows individual assessment of predictions. The integrated framework we present comprises two stages. The first involves the use of exploratory methods to help visually understand the data and identify a parsimonious set of explanatory variables. The second encompasses a two step modelling process, where the use of non-parametric methods such as decision trees and generalized additive models are promoted to identify important variables and their modelling relationship with the response before a final predictive model is considered. We focus on fitting the predictive model using parametric, non-parametric and Bayesian approaches. This paper is motivated by a medical problem where interest focuses on developing a risk stratification system for morbidity of 1,710 cardiac patients given a suite of demographic, clinical and preoperative variables. Although the methods we use are applied specifically to this case study, these methods can be applied across any field, irrespective of the type of response.

Segmenting the market of West Australian senior tourists using an artificial neural network

Measuring perceptions of customers can be a major problem for marketers of tourism and travel services. Much of the problem is to determine which attributes carry most weight in the purchasing decision. Older travellers weigh many travel features before making their travel decisions. This paper presents a descriptive analysis of neural network methodology and provides a research technique that assesses the weighting of different attributes and uses an unsupervised neural network model to describe a consumer-product relationship. The development of this rich class of models was inspired by the neural architecture of the human brain. These models mathematically emulate the neurophysical structure and decision making of the human brain, and, from a statistical perspective, are closely related to generalised linear models. Artificial neural networks or neural networks are, however, nonlinear and do not require the same restrictive assumptions about the relationship between the independent variables and dependent variables. Using neural networks is one way to determine what trade-offs older travellers make as they decide their travel plans. The sample of this study is from a syndicated data source of 200 valid cases from Western Australia. From senior groups, active learner, relaxed family body, careful participants and elementary vacation were identified and discussed. (C) 2003 Published by Elsevier Science Ltd.

Independent contribution of plaque complexity to myocardial ischemia during Dobutamine stress echocardiography

The influence of complex plaque morphology on the extent of demand-induced ischemia in unselected patients is not well defined. We sought to investigate the functional significance of lesion morphology in patients who underwent coronary angiography and dobutamine stress echocardiography (DSE).,Angiography and DSE were performed within a 6-month period (mean 1 +/- 1 month) in 196 patients. Angiographic assessments involved quantification of stenosis severity, assessment of the extent of jeopardized myocardium, and categorization of plaque morphology according to the Ambrose classification. DSE was interpreted by separate investigators with respect to wall motion score index (WMSI) and number of coronary territories involved. A general linear model was constructed to assess,the independent contribution of patient characteristics and angiographic and DSE results with respect to extent of ischemic myocardium. Complex lesion morphology was seen in 62 patients (32%). Patients with complex lesions were more likely to have had prior myocardial infarction (p < 0.001) and be current smokers (p = 0.03). During angiography, they exhibited a trend toward a greater number of diseased vessels, had a greater coronary jeopardy score (p < 0.001) and more frequent collateral flow (p = 0.03). During echocardiography, patients had a higher stress WMSI (p < 0.001) and were more likely to show ischemia in all 3 arterial territories (p < 0.01). On multivariate regression, the coronary artery jeopardy score and the presence of complex plaque morphology were independent predictors of the extent of ischemic myocardium (R 2 = 34%, p < 0.001). Thus, patients with complex plaque morphology are older, more likely to smoke, and more likely to have had prior myocardial. infarction. They exhibit more extensive disease with higher coronary jeopardy scores and a higher resting and peak stress WMSI. Despite these differences, complex plaque morphology remains an independent predictor of the extent of ischemia during stress. (C) 2003 by Excerpta Medica, Inc.

Two-way MANCOVA: an application to public health

The aim of this work is to use the MANCOVA model to study the influence of the phenotype of an enzyme - Acid phosphatase - and a genetic factor - Haptoglobin genotype - on two dependent variables - Activity of Acid Phosphatase (ACP1) and the Body Mass Index (BMI). Therefore it's used a general linear model, namely a multivariate analysis of covariance (Two-way MANCOVA). The covariate is the age of the subject. This covariate works as control variable for the independent factors, serving to reduce the error term in the model. The main results showed that only the ACP1 phenotype has a significant effect on the activity of ACP1 and the covariate has a significant effect in both dependent variables. The univariate analysis showed that ACP1 phenotype accounts for about 12.5% of the variability in the activity of ACP1. In respect to this covariate it can be seen that accounts for about 4.6% of the variability in the activity of ACP1 and 37.3% in the BMI.

Air pollution effects on myocardial infarction

OBJECTIVE: Myocardial infarction is an acute and severe cardiovascular disease that generally leads to patient admissions to intensive care units and few cases are initially admitted to infirmaries. The objective of the study was to assess whether estimates of air pollution effects on myocardial infarction morbidity are modified by the source of health information. METHODS: The study was carried out in hospitals of the Brazilian Health System in the city of São Paulo, Southern Brazil. A time series study (1998-1999) was performed using two outcomes: infarction admissions to infirmaries and to intensive care units, both for people older than 64 years of age. Generalized linear models controlling for seasonality (long and short-term trends) and weather were used. The eight-day cumulative effects of air pollutants were assessed using third degree polynomial distributed lag models. RESULTS: Almost 70% of daily hospital admissions due to myocardial infarction were to infirmaries. Despite that, the effects of air pollutants on infarction were higher for intensive care units admissions. All pollutants were positively associated with the study outcomes but SO2 presented the strongest statistically significant association. An interquartile range increase on SO2 concentration was associated with increases of 13% (95% CI: 6-19) and 8% (95% CI: 2-13) of intensive care units and infirmary infarction admissions, respectively. CONCLUSIONS: It may be assumed there is a misclassification of myocardial infarction admissions to infirmaries leading to overestimation. Also, despite the absolute number of events, admissions to intensive care units data provides a more adequate estimate of the magnitude of air pollution effects on infarction admissions.

Habitat predictive modelling of demersal fish species in the Azores

Dissertação de Mestrado, Estudos Integrados dos Oceanos, 25 de Março de 2013, Universidade dos Açores.

Functional biogeography of oceanic islands and the scaling of functional diversity in the Azores

Analyses of species-diversity patterns of remote islands have been crucial to the development of biogeographic theory, yet little is known about corresponding patterns in functional traits on islands and how, for example, they may be affected by the introduction of exotic species. We collated trait data for spiders and beetles and used a functional diversity index (FRic) to test for nonrandomness in the contribution of endemic, other native (also combined as indigenous), and exotic species to functional-trait space across the nine islands of the Azores. In general, for both taxa and for each distributional category, functional diversity increases with species richness, which, in turn scales with island area. Null simulations support the hypothesis that each distributional group contributes to functional diversity in proportion to their species richness. Exotic spiders have added novel trait space to a greater degree than have exotic beetles, likely indicating greater impact of the reduction of immigration filters and/or differential historical losses of indigenous species. Analyses of species occurring in native-forest remnants provide limited indications of the operation of habitat filtering of exotics for three islands, but only for beetles. Although the general linear (not saturating) pattern of trait-space increase with richness of exotics suggests an ongoing process of functional enrichment and accommodation, further work is urgently needed to determine how estimates of extinction debt of indigenous species should be adjusted in the light of these findings.

Assessment of data-driven modeling strategies for water delivery canals

The aim of this paper is to develop models for experimental open-channel water delivery systems and assess the use of three data-driven modeling tools toward that end. Water delivery canals are nonlinear dynamical systems and thus should be modeled to meet given operational requirements while capturing all relevant dynamics, including transport delays. Typically, the derivation of first principle models for open-channel systems is based on the use of Saint-Venant equations for shallow water, which is a time-consuming task and demands for specific expertise. The present paper proposes and assesses the use of three data-driven modeling tools: artificial neural networks, composite local linear models and fuzzy systems. The canal from Hydraulics and Canal Control Nucleus (A parts per thousand vora University, Portugal) will be used as a benchmark: The models are identified using data collected from the experimental facility, and then their performances are assessed based on suitable validation criterion. The performance of all models is compared among each other and against the experimental data to show the effectiveness of such tools to capture all significant dynamics within the canal system and, therefore, provide accurate nonlinear models that can be used for simulation or control. The models are available upon request to the authors.