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  • 1
    In: Medicine & Science in Sports & Exercise, 2014, Vol.46(9), pp.1831-1839
    Description: PURPOSE: Precise measures of energy expenditure (EE) during everyday activities are needed. This study assessed the validity of novel shorts measuring EMG and compared this method with HR and accelerometry (ACC) when estimating EE. METHODS: Fifty-four volunteers (39.4 ± 13.9 yr) performed a maximal treadmill test (3-min loads) including walking with different speeds uphill, downhill, and on level ground and one running load. The data were categorized into all, low, and level loads. EE was measured by indirect calorimetry, whereas HR, ACC, and EMG were measured continuously. EMG from quadriceps (Q) and hamstrings (H) was measured using shorts with textile electrodes. Validity of the methods used to estimate EE was compared using Pearson correlations, regression coefficients, linear mixed models providing Akaike information criteria, and root mean squared error (RMSE) from cross-validation at the individual and population levels. RESULTS: At all loads, correlations with EE were as follows: EMG(QH), 0.94 ± 0.03; EMG(Q), 0.91 ± 0.03; EMG(H), 0.94 ± 0.03; HR, 0.96 ± 0.04; and ACC, 0.77 ± 0.10. The corresponding correlations at low loads were 0.89 ± 0.08, 0.79 ± 0.10, 0.93 ± 0.07, 0.89 ± 0.23, and 0.80 ± 0.07, and at level loads, they were 0.97 ± 0.03, 0.97 ± 0.05, 0.96 ± 0.04, 0.95 ± 0.08, and 0.99 ± 0.02, respectively. Akaike information criteria ranked the methods in accordance with the individual correlations. CONCLUSIONS: It is shown for the first time that EMG shorts can be used for EE estimations across a wide range of physical activity intensities in a heterogeneous group. Across all loads, HR is a superior method of predicting EE, whereas ACC is most accurate for level loads at the population level. At low levels of physical activity in changing terrains, thigh muscle EMG provides more accurate EE estimations than those in ACC and HR if individual calibrations are performed.
    Keywords: Accelerometry–Instrumentation ; Adult–Physiology ; Clothing–Physiology ; Electrodes–Physiology ; Electromyography–Physiology ; Energy Metabolism–Physiology ; Exercise Test–Physiology ; Female–Physiology ; Heart Rate–Physiology ; Humans–Physiology ; Male–Physiology ; Middle Aged–Physiology ; Physical Exertion–Physiology ; Quadriceps Muscle–Physiology ; Running–Physiology ; Walking–Physiology;
    ISSN: 0195-9131
    E-ISSN: 15300315
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  • 2
    In: Ecological Applications, July 2018, Vol.28(5), pp.1260-1272
    Description: The ecological assessment of freshwaters is currently primarily based on biological communities and the reference condition approach (). In the , the communities in streams and lakes disturbed by humans are compared with communities in reference conditions with no or minimal anthropogenic influence. The currently favored rationale is using selected community metrics for which the expected values () for each site are typically estimated from environmental variables using a predictive model based on the reference data. The proportional differences between the observed values () and are then derived, and the decision rules for status assessment are based on fixed (typically 10th or 25th) percentiles of the / ratios among reference sites. Based on mathematical formulations, illustrations by simulated data and real case studies representing such an assessment approach, we demonstrate that the use of a common quantile of / ratios will, under certain conditions, cause severe bias in decision making even if the predictive model would be unbiased. This is because the variance of / under these conditions, which seem to be quite common among the published applications, varies systematically with . We propose a correction method for the bias and compare the novel approach to the conventional one in our case studies, with data from both reference and impacted sites. The results highlight a conceptual issue of employing ratios in the status assessment. In some cases using the absolute deviations instead provides a simple solution for the bias identified and might also be more ecologically relevant and defensible.
    Keywords: Bioassessment ; Classification Error ; Ecological Status ; Freshwaters ; Predictive Models ; Reference Condition Approach
    ISSN: 1051-0761
    E-ISSN: 1939-5582
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  • 3
    Language: English
    In: Image Analysis and Stereology, 01 May 2011, Vol.20(3), pp.199-202
    Description: In this paper, estimation of fibre orientation is studied for fibre systems observable as a blurred greyscale image. The estimation method is based on scaled variograms observed along a set of sampling lines in different directions. The parameters...
    Keywords: Boolean Model ; Digitization ; Fibre Orientation ; Image Analysis ; Simulation ; Stereology ; Engineering
    ISSN: 1580-3139
    E-ISSN: 1854-5165
    Source: Directory of Open Access Journals (DOAJ)
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  • 4
    Language: English
    In: Image Analysis and Stereology, 01 June 2014, Vol.33(2), pp.147-155
    Description: A novel estimator for estimating the mean length of fibres is proposed for censored data observed in square shaped windows. Instead of observing the fibre lengths, we observe the ratio between the intensity estimates of minus-sampling and plus-sampling. It is well-known that both intensity...
    Keywords: Boolean Model ; Exponential Length Distribution ; Line Segments ; Mean Length ; Minus-Sampling ; Nanocellulose Crystalline ; Plus-Sampling ; Ratio of Estimates ; Variance ; Engineering
    ISSN: 1580-3139
    E-ISSN: 1854-5165
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  • 5
    Language: English
    In: Image Analysis and Stereology, 01 March 2012, Vol.31(1), pp.17-26
    Description: Methods are introduced for analysing the shape and orientation of planar fibres from greyscale images of fibrous systems. The sequence of image processing techniques needed for segmentation of fibres is described. The identified fibres were interpreted as deformed line segments for which two...
    Keywords: Binarization ; Carbon Nanotubes ; Deformed Line Segments ; Multivariate Von Mises Distribution ; 2d Fibre Identification ; Engineering
    ISSN: 1580-3139
    E-ISSN: 1854-5165
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  • 6
    Language: English
    In: Stochastic Environmental Research and Risk Assessment, 2016, Vol.30(7), pp.1981-2008
    Description: The percent model affinity ( PMA ) index is used to measure the similarity of two probability profiles representing, for example, an ideal profile (i.e. reference condition) and a monitored profile (i.e. possibly impacted condition). The goal of this work is to study the effects of sample size, evenness, true value of the index and number of classes on the statistical properties of the estimator of the PMA index. We derive and extend previous formulas of the expectation and variance of the estimator for estimated monitored profile and fixed reference profile. Using the obtained extension, we find that the estimator is asymptotically unbiased, converging faster when the profiles differ. When both profiles are estimated, we calculate the expectation using transformation rules for expectation and in addition derive the formula for the estimator’s variance. Since the computation of the probabilities in the variance formula is slow, we study the behavior of the variance with simulation experiments and assess whether it could be approximated with the variance for the fixed reference profile. Finally, we provide a set of recommendations for the users of the PMA index to avoid the most common caveats of the index.
    Keywords: Percent model affinity index ; Similarity measure ; Statistical properties ; Decision making ; Biomonitoring
    ISSN: 1436-3240
    E-ISSN: 1436-3259
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  • 7
    Description: The assessment of diversity and similarity is relevant in monitoring the status of ecosystems. The respective indicators are based on the taxonomic composition of biological communities of interest, currently estimated through the proportions computed from sampling multivariate counts. In this work we present a novel method able to work with only one sample to estimate the taxonomic composition when the data are affected by overdispersion. The presence of overdispersion in taxonomic counts may be the result of significant environmental factors which are often unobservable but influence communities. Following the empirical Bayes approach, we combine a Bayesian model with the marginal likelihood method to jointly estimate the taxonomic proportions and the level of overdispersion from one sample of multivariate counts. Our proposal is compared to the classical maximum likelihood method in an extensive simulation study with different realistic scenarios. An application to real data from aquatic biomonitoring is also presented. In both the simulation study and the real data application, we consider communities characterized by a large number of taxonomic categories, such as aquatic macroinvertebrates or bacteria which are often overdispersed. The applicative results demonstrate an overall superiority of the empirical Bayes method in almost all examined cases, for both assessments of diversity and similarity. We would recommend practitioners in biomonitoring to use the proposed approach in addition to the traditional procedures. The empirical Bayes estimation allows to better control the error propagation due to the presence of overdispersion in biological data, with a more efficient managerial decision making. Comment: 40 pages, 10 figures, 5 tables, 2 appendices
    Keywords: Statistics - Applications
    Source: Cornell University
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  • 8
    Language: English
    In: Image and Vision Computing, October 2018, Vol.78, pp.73-83
    Description: Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and proportion of certain species. This paper presents a benchmark database for automatic visual classification methods to evaluate their ability for distinguishing visually similar categories of aquatic macroinvertebrate taxa. We make publicly available a new database, containing 64 types of freshwater macroinvertebrates, ranging in number of images per category from 7 to 577. The database is divided into three datasets, varying in number of categories (64, 29, and 9 categories). Furthermore, in order to accomplish a baseline evaluation performance, we present the classification results of Convolutional Neural Networks (CNNs) that are widely used for deep learning tasks in large databases. Besides CNNs, we experimented with several other well-known classification methods using deep features extracted from the data.
    Keywords: Biomonitoring ; Fine-Grained Classification ; Benthic Macroinvertebrates ; Deep Learning ; Convolutional Neural Networks ; Engineering ; Applied Sciences
    ISSN: 0262-8856
    E-ISSN: 1872-8138
    Source: ScienceDirect Journals (Elsevier)
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  • 9
    Language: English
    In: Computers in Biology and Medicine, 2011, Vol.41(7), pp.463-472
    Description: Aquatic ecosystems are continuously threatened by a growing number of human induced changes. Macroinvertebrate biomonitoring is particularly efficient in pinpointing the cause–effect structure between slow and subtle changes and their detrimental consequences in aquatic ecosystems. The greatest obstacle to implementing efficient biomonitoring is currently the cost-intensive human expert taxonomic identification of samples. While there is evidence that automated recognition techniques can match human taxa identification accuracy at greatly reduced costs, so far the development of automated identification techniques for aquatic organisms has been minimal. In this paper, we focus on advancing classification and data retrieval that are instrumental when processing large macroinvertebrate image datasets. To accomplish this for routine biomonitoring, in this paper we shall investigate the feasibility of automated river macroinvertebrate classification and retrieval with high precision. Besides the state-of-the-art classifiers such as Support Vector Machines (SVMs) and Bayesian Classifiers (BCs), the focus is particularly drawn on feed-forward artificial neural networks (ANNs), namely multilayer perceptrons (MLPs) and radial basis function networks (RBFNs). Since both ANN types have been proclaimed superior by different investigations even for the same benchmark problems, we shall first show that the main reason for this ambiguity lies in the static and rather poor comparison methodologies applied in most earlier works. Especially the most common drawback occurs due to the limited evaluation of the ANN performances over just one or few network architecture(s). Therefore, in this study, an extensive evaluation of each classifier performance over an ANN architecture space is performed. The best classifier among all, which is trained over a dataset of river macroinvertebrate specimens, is then used in the MUVIS framework for the efficient search and retrieval of particular macroinvertebrate peculiars. Classification and retrieval results present high accuracy and can match an experts' ability for taxonomic identification.
    Keywords: Biomonitoring ; Classification ; Radial Basis Function Networks ; Multilayer Perceptrons ; Bayesian Networks ; Support Vector Machines ; Benthic Macroinvertebrate ; Medicine
    ISSN: 0010-4825
    E-ISSN: 1879-0534
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  • 10
    Language: English
    In: Ecological Informatics, March 2014, Vol.20, pp.1-12
    Description: Macroinvertebrates form an important functional component of aquatic ecosystems. Their ability to indicate various types of anthropogenic stressors is widely recognized which has made them an integral component of freshwater biomonitoring. The use of macroinvertebrates in biomonitoring is dependent on manual taxa identification which is currently a time-consuming and cost-intensive process conducted by highly trained taxonomical experts. Automated taxa identification of macroinvertebrates is a relatively recent research development. Previous studies have displayed great potential for solutions to this demanding data mining application. In this research we have a collection of 1350 images from eight different macroinvertebrate taxa and the aim is to examine the suitability of artificial neural networks (ANNs) for automated taxa identification of macroinvertebrates. More specifically, the focus is drawn on different training algorithms of Multi-Layer Perceptron (MLP), probabilistic neural network (PNN) and Radial Basis Function network (RBFN). We performed thorough experimental tests and we tested altogether 13 training algorithms for MLPs. The best classification accuracy of MLPs, 95.3%, was obtained by two conjugate gradient backpropagation variations and scaled conjugate gradient backpropagation. For PNN 92.8% and for RBFN 95.7% accuracies were achieved. The results show how important a proper choice of ANN is in order to obtain high accuracy in the automated taxa identification of macroinvertebrates and the obtained model can outperform the level of identification which is made by a taxonomist.
    Keywords: Benthic Macroinvertebrates ; Artificial Neural Networks ; Multi-Layer Perceptron ; Radial Basis Function Network ; Probabilistic Neural Network ; Classification ; Ecology
    ISSN: 1574-9541
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