3,388 research outputs found

    Characterising epithelial tissues using persistent entropy

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    In this paper, we apply persistent entropy, a novel topological statistic, for characterization of images of epithelial tissues. We have found out that persistent entropy is able to summarize topological and geometric information encoded by \alpha-complexes and persistent homology. After using some statistical tests, we can guarantee the existence of significant differences in the studied tissues.Comment: 12 pages, 7 figures, 4 table

    Effects of movement velocity and training frequency of resistance exercise on functional performance in older adults: a randomised controlled trial

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    Objectives: To investigate the effects that high-velocity, low-load (HVLL) and low-velocity, high-load (LVHL) resistance exercise, performed once or twice-weekly, have on indices of functional performance (primary outcome), maximal strength, and body composition (secondary outcomes) in older adults. Methods: In a randomised, controlled, multi-armed, parallel design, 54 moderately-highly active, but resistance exercise naïve older adults (aged 60–79 years), attended baseline and post-10-week intervention assessment sessions. Physical and functional assessments were completed, and predicted one-repetition maximums (1-RM) were obtained for eight exercises. Participants were then randomised into one of five conditions: HVLL once-weekly (HVLL1: n = 11) or twice-weekly (HVLL2: n = 11), LVHL once-weekly (LVHL1: n = 10) or twice-weekly (LVHL2: n = 11), no-exercise control condition (CON: n = 11). The HVLL conditions completed 3 sets of 14 repetitions at 40% 1-RM and the LVHL conditions, 3 sets of 7 repetitions at 80% 1-RM. In total, 50 participants completed all testing and were included in analyses. Results: Only LVHL2 improved 30-sec chair stand performance (p =.035; g = 0.89), arm curls (p =.011; g = 1.65) and grip-strength (p =.015; g = 0.34) compared to CON. LVHL2 improved maximal strength compared to CON for 7/8 exercises (p <.05). Whereas, LVHL1 and HVLL2 only improved seated row and chest press compared to CON (p <.05). Conclusion: Possibly due to the lower intensity nature of the HVLL conditions, LVHL, twice-weekly was most beneficial for improving functional performance and strength in moderately-highly active older adults. Therefore, we recommend that exercise professionals ensure resistance exercise sessions have sufficient intensity of effort and volume, in order to maximise functional performance and strength gains in older adults. 

    SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo

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    The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work

    The glyoxal budget and its contribution to organic aerosol for Los Angeles, California, during CalNex 2010

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    Recent laboratory and field studies have indicated that glyoxal is a potentially large contributor to secondary organic aerosol mass. We present in situ glyoxal measurements acquired with a recently developed, high sensitivity spectroscopic instrument during the CalNex 2010 field campaign in Pasadena, California. We use three methods to quantify the production and loss of glyoxal in Los Angeles and its contribution to organic aerosol. First, we calculate the difference between steady state sources and sinks of glyoxal at the Pasadena site, assuming that the remainder is available for aerosol uptake. Second, we use the Master Chemical Mechanism to construct a two-dimensional model for gas-phase glyoxal chemistry in Los Angeles, assuming that the difference between the modeled and measured glyoxal concentration is available for aerosol uptake. Third, we examine the nighttime loss of glyoxal in the absence of its photochemical sources and sinks. Using these methods we constrain the glyoxal loss to aerosol to be 0-5 × 10-5 s-1 during clear days and (1 ± 0.3) × 10-5 s-1 at night. Between 07:00-15:00 local time, the diurnally averaged secondary organic aerosol mass increases from 3.2 μg m-3 to a maximum of 8.8 μg m -3. The constraints on the glyoxal budget from this analysis indicate that it contributes 0-0.2 μg m-3 or 0-4% of the secondary organic aerosol mass. Copyright 2011 by the American Geophysical Union

    Let's Agree to Disagree: Learning Highly Debatable Multirater Labelling

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    Classification and differentiation of small pathological objects may greatly vary among human raters due to differences in training, expertise and their consistency over time. In a radiological setting, objects commonly have high within-class appearance variability whilst sharing certain characteristics across different classes, making their distinction even more difficult. As an example, markers of cerebral small vessel disease, such as enlarged perivascular spaces (EPVS) and lacunes, can be very varied in their appearance while exhibiting high inter-class similarity, making this task highly challenging for human raters. In this work, we investigate joint models of individual rater behaviour and multi-rater consensus in a deep learning setting, and apply it to a brain lesion object-detection task. Results show that jointly modelling both individual and consensus estimates leads to significant improvements in performance when compared to directly predicting consensus labels, while also allowing the characterization of human-rater consistency

    Hip fracture risk assessment: Artificial neural network outperforms conditional logistic regression in an age- and sex-matched case control study

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    Copyright @ 2013 Tseng et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background - Osteoporotic hip fractures with a significant morbidity and excess mortality among the elderly have imposed huge health and economic burdens on societies worldwide. In this age- and sex-matched case control study, we examined the risk factors of hip fractures and assessed the fracture risk by conditional logistic regression (CLR) and ensemble artificial neural network (ANN). The performances of these two classifiers were compared. Methods - The study population consisted of 217 pairs (149 women and 68 men) of fractures and controls with an age older than 60 years. All the participants were interviewed with the same standardized questionnaire including questions on 66 risk factors in 12 categories. Univariate CLR analysis was initially conducted to examine the unadjusted odds ratio of all potential risk factors. The significant risk factors were then tested by multivariate analyses. For fracture risk assessment, the participants were randomly divided into modeling and testing datasets for 10-fold cross validation analyses. The predicting models built by CLR and ANN in modeling datasets were applied to testing datasets for generalization study. The performances, including discrimination and calibration, were compared with non-parametric Wilcoxon tests. Results - In univariate CLR analyses, 16 variables achieved significant level, and six of them remained significant in multivariate analyses, including low T score, low BMI, low MMSE score, milk intake, walking difficulty, and significant fall at home. For discrimination, ANN outperformed CLR in both 16- and 6-variable analyses in modeling and testing datasets (p?<?0.005). For calibration, ANN outperformed CLR only in 16-variable analyses in modeling and testing datasets (p?=?0.013 and 0.047, respectively). Conclusions - The risk factors of hip fracture are more personal than environmental. With adequate model construction, ANN may outperform CLR in both discrimination and calibration. ANN seems to have not been developed to its full potential and efforts should be made to improve its performance.National Health Research Institutes in Taiwa

    Decolorization of synthetic melanoidins-containing wastewater by a bacterial consortium

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    The presence of melanoidins in molasses wastewater leads to water pollution both due to its dark brown color and its COD contents. In this study, a bacterial consortium isolated from waterfall sediment was tested for its decolorization. The identification of culturable bacteria by 16S rDNA based approach showed that the consortium composed of Klebsiella oxytoca, Serratia mercescens, Citrobacter sp. and unknown bacterium. In the context of academic study, prevention on the difficulties of providing effluent as well as its variations in compositions, several synthetic media prepared with respect to color and COD contents based on analysis of molasses wastewater, i.e., Viandox sauce (13.5% v/v), caramel (30% w/v), beet molasses wastewater (41.5% v/v) and sugarcane molasses wastewater (20% v/v) were used for decolorization using consortium with color removal 9.5, 1.13, 8.02 and 17.5%, respectively, within 2 days. However, Viandox sauce was retained for further study. The effect of initial pH and Viandox concentration on decolorization and growth of bacterial consortium were further determined. The highest decolorization of 18.3% was achieved at pH 4 after 2 day of incubation. Experiments on fresh or used medium and used or fresh bacterial cells, led to conclusion that the limitation of decolorization was due to nutritional deficiency. The effect of aeration on decolorization was also carried out in 2 L laboratory-scale suspended cell bioreactor. The maximum decolorization was 19.3% with aeration at KLa = 2.5836 h-1 (0.1 vvm)
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