Conditioning of the Achilles tendon via ankle exercise improves correlations between sonographic measures of tendon thickness and body anthropometry

Although conditioning is routinely used in mechanical tests of tendon in vitro, previous in vivo research evaluating the influence of body anthropometry on Achilles tendon thickness has not considered its potential effects on tendon structure. This study evaluated the relationship between Achilles tendon thickness and body anthropometry in healthy adults both before and after resistive ankle plantarflexion exercise. A convenience sample of 30 healthy male adults underwent sonographic examination of the Achilles tendon in addition to standard anthropometric measures of stature and body weight. A 10-5 MHz linear array transducer was used to acquire longitudinal sonograms of the Achilles tendon, 20 mm proximal to the tendon insertion. Participants then completed a series (90-100 repetitions) of conditioning exercises against an effective resistance between 100% and 150% body weight. Longitudinal sonograms were repeated immediately on completion of the exercise intervention, and anteroposterior Achilles tendon thickness was determined. Achilles tendon thickness was significantly reduced immediately following conditioning exercise (t = 9.71, P < 0.001), resulting in an average transverse strain of -18.8%. In contrast to preexercise measures, Achilles tendon thickness was significantly correlated with body weight (r = 0.72, P < 0.001) and to a lesser extent height (r = 0.45, P < 0.01) and body mass index (r = 0.63, P < 0.001) after exercise. Conditioning of the Achilles tendon via resistive ankle exercises induces alterations in tendon structure that substantially improve correlations between Achilles tendon thickness and body anthropometry. It is recommended that conditioning exercises, which standardize the load history of tendon, are employed before measurements of sonographic tendon thickness in vivo.

Design and fabrication of tubular scaffolds via direct writing in a melt electrospinning mode

Flexible tubular structures fabricated from solution electrospun fibers are finding increasing use in tissue engineering applications. However it is difficult to control the deposition of fibers due to the chaotic nature of the solution electrospinning jet. By using non-conductive polymer melts instead of polymer solutions the path and collection of the fiber becomes predictable. In this work we demonstrate the melt electrospinning of polycaprolactone in a direct writing mode onto a rotating cylinder. This allows the design and fabrication of tubes using 20 μm diameter fibers with controllable micropatterns and mechanical properties. A key design parameter is the fiber winding angle, where it allows control over scaffold pore morphology (e.g. size, shape, number and porosity). Furthermore, the establishment of a finite element model as a predictive design tool is validated against mechanical testing results of melt electrospun tubes to show that a lesser winding angle provides improved mechanical response to uniaxial tension and compression. In addition, we show that melt electrospun tubes support the growth of three different cell types in vitro and are therefore promising scaffolds for tissue engineering applications.

Poly(2-oxazoline) hydrogel monoliths via thiol-ene coupling

Copoly(2-oxazoline)s, prepared by cationic ring-opening polymerization of 2-(dec-9-enyl)-2-oxazoline with either 2-methyl-2-oxazoline or 2-ethyl-2-oxazoline, have been crosslinked with small dithiol molecules under UV-irradiation to form homogeneous networks. In-situ monitoring of the crosslinking reaction by photo-rheology revealed network formation within minutes. The degree of swelling in water was found to be tunable by the hydrophilicity of the starting macromers and the proportion of alkene side arms. Furthermore, degradable hydrogels have been prepared based on a hydrolytically cleavable dithiol crosslinker.

S-allylmercaptocysteine reduces carbon tetrachloride-induced hepatic oxidative stress and necroinflammation via nuclear factor kappa B-dependent pathways in mice.

Purpose To study the protective effects and underlying molecular mechanisms of SAMC on carbon tetrachloride (CCl4)-induced acute hepatotoxicity in the mouse model. Methods Mice were intraperitoneally injected with CCl4 (50 μl/kg; single dose) to induce acute hepatotoxicity with or without a 2-h pre-treatment of SAMC intraperitoneal injection (200 mg/kg; single dose). After 8 h, the blood serum and liver samples of mice were collected and subjected to measurements of histological and molecular parameters of hepatotoxicity. Results SAMC reduced CCl4-triggered cellular necrosis and inflammation in the liver under histological analysis. Since co-treatment of SAMC and CCl4 enhanced the expressions of antioxidant enzymes, reduced the nitric oxide (NO)-dependent oxidative stress, and inhibited lipid peroxidation induced by CCl4. SAMC played an essential antioxidative role during CCl4-induced hepatotoxicity. Administration of SAMC also ameliorated hepatic inflammation induced by CCl4 via inhibiting the activity of NF-κB subunits p50 and p65, thus reducing the expressions of pro-inflammatory cytokines, mediators, and chemokines, as well as promoting pro-regenerative factors at both transcriptional and translational levels. Conclusions Our results indicate that SAMC mitigates cellular damage, oxidative stress, and inflammation in CCl4-induced acute hepatotoxicity mouse model through regulation of NF-κB. Garlic or garlic derivatives may therefore be a potential food supplement in the prevention of liver damage.

Enhancing public transport passenger experience via mobile-mediated applications and services

Despite the rapidly urbanising population, public transport usage in metropolitan areas is not growing at a level that corresponds to the trend. Many people are reluctant to travel using public transport, as it is commonly associated with unpleasant experiences such as limited services, long wait time, and crowded spaces. This study aims to explore the use of mobile spatial interactions and services, and investigate their potential to increase the enjoyment of our everyday commuting experience. The main goal is to develop and evaluate mobile-mediated design interventions to foster interactions for and among passengers, as well as between passengers and public transport infrastructures, with the aim to positively influence the experience of commuting. Ultimately, this study hopes to generate findings and knowledge towards creating a more enjoyable public transport experience, as well as to explore innovative uses of mobile technologies and context-aware services for the urban lifestyle.

Improved foreground detection via block-based classifier cascade with probabilistic decision integration

Background subtraction is a fundamental low-level processing task in numerous computer vision applications. The vast majority of algorithms process images on a pixel-by-pixel basis, where an independent decision is made for each pixel. A general limitation of such processing is that rich contextual information is not taken into account. We propose a block-based method capable of dealing with noise, illumination variations, and dynamic backgrounds, while still obtaining smooth contours of foreground objects. Specifically, image sequences are analyzed on an overlapping block-by-block basis. A low-dimensional texture descriptor obtained from each block is passed through an adaptive classifier cascade, where each stage handles a distinct problem. A probabilistic foreground mask generation approach then exploits block overlaps to integrate interim block-level decisions into final pixel-level foreground segmentation. Unlike many pixel-based methods, ad-hoc postprocessing of foreground masks is not required. Experiments on the difficult Wallflower and I2R datasets show that the proposed approach obtains on average better results (both qualitatively and quantitatively) than several prominent methods. We furthermore propose the use of tracking performance as an unbiased approach for assessing the practical usefulness of foreground segmentation methods, and show that the proposed approach leads to considerable improvements in tracking accuracy on the CAVIAR dataset.

Optimal power system stabilizer tuning in multi-machine system via an improved differential evolution

Power system stabilizer (PSS) is one of the most important controllers in modern power systems for damping low frequency oscillations. Many efforts have been dedicated to design the tuning methodologies and allocation techniques to obtain optimal damping behaviors of the system. Traditionally, it is tuned mostly for local damping performance, however, in order to obtain a globally optimal performance, the tuning of PSS needs to be done considering more variables. Furthermore, with the enhancement of system interconnection and the increase of system complexity, new tools are required to achieve global tuning and coordination of PSS to achieve optimal solution in a global meaning. Differential evolution (DE) is a recognized as a simple and powerful global optimum technique, which can gain fast convergence speed as well as high computational efficiency. However, as many other evolutionary algorithms (EA), the premature of population restricts optimization capacity of DE. In this paper, a modified DE is proposed and applied for optimal PSS tuning of 39-Bus New-England system. New operators are introduced to reduce the probability of getting premature. To investigate the impact of system conditions on PSS tuning, multiple operating points will be studied. Simulation result is compared with standard DE and particle swarm optimization (PSO).

On robust face recognition via sparse encoding : the good, the bad, and the ugly

In the field of face recognition, Sparse Representation (SR) has received considerable attention during the past few years. Most of the relevant literature focuses on holistic descriptors in closed-set identification applications. The underlying assumption in SR-based methods is that each class in the gallery has sufficient samples and the query lies on the subspace spanned by the gallery of the same class. Unfortunately, such assumption is easily violated in the more challenging face verification scenario, where an algorithm is required to determine if two faces (where one or both have not been seen before) belong to the same person. In this paper, we first discuss why previous attempts with SR might not be applicable to verification problems. We then propose an alternative approach to face verification via SR. Specifically, we propose to use explicit SR encoding on local image patches rather than the entire face. The obtained sparse signals are pooled via averaging to form multiple region descriptors, which are then concatenated to form an overall face descriptor. Due to the deliberate loss spatial relations within each region (caused by averaging), the resulting descriptor is robust to misalignment & various image deformations. Within the proposed framework, we evaluate several SR encoding techniques: l1-minimisation, Sparse Autoencoder Neural Network (SANN), and an implicit probabilistic technique based on Gaussian Mixture Models. Thorough experiments on AR, FERET, exYaleB, BANCA and ChokePoint datasets show that the proposed local SR approach obtains considerably better and more robust performance than several previous state-of-the-art holistic SR methods, in both verification and closed-set identification problems. The experiments also show that l1-minimisation based encoding has a considerably higher computational than the other techniques, but leads to higher recognition rates.