Interaction of contractile activity and training history on mRNA abundance in skeletal muscle from trained athletes

Skeletal muscle displays enormous plasticity to respond to contractile activity with muscle from strength- (ST) and endurance-trained (ET) athletes representing diverse states of the adaptation continuum. Training adaptation can be viewed as the accumulation of specific proteins. Hence, the altered gene expression that allows for changes in protein concentration is of major importance for any training adaptation. Accordingly, the aim of the present study was to quantify acute subcellular responses in muscle to habitual and unfamiliar exercise. After 24-h diet/exercise control, 13 male subjects (7 ST and 6 ET) performed a random order of either resistance (8 × 5 maximal leg extensions) or endurance exercise (1 h of cycling at 70% peak O2 uptake). Muscle biopsies were taken from vastus lateralis at rest and 3 h after exercise. Gene expression was analyzed using real-time PCR with changes normalized relative to preexercise values. After cycling exercise, peroxisome proliferator-activated receptor-γ coactivator-1α (ET ∼8.5-fold, ST ∼10-fold, P < 0.001), pyruvate dehydrogenase kinase-4 (PDK-4; ET ∼26-fold, ST ∼39-fold), vascular endothelial growth factor (VEGF; ET ∼4.5-fold, ST ∼4-fold), and muscle atrophy F-box protein (MAFbx) (ET ∼2-fold, ST ∼0.4-fold) mRNA increased in both groups, whereas MyoD (∼3-fold), myogenin (∼0.9-fold), and myostatin (∼2-fold) mRNA increased in ET but not in ST (P < 0.05). After resistance exercise PDK-4 (∼7-fold, P < 0.01) and MyoD (∼0.7-fold) increased, whereas MAFbx (∼0.7-fold) and myostatin (∼0.6-fold) decreased in ET but not in ST. We conclude that prior training history can modify the acute gene responses in skeletal muscle to subsequent exercise.

Early signaling responses to divergent exercise stimuli in skeletal muscle from well-trained humans

Skeletal muscle from strength- and endurance-trained individuals represents diverse adaptive states. In this regard, AMPK-PGC-1α signaling mediates several adaptations to endurance training, while up-regulation of the Akt-TSC2-mTOR pathway may underlie increased protein synthesis after resistance exercise. We determined the effect of prior training history on signaling responses in seven strength-trained and six endurance-trained males who undertook 1 h cycling at 70% VO2peak or eight sets of five maximal repetitions of isokinetic leg extensions. Muscle biopsies were taken at rest, immediately and 3 h postexercise. AMPK phosphorylation increased after cycling in strength-trained (54%; P<0.05) but not endurance-trained subjects. Conversely, AMPK was elevated after resistance exercise in endurance- (114%; P<0.05), but not strengthtrained subjects. Akt phosphorylation increased in endurance- (50%; P<0.05), but not strengthtrained subjects after cycling but was unchanged in either group after resistance exercise. TSC2 phosphorylation was decreased (47%; P<0.05) in endurance-trained subjects following resistance exercise, but cycling had little effect on the phosphorylation state of this protein in either group. p70S6K phosphorylation increased in endurance- (118%; P<0.05), but not strength-trained subjects after resistance exercise, but was similar to rest in both groups after cycling. Similarly, phosphorylation of S6 protein, a substrate for p70 S6K, was increased immediately following resistance exercise in endurance- (129%; P<0.05), but not strength-trained subjects. In conclusion, a degree of “response plasticity” is conserved at opposite ends of the endurancehypertrophic adaptation continuum. Moreover, prior training attenuates the exercise specific signaling responses involved in single mode adaptations to training.

An exercise intervention for women undergoing chemotherapy for ovarian cancer : feasibility and preliminary outcomes

Exercise interventions during adjuvant cancer treatment have been shown to increase functional capacity, relieve fatigue and distress and in one recent study, assist chemotherapy completion. These studies have been limited to breast, prostate or mixed cancer groups and it is not yet known if a similar intervention is even feasible among women diagnosed with ovarian cancer. Women undergoing treatment for ovarian cancer commonly have extensive pelvic surgery followed by high intensity chemotherapy. It is hypothesized that women with ovarian cancer may benefit most from a customised exercise intervention during chemotherapy treatment. This could reduce the number and severity of chemotherapy-related side-effects and optimize treatment adherence. Hence, the aim of the research was to assess feasibility and acceptability of a walking intervention in women with ovarian cancer whilst undergoing chemotherapy, as well as pre-post intervention changes in a range of physical and psychological outcomes. Newly diagnosed women with ovarian cancer were recruited from the Royal Brisbane and Women’s Hospital (RBWH), to participate in a walking program throughout chemotherapy. The study used a one group pre- post-intervention test design. Baseline (conducted following surgery but prior to the first or second chemotherapy cycles) and follow-up (conducted three weeks after the last chemotherapy dose was received) assessments were performed. To accommodate changes in side-effects associated with treatment, specific weekly walking targets with respect to frequency, intensity and duration, were individualised for each participant. To assess feasibility, adherence and compliance with prescribed walking sessions, withdrawals and adverse events were recorded. Physical and psychological outcomes assessed included functional capacity, body composition, anxiety and depression, symptoms experienced during treatment and quality of life. Chemotherapy completion data was also documented and self-reported program helpfulness was assessed using a questionnaire post intervention. Forty-two women were invited to participate. Nine women were recruited, all of whom completed the program. There were no adverse events associated with participating in the intervention and all women reported that the walking program was helpful during their neo-adjuvant or adjuvant chemotherapy treatment. Adherence and compliance to the walking prescription was high. On average, women achieved at least two of their three individual weekly prescription targets 83% of the time (range 42% to 94%). Positive changes were found in functional capacity and quality of life, in addition to reductions in the number and intensity of treatment-associated symptoms over the course of the intervention period. Functional capacity increased for all nine women from baseline to follow-up assessment, with improvements ranging from 10% to 51%. Quality of life improvements were also noted, especially in the physical well-being scale (baseline: median 18; follow-up: median 23). Treatment symptoms reduced in presence and severity, specifically, in constipation, pain and fatigue, post intervention. These positive yet preliminary results suggest that a walking intervention for women receiving chemotherapy for ovarian cancer is safe, feasible and acceptable. Importantly, women perceived the program to be helpful and rewarding, despite being conducted during a time typically associated with elevated distress and treatment symptoms that are often severe enough to alter or cease chemotherapy prescription.

Application of higher order spectra to identify epileptic EEG

Epilepsy is characterized by the spontaneous and seemingly unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic system that detects seizure onsets would allow patients or the people near them to take appropriate precautions, and could provide more insight into this phenomenon. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, we made a comparative study of the performance of Gaussian mixture model (GMM) and Support Vector Machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Results show that the selected HOS based features achieve 93.11% classification accuracy compared to 88.78% with features derived from the power spectrum for a GMM classifier. The SVM classifier achieves an improvement from 86.89% with features based on the power spectrum to 92.56% with features based on the bispectrum.

Pattern recognition using invariants defined from higher order spectra : 2-D image inputs

A new algorithm for extracting features from images for object recognition is described. The algorithm uses higher order spectra to provide desirable invariance properties, to provide noise immunity, and to incorporate nonlinearity into the feature extraction procedure thereby allowing the use of simple classifiers. An image can be reduced to a set of 1D functions via the Radon transform, or alternatively, the Fourier transform of each 1D projection can be obtained from a radial slice of the 2D Fourier transform of the image according to the Fourier slice theorem. A triple product of Fourier coefficients, referred to as the deterministic bispectrum, is computed for each 1D function and is integrated along radial lines in bifrequency space. Phases of the integrated bispectra are shown to be translation- and scale-invariant. Rotation invariance is achieved by a regrouping of these invariants at a constant radius followed by a second stage of invariant extraction. Rotation invariance is thus converted to translation invariance in the second step. Results using synthetic and actual images show that isolated, compact clusters are formed in feature space. These clusters are linearly separable, indicating that the nonlinearity required in the mapping from the input space to the classification space is incorporated well into the feature extraction stage. The use of higher order spectra results in good noise immunity, as verified with synthetic and real images. Classification of images using the higher order spectra-based algorithm compares favorably to classification using the method of moment invariants

Shape discrimination using invariants defined from higher-order spectra

An approach to pattern recognition using invariant parameters based on higher-order spectra is presented. In particular, bispectral invariants are used to classify one-dimensional shapes. The bispectrum, which is translation invariant, is integrated along straight lines passing through the origin in bifrequency space. The phase of the integrated bispectrum is shown to be scale- and amplification-invariant. A minimal set of these invariants is selected as the feature vector for pattern classification. Pattern recognition using higher-order spectral invariants is fast, suited for parallel implementation, and works for signals corrupted by Gaussian noise. The classification technique is shown to distinguish two similar but different bolts given their one-dimensional profiles

A general procedure for the derivation of principal domains of higher-order spectra

A general procedure to determine the principal domain (i.e., nonredundant region of computation) of any higher-order spectrum is presented, using the bispectrum as an example. The procedure is then applied to derive the principal domain of the trispectrum of a real-valued, stationary time series. These results are easily extended to compute the principal domains of other higher-order spectra

Position, rotation, and scale invariant recognition of images using higher-order spectra

A new approach to recognition of images using invariant features based on higher-order spectra is presented. Higher-order spectra are translation invariant because translation produces linear phase shifts which cancel. Scale and amplification invariance are satisfied by the phase of the integral of a higher-order spectrum along a radial line in higher-order frequency space because the contour of integration maps onto itself and both the real and imaginary parts are affected equally by the transformation. Rotation invariance is introduced by deriving invariants from the Radon transform of the image and using the cyclic-shift invariance property of the discrete Fourier transform magnitude. Results on synthetic and actual images show isolated, compact clusters in feature space and high classification accuracies

Time-dependent fractional advection–diffusion equations by an implicit MLS meshless method

Recently, many new applications in engineering and science are governed by a series of fractional partial differential equations (FPDEs). Unlike the normal partial differential equations (PDEs), the differential order in a FPDE is with a fractional order, which will lead to new challenges for numerical simulation, because most existing numerical simulation techniques are developed for the PDE with an integer differential order. The current dominant numerical method for FPDEs is Finite Difference Method (FDM), which is usually difficult to handle a complex problem domain, and also hard to use irregular nodal distribution. This paper aims to develop an implicit meshless approach based on the moving least squares (MLS) approximation for numerical simulation of fractional advection-diffusion equations (FADE), which is a typical FPDE. The discrete system of equations is obtained by using the MLS meshless shape functions and the meshless strong-forms. The stability and convergence related to the time discretization of this approach are then discussed and theoretically proven. Several numerical examples with different problem domains and different nodal distributions are used to validate and investigate accuracy and efficiency of the newly developed meshless formulation. It is concluded that the present meshless formulation is very effective for the modeling and simulation of the FADE.

High-Order Concept Associations Mining and Inferential Language Modeling for Online Review Spam Detection

Despite many incidents about fake online consumer reviews have been reported, very few studies have been conducted to date to examine the trustworthiness of online consumer reviews. One of the reasons is the lack of an effective computational method to separate the untruthful reviews (i.e., spam) from the legitimate ones (i.e., ham) given the fact that prominent spam features are often missing in online reviews. The main contribution of our research work is the development of a novel review spam detection method which is underpinned by an unsupervised inferential language modeling framework. Another contribution of this work is the development of a high-order concept association mining method which provides the essential term association knowledge to bootstrap the performance for untruthful review detection. Our experimental results confirm that the proposed inferential language model equipped with high-order concept association knowledge is effective in untruthful review detection when compared with other baseline methods.