Undercarboxylated osteocalcin, muscle strength and indices of bone health in older women

We investigated the association between undercarboxylated osteocalcin (ucOC) and lower-limb muscle strength in women over the age of 70years. The study also aims to confirm the association between bone turnover markers and heel ultrasound measures. A post-hoc analysis using data collected as part of a randomized placebo-controlled trial of vitamin D supplementation. An immunoassay was used to quantify total OC (tOC), with hydroxyapatite pre-treatment for ucOC. We determined associations of absolute and relative (ucOC/tOC; ucOC%) measures of ucOC with lower-limb muscle strength, heel ultrasound measures of speed of sound (SOS) and broadband ultrasound attenuation (BUA), bone turnover markers (BTMs; P1NP and CTx) and the acute phase protein alpha-1-antichymotrypsin (α-ACT). ucOC%, but not absolute ucOC concentration, was positively associated with hip flexor, hip abductor and quadriceps muscle strength (all p<0.05). ucOC% was negatively associated with α-ACT (β-coefficient=-0.24, p=0.02). tOC was positively associated with both P1NP and CTx (p<0.001). For each per unit increase in tOC (μg/L) there was a corresponding lower BUA, SOS and SI (β-coefficient = -0.28; -0.23 and -0.23, respectively; all p<0.04). In conclusion, ucOC% is positively associated with muscle strength and negatively associated with α-ACT. These data support a role for ucOC in musculoskeletal interactions in humans. Whilst tOC is associated with bone health, ucOC% and ucOC may also be linked to falls and fracture risk by influencing muscle function.

The effects of ethinylestradiol and progestins ("the pill") on cognitive function in pre-menopausal women

Oral contraceptives (OCs), often referred to as "the pill", are the most commonly employed form of reversible contraception. OCs are comprised of combined synthetic estrogen and progestin, which work to suppress ovulation and subsequently protect against pregnancy. To date, almost 200 million women have taken various formulations of OC, making it one of the most widely consumed classes of medication in the world. While a substantial body of literature has been dedicated to understanding the physical effects of OCs, much less is known about the long term consequences of OC use on brain anatomy and the associated cognitive effects. Accumulating evidence suggests that sex hormones may significantly affect human cognition. This phenomenon has been commonly studied in older populations, such as in post-menopausal women, while research in healthy, pre-menopausal women remains limited. The current review focused on the effects of OCs on human cognition, with the majority of studies comparing pre-menopausal OC users to naturally cycling women. Human neuroimaging data and animal studies are also described herein. Taken together, the published findings on OC use and human cognition are varied. Of those that do report positive results, OC users appear to have improved verbal memory, associative learning and spatial attention. We recommend future research to employ blinding procedures and randomised designs. Further, more detailed information pertaining to the specific generation and phasic type of OCs, as well as menstrual cycle phase of the OC non-users should be considered to help unmask the potential impact of OC use on human cognition.

The effect of environmental conditions on performance in timed cycling events

Air temperature, pressure and humidity are environmental factors that affect air density and therefore the relationship between a cyclist’s power output and their velocity. These environmental factors are changeable and are routinely quite different at elite cycling competitions conducted around the world, which means that they have a variable effect on performance in timed events. The present work describes a method of calculating the effect of these environmental factors on timed cycling events and illustrates the magnitude and significance of these effects in a case study. Formulas are provided to allow the calculation of the effect of environmental conditions on performance in a time trial cycling event. The effect of environmental factors on time trial performance can be in the order of 1.5%, which is significant given that the margins between ranked performances is often less than this. Environmental factors may enhance or hinder performance depending upon the conditions and the comparison conditions. To permit the fair comparison of performances conducted in different environmental conditions, it is recommended that performance times are corrected to the time that would be achieved in standard environmental conditions, such as 20 oC, 760 mmHg (1013.25 hPa) and 50% RH.

Prediction interval estimation for electricity price and demand using support vector machines

Uncertainty is known to be a concomitant factor of almost all the real world commodities such as oil prices, stock prices, sales and demand of products. As a consequence, forecasting problems are becoming more and more challenging and ridden with uncertainty. Such uncertainties are generally quantified by statistical tools such as prediction intervals (Pis). Pis quantify the uncertainty related to forecasts by estimating the ranges of the targeted quantities. Pis generated by traditional neural network based approaches are limited by high computational burden and impractical assumptions about the distribution of the data. A novel technique for constructing high quality Pis using support vector machines (SVMs) is being proposed in this paper. The proposed technique directly estimates the upper and lower bounds of the PI in a short time and without any assumptions about the data distribution. The SVM parameters are tuned using particle swarm optimization technique by minimization of a modified Pi-based objective function. Electricity price and demand data of the Ontario electricity market is used to validate the performance of the proposed technique. Several case studies for different months indicate the superior performance of the proposed method in terms of high quality PI generation and shorter computational times.

Prediction interval estimation of electricity prices using PSO-tuned support vector machines

Uncertainty of the electricity prices makes the task of accurate forecasting quite difficult for the electricity market participants. Prediction intervals (PIs) are statistical tools which quantify the uncertainty related to forecasts by estimating the ranges of the future electricity prices. Traditional approaches based on neural networks (NNs) generate PIs at the cost of high computational burden and doubtful assumptions about data distributions. In this work, we propose a novel technique that is not plagued with the above limitations and it generates high-quality PIs in a short time. The proposed method directly generates the lower and upper bounds of the future electricity prices using support vector machines (SVM). Optimal model parameters are obtained by the minimization of a modified PI-based objective function using a particle swarm optimization (PSO) technique. The efficiency of the proposed method is illustrated using data from Ontario, Pennsylvania-New Jersey-Maryland (PJM) interconnection day-ahead and real-time markets.

Asymmetric risk and return: evidence from the Australian Stock Exchange

This paper examines volatility asymmetry in a financial market using a stochastic volatility framework. We use the MCMC method for model estimations. There is evidence of volatility asymmetry in the data. Our asymmetric stochastic volatility in mean model, which nests both asymmetric stochastic volatility (ASV) and stochastic volatility in mean models (SVM), indicates ASV sufficiently captures the risk-return relationship; therefore, augmenting it with volatility in mean does not improve its performance. ASV fits the data better and yields more accurate out-of-sample forecasts than alternatives. We also demonstrate that asymmetry mainly emanates from the systematic parts of returns. As a result, it is more pronounced at the market level and the volatility feedback effect dominates the leverage effect.

Classification of team sport activities using a single wearable tracking device

Wearable tracking devices incorporating accelerometers and gyroscopes are increasingly being used for activity analysis in sports. However, minimal research exists relating to their ability to classify common activities. The purpose of this study was to determine whether data obtained from a single wearable tracking device can be used to classify team sport-related activities. Seventy-six non-elite sporting participants were tested during a simulated team sport circuit (involving stationary, walking, jogging, running, changing direction, counter-movement jumping, jumping for distance and tackling activities) in a laboratory setting. A MinimaxX S4 wearable tracking device was worn below the neck, in-line and dorsal to the first to fifth thoracic vertebrae of the spine, with tri-axial accelerometer and gyroscope data collected at 100Hz. Multiple time domain, frequency domain and custom features were extracted from each sensor using 0.5, 1.0, and 1.5s movement capture durations. Features were further screened using a combination of ANOVA and Lasso methods. Relevant features were used to classify the eight activities performed using the Random Forest (RF), Support Vector Machine (SVM) and Logistic Model Tree (LMT) algorithms. The LMT (79-92% classification accuracy) outperformed RF (32-43%) and SVM algorithms (27-40%), obtaining strongest performance using the full model (accelerometer and gyroscope inputs). Processing time can be reduced through feature selection methods (range 1.5-30.2%), however a trade-off exists between classification accuracy and processing time. Movement capture duration also had little impact on classification accuracy or processing time. In sporting scenarios where wearable tracking devices are employed, it is both possible and feasible to accurately classify team sport-related activities.

Stable feature selection with support vector machines

The support vector machine (SVM) is a popular method for classification, well known for finding the maximum-margin hyperplane. Combining SVM with l1-norm penalty further enables it to simultaneously perform feature selection and margin maximization within a single framework. However, l1-norm SVM shows instability in selecting features in presence of correlated features. We propose a new method to increase the stability of l1-norm SVM by encouraging similarities between feature weights based on feature correlations, which is captured via a feature covariance matrix. Our proposed method can capture both positive and negative correlations between features. We formulate the model as a convex optimization problem and propose a solution based on alternating minimization. Using both synthetic and real-world datasets, we show that our model achieves better stability and classification accuracy compared to several state-of-the-art regularized classification methods.

Robust network traffic classification

As a fundamental tool for network management and security, traffic classification has attracted increasing attention in recent years. A significant challenge to the robustness of classification performance comes from zero-day applications previously unknown in traffic classification systems. In this paper, we propose a new scheme of Robust statistical Traffic Classification (RTC) by combining supervised and unsupervised machine learning techniques to meet this challenge. The proposed RTC scheme has the capability of identifying the traffic of zero-day applications as well as accurately discriminating predefined application classes. In addition, we develop a new method for automating the RTC scheme parameters optimization process. The empirical study on real-world traffic data confirms the effectiveness of the proposed scheme. When zero-day applications are present, the classification performance of the new scheme is significantly better than four state-of-the-art methods: random forest, correlation-based classification, semi-supervised clustering, and one-class SVM.

Fast one-class support vector machine for novelty detection

Novelty detection arises as an important learning task in several applications. Kernel-based approach to novelty detection has been widely used due to its theoretical rigor and elegance of geometric interpretation. However, computational complexity is a major obstacle in this approach. In this paper, leveraging on the cutting-plane framework with the well-known One-Class Support Vector Machine, we present a new solution that can scale up seamlessly with data. The first solution is exact and linear when viewed through the cutting-plane; the second employed a sampling strategy that remarkably has a constant computational complexity defined relatively to the probability of approximation accuracy. Several datasets are benchmarked to demonstrate the credibility of our framework.

BacHbpred: support vector machine methods for the prediction of bacterial hemoglobin-like proteins

The recent upsurge in microbial genome data has revealed that hemoglobin-like (HbL) proteins may be widely distributed among bacteria and that some organisms may carry more than one HbL encoding gene. However, the discovery of HbL proteins has been limited to a small number of bacteria only. This study describes the prediction of HbL proteins and their domain classification using a machine learning approach. Support vector machine (SVM) models were developed for predicting HbL proteins based upon amino acid composition (AC), dipeptide composition (DC), hybrid method (AC + DC), and position specific scoring matrix (PSSM). In addition, we introduce for the first time a new prediction method based on max to min amino acid residue (MM) profiles. The average accuracy, standard deviation (SD), false positive rate (FPR), confusion matrix, and receiver operating characteristic (ROC) were analyzed. We also compared the performance of our proposed models in homology detection databases. The performance of the different approaches was estimated using fivefold cross-validation techniques. Prediction accuracy was further investigated through confusion matrix and ROC curve analysis. All experimental results indicate that the proposed BacHbpred can be a perspective predictor for determination of HbL related proteins. BacHbpred, a web tool, has been developed for HbL prediction.

Analysis and prediction of major blood proteins based on their amino acid and dipeptide composition

A method has been developed for predicting blood proteins using the SVM based machine learning approach. In this prediction method a two-step strategy was deployed to predict blood proteins and their subclasses. We have developed models of blood proteins and achieved the maximum accuracies of 90.57% and 91.39% with Matthews correlation coefficient (MCC) of 0.89 and 0.90 using single amino acid and dipeptide composition respectively. Furthermore, the method is able to predict major subclasses of blood proteins; developed based on amino acid (AC) and dipeptide composition (DC) with a maximum accuracy 90.38%, 92.83%, 87.41%, 92.52% and 85.27%, 89.07%, 94.82%, 86.31 for albumin, globulin, fibrinogen, and regulatory proteins respectively. All modules were trained, tested, and evaluated using the five-fold cross-validation technique.