LGB Cancer Survivors are More Likely to Participate in Risky Behaviors than Straight Cancer Survivors, United States, Behavioral Risk Factor Surveillance System, 2014

Cancer registries do not collect sexual orientation in their records, leading to limited information about LGB cancer survivorship. Studies have shown that both the LGB population and the population of cancer survivors participate in risky behaviors (i.e. smoking, drinking, and being overweight/obese; sleep inadequacy among cancer survivors), but information about LGB cancer survivors is limited. 2014 Behavioral Risk Factor Surveillance System (BRFSS) data was used to determine if LGB cancer survivors were more likely to participate in risky behaviors than straight cancer survivors. LGB survivors were more likely to drink at least one alcoholic beverage within the past 30 days (AOR: 1.99, 95% CI: 1.44-2.75), to report being an ever smoker (AOR: 1.59, 95% CI: 1.12-2.25), and to binge drink (AOR: 1.99, 95% CI: 1.21-3.28) than straight cancer survivors. There is a strong association between sexual orientation among cancer survivors and risky behaviors. The findings of this study concludes that risky behaviors may be detrimental to the health and survivorship of LGB cancer survivors and further research is needed to determine the association between LGB cancer survivorship, being an adolescent and young adult (AYA), and risky behavior

Energy forward price prediction with a hybrid adaptive model

This paper presents a forecasting technique for forward electricity/gas prices, one day ahead. This technique combines a Kalman filter (KF) and a generalised autoregressive conditional heteroschedasticity (GARCH) model (often used in financial forecasting). The GARCH model is used to compute next value of a time series. The KF updates parameters of the GARCH model when the new observation is available. This technique is applied to real data from the UK energy markets to evaluate its performance. The results show that the forecasting accuracy is improved significantly by using this hybrid model. The methodology can be also applied to forecasting market clearing prices and electricity/gas loads

Combining the wavelet transform and forecasting models to predict gas forward prices

This paper presents a forecasting technique for forward energy prices, one day ahead. This technique combines a wavelet transform and forecasting models such as multi- layer perceptron, linear regression or GARCH. These techniques are applied to real data from the UK gas markets to evaluate their performance. The results show that the forecasting accuracy is improved significantly by using the wavelet transform. The methodology can be also applied to forecasting market clearing prices and electricity/gas loads

Variational inference for Student-t MLP models

This paper presents a novel methodology to infer parameters of probabilistic models whose output noise is a Student-t distribution. The method is an extension of earlier work for models that are linear in parameters to nonlinear multi-layer perceptrons (MLPs). We used an EM algorithm combined with variational approximation, the evidence procedure, and an optimisation algorithm. The technique was tested on two regression applications. The first one is a synthetic dataset and the second is gas forward contract prices data from the UK energy market. The results showed that forecasting accuracy is significantly improved by using Student-t noise models

Convergence of Augmented Lagrangian Methods for Composite Optimization Problems

Local convergence analysis of the augmented Lagrangian method (ALM) is established for a large class of composite optimization problems with nonunique Lagrange multipliers under a second-order sufficient condition. We present a new second-order variational property, called the semi-stability of second subderivatives, and demonstrate that it is widely satisfied for numerous classes of functions, important for applications in constrained and composite optimization problems. Using the latter condition and a certain second-order sufficient condition, we are able to establish Q-linear convergence of the primal-dual sequence for an inexact version of the ALM for composite programs

The spindle of oocytes observed by polarized light microscope can predict embryo quality

Background: The aim is to evaluate spindle position of metaphase II oocyte and the development of embryos originated from oocytes with spindle and without spindle.Methods: Cross-sectional analysis Research: 250 MII oocytes were analyzed with polarized microscope in Military Institute of Clinical Embryology and Histology, Vietnam Military Medical University.Results: Spindles were detected in 170 (77.98%) of 218 metaphase II oocytes, 115 spindles (67.65%) of MII oocytes is beneath or adjacent to the first polar body, 55 oocytes had the spindle located between 300 and 1800 away from the first polar body. Fertilization rate and the rate of good quality embryos in oocytes with a visible spindle (77.98% and 61.02%) were higher than those in oocytes without a visible spindle (22.02% and 36.84%), the difference was statistically significant with p <0.001 and p <0.05.Conclusions: The spindle position of metaphase II oocytes is not always beneath or adjacent to the first polar body. Fertilization rate and the rate of good quality embryos in oocytes with a visible spindle were higher than those in oocytes without a visible spindle

Phoebe Framework and Experimental Results for Estimating Fetal Age and Weight

Fetal age and weight estimation plays an important role in pregnant treatments. There are many estimation formulas created by the combination of statistics and obstetrics. However, such formulas give optimal estimation if and only if they are applied into specified community. This research proposes a so-called Phoebe framework that supports physicians and scientists to find out most accurate formulas with regard to the community where scientists do their research. The built-in algorithm of Phoebe framework uses statistical regression technique for fetal age and weight estimation based on fetal ultrasound measures such as bi-parietal diameter, head circumference, abdominal circumference, fetal length, arm volume, and thigh volume. This algorithm is based on heuristic assumptions, which aim to produce good estimation formulas as fast as possible. From experimental results, the framework produces optimal formulas with high adequacy and accuracy. Moreover, the framework gives facilities to physicians and scientists for exploiting useful statistical information under pregnant data. Phoebe framework is a computer software available at http://phoebe.locnguyen.net

Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their MSEs are 0.02314 and 0.15384 respectively