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Normalization with proper reference genes is a crucial step in obtaining accurate mRNA expression levels in RT-qPCR experiments. GeNorm and NormFinder are two commonly used software packages that help in selecting the best reference genes, based on their expression stability. However, GeNorm does not take into account a group variable, such as sample sex, in its calculation. We demonstrate a simple calculation step to assess the variability of such parameters by multiplying the GeNorm M value with the difference of Cq values between groups. To test this, we used 28 reference gene candidates, to analyze 20 placental samples (10 of each sex), and by using HPRT1 (lower Cq values in male placentas (P = 0.017)), as a target gene. Our calculation demonstrates that the RPL30 – GAPDH reference gene combination is the better option to assess small placental sex differences in mRNA level, versus the selection obtained from GeNorm or NormFinder. The HPRT1 normalized mRNA expression level is different between placental sexes, using RPL30 and GAPDH as reference genes (P = 0.01), but not when using genes suggested by GeNorm or NormFinder. These results indicate that the proposed calculation is appropriate to assess small variations in mRNA expression between 2 groups.

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In order to mechanically predict audiovisual quality in interactive multimedia services, we have developed machine learning--based no-reference parametric models. We have compared Decision Trees--based ensemble methods, Genetic Programming and Deep Learning models that have one and more hidden layers. We have used the Institut national de la recherche scientifique (INRS) audiovisual quality dataset specifically designed to include ranges of parameters and degradations typically seen in real-time communications. Decision Trees--based ensemble methods have outperformed both Deep Learning-- and Genetic Programming--based models in terms of Root-Mean-Square Error (RMSE) and Pearson correlation values. We have also trained and developed models on various publicly available datasets and have compared our results with those of these original models. Our studies show that Random Forests--based prediction models achieve high accuracy for both the INRS audiovisual quality dataset and other publicly available comparable datasets.

Abstract-We have developed reduced reference parametric models for estimating perceived quality in audiovisual multimedia services. We have created 144 unique configurations for audiovisual content including various application and network parameters such as bitrates and distortions in terms of bandwidth, packet loss rate and jitter. To generate the data needed for model training and validation we have tasked 24 subjects, in a controlled environment, to rate the overall audiovisual quality on the absolute category rating (ACR) 5-level quality scale. We have developed models using Random Forest and Neural Network based machine learning methods in order to estimate Mean Opinion Scores (MOS) values. We have used information retrieved from the packet headers and side information provided as network parameters for model training. Random Forest based models have performed better in terms of Root Mean Square Error (RMSE) and Pearson correlation coefficient. The side information proved to be very effective in developing the model. We have found that, while the model performance might be improved by replacing the side information with more accurate bit stream level measurements, they are performing well in estimating perceived quality in audiovisual multimedia services.