Atmospheric tides and their consequences on the rotational dynamics of terrestrial planets

Atmospheric tides can have a strong impact on the rotational dynamics of planets. They are of most importance for terrestrial planets located in the habitable zone of their host star, where their competition with solid tides is likely to drive the body towards non-synchronized rotation states of equilibrium, as observed in the case of Venus. Contrary to other planetary layers, the atmosphere is sensitive to both gravitational and thermal forcings, through a complex dynamical coupling involving the effects of Coriolis acceleration and characteristics of the atmospheric structure. These key physics are usually not taken into account in modelings used to compute the evolution of planetary systems, where tides are described with parametrised prescriptions. In this work, we present a new ab initio modeling of atmospheric tides adapting the theory of the Earth's atmospheric tides (Chapman & Lindzen 1970) to other terrestrial planets. We derive analytic expressions of the tidal torque, as a function of the tidal frequency and parameters characterizing the internal structure (e.g. the Brunt-V\"ais\"al\"a frequency, the radiative frequency, the pressure heigh scale). We show that stratification plays a key role, the tidal torque being strong in the case of convective atmospheres (i.e. with a neutral stratification) and weak in case of atmosphere convectively stable. In a second step, the model is used to determine the non-synchronized rotation states of equilibrium of Venus-like planets as functions of the physical parameters of the system. These results are detailed in Auclair-Desrotour et al. (2017a) and Auclair-Desrotour et al. (2017b).Comment: Proceedings for Astro Fluid conference in memory of Jean-Paul Zahn (Paris, June 2016), 9 pages, 5 figure

Additional file 11: Figure S7. of A comprehensive genomic pan-cancer classification using The Cancer Genome Atlas gene expression data

Proportion of test-set samples predicted to be each of the 23 sex non-specific tumor types in male patients. Y-axis lists the 23 actual tumor types; X-axis lists the 24 possible classification categories (23 tumor types plus “unclassified” [UC]). Each bar represents one of the 24 proportions that samples from the actual tumor type were predicted to be. The 24 plotted proportions represent averages from the corresponding proportions for all samples of the actual tumor type. (DOCX 1745 kb

Additional file 9: Figure S5. of A comprehensive genomic pan-cancer classification using The Cancer Genome Atlas gene expression data

Classification accuracies between GA/KNN and XGBoost for 10 testing sets. (DOCX 560 kb

Additional file 6: Figure S2. of A comprehensive genomic pan-cancer classification using The Cancer Genome Atlas gene expression data

Heatmap representation of the expression patterns of the top 50 genes across all (a) ACC, (b) BLCA, (c) BRCA, (d) KIRC, (e) KIRP, (f) LGG, and (g) PAAD samples. See Fig. 3 legend for details. The colors of the horizontal bar represent the subgroups identified by k-means clustering analysis. (DOCX 60 kb

Additional file 13: Figure S8. of A comprehensive genomic pan-cancer classification using The Cancer Genome Atlas gene expression data

Heatmap representations of the expression patterns of the top genes across all male and female samples. Each row (gene) was centered by the median expression value across all samples. A hierarchical clustering analysis was carried out for both samples and genes using the Euclidean distance as the similarity metric. (DOCX 15 kb

Additional file 8: Figure S4. of A comprehensive genomic pan-cancer classification using The Cancer Genome Atlas gene expression data

Heatmap representation of the expression patterns of the top 50 genes across all 602 “normal” samples taken adjacent to tumors from 17 tumor types. Each row (gene) was centered by the median expression value across all samples. A hierarchical clustering analysis was carried out for both samples and genes using the Euclidean distance as the similarity metric. (DOCX 16 kb