Identifying RNA contacts from SHAPE-MaP by partial correlation analysis

In a recent paper Siegfried et al. published a new sequence-based structural RNA assay that utilizes mutational profiling to detect base pairing (MaP). Output from MaP provides information about both pairing (via reactivities) and contact (via correlations). Reactivities can be coupled to partition function folding models for structural inference, while correlations can reveal pairs of sites that may be in structural proximity. The possibility for inference of 3D contacts via MaP suggests a novel approach to structural prediction for RNA analogous to covariance structural prediction for proteins. We explore this approach and show that partial correlation analysis outperforms na\"ive correlation analysis. Our results should be applicable to a wide range of high-throughput sequencing based RNA structural assays that are under development

Relationships among catches, fishing effort and river morphology for eight rivers in Amazonas State (Brazil), during 1976-1978

For eight rivers in the Amazonas State (Brazil), it is shown that the annual number of fìshermen and dummy variables, which identify the rivers, explain 98.8 % of the landings at Manaus market. Partial correlation analysis suggests that only the most productive floodplains are actively sought by professional fishermen. Keywords: river ecology, floodplain fisheries, multiple regressio

Studying alternative splicing regulatory networks through partial correlation analysis

The identification of links between exons and their regulators or targets and between co-spliced exons in human, mouse and rat provides novel insights into the alternative splicing regulatory network

Partial correlation analysis: applications for financial markets

The presence of significant cross-correlations between the synchronous time evolution of a pair of equity returns is a well-known empirical fact. The Pearson correlation is commonly used to indicate the level of similarity in the price changes for a given pair of stocks, but it does not measure whether other stocks influence the relationship between them. To explore the influence of a third stock on the relationship between two stocks, we use a partial correlation measurement to determine the underlying relationships between financial assets. Building on previous work, we present a statistically robust approach to extract the underlying relationships between stocks from four different financial markets: the United States, the United Kingdom, Japan, and India. This methodology provides new insights into financial market dynamics and uncovers implicit influences in play between stocks. To demonstrate the capabilities of this methodology, we (i) quantify the influence of different companies and, by studying market similarity across time, present new insights into market structure and market stability, and (ii) we present a practical application, which provides information on the how a company is influenced by different economic sectors, and how the sectors interact with each other. These examples demonstrate the effectiveness of this methodology in uncovering information valuable for a range of individuals, including not only investors and traders but also regulators and policy makers.We wish to thank ONR (Grant N00014-09-1-0380, Grant N00014-12-1-0548), DTRA (Grant HDTRA-1-10-1- 0014, Grant HDTRA-1-09-1-0035), NSF (Grant CMMI 1125290), the European MULTIPLEX (EU-FET project 317532), CONGAS (Grant FP7-ICT-2011-8-317672), FET Open Project FOC 255987 and FOC-INCO 297149, and LINC (no. 289447 funded by the ECs Marie-Curie ITN program) projects, DFG, the Next Generation Infrastructure (Bsik), Bi-national US-Israel Science Foundation (BSF) and the Israel Science Foundation for financial support. (N00014-09-1-0380 - ONR; N00014-12-1-0548 - ONR; HDTRA-1-10-1- 0014 - DTRA; HDTRA-1-09-1-0035 - DTRA; CMMI 1125290 - NSF; 317532 - European MULTIPLEX (EU-FET project); FP7-ICT-2011-8-317672 - CONGAS; FOC 255987 - FET Open Project; FOC-INCO 297149 - FET Open Project; 289447 - LINC project - (ECs Marie-Curie ITN program); DFG; Next Generation Infrastructure (Bsik); Bi-national US-Israel Science Foundation (BSF); Israel Science Foundation)Accepted manuscrip

The Relation between Black Hole Mass, Bulge Mass, and Near-Infrared Luminosity

We present new accurate near-infrared (NIR) spheroid (bulge) structural parameters obtained by two-dimensional image analysis for all galaxies with a direct black hole (BH) mass determination. As expected, NIR bulge luminosities Lbul and BH masses are tightly correlated, and if we consider only those galaxies with secure BH mass measurement and accurate Lbul (27 objects), the spread of MBH-Lbul is similar to MBH-sigma, where sigma is the effective stellar velocity dispersion. We find an intrinsic rms scatter of ~0.3 dex in log MBH. By combining the bulge effective radii R_e measured in our analysis with sigma, we find a tight linear correlation (rms ~ 0.25 dex) between MBH and the virial bulge mass (propto R_e sigma^2), with ~ 0.002. A partial correlation analysis shows that MBH depends on both sigma and R_e, and that both variables are necessary to drive the correlations between MBH and other bulge properties.Comment: Astrophysical Journal Letters, in pres