26 research outputs found
Binding Site Turnover Produces Pervasive Quantitative Changes in Transcription Factor Binding between Closely Related Drosophila Species
Genome-wide comparison of transcription factor binding between related Drosophila species highlights how sequence changes affect the biochemical events that underlie animal development
Small worlds and board interlocking in Brazil: a longitudinal study of corporate networks, 1997-2007
Social Network Analysis (SNA) is an emerging research field in finance, above all in Brazil. This work is pioneering in that it is supported by reference to different areas of knowledge: social network analysis and corporate governance, for dealing with a similarly emerging topic in finance; interlocking boards, the purpose being to check the validity of the small-world model in the Brazilian capital market, and the existence of associations between the positioning of the firm in the network of corporate relationships and its worth. To do so official data relating to more than 400 companies listed in Brazil between 1997 and 2007 were used. The main results obtained suggest that the configuration of the networks of relationships between board members and companies reflects the small-world model. Furthermore, there seems to be a significant relationship between the firm’s centrality and its worth, described according to an “inverted U” curve, which suggests the existence of optimum values of social prominence in the corporate network
Small Worlds and Board Interlocking in Brazil: A Longitudinal Study of Corporate Networks, 1997-2007
Predicting intrinsic aqueous solubility by a thermodynamic cycle
We report methods to predict the intrinsic aqueous solubility of crystalline organic molecules from two different thermodynamic cycles. We find that direct computation of solubility, via ab initio calculation of thermodynamic quantities at an affordable level of theory, cannot deliver the required accuracy. Therefore, we have turned to a mixture of direct computation and informatics, using the calculated thermodynamic properties, along with a few other key descriptors, in regression models. The prediction of log intrinsic solubility (referred to mol/L) by a three-variable linear regression equation gave r(2)=0.77 and RMSE=0.71 for an external test set comprising drug molecules. The model includes a calculated crystal lattice energy which provides a computational method to account for the interactions in the solid state. We suggest that it is not necessary to know the polymorphic form prior to prediction. Furthermore, the method developed here may be applicable to other solid-state systems such as salts or cocrystals