New solar axion search in CAST with 4^4He filling

The CERN Axion Solar Telescope (CAST) searches for $a\to\gamma$ conversion in the 9 T magnetic field of a refurbished LHC test magnet that can be directed toward the Sun. Two parallel magnet bores can be filled with helium of adjustable pressure to match the X-ray refractive mass $m_\gamma$ to the axion search mass $m_a$. After the vacuum phase (2003--2004), which is optimal for $m_a\lesssim0.02$ eV, we used $^4$He in 2005--2007 to cover the mass range of 0.02--0.39 eV and $^3$He in 2009--2011 to scan from 0.39--1.17 eV. After improving the detectors and shielding, we returned to $^4$He in 2012 to investigate a narrow $m_a$ range around 0.2 eV ("candidate setting" of our earlier search) and 0.39--0.42 eV, the upper axion mass range reachable with $^4$He, to "cross the axion line" for the KSVZ model. We have improved the limit on the axion-photon coupling to $g_{a\gamma}< 1.47\times10^{-10} {\rm GeV}^{-1}$ (95% C.L.), depending on the pressure settings. Since 2013, we have returned to vacuum and aim for a significant increase in sensitivity.Comment: CAST Collaboration 6 pages 3 figure

Search for chameleons with CAST

In this work we present a search for (solar) chameleons with the CERN Axion Solar Telescope (CAST). This novel experimental technique, in the field of dark energy research, exploits both the chameleon coupling to matter ($\beta_{\rm m}$) and to photons ($\beta_{\gamma}$) via the Primakoff effect. By reducing the X-ray detection energy threshold used for axions from 1$\,$keV to 400$\,$eV CAST became sensitive to the converted solar chameleon spectrum which peaks around 600$\,$eV. Even though we have not observed any excess above background, we can provide a 95% C.L. limit for the coupling strength of chameleons to photons of $\beta_{\gamma}\!\lesssim\!10^{11}$ for $1<\beta_{\rm m}<10^6$.Comment: 8 pages, 12 figure

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

Background: Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Results: Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Conclusions: Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Figure not available: see fulltext. © 2015 Freitas et al.; licensee Springer

Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures

BACKGROUND: Protein-protein interactions (PPIs) mediate the vast majority of biological processes, therefore, significant efforts have been directed to investigate PPIs to fully comprehend cellular functions. Predicting complex structures is critical to reveal molecular mechanisms by which proteins operate. Despite recent advances in the development of new methods to model macromolecular assemblies, most current methodologies are designed to work with experimentally determined protein structures. However, because only computer-generated models are available for a large number of proteins in a given genome, computational tools should tolerate structural inaccuracies in order to perform the genome-wide modeling of PPIs. RESULTS: To address this problem, we developed eRank(PPI), an algorithm for the identification of near-native conformations generated by protein docking using experimental structures as well as protein models. The scoring function implemented in eRank(PPI) employs multiple features including interface probability estimates calculated by eFindSite(PPI) and a novel contact-based symmetry score. In comparative benchmarks using representative datasets of homo- and hetero-complexes, we show that eRank(PPI) consistently outperforms state-of-the-art algorithms improving the success rate by ~10 %. CONCLUSIONS: eRank(PPI) was designed to bridge the gap between the volume of sequence data, the evidence of binary interactions, and the atomic details of pharmacologically relevant protein complexes. Tolerating structure imperfections in computer-generated models opens up a possibility to conduct the exhaustive structure-based reconstruction of PPI networks across proteomes. The methods and datasets used in this study are available at www.brylinski.org/erankppi