Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Fusion pore conductance: experimental approaches and theoretical algorithms.

Time-resolved admittance measurements provide the basis for studies showing that membrane fusion occurs through the formation and widening of an initially small pore, linking two previously separated aqueous compartments. Here we introduce modifications to this method that correct the cell-pipette (source) admittance for attenuation and phase shifts produced by electrophysiological equipment. Two new approaches for setting the right phase angle are discussed. The first uses the displacement of a patch-clamp amplifier C-slow potentiometer for the calculation of phase. This calculation is based on amplitudes of observed and expected (theoretical) changes in the source admittance. The second approach automates the original phase adjustment, the validity of which we prove analytically for certain conditions. The multiple sine wave approach is modified to allow the calculation of target cell membrane parameters and the conductance of the fusion pore. We also show how this technique can be extended for measurements of the resting potential of the first (voltage-clamped) membrane. We introduce an algorithm for calculation of fusion pore conductance despite a concurrent change in the resistance of the clamped membrane. The sensitivity of the capacitance restoration algorithm to phase shift errors is analyzed, and experimental data are used to demonstrate the results of this analysis. Finally, we show how the phase offset can be corrected "off-line" by restoring the shape of the capacitance increment

Theory of the setting of gypsum bonding materials

Peer reviewed: NoNRC publication: Ye

Robust and Collective Entity Disambiguation through Semantic Embeddings

Entity disambiguation is the task of mapping ambiguous terms in natural-language text to its entities in a knowledge base. It finds its application in the extraction of structured data in RDF (Resource Description Framework) from textual documents, but equally so in facilitating artificial intelligence applications, such as Seman-tic Search, Reasoning and Question & Answering. We propose a new collective, graph-based disambiguation algorithm utilizing semantic entity and document embeddings for robust entity disam-biguation. Robust thereby refers to the property of achieving better than state-of-the-art results over a wide range of very different data sets. Our approach is also able to abstain if no appropriate entity can be found for a specific surface form. Our evaluation shows, that our approach achieves significantly (>5%) better results than all other publicly available disambiguation algorithms on 7 of 9 datasets without data set specific tuning. Moreover, we discuss the influence of the quality of the knowledge base on the disambigua-tion accuracy and indicate that our algorithm achieves better results than non-publicly available state-of-the-art algorithms