Responding to reports of comments made by the lawmakers, which suggested the EU would consider legally binding assurances on how the withdrawal agreement would operate, Selmayr tweeted: "On the EU side, nobody is considering this. Asked whether any assurance would help to get the Withdrawal Agreement through the Commons, the answers of MPs were inconclusive."
Reed starts each class by sharing this story, and teaching students how to mitigate pain and prevent injuries of their own. They urge students to spend a few minutes a day going through self-massaging techniques. Reed demonstrates how to massage their diaphragm muscle tissue near the edge of the binder and how to roll a tennis ball against their trapezius muscle using a corner of a wall. In another technique, Reed lies down on a roller and extends their arms toward the floor, opening their chest.
Metal binding is important for the structural and functional characterization of proteins. Previous prediction efforts have only focused on bonding state, i.e. deciding which protein residues act as metal ligands in some binding site. Identifying the geometry of metal-binding sites, i.e. deciding which residues are jointly involved in the coordination of a metal ion is a new prediction problem that has been never attempted before from protein sequence alone. In this paper, we formulate it in the framework of learning with structured outputs. Our solution relies on the fact that, from a graph theoretical perspective, metal binding has the algebraic properties of a matroid, enabling the application of greedy algorithms for learning structured outputs.
RNA-binding proteins (RBPs) play crucial roles in many biological processes, e.g. gene regulation. Computational identification of RBP binding sites on RNAs are urgently needed. In particular, RBPs bind to RNAs by recognizing sequence motifs. Thus, fast locating those motifs on RNA sequences is crucial and time-efficient for determining whether the RNAs interact with the RBPs or not. In this study, we present an attention based convolutional neural network, iDeepA, to predict RNA-protein binding sites from raw RNA sequences. We first encode RNA sequences into one-hot encoding. Next, we design a deep learning model with a convolutional neural network (CNN) and an attention mechanism, which automatically search for important positions, e.g. binding motifs, to learn discriminant high-level features for predicting RBP binding sites. We evaluate iDeepA on publicly gold-standard RBP binding sites derived from CLIP-seq data. The results demonstrate iDeepA achieves comparable performance with other state-of-the-art methods.