Graph-based Neural Multi-Document Summarization

We propose a neural multi-document summarization (MDS) system that incorporates sentence relation graphs. We employ a Graph Convolutional Network (GCN) on the relation graphs, with sentence embeddings obtained from Recurrent Neural Networks as input node features. Through multiple layer-wise propagation, the GCN generates high-level hidden sentence features for salience estimation. We then use a greedy heuristic to extract salient sentences while avoiding redundancy. In our experiments on DUC 2004, we consider three types of sentence relation graphs and demonstrate the advantage of combining sentence relations in graphs with the representation power of deep neural networks. Our model improves upon traditional graph-based extractive approaches and the vanilla GRU sequence model with no graph, and it achieves competitive results against other state-of-the-art multi-document summarization systems.Comment: In CoNLL 201

In silico structural evaluation of short cationic antimicrobial peptides

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Cationic peptides with antimicrobial properties are ubiquitous in nature and have been studied for many years in an attempt to design novel antibiotics. However, very few molecules are used in the clinic so far, sometimes due to their complexity but, mostly, as a consequence of the unfavorable pharmacokinetic profile associated with peptides. The aim of this work is to investigate cationic peptides in order to identify common structural features which could be useful for the design of small peptides or peptido-mimetics with improved drug-like properties and activity against Gram negative bacteria. Two sets of cationic peptides (AMPs) with known antimicrobial activity have been investigated. The first reference set comprised molecules with experimentally-known conformations available in the protein databank (PDB), and the second one was composed of short peptides active against Gram negative bacteria but with no significant structural information available. The predicted structures of the peptides from the first set were in excellent agreement with those experimentally-observed, which allowed analysis of the structural features of the second group using computationally-derived conformations. The peptide conformations, either experimentally available or predicted, were clustered in an “all vs. all” fashion and the most populated clusters were then analyzed. It was confirmed that these peptides tend to assume an amphipathic conformation regardless of the environment. It was also observed that positively-charged amino acid residues can often be found next to aromatic residues. Finally, a protocol was evaluated for the investigation of the behavior of short cationic peptides in the presence of a membrane-like environment such as dodecylphosphocholine (DPC) micelles. The results presented herein introduce a promising approach to inform the design of novel short peptides with a potential antimicrobial activity.Peer reviewedFinal Published versio

How organized is deep convection over Germany?

Deep moist convection shows a tendency to organize into mesoscale structures. To be able to understand the potential effect of convective organization on the climate, one needs first to characterize organization. In this study, we systematically characterize the organizational state of convection over Germany based on two years of cloud-top observations derived from the Meteosat Second Generation satellite and of precipitation cores detected by the German C-band radar network. The organizational state of convection is characterized by commonly employed organization indices, which are mostly based on the object numbers, sizes and nearest-neighbour distances. According to the organization index Iorg, cloud tops and precipitation cores are found to be in an organized state for 69% and 92% of the time, respectively. There is an increase in rainfall when the number of objects and their sizes increase, independently of the organizational state. Case-studies of specific days suggest that convectively organized states correspond to either local multi-cell clusters, with less numerous, larger objects close to each other, or to scattered clusters, with more numerous, smaller organized objects spread out over the domain. For those days, simulations are performed with the large-eddy model ICON with grid spacings of 625, 312 and 156?m. Although the model underestimates rainfall and shows a too large cold cloud coverage, the organizational state is reasonably well represented without significant differences between the grid spacings

Abstract Meaning Representation for Multi-Document Summarization

Generating an abstract from a collection of documents is a desirable capability for many real-world applications. However, abstractive approaches to multi-document summarization have not been thoroughly investigated. This paper studies the feasibility of using Abstract Meaning Representation (AMR), a semantic representation of natural language grounded in linguistic theory, as a form of content representation. Our approach condenses source documents to a set of summary graphs following the AMR formalism. The summary graphs are then transformed to a set of summary sentences in a surface realization step. The framework is fully data-driven and flexible. Each component can be optimized independently using small-scale, in-domain training data. We perform experiments on benchmark summarization datasets and report promising results. We also describe opportunities and challenges for advancing this line of research.Comment: 13 page

Methods for protein complex prediction and their contributions towards understanding the organization, function and dynamics of complexes

Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organization of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight challenges faced by these methods, in particular detection of sparse and small or sub- complexes and discerning of overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time-based assembly of complex subunits and formation of fuzzy complexes from intrinsically disordered proteins. Finally, we discuss methods for identifying dysfunctional complexes in human diseases, an application that is proving invaluable to understand disease mechanisms and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area.Comment: 1 Tabl

Recent Advances in Noble Metal (Pt, Ru, and Ir)-Based Electrocatalysts for Efficient Hydrogen Evolution Reaction

Noble metal (Pt, Ru, and Ir)-based electrocatalysts are currently considered the most active materials for the hydrogen evolution reaction (HER). Although they have been associated with high cost, easy agglomeration, and poor stability during the HER reaction, recent efforts to intentionally tailor noble-metal-based catalysts have led to promising improvements, with lower cost and superior activity, which are critical to achieving large-scale production of pure hydrogen. In this mini-review, we focus on the recent advances in noble-metal-based HER electrocatalysts. In particular, the synthesis strategies to enhance cost-effectiveness and the catalytic activity for HER are highlighted

Energy Efficiency Prediction using Artificial Neural Network

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%