Two problems arise when using distant supervision for relation extraction. First, in this method, an already existing knowledge base is heuristically aligned to texts, and the alignment results are treated as labeled data. However, the heuristic alignment can fail, resulting in wrong label problem. In addition, in previous approaches, statistical models have typically been applied to ad hoc features. The noise that originates from the feature extraction process can cause poor performance.In this paper, we propose a novel model dubbed the Piecewise Convolutional Neural Networks (PCNNs) with multi-instance learning to address these two problems. To solve the first problem, distant supervised relation extraction is treated as a multi-instance problem in which the uncertainty of instance labels is taken into account. To address the latter problem, we avoid feature engineering and instead adopt convolutional architecture with piecewise max pooling to automatically learn relevant features. Experiments show that our method is effective and outperforms several competitive baseline methods.

Traditional approaches to the task of ACE event extraction primarily rely on elaborately designed features and complicated natural language processing (NLP) tools. These traditional approaches lack generalization, take a large amount of human effort and are prone to error propagation and data sparsity problems. This paper proposes a novel event-extraction method, which aims to automatically extract lexical-level and sentence-level features without using complicated NLP tools. We introduce a word-representation model to capture meaningful semantic regularities for words and adopt a framework based on a convolutional neural network (CNN) to capture sentence-level clues. However, CNN can only capture the most important information in a sentence and may miss valuable facts when considering multiple-event sentences. We propose a dynamic multi-pooling convolutional neural network (DMCNN), which uses a dynamic multi-pooling layer according to event triggers and arguments, to reserve more crucial information. The experimental results show that our approach significantly outperforms other state-of-the-art methods.

Development of cost-effective and efficient electrocatalysts for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) is of prime importance to emerging renewable energy technologies. Here, we report a simple and effective strategy for enhancing ORR and OER electrocatalytic activity in alkaline solution by introducing Asite cation deficiency in LaFeO 3 perovskite; the enhancement effect is more pronounced for the OER than the ORR. Among the A-site cation deficient perovskites studied, La 0.95 FeO 3-δ (L0.95F) demonstrates the highest ORR and OER activity and, hence, the best bifunctionality. The dramatic enhancement is attributed to the creation of surface oxygen vacancies and a small amount of Fe 4+ species. This work highlights the importance of tuning cation deficiency in perovskites as an effective strategy for enhancing ORR and OER activity for applications in various oxygen-based energy storage and conversion processes.

The development of oxygen evolution reaction (OER) electrocatalysts remains a major challenge that requires significant advances in both mechanistic understanding and material design. Recent studies show that oxygen from the perovskite oxide lattice could participate in the OER via a lattice oxygen-mediated mechanism, providing possibilities for the development of alternative electrocatalysts that could overcome the scaling relations-induced limitations found in conventional catalysts utilizing the adsorbate evolution mechanism. Here we distinguish the extent to which the participation of lattice oxygen can contribute to the OER through the rational design of a model system of silicon-incorporated strontium cobaltite perovskite electrocatalysts with similar surface transition metal properties yet different oxygen diffusion rates. The as-derived silicon-incorporated perovskite exhibits a 12.8-fold increase in oxygen diffusivity, which matches well with the 10-fold improvement of intrinsic OER activity, suggesting that the observed activity increase is dominantly a result of the enhanced lattice oxygen participation.

The sequenced genomes of oomycete plant pathogens contain large superfamilies of effector proteins containing the protein translocation motif RXLR-dEER. However, the contributions of these effectors to pathogenicity remain poorly understood. Here, we show that the Phytophthora sojae effector protein Avr1b can contribute positively to virulence and can suppress programmed cell death (PCD) triggered by the mouse BAX protein in yeast, soybean (Glycine max), and Nicotiana benthamiana cells. We identify three conserved motifs (K, W, and Y) in the C terminus of the Avr1b protein and show that mutations in the conserved residues of the W and Y motifs reduce or abolish the ability of Avr1b to suppress PCD and also abolish the avirulence interaction of Avr1b with the Rps1b resistance gene in soybean. W and Y motifs are present in at least half of the identified oomycete RXLR-dEER effector candidates, and we show that three of these candidates also suppress PCD in soybean. Together, these results indicate that the W and Y motifs are critical for the interaction of Avr1b with host plant target proteins and support the hypothesis that these motifs are critical for the functions of the very large number of predicted oomycete effectors that contain them.