End-to-End Reinforcement Learning for Automatic Taxonomy Induction

We present a novel end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms. While prior methods treat the problem as a two-phase task (i.e., detecting hypernymy pairs followed by organizing these pairs into a tree-structured hierarchy), we argue that such two-phase methods may suffer from error propagation, and cannot effectively optimize metrics that capture the holistic structure of a taxonomy. In our approach, the representations of term pairs are learned using multiple sources of information and used to determine \textit{which} term to select and \textit{where} to place it on the taxonomy via a policy network. All components are trained in an end-to-end manner with cumulative rewards, measured by a holistic tree metric over the training taxonomies. Experiments on two public datasets of different domains show that our approach outperforms prior state-of-the-art taxonomy induction methods up to 19.6\% on ancestor F1.Comment: 11 Pages. ACL 2018 Camera Read

Growth, chemical components and ensiling characteristics of king grass at different cuttings

In order to effectively use and ensile king grass (Pennisetum purpureum × Pennisetum americanum), the present research investigated growth rate, yield, chemical components and silage fermentation quality of different cuttings. King grass was harvested four times, and the 1st and 3rd cuttings were ensiled directly or after wilting for 12 and 24 h. The results showed that the dry matter daily growth of 2nd cutting was significantly higher than that of other cuttings, and the 4th cutting was the lowest (P < 0.05). The contents of crude protein (CP), crude fat and water-soluble carbohydrates (WSC) tended to reduce, and crude ash tended to increase with the increase of cutting times. All four cuttings of king grass had higher WSC content, lower buffer capacity and much lactic acid bacteria, the silages made from unwilted 1st cutting and 3rd cutting were of good fermentation quality, indicated by low pH values and high V-scores. Wilting had different effects on the 1st cutting and 3rd cutting silages in pH value and NH3-N content, the 1st cutting silage tended to increase the pH values and NH3-N content, with moisture content reduction, while the 3rd cutting silage tended to reduce NH3-N content and its pH value was not affected by wilting (P > 0.05). Although the 3rd cutting silage had better aerobic stability than the 1st cutting silage, they all were not stable within 6 days of aerobic exposure. Considering the contents of CP, crude fat, crude fiber, crude ash and WSC, the 1st cutting of king grass might have best nutrient value, while the 4th cutting was contrary. Different cuttings of king grass could be well preserved by natural fermentation, but their aerobic stability was poor.Keywords: Cuttings, ensiling, king grass, nutrient component, wiltin

Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution

Real-world face super-resolution (SR) is a highly ill-posed image restoration task. The fully-cycled Cycle-GAN architecture is widely employed to achieve promising performance on face SR, but prone to produce artifacts upon challenging cases in real-world scenarios, since joint participation in the same degradation branch will impact final performance due to huge domain gap between real-world and synthetic LR ones obtained by generators. To better exploit the powerful generative capability of GAN for real-world face SR, in this paper, we establish two independent degradation branches in the forward and backward cycle-consistent reconstruction processes, respectively, while the two processes share the same restoration branch. Our Semi-Cycled Generative Adversarial Networks (SCGAN) is able to alleviate the adverse effects of the domain gap between the real-world LR face images and the synthetic LR ones, and to achieve accurate and robust face SR performance by the shared restoration branch regularized by both the forward and backward cycle-consistent learning processes. Experiments on two synthetic and two real-world datasets demonstrate that, our SCGAN outperforms the state-of-the-art methods on recovering the face structures/details and quantitative metrics for real-world face SR. The code will be publicly released at https://github.com/HaoHou-98/SCGAN

O-GlcNAcylation of G6PD Promotes the Pentose Phosphate Pathway and Tumor Growth

The pentose phosphate pathway (PPP) plays a critical role in macromolecule biosynthesis and maintaining cellular redox homoeostasis in rapidly proliferating cells. Upregulation of the PPP has been shown in several types of cancer. However, how the PPP is regulated to confer a selective growth advantage on cancer cells is not well understood. Here we show that glucose-6-phosphate dehydrogenase (G6PD), the rate-limiting enzyme of the PPP, is dynamically modified with an O-linked b-N-acetylglucosamine sugar in response to hypoxia. Glycosylation activates G6PD activity and increases glucose flux through the PPP, thereby providing precursors for nucleotide and lipid biosynthesis, and reducing equivalents for antioxidant defense. Blocking glycosylation of G6PD reduces cancer cell proliferation in vitro and impairs tumor growth in vivo. Importantly, G6PD glycosylation is increased in human lung cancers. Our findings reveal a mechanistic understanding of how O-glycosylation directly regulates the PPP to confer a selective growth advantage to tumours

State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event

The exposome recognizes that individuals are exposed simultaneously to a multitude of different environmental factors and takes a holistic approach to the discovery of etiological factors for disease. However, challenges arise when trying to quantify the health effects of complex exposure mixtures. Analytical challenges include dealing with high dimensionality, studying the combined effects of these exposures and their interactions, integrating causal pathways, and integrating high-throughput omics layers. To tackle these challenges, the Barcelona Institute for Global Health (ISGlobal) held a data challenge event open to researchers from all over the world and from all expertises. Analysts had a chance to compete and apply state-of-the-art methods on a common partially simulated exposome dataset (based on real case data from the HELIX project) with multiple correlated exposure variables (P > 100 exposure variables) arising from general and personal environments at different time points, biological molecular data (multi-omics: DNA methylation, gene expression, proteins, metabolomics) and multiple clinical phenotypes in 1301 mother–child pairs. Most of the methods presented included feature selection or feature reduction to deal with the high dimensionality of the exposome dataset. Several approaches explicitly searched for combined effects of exposures and/or their interactions using linear index models or response surface methods, including Bayesian methods. Other methods dealt with the multi-omics dataset in mediation analyses using multiple-step approaches. Here we discuss features of the statistical models used and provide the data and codes used, so that analysts have examples of implementation and can learn how to use these methods. Overall, the exposome data challenge presented a unique opportunity for researchers from different disciplines to create and share state-of-the-art analytical methods, setting a new standard for open science in the exposome and environmental health field

Identification and Molecular Characterization of a New Ovarian Cancer Susceptibility Locus at 17q21.31

Epithelial ovarian cancer (EOC) has a heritable component that remains to be fully characterized. Most identified common susceptibility variants lie in non-protein-coding sequences. We hypothesized that variants in the 3 ′ untranslated region at putative microRNA (miRNA) binding sites represent functional targets that influence EOC susceptibility. Here, we evaluate the association between 767 miRNA binding site single nucleotide polymorphisms (miRSNPs) and EOC risk in 18,174 EOC cases and 26,134 controls from 43 studies genotyped through the Collaborative Oncological Gene-environment Study. We identify several miRSNPs associated with invasive serous EOC risk (OR=1.12, P =10−8 ) mapping to an inversion polymorphism at 17q21.31. Additional genotyping of non-miRSNPs at 17q21.31 reveals stronger signals outside the inversion ( P =10−10 ). Variation at 17q21.31 associates with neurological diseases, and our collaboration is the first to report an association with EOC susceptibility. An integrated molecular analysis in this region provides evidence for ARHGAP27 and PLEKHM1 as candidate EOC susceptibility genes