SocialLink: exploiting graph embeddings to link DBpedia entities to Twitter profiles

SocialLink is a project designed to match social media profiles on Twitter to corresponding entities in DBpedia. Built to bridge the vibrant Twitter social media world and the Linked Open Data cloud, SocialLink enables knowledge transfer between the two, both assisting Semantic Web practitioners in better harvesting the vast amounts of information available on Twitter and allowing leveraging of DBpedia data for social media analysis tasks. In this paper, we further extend the original SocialLink approach by exploiting graph-based features based on both DBpedia and Twitter, represented as graph embeddings learned from vast amounts of unlabeled data. The introduction of such new features required to redesign our deep neural network-based candidate selection algorithm and, as a result, we experimentally demonstrate a significant improvement of the performances of SocialLink

Investigating heterogeneous protein annotations toward cross-corpora utilization

<p>Abstract</p> <p>Background</p> <p>The number of corpora, collections of structured texts, has been increasing, as a result of the growing interest in the application of natural language processing methods to biological texts. Many named entity recognition (NER) systems have been developed based on these corpora. However, in the biomedical community, there is yet no general consensus regarding named entity annotation; thus, the resources are largely incompatible, and it is difficult to compare the performance of systems developed on resources that were divergently annotated. On the other hand, from a practical application perspective, it is desirable to utilize as many existing annotated resources as possible, because annotation is costly. Thus, it becomes a task of interest to integrate the heterogeneous annotations in these resources.</p> <p>Results</p> <p>We explore the potential sources of incompatibility among gene and protein annotations that were made for three common corpora: GENIA, GENETAG and AIMed. To show the inconsistency in the corpora annotations, we first tackle the incompatibility problem caused by corpus integration, and we quantitatively measure the effect of this incompatibility on protein mention recognition. We find that the F-score performance declines tremendously when training with integrated data, instead of training with pure data; in some cases, the performance drops nearly 12%. This degradation may be caused by the newly added heterogeneous annotations, and cannot be fixed without an understanding of the heterogeneities that exist among the corpora. Motivated by the result of this preliminary experiment, we further qualitatively analyze a number of possible sources for these differences, and investigate the factors that would explain the inconsistencies, by performing a series of well-designed experiments. Our analyses indicate that incompatibilities in the gene/protein annotations exist mainly in the following four areas: the boundary annotation conventions, the scope of the entities of interest, the distribution of annotated entities, and the ratio of overlap between annotated entities. We further suggest that almost all of the incompatibilities can be prevented by properly considering the four aspects aforementioned.</p> <p>Conclusion</p> <p>Our analysis covers the key similarities and dissimilarities that exist among the diverse gene/protein corpora. This paper serves to improve our understanding of the differences in the three studied corpora, which can then lead to a better understanding of the performance of protein recognizers that are based on the corpora.</p

Biodiversity of the Deep-Sea Continental Margin Bordering the Gulf of Maine (NW Atlantic): Relationships among Sub-Regions and to Shelf Systems

Background: In contrast to the well-studied continental shelf region of the Gulf of Maine, fundamental questions regarding the diversity, distribution, and abundance of species living in deep-sea habitats along the adjacent continental margin remain unanswered. Lack of such knowledge precludes a greater understanding of the Gulf of Maine ecosystem and limits development of alternatives for conservation and management. Methodology/Principal Findings: We use data from the published literature, unpublished studies, museum records and online sources, to: (1) assess the current state of knowledge of species diversity in the deep-sea habitats adjacent to the Gulf of Maine (39–43uN, 63–71uW, 150–3000 m depth); (2) compare patterns of taxonomic diversity and distribution of megafaunal and macrofaunal species among six distinct sub-regions and to the continental shelf; and (3) estimate the amount of unknown diversity in the region. Known diversity for the deep-sea region is 1,671 species; most are narrowly distributed and known to occur within only one sub-region. The number of species varies by sub-region and is directly related to sampling effort occurring within each. Fishes, corals, decapod crustaceans, molluscs, and echinoderms are relatively well known, while most other taxonomic groups are poorly known. Taxonomic diversity decreases with increasing distance from the continental shelf and with changes in benthic topography. Low similarity in faunal composition suggests the deep-sea region harbours faunal communities distinct from those of the continental shelf. Non-parametric estimators of species richness suggest a minimum of 50% of the deep-sea species inventory remains to be discovered. Conclusions/Significance: The current state of knowledge of biodiversity in this deep-sea region is rudimentary. Our ability to answer questions is hampered by a lack of sufficient data for many taxonomic groups, which is constrained by sampling biases, life-history characteristics of target species, and the lack of trained taxonomists

Overview of the ID, EPI and REL tasks of BioNLP Shared Task 2011

We present the preparation, resources, results and analysis of three tasks of the BioNLP Shared Task 2011: the main tasks on Infectious Diseases (ID) and Epigenetics and Post-translational Modifications (EPI), and the supporting task on Entity Relations (REL). The two main tasks represent extensions of the event extraction model introduced in the BioNLP Shared Task 2009 (ST'09) to two new areas of biomedical scientific literature, each motivated by the needs of specific biocuration tasks. The ID task concerns the molecular mechanisms of infection, virulence and resistance, focusing in particular on the functions of a class of signaling systems that are ubiquitous in bacteria. The EPI task is dedicated to the extraction of statements regarding chemical modifications of DNA and proteins, with particular emphasis on changes relating to the epigenetic control of gene expression. By contrast to these two application-oriented main tasks, the REL task seeks to support extraction in general by separating challenges relating to part-of relations into a subproblem that can be addressed by independent systems. Seven groups participated in each of the two main tasks and four groups in the supporting task. The participating systems indicated advances in the capability of event extraction methods and demonstrated generalization in many aspects: from abstracts to full texts, from previously considered subdomains to new ones, and from the ST'09 extraction targets to other entities and events. The highest performance achieved in the supporting task REL, 58% F-score, is broadly comparable with levels reported for other relation extraction tasks. For the ID task, the highest-performing system achieved 56% F-score, comparable to the state-of-the-art performance at the established ST'09 task. In the EPI task, the best result was 53% F-score for the full set of extraction targets and 69% F-score for a reduced set of core extraction targets, approaching a level of performance sufficient for user-facing applications. In this study, we extend on previously reported results and perform further analyses of the outputs of the participating systems. We place specific emphasis on aspects of system performance relating to real-world applicability, considering alternate evaluation metrics and performing additional manual analysis of system outputs. We further demonstrate that the strengths of extraction systems can be combined to improve on the performance achieved by any system in isolation. The manually annotated corpora, supporting resources, and evaluation tools for all tasks are available from http://www.bionlp-st.org and the tasks continue as open challenges for all interested parties

What Do We Really Know about Cognitive Inhibition? Task Demands and Inhibitory Effects across a Rang

Our study explores inhibitory control across a range of widely recognised memory and behavioural tasks. Eighty-seven never-depressed participants completed a series of tasks designed to measure inhibitory control in memory and behaviour. Specifically, a variant of the selective retrieval-practice and the Think/No-Think tasks were employed as measures of memory inhibition. The Stroop-Colour Naming and the Go/No-Go tasks were used as measures of behavioural inhibition. Participants completed all 4 tasks. Task presentation order was counterbalanced across 3 separate testing sessions for each participant. Standard inhibitory forgetting effects emerged on both memory tasks but the extent of forgetting across these tasks was not correlated. Furthermore, there was no relationship between memory inhibition tasks and either of the main behavioural inhibition measures. At a time when cognitive inhibition continues to gain acceptance as an explanatory mechanism, our study raises fundamental questions about what we actually know about inhibition and how it is affected by the processing demands of particular inhibitory tasks

The Toll-Like Receptor 4 (TLR4) Variant rs2149356 and Risk of Gout in European and Polynesian Sample Sets

Deposition of crystallized monosodium urate (MSU) in joints as a result of hyperuricemia is a central risk factor for gout. However other factors must exist that control the progression from hyperuricaemia to gout. A previous genetic association study has implicated the toll-like receptor 4 (TLR4) which activates the NLRP3 inflammasome via the nuclear factor-κB signaling pathway upon stimulation by MSU crystals. The T-allele of single nucleotide polymorphism rs2149356 in TLR4 is a risk factor associated with gout in a Chinese study. Our aim was to replicate this observation in participants of European and New Zealand Polynesian (Māori and Pacific) ancestry. A total of 2250 clinically-ascertained prevalent gout cases and 13925 controls were used. Non-clinically-ascertained incident gout cases and controls from the Health Professional Follow-up (HPFS) and Nurses Health Studies (NHS) were also used. Genotypes were derived from genome-wide genotype data or directly obtained using Taqman. Logistic regression analysis was done including age, sex, diuretic exposure and ancestry as covariates as appropriate. The T-allele increased the risk of gout in the clinically-ascertained European samples (OR = 1.12, P = 0.012) and decreased the risk of gout in Polynesians (OR = 0.80, P = 0.011). There was no evidence for association in the HPFS or NHS sample sets. In conclusion TLR4 SNP rs2143956 associates with gout risk in prevalent clinically-ascertained gout in Europeans, in a direction consistent with previously published results in Han Chinese. However, with an opposite direction of association in Polynesians and no evidence for association in a non-clinically-ascertained incident gout cohort this variant should be analysed in other international gout genetic data sets to determine if there is genuine evidence for association

Epsin 1 Promotes Synaptic Growth by Enhancing BMP Signal Levels in Motoneuron Nuclei

We thank Carl-Henrik Heldin (Uppsala University, Sweden) for his generous gift of the PS1 pMad antibody, Hugo Bellen, Corey Goodman, Janis Fischer, Graeme Davis, Guillermo Marques, Michael O'Connor, Kate O'Connor-Giles, and the Bloomington Drosophila Stock Center for flies strains, the Developmental Studies Hybridoma Bank at the University of Iowa for antibodies to Wit and CSP; Marie Phillips for advice on membrane fractionation; Avital Rodal, Kate O'Connor-Giles, Ela Serpe, Kristi Wharton, Mojgan Padash-Barmchi for discussions or comments on the manuscript. We also thank Jody Summers at OUHSC for her generosity in letting us to use her confocal microscope.Conceived and designed the experiments: PAV TRF LRC BZ. Performed the experiments: PAV TRF LRC SMR HB NER BZ. Analyzed the data: PAV TRF LRC SMR HB NER BZ. Wrote the paper: PAV TRF BZ.Bone morphogenetic protein (BMP) retrograde signaling is crucial for neuronal development and synaptic plasticity. However, how the BMP effector phospho-Mother against decapentaplegic (pMad) is processed following receptor activation remains poorly understood. Here we show that Drosophila Epsin1/Liquid facets (Lqf) positively regulates synaptic growth through post-endocytotic processing of pMad signaling complex. Lqf and the BMP receptor Wishful thinking (Wit) interact genetically and biochemically. lqf loss of function (LOF) reduces bouton number whereas overexpression of lqf stimulates bouton growth. Lqf-stimulated synaptic overgrowth is suppressed by genetic reduction of wit. Further, synaptic pMad fails to accumulate inside the motoneuron nuclei in lqf mutants and lqf suppresses synaptic overgrowth in spinster (spin) mutants with enhanced BMP signaling by reducing accumulation of nuclear pMad. Interestingly, lqf mutations reduce nuclear pMad levels without causing an apparent blockage of axonal transport itself. Finally, overexpression of Lqf significantly increases the number of multivesicular bodies (MVBs) in the synapse whereas lqf LOF reduces MVB formation, indicating that Lqf may function in signaling endosome recycling or maturation. Based on these observations, we propose that Lqf plays a novel endosomal role to ensure efficient retrograde transport of BMP signaling endosomes into motoneuron nuclei.Yeshttp://www.plosone.org/static/editorial#pee