Automatically Acquiring A Semantic Network Of Related Concepts

We describe the automatic acquisition of a semantic network in which over 7,500 of the most frequently occurring nouns in the English language are linked to their semantically related concepts in the WordNet noun ontology. Relatedness between nouns is discovered automatically from lexical co-occurrence in Wikipedia texts using a novel adaptation of an information theoretic inspired measure. Our algorithm then capitalizes on salient sense clustering among these semantic associates to automatically disambiguate them to their corresponding WordNet noun senses (i.e., concepts). The resultant concept-to-concept associations, stemming from 7,593 target nouns, with 17,104 distinct senses among them, constitute a large-scale semantic network with 208,832 undirected edges between related concepts. Our work can thus be conceived of as augmenting the WordNet noun ontology with RelatedTo links. The network, which we refer to as the Szumlanski-Gomez Network (SGN), has been subjected to a variety of evaluative measures, including manual inspection by human judges and quantitative comparison to gold standard data for semantic relatedness measurements. We have also evaluated the network’s performance in an applied setting on a word sense disambiguation (WSD) task in which the network served as a knowledge source for established graph-based spreading activation algorithms, and have shown: a) the network is competitive with WordNet when used as a stand-alone knowledge source for WSD, b) combining our network with WordNet achieves disambiguation results that exceed the performance of either resource individually, and c) our network outperforms a similar resource, WordNet++ (Ponzetto & Navigli, 2010), that has been automatically derived from annotations in the Wikipedia corpus. iii Finally, we present a study on human perceptions of relatedness. In our study, we elicited quantitative evaluations of semantic relatedness from human subjects using a variation of the classical methodology that Rubenstein and Goodenough (1965) employed to investigate human perceptions of semantic similarity. Judgments from individual subjects in our study exhibit high average correlation to the elicited relatedness means using leave-one-out sampling (r = 0.77, σ = 0.09, N = 73), although not as high as average human correlation in previous studies of similarity judgments, for which Resnik (1995) established an upper bound of r = 0.90 (σ = 0.07, N = 10). These results suggest that human perceptions of relatedness are less strictly constrained than evaluations of similarity, and establish a clearer expectation for what constitutes human-like performance by a computational measure of semantic relatedness. We also contrast the performance of a variety of similarity and relatedness measures on our dataset to their performance on similarity norms and introduce our own dataset as a supplementary evaluative standard for relatedness measures

Automatically acquiring a semantic network of related concepts

ABSTRACT We describe the automatic construction of a semantic network 1 , in which over 3000 of the most frequently occurring monosemous nouns 2 in Wikipedia (each appearing between 1,500 and 100,000 times) are linked to their semantically related concepts in the WordNet noun ontology. Relatedness between nouns is discovered automatically from cooccurrence in Wikipedia texts using an information theoretic inspired measure. Our algorithm then capitalizes on salient sense clustering among related nouns to automatically disambiguate them to their appropriate senses (i.e., concepts). Through the act of disambiguation, we begin to accumulate relatedness data for concepts denoted by polysemous nouns, as well. The resultant concept-to-concept associations, covering 17,543 nouns, and 27,312 distinct senses among them, constitute a large-scale semantic network of related concepts that can be conceived of as augmenting the WordNet noun ontology with related-to links

Rho-GTPase–dependent filamentous actin dynamics coordinate vesicle targeting and exocytosis during tip growth

The dynamic activity of tip-localized filamentous actin (F-actin) in pollen tubes is controlled by counteracting RIC4 and RIC3 pathways downstream of the ROP1 guanosine triphosphatase promoting actin assembly and disassembly, respectively. We show here that ROP1 activation is required for both the polar accumulation and the exocytosis of vesicles at the plasma membrane apex. The apical accumulation of exocytic vesicles oscillated in phase with, but slightly behind, apical actin assembly and was enhanced by overexpression of RIC4. However, RIC4 overexpression inhibited exocytosis, and this inhibition could be suppressed by latrunculin B treatment or RIC3 overexpression. We conclude that RIC4-dependent actin assembly is required for polar vesicle accumulation, whereas RIC3-mediated actin disassembly is required for exocytosis. Thus ROP1-dependent F-actin dynamics control tip growth through spatiotemporal coordination of vesicle targeting and exocytosis

Nomenclature for alleles of the thiopurine methyltransferase gene

The drug-metabolizing enzyme thiopurine methyltransferase (TPMT) has become one of the best examples of pharmacogenomics to be translated into routine clinical practice. TPMT metabolizes the thiopurines 6-mercaptopurine, 6-thioguanine, and azathioprine, drugs that are widely used for treatment of acute leukemias, inflammatory bowel diseases, and other disorders of immune regulation. Since the discovery of genetic polymorphisms in the TPMT gene, many sequence variants that cause a decreased enzyme activity have been identified and characterized. Increasingly, to optimize dose, pretreatment determination of TPMT status before commencing thiopurine therapy is now routine in many countries. Novel TPMT sequence variants are currently numbered sequentially using PubMed as a source of information; however, this has caused some problems as exemplified by two instances in which authors' articles appeared on PubMed at the same time, resulting in the same allele numbers given to different polymorphisms. Hence, there is an urgent need to establish an order and consensus to the numbering of known and novel TPMT sequence variants. To address this problem, a TPMT nomenclature committee was formed in 2010, to define the nomenclature and numbering of novel variants for the TPMT gene. A website (http://www.imh.liu.se/tpmtalleles) serves as a platform for this work. Researchers are encouraged to submit novel TPMT alleles to the committee for designation and reservation of unique allele numbers. The committee has decided to renumber two alleles: nucleotide position 106 (G>A) from TPMT*24 to TPMT*30 and position 611 (T>C, rs79901429) from TPMT*28 to TPMT*31. Nomenclature for all other known alleles remains unchanged

EFSA Panel on Food Contact Materials, Enzymes, Flavourings and Processing Aids (CEF); Scientific Opinion on Flavouring Group Evaluation 08, Revision 5 (FGE.08Rev5): Aliphatic and alicyclic mono-, di-, tri-, and polysulphides with or without additional oxygenated functional groups from chemical groups 20 and 30

<p>The CEF Panel of the European Food Safety Authority was requested to evaluate 80 flavouring substances in the Flavouring Group Evaluation 08, Revision 4, using the Procedure in Commission Regulation (EC) No 1565/2000. Since the publication of the last revision of this FGE, the EFSA has been requested to evaluate additional toxicological data submitted for two flavouring substances, one substance 2,5-dihydroxy-2,5-dimethyl-1,4-dithiane [FL-no: 15.006], which support the evaluation of the candidate substance 2,5-dihydroxy-1,4-dithiane [FL-no: 15.134] and one on the candidate substance spiro(2,4-dithia-1-methyl-8-oxabicyclo[3.3.0]octane-3,3’-(1’-oxa-2’-methyl)-cyclopentane) and spiro(2,4-dithia-6-methyl-7-oxabicyclo[3.3.0]octane-3,3’-(1’-oxa-2’-methyl)-cyclopentane) [FL-no: 15.007], which have been included in the present revision of FGE.08. For the substances methyl methanethiosulphonate [FL-no: 12.159], 2-methylbutane-2-thiol [FL-no: 12.172], 2-methylpropane-2-thiol [FL-no: 12.174], ethyl-2-mercapto-2-methyl propanoate [FL-no: 12.304] and 2,4,4-trimethyl-1,3-oxathiane [FL-no: 16.057] there is an indication of a genotoxic potential in vitro. Therefore, without further genotoxicity data, the Panel concluded that the Procedure could not be applied to these five substances. For four substances, 3-mercaptooctanal [FL-no: 12.268], 3-mercaptodecanal [FL-no: 12.269], methanedithiol diacetate [FL-no: 12.271] and 3,5-dimethyl-1,2-dithiolane-4-one [FL-no: 12.295] no data on use as flavouring substances in Europe are available and no intake figures could be calculated, which preclude the evaluation of the four substances using the Procedure. The remaining 71 substances were evaluated through a stepwise approach that integrates information on the structure-activity relationships, intake from current uses, toxicological threshold of concern, and available data on metabolism and toxicity. The Panel concluded that 59 substances do not rise safety concerns at their levels of dietary intake, estimated on the basis of the MSDI approach. For 12 substances [FL-no: 12.093, 12.094, 12.097, 12.100, 12.112, 12.116, 12.120, 12.164, 12.167, 12.199, 15.102 and 15.125], evaluated through the Procedure, no appropriate NOAEL was available and additional data are required. The specifications for the materials of commerce have also been considered and for three substances, information on the specifications is lacking.</p&gt

Resolving the homology-function relationship through comparative genomics of membrane-trafficking machinery and parasite cell biology

With advances in DNA sequencing technology, it is increasingly common and tractable to informatically look for genes of interest in the genomic databases of parasitic organisms and infer cellular states. Assignment of a putative gene function based on homology to functionally characterized genes in other organisms, though powerful, relies on the implicit assumption of functional homology, i.e. that orthology indicates conserved function. Eukaryotes reveal a dazzling array of cellular features and structural organization, suggesting a concomitant diversity in their underlying molecular machinery. Significantly, examples of novel functions for pre-existing or new paralogues are not uncommon. Do these examples undermine the basic assumption of functional homology, especially in parasitic protists, which are often highly derived? Here we examine the extent to which functional homology exists between organisms spanning the eukaryotic lineage. By comparing membrane trafficking proteins between parasitic protists and traditional model organisms, where direct functional evidence is available, we find that function is indeed largely conserved between orthologues, albeit with significant adaptation arising from the unique biological features within each lineage

Code Vectors: Understanding Programs Through Embedded Abstracted Symbolic Traces

With the rise of machine learning, there is a great deal of interest in treating programs as data to be fed to learning algorithms. However, programs do not start off in a form that is immediately amenable to most off-the-shelf learning techniques. Instead, it is necessary to transform the program to a suitable representation before a learning technique can be applied. In this paper, we use abstractions of traces obtained from symbolic execution of a program as a representation for learning word embeddings. We trained a variety of word embeddings under hundreds of parameterizations, and evaluated each learned embedding on a suite of different tasks. In our evaluation, we obtain 93% top-1 accuracy on a benchmark consisting of over 19,000 API-usage analogies extracted from the Linux kernel. In addition, we show that embeddings learned from (mainly) semantic abstractions provide nearly triple the accuracy of those learned from (mainly) syntactic abstractions

Conflict resolution and a framework for collaborative interactive evolution

Abstract Interactive evolutionary computation (IEC) has proven useful in a variety of applications by combining the subjective evaluation of a user with the massive parallel search power of the genetic algorithm (GA). Here, we articulate a framework for an extension of IEC into collaborative interactive evolution, in which multiple users guide the evolutionary process. In doing so, we introduce the ability for users to combine their efforts for the purpose of evolving effective solutions to problems. This necessarily gives rise to the possibility of conflict between users. We draw on the salient features of the GA to resolve these conflicts and lay the foundation for this new paradigm to be used as a tool for conflict resolution in complex group-wise human-computer interaction tasks

Thiopurine Methyltransferase Predicts the Extent of Cytotoxicty and DNA Damage in Astroglial Cells after Thioguanine Exposure

Thiopurine methyltransferase (Tpmt) is the primary enzyme responsible for deactivating thiopurine drugs. Thiopurine drugs (i.e., thioguanine [TG], mercaptopurine, azathioprine) are commonly used for the treatment of cancer, organ transplant, and autoimmune disorders. Chronic thiopurine therapy has been linked to the development of brain cancer (most commonly astrocytomas), and Tpmt status has been associated with this risk. Therefore, we investigated whether the level of Tpmt protein activity could predict TG-associated cytotoxicity and DNA damage in astrocytic cells. We found that TG induced cytotoxicity in a dose-dependent manner in Tpmt+/+, Tpmt+/− and Tpmt−/− primary mouse astrocytes and that a low Tpmt phenotype predicted significantly higher sensitivity to TG than did a high Tpmt phenotype. We also found that TG exposure induced significantly more DNA damage in the form of single strand breaks (SSBs) and double strand breaks (DSBs) in primary astrocytes with low Tpmt versus high Tpmt. More interestingly, we found that Tpmt+/− astrocytes had the highest degree of cytotoxicity and genotoxicity (i.e., IC50, SSBs and DSBs) after TG exposure. We then used human glioma cell lines as model astroglial cells to represent high (T98) and low (A172) Tpmt expressers and found that A172 had the highest degree of cytoxicity and SSBs after TG exposure. When we over-expressed Tpmt in the A172 cell line, we found that TG IC50 was significantly higher and SSB's were significantly lower as compared to mock transfected cells. This study shows that low Tpmt can lead to greater sensitivity to thiopurine therapy in astroglial cells. When Tpmt deactivation at the germ-line is considered, this study also suggests that heterozygosity may be subject to the greatest genotoxic effects of thiopurine therapy