We describe one approach to build an automatically trainable anaphora resolution system. In this approach, we use Japanese newspaper articles tagged with discourse information as training examples for a machine learning algorithm which employs the C4.5 decision tree algorithm by Quinlan (Quinlan, 1993). Then, we evaluate and compare the results of several variants of the machine learning-based approach with those of our existing anaphora resolution system which uses manually-designed knowledge sources. Finally, we compare our algorithms with existing theories of anaphora, in particular, Japanese zero pronouns.

RNA structural elements occur in numerous single stranded (+)-sense RNA viruses. The stem-loop 2 motif (s2m) is one such element with an unusually high degree of sequence conservation, being found in the 3′ UTR in the genomes of many astroviruses, some picornaviruses and noroviruses, and a variety of coronaviruses, including SARS-CoV and SARS-CoV-2. The evolutionary conservation and its occurrence in all viral subgenomic transcripts implicates a key role of s2m in the viral infection cycle. Our findings indicate that the element, while stably folded, can nonetheless be invaded and remodelled spontaneously by antisense oligonucleotides (ASOs) that initiate pairing in exposed loops and trigger efficient sequence-specific RNA cleavage in reporter assays. ASOs also act to inhibit replication in an astrovirus replicon model system in a sequence-specific, dose-dependent manner and inhibit SARS-CoV-2 replication in cell culture. Our results thus permit us to suggest that the s2m element is readily targeted by ASOs, which show promise as anti-viral agents. IMPORTANCE The highly conserved stem-loop 2 motif (s2m) is found in the genomes of many RNA viruses including SARS-CoV-2. Our findings indicate that the s2m element can be targeted by antisense oligonucleotides. The anti-viral potential of this conserved element represents a promising start for further research into targeting conserved elements in RNA viruses.

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The conforn1ational features of an RNA A-fonn duplex with single adenosine bulges in two crystal structures will be discussed. Bulged nucleotides are frequent secondary structure motifs in RNA molecules and are often involved in Rl"\fA-RNA and Rl"\fA-protein interactions. RNA can be efficiently and selectively cleaved at bulge sites in the presence of divalent metal cations. The bulged As are looped-out, kink the duplex into the minor groove, and cause a marked opening of the normally cavernous Rl"\!A major .groove. The distinct geometries around the A-bulges in the two structures indicate that bulges can confer considerable local plasticity on the usually rigid Rl"\fA double helix. The enhanced backbone flexibility can provide for a linear preorientation of the attacking 2'-oxygen of the bulge nucleotide and the P-05' bond of the adjacent phosphate, consistent with Rl"\fA self-cleavage at bulged sites.The crystal structure of the RNA duplex [r(CCCCGGGG)h was refined to 1.45 A resolution using room temperature synchrotron diffraction data. This represents the highest resolution reported to date for an all-RNA oligonucleotide. Analysis of the ordered hydration around the octamer duplex reveals conserved regular arrangements of water molecules in both grooves. Evidence will be provided for an important role of the 2'-hydroxyl groups in the thermodynamic stabilization ofRl"\fA relative to DNA beyond their known functions of locking the sugar· pucker and mediating 3'->5' intrastrand 02' .. ·04' hydrogen bonds. We have solved the crystal structure of an all-RNA hammerhead ribozyme by isomorphous replacement. We desc1ibe the hammerhead RNA structure from a point of view which shows how the structural elements ar·e disposed to b!ing the active site nucleotide into the catalytic pocket of the ribozyme. Five potential Mg(II) binding sites can be identified in the hammerhead RNA electron density maps. Of these, one is a newly identified metal site positioned near the ribozyme catalytic pocket, and another site corresponds to a Mn(II) site identified in the previous hammerhead RNA structure. We propose a mechanism for Rl"\fA catalytic cleavage on the basis of this new metal-binding site, as well as upon comparisons between the catalytic pocket of the hanm1er-head RNA and the metal-binding sites in the structurally homologous uridine turn of the anticodon loop in tRNAPhe. In this paper, we investigate the geometry of DNA dinucleotide steps as revealed by single crystal structures of DNA oligomers. We set up a database of 60 naked ( = not bound to protein or drug) DNA oligomers, including A-form and B-forn1 oligomers alike. The database contains a total of 400 dinucleotide steps. The geometry of a dinucleotide step is desc1ibed by the six step parameters (Twist, Roll, Tilt, Rise, Slide and Shift) and the six base-pair parameters (Propeller, Buckle, Opening, Stretch, Shear and Stagger) as given by the Cambridge Accord!. The step and base-pair parameters are extracted from the atomic co-ordinates data of the various oligomers ...

Information on social media comprises of various modalities such as textual, visual and audio. NLP and Computer Vision communities often leverage only one prominent modality in isolation to study social media. However, computational processing of Internet memes needs a hybrid approach. The growing ubiquity of Internet memes on social media platforms such as Facebook, Instagram, and Twitter further suggests that we can not ignore such multimodal content anymore. To the best of our knowledge, there is not much attention towards meme emotion analysis. The objective of this proposal is to bring the attention of the research community towards the automatic processing of Internet memes. The task Memotion analysis released approx 10K annotated memes-with human annotated labels namely sentiment(positive, negative, neutral), type of emotion(sarcastic,funny,offensive, motivation) and their corresponding intensity. The challenge consisted of three subtasks: sentiment (positive, negative, and neutral) analysis of memes, overall emotion (humor, sarcasm, offensive, and motivational) classification of memes, and classifying intensity of meme emotion. The best performances achieved were F 1 (macro average) scores of 0.35, 0.51 and 0.32, respectively for each of the three subtasks.