\u3ci\u3eThe Pine Tree\u3c/i\u3e

Bang! The bat hit the ball and Walter raced to first base. The outfielder picked up the ball and threw it to Richie who was pitching

Representations of Gender-Diverse Characters in Adventure Time and Steven Universe

Television is a strong educational and socializing agent for children. Watching television can teach children appropriate language and vocabulary to use, as well as the social norms about gender behaviors or activities. Previous research on gender representations in children’s television has been limited to studying male and female characters because children’s programming has historically presented audiences with cisgender characters (e.g., boy and girls). Recently, television shows aimed at children have provided audiences with nonbinary and gender-diverse characters. This study is the first exploratory content analysis, to my knowledge, to examine the portrayal and representation of nonbinary and gender-diverse characters in children’s television. The current study examined the gender-neutral pronoun and gendered language use toward nonbinary and gender-diverse characters, as well as the portrayal of these characters as leaders, and with special skills in Adventure Time and Steven Universe. Overall, nonbinary and gender-diverse characters were portrayed as strong, positive, characters, and were represented similarly to their cisgender counterparts. This represents a promising shift toward more inclusive and equitable television representation, which may lead to the acceptance and appropriate use of gender-neutral pronouns toward peers by cisgender children, and the feeling of visibility and validation by nonbinary children. Future research should examine the impacts of these characters on viewers. RELEVANCE STATEMENT: As children’s television becomes more diverse it has the potential to positively impact the lives of cisgender (e.g., boys and girls) and nonbinary children. Because television has the potential to influence young children, gender-diverse representations in children’s television may lead to children developing more accepting attitudes and behaviors toward nonbinary peers

Edition 1.1 of the PARSEME shared task on automatic identification of verbal multiword expressions

This paper describes the PARSEME Shared Task 1.1 on automatic identification of verbal multiword expressions. We present the annotation methodology, focusing on changes from last year’s shared task. Novel aspects include enhanced annotation guidelines, additional annotated data for most languages, corpora for some new languages, and new evaluation settings. Corpora were created for 20 languages, which are also briefly discussed. We report organizational principles behind the shared task and the evaluation metrics employed for ranking. The 17 participating systems, their methods and obtained results are also presented and analysed

The automatic processing of multiword expressions in Irish

It is well-documented that Multiword Expressions (MWEs) pose a unique challenge to a variety of NLP tasks such as machine translation, parsing, information retrieval, and more. For low-resource languages such as Irish, these challenges can be exacerbated by the scarcity of data, and a lack of research in this topic. In order to improve handling of MWEs in various NLP tasks for Irish, this thesis will address both the lack of resources specifically targeting MWEs in Irish, and examine how these resources can be applied to said NLP tasks. We report on the creation and analysis of a number of lexical resources as part of this PhD research. Ilfhocail, a lexicon of Irish MWEs, is created through extract- ing MWEs from other lexical resources such as dictionaries. A corpus annotated with verbal MWEs in Irish is created for the inclusion of Irish in the PARSEME Shared Task 1.2. Additionally, MWEs were tagged in a bilingual EN-GA corpus for inclusion in experiments in machine translation. For the purposes of annotation, a categorisation scheme for nine categories of MWEs in Irish is created, based on combining linguistic analysis on these types of constructions and cross-lingual frameworks for defining MWEs. A case study in applying MWEs to NLP tasks is undertaken, with the exploration of incorporating MWE information while training Neural Machine Translation systems. Finally, the topic of automatic identification of Irish MWEs is explored, documenting the training of a system capable of automatically identifying Irish MWEs from a variety of categories, and the challenges associated with developing such a system. This research contributes towards a greater understanding of Irish MWEs and their applications in NLP, and provides a foundation for future work in exploring other methods for the automatic discovery and identification of Irish MWEs, and further developing the MWE resources described above

Annotating verbal MWEs in Irish for the PARSEME Shared Task 1.2

This paper describes the creation of two Irish corpora (labelled and unlabelled) for verbal MWEs for inclusion in the PARSEME Shared Task 1.2 on automatic identification of verbal MWEs, and the process of developing verbal MWE categories for Irish. A qualitative analysis on the two corpora is presented, along with discussion of Irish verbal MWEs

Membrane integrity contributes to resistance of Cryptococcus neoformans to the cell wall inhibitor caspofungin

The fungal pathogen Cryptococcus neoformans causes up to 278 000 infections each year globally, resulting in up to 180,000 deaths annually, mostly impacting immunocompromised people. Therapeutic options for C. neoformans infections are very limited. Caspofungin, a member of the echinocandin class of antifungals, is generally well tolerated but clinically ineffective against C. neoformans. We sought to identify biological processes that can be targeted to render the cell more susceptible to echinocandins by screening the available libraries of gene deletion mutants made in the KN99α background for caspofungin sensitivity. We adapted a Candida albicans fungal biofilm assay for the growth characteristics of C. neoformans and systematically screened 4,030 individual gene deletion mutants in triplicate plate assays. We identified 25 strains that showed caspofungin sensitivity. We followed up with a dose dependence assay, and 17 of the 25 were confirmed sensitive, 5 of which were also sensitive in an agar plate assay. We made new deletion mutant strains for four of these genes

Identification of a Homology-Independent Linchpin Domain Controlling Mouse and Bank Vole Prion Protein Conversion

Prions are unorthodox pathogens that cause fatal neurodegenerative diseases in humans and other mammals. Prion propagation occurs through the self-templating of the pathogenic conformer PrPSc, onto the cell-expressed conformer, PrPC. Here we study the conversion of PrPC to PrPSc using a recombinant mouse PrPSc conformer (mouse protein-only recPrPSc) as a unique tool that can convert bank vole but not mouse PrPC substrates in vitro. Thus, its templating ability is not dependent on sequence homology with the substrate. In the present study, we used chimeric bank vole/mouse PrPC substrates to systematically determine the domain that allows for conversion by Mo protein-only recPrPSc. Our results show that that either the presence of the bank vole amino acid residues E227 and S230 or the absence of the second N-linked glycan are sufficient to allow PrPC substrates to be converted by Mo protein-only recPrPSc and several native infectious prion strains. We propose that residues 227 and 230 and the second glycan are part of a C-terminal domain that acts as a linchpin for bank vole and mouse prion conversion

gaBERT — an Irish Language model

The BERT family of neural language models have become highly popular due to their ability to provide sequences of text with rich context-sensitive token encodings which are able to generalise well to many NLP tasks. We introduce gaBERT, a monolingual BERT model for the Irish language. We compare our gaBERT model to multilingual BERT and the monolingual Irish WikiBERT, and we show that gaBERT provides better representations for a downstream parsing task. We also show how different filtering criteria, vocabulary size and the choice of subword tokenisation model affect downstream performance. We compare the results of fine-tuning a gaBERT model with an mBERT model for the task of identifying verbal multiword expressions, and show that the fine-tuned gaBERT model also performs better at this task. We release gaBERT and related code to the community