We report a new surgical technique that allows intrascleral fixation of a posterior chamber intraocular lens (IOL) without sutures. The Y-fixation technique does not involve complicated intraocular manipulation and achieves safe sutureless fixation. A Y-shaped incision is made in the sclera and a 24-gauge microvitreoretinal (MVR) knife is used to create the sclerotomy instead of a needle. The Y-shaped incision eliminates the need to raise a large lamellar scleral flap and to use fibrin glue because the haptic can be fixed both inside the tunnel and in the groove, and performing the sclerotomy with the 24-gauge MVR knife simplifies extraction of the haptic and improves wound closure. There is no risk of infection from exposure of the haptic on the sclera and no use of fibrin glue. There was significantly less IOL decentration and tilt than with suture fixation.

Large‐scale learner corpora collected from online language learning platforms, such as the EF‐Cambridge Open Language Database (EFCAMDAT), provide opportunities to analyze learner data at an unprecedented scale. However, interpreting the learner language in such corpora requires a precise understanding of tasks: How does the prompt and input of a task and its functional requirements influence task‐based linguistic performance? This question is vital for making large‐scale task‐based corpora fruitful for second language acquisition research. We explore the issue through an analysis of selected tasks in EFCAMDAT and the complexity and accuracy of the language they elicit.

This paper introduces topic modelling, a machine learning technique that automatically identifies ‘topics’ in a given corpus. The paper illustrates its use in the exploration of a corpus of academic English. It first offers the intuitive explanation of the underlying mechanism of topic modelling and describes the procedure for building a model, including the decisions involved in the model-building process. The paper then explores the model. A topic in topic models is characterised by a set of co-occurring words, and we will demonstrate that such topics bring us rich insights into the nature of a corpus. As exemplary tasks, this paper identifies the prominent topics in different parts of papers, investigates the chronological change of a journal, and reveals different types of papers in the journal. The paper further compares topic modelling to two more traditional techniques in corpus linguistics, semantic annotation and keywords analysis, and highlights the strengths of topic modelling. We believe that topic modelling is particularly useful in the initial exploration of a corpus.

KIT, a transmembrane receptor tyrosine kinase, is one of the specific targets for anti-cancer therapy. In humans, its expression and mutations have been identified in malignant melanomas and therapies using molecular-targeted agents have been promising in these tumours. As human malignant melanoma, canine malignant melanoma is a fatal disease with metastases and the poor response has been observed with all standard protocols. In our study, KIT expression and exon 11 mutations in dogs with histologically confirmed malignant oral melanomas were evaluated. Although 20 of 39 cases were positive for KIT protein, there was no significant difference between KIT expression and overall survival. Moreover, polymerase chain reaction amplification and sequencing of KIT exon 11 in 17 samples did not detect any mutations and proved disappointing. For several reasons, however, KIT expression and mutations of various exons including exon 11 should be investigated in more cases.

This article introduces two sophisticated statistical modeling techniques that allow researchers to analyze systematicity, individual variation, and nonlinearity in second language (L2) development. Generalized linear mixed-effects models can be used to quantify individual variation and examine systematic effects simultaneously, and generalized additive mixed models allow for the examination of systematicity, individuality, and nonlinearity within a single model. Based on a longitudinal learner corpus, this article illustrates the usefulness of these models in the context of L2 accuracy development of English grammatical morphemes. I discuss the strengths of each technique and the ways in which these techniques can benefit L2 acquisition research, further highlighting the importance of accounting for individual variation in modeling L2 development.