A VetCompass Australia Study of Antimicrobial Use in Dog-to-Dog Bite Wounds (1998–2018)

Although dog-to-dog bite wounds (DBW) are a common presentation to veterinary clinics, antimicrobial prescribing habits of Australian clinics have not been reported. This study determined the frequency and results of DBW cultures; antimicrobial selection; and importance class of antimicrobials prescribed relative to wound severity, geographic location, or year. A systematic sample of 72,507 patient records was retrieved from the VetCompass Australia database. Records for 1713 dog bite events involving 1655 dogs were reviewed for presenting signs, results of culture and susceptibility testing (C&S), antimicrobial treatment, geographical location, and outcome. A crossed random effects multivariable logistic regression model was used to determine if antimicrobial importance was associated with wound severity, year, and location, and to assess the differences in antimicrobial prescription between geographical locations, clinics, and veterinarians. Antimicrobials were prescribed in 86.1% of DBW. Amoxicillin-clavulanic acid was prescribed in 70% (1202/1713) with underdosing in 15.8% (191/1202). High-importance antimicrobial use was associated with wound severity (p < 0.001), year category (p = 0.007), and surgery (p = 0.03). C&S testing was recorded as having been performed in only one case. Differences in individual veterinarian prescribing habits were stronger than the clinic culture, suggesting that education utilizing clinic-wide antimicrobial guidelines may aid in improving antimicrobial stewardship.

Effective playing time in the Chinese Super League

The purpose of this study was to analyse the effective playing time during the 2012-2018 Chinese Super League seasons. A linear mixed model with crossed random effects was used to assess the effects of match and technique characteristics on effective playing time. The key findings are as follows. (1) Matches played between the top eight teams, a tie at half time and increased audience participation were associated with reduced effective playing time; (2) The effective playing time tended to be smaller in matches with red cards than in matches without red cards. In contrast, the effective playing time did not significantly vary among the outcomes of the matches; (3) Game interruptions (goals, corner-kicks, free-kicks and throw-ins) and destructive actions (yellow cards, red cards and fouls) had a significant negative impact on the effective playing time, while productive actions (shots on target) had a significant positive impact on the effective playing time.

The Effect of Facilitative vs. Inhibitory Word Training Corpora on Word Reading Accuracy Growth in Children with Dyslexia

We modeled word reading growth in typically developing (n = 118) and children with dyslexia (n = 20), grades 2-5, across multiple exposures to 30 words. We explored the facilitative vs. inhibitory effects of exposures to differential mixes of words that support high vs. low frequency vowel pronunciations. One training corpus contained a ratio of 80%-20% high to low frequency pronunciations (e.g. for ea; 80% ea pronounced as /i/ as in bead and 20% ea pronounced /ε/ as in dead) while the other consisted of a ratio of 20%-80%. We also modeled accuracy at the final exposure for a subset of 12 shared words across conditions using item-level crossed random effects models with reading skill (i.e., typically developing vs. dyslexic), condition, word frequency, and vowel pronunciation (i.e., high vs. low frequency vowel pronunciation) as predictors in the model. We were particularly interested in the interaction between condition and vowel pronunciation across reading groups. Results suggest typically developing children were influenced by the interaction between condition and vowel pronunciation, suggesting both facilitation and inhibition; whereas children with dyslexia were influenced by condition and vowel pronunciation without an interaction. Results are interpreted within the overfitting model of dyslexia (Harm &amp; Seidenberg, 1999).

The Effect of Facilitative Versus Inhibitory Word Training Corpora on Word Reading Accuracy Growth in Children With Dyslexia

We modeled word reading growth in typically developing ( n = 118) and children with dyslexia ( n = 20), Grades 2–5, across multiple exposures to 30 words. We explored the facilitative versus inhibitory effects of exposures to differential mixes of words that support high- versus low-frequency vowel pronunciations. One training corpus contained a ratio of 80%–20% high- to low-frequency pronunciations (e.g., for ea; 80% ea pronounced as /i/ as in bead and 20% ea pronounced /ε/ as in dead), whereas the other consisted of a ratio of 20%–80%. We also modeled accuracy at the final exposure for a subset of 12 shared words across conditions using item-level crossed-random effects models with reading skill (i.e., typically developing vs. dyslexic), condition, word frequency, and vowel pronunciation (i.e., high- vs. low-frequency vowel pronunciation) as predictors in the model. We were particularly interested in the interaction between condition and vowel pronunciation across reading groups. Results suggest typically developing children were influenced by the interaction between condition and vowel pronunciation, suggesting both facilitation and inhibition, whereas children with dyslexia were influenced by condition and vowel pronunciation without an interaction. Results are interpreted within the overfitting model of dyslexia.

The role of iconicity, construal, and proficiency in the online processing of handshape

abstractIconicity has traditionally been considered an objective, fixed, unidimensional property of language forms, often operationalized as transparency for experimental purposes. Within a Cognitive Linguistics framework, iconicity is a mapping between an individual’s construal of form and construal of meaning, such that iconicity is subjective, dynamic, and multidimensional. We test the latter alternative by asking signers who differed in ASL proficiency to complete a handshape monitoring task in which we manipulated the number of form–meaning construals that target handshapes participated in. We estimated the interaction of iconicity, proficiency, and construal density using mixed-effects models for response time and accuracy with crossed random effects for participants and items.Results show a significant three-way interaction between iconicity, proficiency, and construal density such that less-proficient signers detected handshapes in more iconic signs faster than less iconic signs regardless of the handshape they were monitoring, but highly proficient signers’ performance was only improved by iconicity for handshapes that participate in many construals. Taken in conjunction with growing evidence of the subjectivity of iconicity, we interpret these results as support for the claim that construal is a core mechanism underlying iconicity, both for transparent and systematic language-internal form–meaning mappings.

Scalable inference for crossed random effects models

Summary We develop methodology and complexity theory for Markov chain Monte Carlo algorithms used in inference for crossed random effects models in modern analysis of variance. We consider a plain Gibbs sampler and propose a simple modification, referred to as a collapsed Gibbs sampler. Under some balancedness conditions on the data designs and assuming that precision hyperparameters are known, we demonstrate that the plain Gibbs sampler is not scalable, in the sense that its complexity is worse than proportional to the number of parameters and data, but the collapsed Gibbs sampler is scalable. In simulated and real datasets we show that the explicit convergence rates predicted by our theory closely match the computable, but nonexplicit rates in cases where the design assumptions are violated. We also show empirically that the collapsed Gibbs sampler extended to sample precision hyperparameters significantly outperforms alternative state-of-the-art algorithms.

Maximum likelihood estimation of nonlinear mixed-effects models with crossed random effects by combining first-order conditional linearization and sequential quadratic programming

Nonlinear mixed-effects (NLME) models have become popular in various disciplines over the past several decades. However, the existing methods for parameter estimation implemented in standard statistical packages such as SAS and R/S-Plus are generally limited to single- or multi-level NLME models that only allow nested random effects and are unable to cope with crossed random effects within the framework of NLME modeling. In this study, we propose a general formulation of NLME models that can accommodate both nested and crossed random effects, and then develop a computational algorithm for parameter estimation based on normal assumptions. The maximum likelihood estimation is carried out using the first-order conditional expansion (FOCE) for NLME model linearization and sequential quadratic programming (SQP) for computational optimization while ensuring positive-definiteness of the estimated variance-covariance matrices of both random effects and error terms. The FOCE-SQP algorithm is evaluated using the height and diameter data measured on trees from Korean larch (L. olgensis var. Changpaiensis) experimental plots as well as simulation studies. We show that the FOCE-SQP method converges fast with high accuracy. Applications of the general formulation of NLME models are illustrated with an analysis of the Korean larch data.