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1. Contemporary Safety Topics in Animal Research

   Swearengen, James R.; Colby, Lesley A.; Zitzow, Lois; Asfaw, Yohannes G.; Reynolds, Randall; Alderman, Scott; Norton, John N.; Villano, Jason S.; Vleck, Susan E.; Felt, Stephen A.; et al (July 2018, ILAR journal)

   Bradfield, John; Meyer, Esmeralda; Norton, John N. (Ed.)

   Institutions with animal care and use programs are obligated to provide for the health and well-being of the animals, but are equally obligated to provide for safety of individuals associated with the program. The topics in this issue of the ILAR Journal, in association with those within the complimentary issue of the Journal of Applied Biosafety, provide a variety of contemporary occupational health and safety considerations in today’s animal research programs. Each article addresses key or emerging occupational health and safety topics in institutional animal care and use programs, where the status of the topic, contemporary challenges, and future directions are provided. 

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2. Discussion of ‘A Unified Framework for De-Duplication and Population Size Estimation’ by Tancredi, Steorts, and Liseo.

   Sadinle, Mauricio (January 2020, Bayesian analysis)

   Population size estimation techniques, such as multiple-systems or capture-recapture estimation, typically require multiple samples from the study population, in addition to the information on which individuals are included in which samples. In many contexts, these samples come from existing data sources that contain certain information on the individuals but no unique identifiers. The goal of record linkage and duplicate detection techniques is to identify unique individuals across and within samples based on the information collected on them, which might correspond to basic partial identifiers, such as given and family name, and other demographic information. Therefore, record linkage and duplicate detection are often needed to generate the input for a given population size estimation technique that a researcher might want to use. Linkage decisions, however, are subject to uncertainty when partial identifiers are limited or contain errors and missingness, and therefore, intuitively, uncertainty in the linkage and deduplication process should somehow be taken into account in the stage of population size estimation. 

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3. Ranking with multiple types of pairwise comparisons

   https://doi.org/10.1098/rspa.2022.0517  

   Newman, Mark E. (October 2022, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences)

   The task of ranking individuals or teams, based on a set of comparisons between pairs, arises in various contexts, including sporting competitions and the analysis of dominance hierarchies among animals and humans. Given data on which competitors beat which others, the challenge is to rank the competitors from best to worst. Here we study the problem of computing rankings when there are multiple, potentially conflicting types of comparison, such as multiple types of dominance behaviours among animals. We assume that we do not know a priori what information each behaviour conveys about the ranking, or even whether they convey any information at all. Nonetheless, we show that it is possible to compute a ranking in this situation and present a fast method for doing so, based on a combination of an expectation–maximization algorithm and a modified Bradley–Terry model. We give a selection of example applications to both animal and human competition. 

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4. Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing

   https://doi.org/s13059-020-02001-7  

   Huang, J. (April 2020, Genome biology)

   Background Epigenome-wide association studies (EWAS), which seek the association between epigenetic marks and an outcome or exposure, involve multiple hypothesis testing. False discovery rate (FDR) control has been widely used for multiple testing correction. However, traditional FDR control methods do not use auxiliary covariates, and they could be less powerful if the covariates could inform the likelihood of the null hypothesis. Recently, many covariate-adaptive FDR control methods have been developed, but application of these methods to EWAS data has not yet been explored. It is not clear whether these methods can significantly improve detection power, and if so, which covariates are more relevant for EWAS data. Results In this study, we evaluate the performance of five covariate-adaptive FDR control methods with EWAS-related covariates using simulated as well as real EWAS datasets. We develop an omnibus test to assess the informativeness of the covariates. We find that statistical covariates are generally more informative than biological covariates, and the covariates of methylation mean and variance are almost universally informative. In contrast, the informativeness of biological covariates depends on specific datasets. We show that the independent hypothesis weighting (IHW) and covariate adaptive multiple testing (CAMT) method are overall more powerful, especially for sparse signals, and could improve the detection power by a median of 25% and 68% on real datasets, compared to the ST procedure. We further validate the findings in various biological contexts. Conclusions Covariate-adaptive FDR control methods with informative covariates can significantly increase the detection power for EWAS. For sparse signals, IHW and CAMT are recommended. 

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5. Behavior and detection method influence detection probability of a translocated, endangered amphibian

   https://doi.org/10.1111/acv.12645  

   Hammond, Talisin T.; Curtis, Michelle J.; Jacobs, Leah E.; Tobler, Mathias W.; Swaisgood, Ronald R.; Shier, Debra M. (October 2020, Animal Conservation)

   Abstract Accurate estimates of survival are crucial for many management decisions in translocation programs. Maximizing detection probabilities and reducing sampling biases for released animals can aid in estimates of survival. One important source of sampling bias is an animal’s behavior. For example, individuals that are consistently more exploratory or active may be more likely to be detected visually. Behavioral traits can be related to survival after reintroduction, and because many pre‐release treatments aim to manipulate animal behavior, it is critical to tease apart relationships between behavior and detection probability. Here, we assessed the repeatability (intra‐individual consistency and inter‐individual variation) of behavioral traits for an endangered amphibian, the mountain yellow‐legged frog (Rana muscosa). Because new technological tools offer one potential solution for reducing sampling biases while increasing detection, we also tested whether a long‐range passive integrated transponder (PIT) tag reader could enhance surveys for these individuals after translocation into the wild. After confirming thatex situbredR. muscosaexhibit repeatable behavioral traits (repeatability = 0.25–0.41) and releasing these frogs (N = 196) into the wild, we conducted post‐release surveys visually and with the long‐range PIT tag reader. Integrating the long‐range reader into surveys improved detection probability four‐fold in comparison to visual surveys alone (~0.09 to ~0.36). Moreover, mark–recapture modeling revealed that tag reader detection probability was not biased toward detecting individuals of specific behavioral types, while visual detection was significantly related to behavioral traits. These results will enable a more accurate understanding of individual differences in post‐release success in translocations. This may be particularly important for amphibian species, which can be difficult to detect and are expected to increasingly be involved in human‐managed breeding and translocation programs due to their vulnerable conservation status. 

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