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"Henry, A"
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Student evaluations of teaching are an inadequate assessment tool for evaluating faculty performance
Literature is examined to support the contention that student evaluations of teaching (SET) should not be used for summative evaluation of university faculty. Recommendations for alternatives to SET are provided.
Journal Article
Environmental (e)RNA advances the reliability of eDNA by predicting its age
Environmental DNA (eDNA) analysis has advanced conservation biology and biodiversity management. However, accurate estimation of age and origin of eDNA is complicated by particle transport and the presence of legacy genetic material, which can obscure accurate interpretation of eDNA detection and quantification. To understand the state of genomic material within the environment, we investigated the degradation relationships between (a) size of fragments (long vs short), (b) genomic origins (mitochondrial vs nuclear), (c) nucleic acids (eDNA vs eRNA), and (d) RNA types (messenger (m)RNA vs ribosomal (r)RNA) from non-indigenous
Dreissena
mussels. Initial concentrations of eRNA followed expected transcriptional trends, with rRNAs found at > 1000 × that of eDNA, and a mitosis-associated mRNA falling below detection limits within 24 h. Furthermore, the ratio of eRNA:eDNA significantly decreased throughout degradation, potentially providing an estimate for the age of genomic material. Thus, eRNA quantification can increase detection due to the high concentrations of rRNAs. Furthermore, it may improve interpretation of positive detections through the eRNA:eDNA ratio and/or by detecting low abundant mitosis-associated mRNAs that degrade within ~ 24 h.
Journal Article
Climate change and soil freezing dynamics: historical trends and projected changes
Changes to soil freezing dynamics with climate change can modify ecosystem carbon and nutrient losses. Soil freezing is influenced strongly by both air temperature and insulation by the snowpack, and it has been hypothesized that winter climate warming may lead to increased soil freezing as a result of reduced snowpack thickness. I used weather station data to explore the relationships between winter air temperature, precipitation and soil freezing for 31 sites in Canada, ranging from the temperate zone to the high Arctic. Inter-annual climate variation and associated soil temperature variation over the last 40 years were examined and used to interpolate the effects of projected climate change on soil freezing dynamics within sites using linear regression models. Annual soil freezing days declined with increasing mean winter air temperature despite decreases in snow depth and cover, and reduced precipitation only increased annual soil freezing days in the warmest sites. Annual soil freeze-thaw cycles increased in both warm and dry winters, although the effects of precipitation were strongest in sites that experience low mean winter precipitation. Overall, it was projected that by 2050, changes in winter temperature will have a much stronger effect on annual soil freezing days and freeze-thaw cycles than changes in total precipitation, with sites close to but below freezing experiencing the largest changes in soil freezing days. These results reveal that experimental data relevant to the effects of climate changes on soil freezing dynamics and changes in associated soil physical and biological processes are lacking.
Journal Article
MAIA—A machine learning assisted image annotation method for environmental monitoring and exploration
Digital imaging has become one of the most important techniques in environmental monitoring and exploration. In the case of the marine environment, mobile platforms such as autonomous underwater vehicles (AUVs) are now equipped with high-resolution cameras to capture huge collections of images from the seabed. However, the timely evaluation of all these images presents a bottleneck problem as tens of thousands or more images can be collected during a single dive. This makes computational support for marine image analysis essential. Computer-aided analysis of environmental images (and marine images in particular) with machine learning algorithms is promising, but challenging and different to other imaging domains because training data and class labels cannot be collected as efficiently and comprehensively as in other areas. In this paper, we present Machine learning Assisted Image Annotation (MAIA), a new image annotation method for environmental monitoring and exploration that overcomes the obstacle of missing training data. The method uses a combination of autoencoder networks and Mask Region-based Convolutional Neural Network (Mask R-CNN), which allows human observers to annotate large image collections much faster than before. We evaluated the method with three marine image datasets featuring different types of background, imaging equipment and object classes. Using MAIA, we were able to annotate objects of interest with an average recall of 84.1% more than twice as fast as compared to \"traditional\" annotation methods, which are purely based on software-supported direct visual inspection and manual annotation. The speed gain increases proportionally with the size of a dataset. The MAIA approach represents a substantial improvement on the path to greater efficiency in the annotation of large benthic image collections.
Journal Article