Insights into mountain wetland resilience to climate change: An evaluation of the hydrological processes contributing to the hydrodynamics of alpine wetlands in the Canadian Rocky Mountains

Hydrological conditions play an important role in provisioning the exceptionally valuable ecosystem services and functions of wetlands. In alpine areas, wetland functions and services are expected to be very sensitive to climate-mediated changes in hydrology. However, few field studies of alpine wetland hydrology currently exist, thus limiting understanding of how wetlands will respond to warming and drying, and how their ecosystem services and functions will change. This study examines key processes contributing to the hydrological stability of alpine wetlands in Banff National Park, AB, Canada. During the two-year study, snowmelt timing differed by over three weeks, allowing for the examination of water table patterns under comparatively wet and dry conditions. Contrary to expectations, water table positions were relatively stable in each study year, particularly in the peat-bearing soils. Hydrophysical and hydrochemical data together provide evidence that the observed stability is in part due to groundwater contributions, which made up as much as 53% of the water budget in one peatland. Soil conditions also appear to play a role in stabilizing water table regimes. The results suggest that alpine wetlands, and peatlands in particular, may be more resilient to changes in climate than currently thought. Mineral wetlands, comparatively, may have limited adaptive capacity

Open-source single-particle analysis for super-resolution microscopy with virusmapper

Super-resolution fluorescence microscopy is currently revolutionizing cell biology research. Its capacity to break the resolution limit of around 300 nm allows for the routine imaging of nanoscale biological complexes and processes. This increase in resolution also means that methods popular in electron microscopy, such as single-particle analysis, can readily be applied to super-resolution fluorescence microscopy. By combining this analytical approach with super-resolution optical imaging, it becomes possible to take advantage of the molecule-specific labeling capacity of fluorescence microscopy to generate structural maps of molecular elements within a metastable structure. To this end, we have developed a novel algorithm - VirusMapper - packaged as an easy-to-use, high-performance, and high-throughput ImageJ plugin. This article presents an in-depth guide to this software, showcasing its ability to uncover novel structural features in biological molecular complexes. Here, we present how to assemble compatible data and provide a step-by-step protocol on how to use this algorithm to apply single-particle analysis to super-resolution images

Ultrahigh-resolution mapping of peatland microform using ground-based structure from motion with multiview stereo

This paper was published by AGU. Published 2016 American Geophysical Union.Global Institute for Water Security, The Natural Sciences and Engineering Research Council of Canada (Discovery Grant RGPIN 32837-20), The Canadian Foundation for Innovation, The Society of Wetland Scientists student research grants programPeer ReviewedMicroform is important in understanding wetland functions and processes. But collecting imagery of and mapping the physical structure of peatlands is often expensive and requires specialized equipment. We assessed the utility of coupling computer vision-based structure from motion with multiview stereo photogrammetry (SfM-MVS) and ground-based photos to map peatland topography. The SfM-MVS technique was tested on an alpine peatland in Banff National Park, Canada, and guidance was provided on minimizing errors. We found that coupling SfM-MVS with ground-based photos taken with a point and shoot camera is a viable and competitive technique for generating ultrahigh-resolution elevations (i.e., <0.01 m, mean absolute error of 0.083 m). In evaluating 100+ viable SfM-MVS data collection and processing scenarios, vegetation was found to considerably influence accuracy. Vegetation class, when accounted for, reduced absolute error by as much as 50%. The logistic flexibility of ground-based SfM-MVS paired with its high resolution, low error, and low cost makes it a research area worth developing as well as a useful addition to the wetland scientists’ toolkit

Seed fates in crop–wild hybrid sunflower: crop allele and maternal effects

Domestication has resulted in selection upon seed traits found in wild populations, yet crop-wild hybrids retain some aspects of both parental phenotypes. Seed fates of germination, dormancy, and mortality can influence the success of crop allele introgression in crop-wild hybrid zones, especially if crop alleles or crop-imparted seed coverings result in out-of-season germination. We performed a seed burial experiment using crop, wild, and diverse hybrid sunflower (Helianthus annuus) cross types to test how a cross type's maternal parent and nuclear genetic composition might affect its fate under field conditions. We observed higher maladaptive fall germination in the crop- and F1- produced seeds than wild-produced seeds and, due to an interaction with percent crop alleles, fall germination was higher for cross types with more crop-like nuclear genetics. By spring, crop-produced cross types had the highest overwintering mortality, primarily due to higher fall germination. Early spring germination was identical across maternal types, but germination continued for F1-produced seeds. In conclusion, the more wild-like the maternal parent or the less proportion of the cross type's genome contributed by the crop, the greater likelihood a seed will remain ungerminated than die. Wild-like dormancy may facilitate introgression through future recruitment from the soil seed bank

A RNA-seq approach to identify putative toxins from acrorhagi in aggressive and non-aggressive Anthopleura elegantissima polyps

Background The use of venom in intraspecific aggression is uncommon and venom-transmitting structures specifically used for intraspecific competition are found in few lineages of venomous taxa. Next-generation transcriptome sequencing allows robust characterization of venom diversity and exploration of functionally unique tissues. Using a tissue-specific RNA-seq approach, we investigate the venom composition and gene ontology diversity of acrorhagi, specialized structures used in intraspecific competition, in aggressive and non-aggressive polyps of the aggregating sea anemone Anthopleura elegantissima (Cnidaria: Anthozoa: Hexacorallia: Actiniaria: Actiniidae). Results Collectively, we generated approximately 450,000 transcripts from acrorhagi of aggressive and non-aggressive polyps. For both transcriptomes we identified 65 candidate sea anemone toxin genes, representing phospholipase A2s, cytolysins, neurotoxins, and acrorhagins. When compared to previously characterized sea anemone toxin assemblages, each transcriptome revealed greater within-species sequence divergence across all toxin types. The transcriptome of the aggressive polyp had a higher abundance of type II voltage gated potassium channel toxins/Kunitz-type protease inhibitors and type II acrorhagins. Using toxin-like proteins from other venomous taxa, we also identified 612 candidate toxin-like transcripts with signaling regions, potentially unidentified secretory toxin-like proteins. Among these, metallopeptidases and cysteine rich (CRISP) candidate transcripts were in high abundance. Furthermore, our gene ontology analyses identified a high prevalence of genes associated with “blood coagulation” and “positive regulation of apoptosis”, as well as “nucleoside: sodium symporter activity” and “ion channel binding”. The resulting assemblage of expressed genes may represent synergistic proteins associated with toxins or proteins related to the morphology and behavior exhibited by the aggressive polyp. Conclusion We implement a multifaceted approach to investigate the assemblage of expressed genes specifically within acrorhagi, specialized structures used only for intraspecific competition. By combining differential expression, phylogenetic, and gene ontology analyses, we identify several candidate toxins and other potentially important proteins in acrorhagi of A. elegantissima. Although not all of the toxins identified are used in intraspecific competition, our analysis highlights some candidates that may play a vital role in intraspecific competition. Our findings provide a framework for further investigation into components of venom used exclusively for intraspecific competition in acrorhagi-bearing sea anemones and potentially other venomous animals. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1417-4) contains supplementary material, which is available to authorized users

Mimicry Embedding Facilitates Advanced Neural Network Training for Image-Based Pathogen Detection.

The use of deep neural networks (DNNs) for analysis of complex biomedical images shows great promise but is hampered by a lack of large verified data sets for rapid network evolution. Here, we present a novel strategy, termed "mimicry embedding," for rapid application of neural network architecture-based analysis of pathogen imaging data sets. Embedding of a novel host-pathogen data set, such that it mimics a verified data set, enables efficient deep learning using high expressive capacity architectures and seamless architecture switching. We applied this strategy across various microbiological phenotypes, from superresolved viruses to in vitro and in vivo parasitic infections. We demonstrate that mimicry embedding enables efficient and accurate analysis of two- and three-dimensional microscopy data sets. The results suggest that transfer learning from pretrained network data may be a powerful general strategy for analysis of heterogeneous pathogen fluorescence imaging data sets.IMPORTANCE In biology, the use of deep neural networks (DNNs) for analysis of pathogen infection is hampered by a lack of large verified data sets needed for rapid network evolution. Artificial neural networks detect handwritten digits with high precision thanks to large data sets, such as MNIST, that allow nearly unlimited training. Here, we developed a novel strategy we call mimicry embedding, which allows artificial intelligence (AI)-based analysis of variable pathogen-host data sets. We show that deep learning can be used to detect and classify single pathogens based on small differences