ASU Scholarship Showcase

Given species inventories of all sites in a planning area, integer programming or heuristic algorithms can prioritize sites in terms of the site's complementary value, that is, the ability of the site to complement (add unrepresented species to) other sites prioritized for conservation. The utility of these procedures is limited because distributions of species are typically available only as coarse atlases or range maps, whereas conservation planners need to prioritize relatively small sites. If such coarse-resolution information can be used to identify small sites that efficiently represent species (i.e., downscaled), then such data can be useful for conservation planning. We develop and test a new type of surrogate for biodiversity, which we call downscaled complementarity. In this approach, complementarity values from large cells are downscaled to small cells, using statistical methods or simple map overlays. We illustrate our approach for birds in Spain by building models at coarse scale (50 × 50 km atlas of European birds, and global range maps of birds interpreted at the same 50 × 50 km grid size), using this model to predict complementary value for 10 × 10 km cells in Spain, and testing how well-prioritized cells represented bird distributions in an independent bird atlas of those 10 × 10 km cells. Downscaled complementarity was about 63–77% as effective as having full knowledge of the 10-km atlas data in its ability to improve on random selection of sites. Downscaled complementarity has relatively low data acquisition cost and meets representation goals well compared with other surrogates currently in use. Our study justifies additional tests to determine whether downscaled complementarity is an effective surrogate for other regions and taxa, and at spatial resolution finer than 10 × 10 km cells. Until such tests have been completed, we caution against assuming that any surrogate can reliably prioritize sites for species representation.

Lack of biodiversity data is a major impediment to prioritizing sites for species representation. Because comprehensive species data are not available in any planning area, planners often use surrogates (such as vegetation communities, or mapped occurrences of a well-inventoried taxon) to prioritize sites. We propose and demonstrate the effectiveness of predicted rarity-weighted richness (PRWR) as a surrogate in situations where species inventories may be available for a portion of the planning area. Use of PRWR as a surrogate involves several steps. First, rarity-weighted richness (RWR) is calculated from species inventories for a q% subset of sites. Then random forest models are used to model RWR as a function of freely available environmental variables for that q% subset. This function is then used to calculate PRWR for all sites (including those for which no species inventories are available), and PRWR is used to prioritize all sites. We tested PRWR on plant and bird datasets, using the species accumulation index to measure efficiency of PRWR. Sites with the highest PRWR represented species with median efficiency of 56% (range 32%–77% across six datasets) when q = 20%, and with median efficiency of 39% (range 20%–63%) when q = 10%. An efficiency of 56% means that selecting sites in order of PRWR rank was 56% as effective as having full knowledge of species distributions in PRWR's ability to improve on the number of species represented in the same number of randomly selected sites. Our results suggest that PRWR may be able to help prioritize sites to represent species if a planner has species inventories for 10%–20% of the sites in the planning area.

MALDI-TOF MS profiling has been shown to be a rapid and reliable method to characterize pure cultures of bacteria. Currently, there is keen interest in using this technique to identify bacteria in mixtures. Promising results have been reported with two- or three-isolate model systems using biomarker-based approaches. In this work, we applied MALDI-TOF MS-based methods to a more complex model mixture containing six bacteria. We employed: 1) a biomarker-based approach that has previously been shown to be useful in identification of individual bacteria in pure cultures and simple mixtures and 2) a similarity coefficient-based approach that is routinely and nearly exclusively applied to identification of individual bacteria in pure cultures. Both strategies were developed and evaluated using blind-coded mixtures. With regard to the biomarker-based approach, results showed that most peaks in mixture spectra could be assigned to those found in spectra of each component bacterium; however, peaks shared by two isolates as well as peaks that could not be assigned to any individual component isolate were observed. For two-isolate blind-coded samples, bacteria were correctly identified using both similarity coefficient- and biomarker-based strategies, while for blind-coded samples containing more than two isolates, bacteria were more effectively identified using a biomarker-based strategy.

Background: Many studies used the older ActiGraph (7164) for physical activity measurement, but this model has been replaced with newer ones (e.g., GT3X+). The assumption that new generation models are more accurate has been questioned, especially for measuring lower intensity levels. The low-frequency extension (LFE) increases the low-intensity sensitivity of newer models, but its comparability with older models is unknown. This study compared step counts and physical activity collected with the 7164 and GT3X + using the Normal Filter and the LFE (GT3X+N and GT3X+LFE, respectively).

Findings: Twenty-five adults wore 2 accelerometer models simultaneously for 3Âdays and were instructed to engage in typical behaviors. Average daily step counts and minutes per day in nonwear, sedentary, light, moderate, and vigorous activity were calculated. Repeated measures ANOVAs with post-hoc pairwise comparisons were used to compare mean values. Means for the GT3X+N and 7164 were significantly different in 4 of the 6 categories (p < .05). The GT3X+N showed 2041 fewer steps per day and more sedentary, less light, and less moderate than the 7164 (+25.6, -31.2, -2.9 mins/day, respectively). The GT3X+LFE showed non-significant differences in 5 of 6 categories but recorded significantly more steps (+3597 steps/day; p < .001) than the 7164.

Conclusion: Studies using the newer ActiGraphs should employ the LFE for greater sensitivity to lower intensity activity and more comparable activity results with studies using the older models. Newer generation ActiGraphs do not produce comparable step counts to the older generation devices with the Normal filter or the LFE.

Students often self-identify as visual learners and prefer to engage with a topic in an active, hands-on way. Indeed, much research has shown that students who actively engage with the material and are engrossed in the topics retain concepts better than students who are passive receivers of information. However, much of learning life science concepts is still driven by books and static pictures. One concept students have a hard time grasping is how a linear chain of amino acids folds to becomes a 3D protein structure. Adding three dimensional activities to the topic of protein structure and function should allow for a deeper understanding of the primary, secondary, tertiary, and quaternary structure of proteins and how proteins function in a cell. Here, I review protein folding activities and describe using Apps and 3D visualization to enhance student understanding of protein structure.

I teach an upper-level writing course, Genes, Race, Gender, and Society, designed for Life Science majors, in which I utilize a case study to expose students to ethical ways of thinking. Students first work through the topical case study and then are challenged to rethink their responses through the lenses of ethics, taking into account different ethical frameworks. Students then develop their own case study, integrating ethical components. I want to expose my students to this way of thinking because I see technology being driven by the Jurassic Park phenomenon, “Your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should,” and want future physicians grounded in a sense of how their actions relate to the greater good.

We used the Affymetrix® Genome-Wide Human SNP Array 6.0 to identify heterospecific markers and compare copy number and structural genomic variation between humans and rhesus macaques. Over 200,000 human copy number variation (CNV) probes were mapped to a Chinese and an Indian rhesus macaque sample. Observed genomic rearrangements and synteny were in agreement with the results of a previously published genomic comparison between humans and rhesus macaques. Comparisons between each of the two rhesus macaques and humans yielded 206 regions with copy numbers that differed by at least two fold in the Indian rhesus macaque and human, 32 in the Chinese rhesus macaque and human, and 147 in both rhesus macaques. The detailed genomic map and preliminary CNV data are useful for better understanding genetic variation in rhesus macaques, identifying derived changes in human CNVs that may have evolved by selection, and determining the suitability of rhesus macaques as human models for particular biomedical studies.

Recent outbreaks of Zika virus (ZIKV) highlight the urgent need to develop efficacious interventions against flaviviruses, many of which cause devastating epidemics around the world. Monoclonal antibodies (mAb) have been at the forefront of treatment for cancer and a wide array of other diseases due to their specificity and potency. While mammalian cell-produced mAbs have shown promise as therapeutic candidates against several flaviviruses, their eventual approval for human application still faces several challenges including their potential risk of predisposing treated patients to more severe secondary infection by a heterologous flavivirus through antibody-dependent enhancement (ADE). The high cost associated with mAb production in mammalian cell cultures also poses a challenge for the feasible application of these drugs to the developing world where the majority of flavivirus infection occurs. Here, we review the current therapeutic mAb candidates against various flaviviruses including West Nile (WNV), Dengue virus (DENV), and ZIKV. The progress of using plants for developing safer and more economical mAb therapeutics against flaviviruses is discussed within the context of their expression, characterization, downstream processing, neutralization, and in vivo efficacy. The progress of using plant glycoengineering to address ADE, the major impediment of flavivirus therapeutic development, is highlighted. These advancements suggest that plant-based systems are excellent alternatives for addressing the remaining challenges of mAb therapeutic development against flavivirus and may facilitate the eventual commercialization of these drug candidates.

The elongases of very long chain fatty acid (ELOVL or ELO) are essential in the biosynthesis of fatty acids longer than C14. Here, two ELO full-length cDNAs (TmELO1, TmELO2) from the yellow mealworm (Tenebrio molitor L.) were isolated and the functions were characterized. The open reading frame (ORF) lengths of TmELO1 and TmELO2 were 1005 bp and 972 bp, respectively and the corresponding peptide sequences each contained several conserved motifs including the histidine-box motif HXXHH. Phylogenetic analysis demonstrated high similarity with the ELO of Tribolium castaneum and Drosophila melanogaster. Both TmELO genes were expressed at various levels in eggs, 1st and 2nd instar larvae, mature larvae, pupae, male and female adults. Injection of dsTmELO1 but not dsTmELO2 RNA into mature larvae significantly increased mortality although RNAi did not produce any obvious changes in the fatty acid composition in the survivors. Heterologous expression of TmELO genes in yeast revealed that TmELO1 and TmELO2 function to synthesize long chain and very long chain fatty acids.

Essential or enduring understandings are often defined as the underlying core concepts or “big ideas” we’d like our students to remember when much of the course content has been forgotten. The central dogma of molecular biology and how cellular information is stored, used, and conveyed is one of the essential understandings students should retain after a course or unit in molecular biology or genetics. An additional enduring understanding is the relationships between DNA sequence, RNA sequence, mRNA production and processing, and the resulting polypeptide/protein product. A final big idea in molecular biology is the relationship between DNA mutation and polypeptide change. To engage students in these essential understandings in a Genetics course, I have developed a hands-on activity to simulate VDJ recombination. Students use a foldable type activity to splice out regions of a mock kappa light chain gene to generate a DNA sequence for transcription and translation. Students fold the activity several different times in multiple ways to “recombine” and generate several different DNA sequences. They then are asked to construct the corresponding mRNA and polypeptide sequence of each “recombined” DNA sequence and reflect on the products in a write-to-learn activity.