Development of a Smoking Cessation Education Protocol in Acute Care Facilities

https://scholarworks.moreheadstate.edu/student_scholarship_posters/1118/thumbnail.jp

Effects of changing climate extremes and vegetation phenology on wildlife associated with grasslands in the southwestern United States

Assessments of the potential responses of animal species to climate change often rely on correlations between long-term average temperature or precipitation and species’ occurrence or abundance. Such assessments do not account for the potential predictive capacity of either climate extremes and variability or the indirect effects of climate as mediated by plant phenology. By contrast, we projected responses of wildlife in desert grasslands of the southwestern United States to future climate means, extremes, and variability and changes in the timing and magnitude of primary productivity. We used historical climate data and remotely sensed phenology metrics to develop predictive models of climate-phenology relations and to project phenology given anticipated future climate. We used wildlife survey data to develop models of wildlife-climate and wildlife-phenology relations. Then, on the basis of the modeled relations between climate and phenology variables, and expectations of future climate change, we projected the occurrence or density of four species of management interest associated with these grasslands: Gambel’s Quail ( Callipepla gambelii ), Scaled Quail ( Callipepla squamat ), Gunnison’s prairie dog ( Cynomys gunnisoni ), and American pronghorn ( Antilocapra americana ). Our results illustrated that climate extremes and plant phenology may contribute more to projecting wildlife responses to climate change than climate means. Monthly climate extremes and phenology variables were influential predictors of population measures of all four species. For three species, models that included climate extremes as predictors outperformed models that did not include extremes. The most important predictors, and months in which the predictors were most relevant to wildlife occurrence or density, varied among species. Our results highlighted that spatial and temporal variability in climate, phenology, and population measures may limit the utility of climate averages-based bioclimatic niche models for informing wildlife management actions, and may suggest priorities for sustained data collection and continued analysis

Tropical sea temperatures in the high-latitude South Pacific during the Eocene

Sea-surface temperature (SST) estimates of ~30 °C from planktic foraminifera and archaeal membrane lipids in bathyal sediments in the Canterbury Basin, New Zealand, support paleontological evidence for a warm subtropical to tropical climate in the early Eocene high-latitude (55°S) southwest Pacific. Such warm SSTs call into question previous estimates based on oxygen isotopes and present a major challenge to climate modelers. Even under hypergreenhouse conditions (2240 ppm CO2), modeled summer SSTs for the New Zealand region do not exceed 20 °C

Early Paleogene temperature history of the Southwest Pacific Ocean: Reconciling proxies and models

We present a new multiproxy (TEX86, ?18O and Mg/Ca), marine temperature history for Canterbury Basin, eastern New Zealand, that extends from middle Paleocene to middle Eocene, including the Paleocene–Eocene thermal maximum (PETM) and early Eocene climatic optimum (EECO). In light of concerns that proxy-based sea surface temperature (SST) estimates are untenably warm for the southwest Pacific during the Eocene, we review the assumptions that underlie the proxies and develop a preliminary paleo-calibration for TEX86 that is based on four multiproxy Eocene records that represent an SST range of 15–34 °C. For the southwest Pacific Paleogene, we show that TEX86L exhibits the best fit with the Eocene paleo-calibration. SSTs derived from related proxies (TEX86H, 1/TEX86) exhibit a systematic warm bias that increases as TEX86 values decrease (a warm bias of 4–7 °C where TEX86<0.7). The TEX86L proxy indicates that southwest Pacific SST increased by ?10 °C from middle Paleocene to early Eocene, with SST maxima of 26–28 °C (tropical) during the PETM and EECO and an SST minimum of 13–16 °C (cool–warm temperate) at the middle/late Paleocene transition (58.7 Ma). The base of the EECO is poorly defined in these records but the top is well-defined in Canterbury Basin by a 2–5 °C decrease in SST and bottom water temperature (BWT) in the latest early Eocene (49.3 Ma); BWT falls from a maximum of 18–20 °C in the EECO to 12–14 °C in the middle Eocene. Overall, cooler temperatures are recorded in the mid-Waipara section, which may reflect a deeper (?500 m water depth) and less neritic depositional setting compared with Hampden and ODP 1172 (?200 m water depth). The high SSTs and BWTs inferred for the PETM and EECO can be reconciled with Eocene coupled climate model results if the proxies are biased towards seasonal maxima and the likely effect of a proto-East Australian Current is taken into account

Genome-Wide Association Study of Serum Creatinine Levels during Vancomycin Therapy

<div><p>Vancomycin, a commonly used antibiotic, can be nephrotoxic. Known risk factors such as age, creatinine clearance, vancomycin dose / dosing interval, and concurrent nephrotoxic medications fail to accurately predict nephrotoxicity. To identify potential genomic risk factors, we performed a genome-wide association study (GWAS) of serum creatinine levels while on vancomycin in 489 European American individuals and validated findings in three independent cohorts totaling 439 European American individuals. In primary analyses, the chromosome 6q22.31 locus was associated with increased serum creatinine levels while on vancomycin therapy (most significant variant rs2789047, risk allele A, β = -0.06, p = 1.1 x 10^-7). SNPs in this region had consistent directions of effect in the validation cohorts, with a meta-p of 1.1 x 10^-7. Variation in this region on chromosome 6, which includes the genes <i>TBC1D32/C6orf170</i> and <i>GJA1</i> (encoding connexin43), may modulate risk of vancomycin-induced kidney injury.</p></div

Identification of primary cohort.

<p>Electronic medical records data were searched to identify 5,665 individuals exposed to vancomycin. Automated and manual algorithms were used to determine if each satisfied inclusion / exclusion criteria, as described in the methods, resulting in 882 confirmed cases. After exclusion of those without DNA, those who failed quality control (QC), and those of non-European-American ancestry, 745 individuals remained. Of those, 489 had serum creatinine measurements for the primary analysis.</p

Association of genome-wide SNPs to peak creatinine while on vancomycin therapy in the primary cohort.

<p>A) Manhattan plot, where each dot represents a genotyped SNP, arranged along the x-axis by position of the SNP on each chromosome. The y-axis plots-log10(p-value) for the linear regression analysis of peak creatinine, adjusted for sex, age at time of vancomycin therapy, height, weight, vancomycin dose / dosing interval, vancomycin trough, and baseline serum creatinine measurements as described in the methods. B) LocusZoom plot of 6q22.31 locus, including genotyped and imputed SNPs. Each dot represents a SNP, arranged by position on chromosome 6 along the x-axis, and the color indicates degree of linkage disequilibrium with the index SNP, rs2789047. The left y-axis plots-log10(p-value) for each SNP. The blue line indicates estimated recombination rate, quantified on the right y-axis. Known genes in the region are indicted below the x-axis.</p

Cohort demographics for primary and validation cohorts investigated by genome-wide association for the outcome of peak serum creatinine during vancomycin therapy.

<p>*Median (interquartile range)</p><p>^N, %.</p><p>Cohort demographics for primary and validation cohorts investigated by genome-wide association for the outcome of peak serum creatinine during vancomycin therapy.</p

Meta-analysis results for chromosome 6 SNP associations to peak creatinine during vancomycin therapy.

<p>RAF—Risk Allele Frequency; SE—Standard Error.</p><p>*—Imputed SNP in Mayo and VGER cohorts.</p><p>Meta-analysis results for chromosome 6 SNP associations to peak creatinine during vancomycin therapy.</p