Development of grassland modelling techniques with weather forecasts

This thesis investigates different aspects of weather, grass growth and statistical modelling. First, the accuracy of weather forecasts is assessed and improved using bias correction methods at twenty-five Irish locations for weather variables that influence grass growth. For the first time, soil temperature observations measured at six depths are verified and bias corrected. Next, the weather forecasts are included in an Irish grass growth model to investigate how the model accuracy is affected. The model predictions at an Irish farm are compared for weather observations and forecasts in multiple years, as well as to on-farm grass growth observations. These studies show that forecasts can be used in place of observations, and model predictions generally describe weekly grass growth accurately. Finally, grass growth modelling methods for experiments involving multiple species are developed for the analysis of a weed invasion study involving a large number of species. These developments include fitting novel random effects over multiple years to describe pairwise interactions between species parsimoniously and incorporating spatial planting pattern treatment into modelling methods

Development of grassland modelling techniques with weather forecasts

This thesis investigates different aspects of weather, grass growth and statistical modelling. First, the accuracy of weather forecasts is assessed and improved using bias correction methods at twenty-five Irish locations for weather variables that influence grass growth. For the first time, soil temperature observations measured at six depths are verified and bias corrected. Next, the weather forecasts are included in an Irish grass growth model to investigate how the model accuracy is affected. The model predictions at an Irish farm are compared for weather observations and forecasts in multiple years, as well as to on-farm grass growth observations. These studies show that forecasts can be used in place of observations, and model predictions generally describe weekly grass growth accurately. Finally, grass growth modelling methods for experiments involving multiple species are developed for the analysis of a weed invasion study involving a large number of species. These developments include fitting novel random effects over multiple years to describe pairwise interactions between species parsimoniously and incorporating spatial planting pattern treatment into modelling methods

Analysis of Poverty In Chicago

This poster displays visualizations of the correlation between select public health indicators and their correlation to impoverished areas in Chicago

Helianthus maximiliani and Species Fine-Scale Spatial Pattern Affect Diversity Interactions in Reconstructed Tallgrass Prairies

1. Biodiversity and Ecosystem Function analyses aim to explain how individual spe‐ cies and their interactions affect ecosystem function. With this study, we asked in what ways do species interact, are these interactions affected by species planting pattern, and are initial (planted) proportions or previous year (realized) propor‐ tions a better reference point for characterizing grassland diversity effects? 2. We addressed these questions with experimental communities compiled from a pool of 16 tallgrass prairie species. We planted all species in monocultures and mixtures that varied in their species richness, evenness, and spatial pattern. We recorded species‐specific biomass production over three growing seasons and fit‐ ted Diversity‐Interactions (DI) models to annual plot biomass yields. 3. In the establishment season, all species interacted equally to form the diversity effect. In years 2 and 3, each species contributed a unique additive coefficient to its interaction with every other species to form the diversity effect. These inter‐ actions were affected by Helianthus maximiliani and the species planting pattern. Models based on species planted proportions better‐fit annual plot yield than models based on species previous contributions to plot biomass. 4. Outcomes suggest that efforts to plant tallgrass prairies to maximize diversity ef‐ fects should focus on the specific species present and in what arrangement they are planted. Furthermore, for particularly diverse grasslands, the effort of collect‐ ing annual species biomass data may not be necessary when quantifying diversity effects with DI models

A Mixed Model for Assessing the Effect of Numerous Plant Species Interactions on Grassland Biodiversity and Ecosystem Function Relationships

In grassland ecosystems, it is well known that increasing plant species diversity can improve ecosystem functions (i.e., ecosystem responses), for example, by increasing productivity and reducing weed invasion. Diversity-Interactions models use species proportions and their interactions as predictors in a regression framework to assess biodiversity and ecosystem function relationships. However, it can be difficult to model numerous interactions if there are many species, and interactions may be temporally variable or dependent on spatial planting patterns. We developed a new Diversity-Interactions mixed model for jointly assessing many species interactions and within-plot species planting pattern over multiple years. We model pairwise interactions using a small number of fixed parameters that incorporate spatial effects and supplement this by including all pairwise interaction variables as random effects, each constrained to have the same variance within each year. The random effects are indexed by pairs of species within plots rather than a plot-level factor as is typical in mixed models, and capture remaining variation due to pairwise species interactions parsimoniously. We apply our novel methodology to three years of weed invasion data from a 16-species grassland experiment that manipulated plant species diversity and spatial planting pattern and test its statistical properties in a simulation study. Supplementary materials accompanying this paper appear online

Carotid artery wall mechanics in young males with high cardiorespiratory fitness

The influence of cardiorespiratory fitness (CRF) on arterial stiffness in young adults remains equivocal. Beyond conventional measures of arterial stiffness, 2D strain imaging of the common carotid artery (CCA) provides novel information related to the intrinsic properties of the arterial wall. Therefore, this study aimed to assess the effect of CRF on both conventional indices of CCA stiffness and 2D strain parameters, at rest and following a bout of aerobic exercise in young healthy males. Short‐axis ultrasound images of the CCA were recorded in 34 healthy men [22 years (95%CI, 19–22)] before, and immediately after 5‐minutes of aerobic exercise (40% VO2max). Images were analysed for arterial diameter, peak circumferential strain (PCS), and peak systolic and diastolic strain rates (S‐SR, D‐SR). Heart rate (HR), systolic and diastolic blood pressure (SBP, DBP) were simultaneously assessed and Petersons' elastic modulus (Ep) and Beta stiffness (β1) were calculated. Participants were separated post hoc into moderate and high fitness groups [VO2max: 48.9 ml.kg‐1 min‐1 (95%CI, 44.7–53.2) vs. 65.6 ml.kg‐1 min‐1 (95%CI, 63.1–68.1); P 0.13) but were elevated in the moderate‐fitness group post‐exercise (P 0.05). High‐fit individuals exhibit elevated CCA PCS and S‐SR, which may reflect training‐induced adaptations that help to buffer the rise in pulse‐pressure and stroke volume during exercise

The Naturalized Gender Order Of Rock and Roll

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66933/2/10.1177_019685999501900104.pd

A practical drug discovery project at the undergraduate level

A practical drug discovery project for third-year undergraduates is described. No previous knowledge of medicinal chemistry is assumed. Initial lecture-workshops cover the basic principles; then students are asked to improve the profile of a weakly potent, poorly soluble PI3K inhibitor (1). Compound array design, molecular modelling and screening data analysis are followed by laboratory work in which each student, as part of a team, attempts to synthesise at least two target compounds. The project benefits from significant industrial support, including lectures, student mentoring and consumables. The aim is to make the learning experience as close as possible to real-life industrial situations. Forty-eight target compounds have been prepared, the best of which (5b, 5j, 6b and 6ap) improved the potency and aqueous solubility of the lead compound (1) by 100-1000 fold and 10-fold, respectively

High-performance liquid chromatography–tandem mass spectrometry in the identification and determination of phase I and phase II drug metabolites

Applications of tandem mass spectrometry (MS/MS) techniques coupled with high-performance liquid chromatography (HPLC) in the identification and determination of phase I and phase II drug metabolites are reviewed with an emphasis on recent papers published predominantly within the last 6 years (2002–2007) reporting the employment of atmospheric pressure ionization techniques as the most promising approach for a sensitive detection, positive identification and quantitation of metabolites in complex biological matrices. This review is devoted to in vitro and in vivo drug biotransformation in humans and animals. The first step preceding an HPLC-MS bioanalysis consists in the choice of suitable sample preparation procedures (biomatrix sampling, homogenization, internal standard addition, deproteination, centrifugation, extraction). The subsequent step is the right optimization of chromatographic conditions providing the required separation selectivity, analysis time and also good compatibility with the MS detection. This is usually not accessible without the employment of the parent drug and synthesized or isolated chemical standards of expected phase I and sometimes also phase II metabolites. The incorporation of additional detectors (photodiode-array UV, fluorescence, polarimetric and others) between the HPLC and MS instruments can result in valuable analytical information supplementing MS results. The relation among the structural changes caused by metabolic reactions and corresponding shifts in the retention behavior in reversed-phase systems is discussed as supporting information for identification of the metabolite. The first and basic step in the interpretation of mass spectra is always the molecular weight (MW) determination based on the presence of protonated molecules [M+H]+ and sometimes adducts with ammonium or alkali-metal ions, observed in the positive-ion full-scan mass spectra. The MW determination can be confirmed by the [M-H]- ion for metabolites providing a signal in negative-ion mass spectra. MS/MS is a worthy tool for further structural characterization because of the occurrence of characteristic fragment ions, either MSn analysis for studying the fragmentation patterns using trap-based analyzers or high mass accuracy measurements for elemental composition determination using time of flight based or Fourier transform mass analyzers. The correlation between typical functional groups found in phase I and phase II drug metabolites and corresponding neutral losses is generalized and illustrated for selected examples. The choice of a suitable ionization technique and polarity mode in relation to the metabolite structure is discussed as well

Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction.

Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction