Soybean Response to Water: Trait Identification and Prediction

The rising demand for soybean [Glycine Max (L.) Merrill] taken in consideration with current climatic trends accentuates the importance of improving soybean seed yield response per unit water (WP). To further our understanding of the quantitative WP trait, a multi-omic approach was implemented for improved trait identification and predictive modeling opportunities. Through the evaluation of two recombinant inbred line populations jointly totaling 439 lines subjected to contrasting irrigation treatments, informative agronomic, phenomic, and genomic associations were identified. Across both populations, relationships were identified between lodging at maturity (r = -0.58, H = 0.86), canopy to air temperature differential at the V5 growth stage (r = -0.31, H = 0.39), the SR680 spectral index collected at the R5 growth stage, (r = 0.62, H = 0.39), and a quantitative trait loci at approximately 30 centimorgans on chromosome 19 (r = 0.27) to WP. Through the integration of significant agronomic, phenomic, and genomic traits, predictive models of WP were developed across environments on an entry mean basis (r = 0.72, RMSE = 0.67 kg ha-1 mm-1) and on a per plot basis (r = 0.95, RMSE = 0.39 kg ha-1 mm-1) using machine learning algorithms. Our results highlight the value of integrating multiple dataset types to study and model quantitative traits. Through the application of our findings, soybean breeders can potentially deploy multi-omic selection models in early generation screening stages to increase the rate of genetic gain in relation to soybean WP. Advisor: George L. Grae

Encryption and Decryption Using Matricies

Mathematician Lester Hill developed the Hill Cipher, the first mathematical encryption method ever developed, in 1929. This method was created in order to strengthen the level of security of previous methods and made it possible to encrypt more than three symbols at a time

Field-Based Scoring of Soybean Iron Deficiency Chlorosis Using RGB Imaging and Statistical Learning

Iron deficiency chlorosis (IDC) is an abiotic stress in soybean that can cause significant biomass and yield reduction. IDC is characterized by stunted growth and yellowing and interveinal chlorosis of early trifoliate leaves. Scoring IDC severity in the field is conventionally done by visual assessment. The goal of this study was to investigate the usefulness of Red Green Blue (RGB) images of soybean plots captured under the field condition for IDC scoring. A total of 64 soybean lines with four replicates were planted in 6 fields over 2 years. Visual scoring (referred to as Field Score, or FS) was conducted at V3–V4 growth stage; and concurrently RGB images of the field plots were recorded with a high-throughput field phenotyping platform. A second set of IDC scores was done on the plot images (displayed on a computer screen) consistently by one person in the office (referred to as Office Score, or OS). Plot images were then processed to remove weeds and extract six color features, which were used to train computer-based IDC scoring models (referred to as Computer Score, or CS) using linear discriminant analysis (LDA) and support vector machine (SVM). The results showed that, in the fields where severe IDC symptoms were present, FS and OS were strongly positively correlated with each other, and both of them were strongly negatively correlated with yield. CS could satisfactorily predict IDC scores when evaluated using FS and OS as the reference (overall classification accuracy \u3e 81%). SVM models appeared to outperform LDA models; and the SVM model trained to predict IDC OS gave the highest prediction accuracy. It was anticipated that coupling RGB imaging from the high-throughput field phenotyping platform with real-time image processing and IDC CS models would lead to a more rapid, cost-effective, and objective scoring pipeline for soybean IDC field screening and breeding

Wrongful Convictions: Science, Experience, and the Law

3.5 MCLE Credit Hours Schedule: 10:30-11:00 - Registration/Check-In 11:00-12:00 - Introduction to the science behind exonerations by Bradford Jenkins of the Virginia Department of Forensic Science 12:00-12:45 - Lunch 12:45-2:15 - Panel on the Human Experience of wrongful convictions from varying perspectives (Mike Herring - Commonwealth\u27s Attorney, Doug Ramseur - Defense Counsel, Shawn Armbrust, Mid-Atlantic Innocence Project) 2:15-2:30 - Break 2:30-3:30 -Legal wrap up of where we are in Virginia on reforms, discussion of case law and relevant statutes by Brandon Garrett, Professor of Law at the University of Virginia 3:30-4:30- Receptio

Detection Of KOI-13.01 Using The Photometric Orbit

We use the KOI-13 transiting star-planet system as a test case for the recently developed BEER algorithm (Faigler & Mazeh 2011), aimed at identifying non-transiting low-mass companions by detecting the photometric variability induced by the companion along its orbit. Such photometric variability is generated by three mechanisms, including the beaming effect, tidal ellipsoidal distortion, and reflection/heating. We use data from three Kepler quarters, from the first year of the mission, while ignoring measurements within the transit and occultation, and show that the planet's ephemeris is clearly detected. We fit for the amplitude of each of the three effects and use the beaming effect amplitude to estimate the planet's minimum mass, which results in M_p sin i = 9.2 +/- 1.1 M_J (assuming the host star parameters derived by Szabo et al. 2011). Our results show that non-transiting star-planet systems similar to KOI-13.01 can be detected in Kepler data, including a measurement of the orbital ephemeris and the planet's minimum mass. Moreover, we derive a realistic estimate of the amplitudes uncertainties, and use it to show that data obtained during the entire lifetime of the Kepler mission, of 3.5 years, will allow detecting non-transiting close-in low-mass companions orbiting bright stars, down to the few Jupiter mass level. Data from the Kepler Extended Mission, if funded by NASA, will further improve the detection capabilities.Comment: Accepted to AJ on October 4, 2011. Kepler Q5 Long Cadence data will become publicly available on MAST by October 23. Comments welcome (V2: minor changes, to reflect proof corrections

Survey on Visual Impairment and Refractive Errors on Ta’u Island, American Samoa

Purpose: To assess the prevalence of presenting visual impairment and refractive errors on the isolated island of Ta′u, American Samoa. Methods: Presenting visual acuity and refractive errors of 124 adults over 40 years of age (55 male and 69 female) were measured using the Snellen chart and an autorefractometer. This sample represented over 50% of the island′s eligible population. Results: In this survey, all presenting visual acuity (VA) was uncorrected. Of the included sample, 10.5% presented with visual impairment (visual acuity lower than 6/18, but equal to or better than 3/60 in the better eye) and 4.8% presented with VA worse than 6/60 in the better eye. Overall, 4.0% of subjects presented with hyperopia (+3 D or more), 3.2% were myopic (‑1 D or less), and 0.8% presented with high myopia (‑5 D or less). There was no significant difference between genders in terms of visual impairment or refractive errors. Conclusion: This study represents the first population-based survey on presenting visual acuity and refractive errors in American Samoa. In addition to providing baseline data on vision and refractive errors, we found that the prevalence of myopia and hyperopia was much lower than expected

Detection of Potential Transit Signals in the First Three Quarters of Kepler Mission Data

We present the results of a search for potential transit signals in the first three quarters of photometry data acquired by the Kepler Mission. The targets of the search include 151,722 stars which were observed over the full interval and an additional 19,132 stars which were observed for only 1 or 2 quarters. From this set of targets we find a total of 5,392 detections which meet the Kepler detection criteria: those criteria are periodicity of the signal, an acceptable signal-to-noise ratio, and a composition test which rejects spurious detections which contain non-physical combinations of events. The detected signals are dominated by events with relatively low signal-to-noise ratio and by events with relatively short periods. The distribution of estimated transit depths appears to peak in the range between 40 and 100 parts per million, with a few detections down to fewer than 10 parts per million. The detected signals are compared to a set of known transit events in the Kepler field of view which were derived by a different method using a longer data interval; the comparison shows that the current search correctly identified 88.1% of the known events. A tabulation of the detected transit signals, examples which illustrate the analysis and detection process, a discussion of future plans and open, potentially fruitful, areas of further research are included

Measuring Transit Signal Recovery in the Kepler Pipeline II: Detection Efficiency as Calculated in One Year of Data

The Kepler planet sample can only be used to reconstruct the underlying planet occurrence rate if the detection efficiency of the Kepler pipeline is known, here we present the results of a second experiment aimed at characterising this detection efficiency. We inject simulated transiting planet signals into the pixel data of ~10,000 targets, spanning one year of observations, and process the pixels as normal. We compare the set of detections made by the pipeline with the expectation from the set of simulated planets, and construct a sensitivity curve of signal recovery as a function of the signal-to-noise of the simulated transit signal train. The sensitivity curve does not meet the hypothetical maximum detection efficiency, however it is not as pessimistic as some of the published estimates of the detection efficiency. For the FGK stars in our sample, the sensitivity curve is well fit by a gamma function with the coefficients a = 4.35 and b = 1.05. We also find that the pipeline algorithms recover the depths and periods of the injected signals with very high fidelity, especially for periods longer than 10 days. We perform a simplified occurrence rate calculation using the measured detection efficiency compared to previous assumptions of the detection efficiency found in the literature to demonstrate the systematic error introduced into the resulting occurrence rates. The discrepancies in the calculated occurrence rates may go some way towards reconciling some of the inconsistencies found in the literature.Comment: 13 pages, 7 figures, 1 electronic table, accepted by Ap