Master of Science / Department of Biological and Agricultural Engineering / Naiqian Zhang / In order to meet food, fiber, and bio-fuel needs of a growing world population, crop-breeding methods must be improved and new technologies must be developed. One area under focus is the decoding of the genetic basis of complex traits, such as yield and drought stress tolerance, and predicting these traits from genetic composition of lines or cultivars. In the last three decades, significant advances in genotyping methods have resulted in a wealth of genomic information; however, little improvement has occurred for methods of collecting corresponding plant trait data, especially for agronomic crops. This study developed a mobile, field-based, high-throughput sensor platform for rapid and repeated measurement of plant characteristics. The platform consisted of three sets of sensors mounted on a high-clearance vehicle. Each set of sensors contained two infrared thermometers (IRT), one ultrasonic sensor, one Crop Circle, and one GreenSeeker. Each sensor set measured canopy temperature, crop height, and spectral reflectance. In addition to the sensors, the platform was equipped with an RTK-GPS system that provided precise, accurate position data for georeferencing sensor measurements. Software for collecting, georeferencing, and logging sensor data was developed using National Instruments LabVIEW and deployed on a laptop computer. Two verification tests were conducted to evaluate the phenotyping system. In the first test, data timestamps were analyzed to determine if the system could collect data at the required rate of 10 Hz and 5 Hz for sensor data and position data, respectively. The determination was made that, on average, IRT, ultrasonic, and Crop Circle data are received in intervals of 100 ms (SD = 10 ms), GreenSeeker data are received in intervals of 122 ms(SD=10 ms), and position data are received in intervals of 200 ms (SD = 32 ms). The second test determined that a statistically significant relationship exists between sensor readings and ambient light intensity and ambient temperatures. Whether the relationship is significant from a practical stand point should be determined based on specific application of the sensors.

<div><p>Wheat is among the most important cereal crops in the world today with respect to the area harvested (219 million ha), production (772 million tonnes), and productivity (3.53 tons/ha). However, global wheat production goals for the coming decades are falling short of needed increases. Among the leading factors hindering yields is abiotic stress which is present in nearly 38% of wheat acres globally. Nevertheless, many standard wheat breeding programs focus on yield and yield related traits (i.e. grain yield, plant height, and test weight) in ideal environments rather than evaluating traits that could lead to enhanced abiotic stress tolerance. In this thesis, we explore the use of root and high-throughput phenotyping strategies to aid in further development of abiotic stress tolerant varieties. </p><p>In the first three experiments, root phenotypes were evaluated in two nitrogen (N) treatments. Over a series of seedling, adult, and multiple-growth-stage destructive plant biomass measurements, above-ground and below-ground traits were analyzed in seven geographically diverse wheat accessions. Root and shoot biomass allocation in fourteen-day-old seedlings were analyzed using paper-roll-supported hydroponic culture in two Hoagland solutions containing 0.5 (low) and 4.0 (high) mM of N. Root traits were digitized using a WINRhizo platform. For biomass analysis at maturity, plants were grown in 7.5-liter pots filled with soil mix using the same concentrations of N. Traits were measured as plants reached maturity. In the third N experiment, above- and below-ground traits were measured at four-leaf stage, stem elongation, heading, post-anthesis, and maturity. At maturity, there was a ~15-fold difference between lines with the largest and smallest root dry matter. However, only ~5-fold difference was observed between genotypes for above-ground biomass. In the third experiment, root growth did not significantly change from stem elongation to maturity. </p><p>In the final experiment, two of these lines were selected for further evaluation under well-watered and drought treatments. This experiment was implemented in a completely randomized design in the Controlled Environment Phenotyping Facility (CEPF) at Purdue University. The differential water treatments were imposed at stem elongation and continued until post-anthesis, when all plants were destructively phenotyped. Image-based height and side-projected area were associated with height and shoot dry matter with correlations of r=1 and r=0.98, respectively. Additionally, 81% of the variation in tiller number was explained using convex hull and side-projected area. Image-based phenotypes were used to model crop growth temporally, through which one of the lines was identified as being relatively more drought tolerant. Finally, the use of the Munsell Color System was explored to investigate drought response.</p><p>These experiments illustrate the value of phenotyping and the use of novel phenotyping strategies in wheat breeding to increase adaptation and development of lines with enhanced abiotic tolerance.</p></div><br>

Indiana University-Purdue University Indianapolis (IUPUI) / In the forensic science community, there is an immense need for tools to help assist investigations where conventional DNA profiling methods have been non-informative. Forensic DNA Phenotyping (FDP) aims to bridge that gap and aid investigations by providing physical appearance information when other investigative methods have been exhausted. To create a “biological eye witness”, it becomes necessary to constantly improve these methods in order to develop a complete and accurate image of the individual who left the sample. To add to our previous prediction systems IrisPlex and HIrisPlex, we have developed the HIrisPlex-S system for the all-in-one combined prediction of eye, hair, and skin color from DNA. The skin color prediction model uses 36 variants that were recently proposed for the accurate prediction of categorical skin color on a global scale, and the system is completed by the developmental validation of a 17-plex capillary electrophoresis (CE) genotyping assay that is run in conjunction with the HIrisPlex assay to generate these genotypes. The predicted skin color output includes Very Pale, Pale, Intermediate, Dark and Dark-to-Black categories in addition to categorical eye (Blue, Intermediate, and Brown) and hair (Black, Brown, Blond, and Red) color predictions. We demonstrate that the HIrisPlex-S assay performs in full agreement with guidelines from the Scientific Working Group on DNA Analysis Methods (SWGDAM), achieving high sensitivity levels with a minimum 63pg DNA input. In addition to adding skin color to complete the pigmentation prediction system termed HIrisPlex-S, we successfully designed a Massively Parallel Sequencing (MPS) assay to complement the system and bring Next Generation Sequencing (NGS) to the forefront of forensic DNA analyses methods. Using Illumina’s MiSeq system enables the generation of HIrisPlex-S’s 41 variants using sequencing data that has the capacity to xiii better deconvolute mixtures and perform with even more sensitivity and accuracy. This transition opens the door for a plethora of new ways in which this physical appearance assay can grow as sequencing technology is not limited by variant number; therefore, in essence many more traits have the potential to be included in this one assay design. For now, the HIrisPlex-S design of 41 variants using MPS is being fully assessed according to SWGDAM validated guidelines; therefore, this design paves the way for Forensic DNA Phenotyping to be used in any forensic laboratory. This new and improved HIrisPlex-S system will have a profound impact on casework, missing persons cases, and anthropological cases, as it is relatively inexpensive to run, HIrisPlex-S is easy to use, developmentally validated and one of the largest systems freely available online for physical appearance prediction from DNA using the freely available online web tool found at https://hirisplex.erasmusmc.nl/. Lastly, moving forward in our aim to include additional traits for prediction from DNA, we contributed to a large-scale research collaboration to unearth variants associated with hair morphology. 1026 samples were successfully sequenced using an inhouse MPS design at 91 proposed hair morphological loci. From this reaction, we were able to contribute to the identification of significant correlations between the SNPs rs2219783, rs310642 and rs80293268 with categorical hair morphology: straight, wavy or curly.

Doctor of Philosophy / Genetics Interdepartmental Program - Plant Pathology / Jesse A. Poland / A major challenge for 21st century plant geneticists is to predict plant performance based on genetic information. This is a daunting challenge, especially when there are thousands of genes that control complex traits as well as the extreme variation that results from the environment where plants are grown. Rapid advances in technology are assisting in overcoming the obstacle of connecting the genotype to phenotype. Next generation sequencing has provided a wealth of genomic information resulting in numerous completely sequenced genomes and the ability to quickly genotype thousands of individuals. The ability to pair the dense genotypic data with phenotypic data, the observed plant performance, will culminate in successfully predicting cultivar performance. While genomics has advanced rapidly, phenomics, the science and ability to measure plant phenotypes, has slowly progressed, resulting in an imbalance of genotypic to phenotypic data. The disproportion of high-throughput phenotyping (HTP) data is a bottleneck to many genetic and association mapping studies as well as genomic selection (GS). To alleviate the phenomics bottleneck, an affordable and portable phenotyping platform, Phenocart, was developed and evaluated. The Phenocart was capable of taking multiple types of georeferenced measurements including normalized difference vegetation index and canopy temperature, throughout the growing season. The Phenocart performed as well as existing manual measurements while increasing the amount of data exponentially. The deluge of phenotypic data offered opportunities to evaluate lines at specific time points, as well as combining data throughout the season to assess for genotypic differences. Finally in an effort to predict crop performance, the phenotypic data was used in GS models. The models combined molecular marker data from genotyping-by-sequencing with high-throughput phenotyping for plant phenotypic characterization. Utilizing HTP data, rather than just the often measured yield, increased the accuracy of GS models. Achieving the goal of connecting genotype to phenotype has direct impact on plant breeding by allowing selection of higher yielding crops as well as selecting crops that are adapted to local environments. This will allow for a faster rate of improvement in crops, which is imperative to meet the growing global population demand for plant products.

Introduction: Asthma and chronic obstructive pulmonary disease (COPD) are heterogeneous diseases with a wide range of clinical manifestations not adequately described within the current diagnostic criteria. Exhaled breath analysis may provide a novel method for diagnosing and phenotyping these diseases. Our aim was to ascertain patterns of breath volatile organic compounds (VOCs) and nuclear magnetic resonance (NMR) spectral regions identifying diseased patients and subgroups determined by treatment requirement, asthma control, exacerbation frequency and inflammatory phenotypes. The validity and reproducibility of the methodology and the outcome were also investigated. Methods: Three separate clinical studies (two involving exhaled gas and one involving breath condensate) were conducted, as well as validation studies. In exhaled gas analysis, the adaptive breath sampler developed by Basanta et al was modified; efficiency of air supply and air filter and the reproducibility and stability of VOCs in storage were determined by comparing breath chromatograms. Concentrated late-expiratory breath samples were collected from asthmatics, COPD subjects and healthy controls. In the asthmatic group, sputum induction with hypertonic saline, fraction exhaled nitric oxide (FeNO) measurement and asthma control questionnaire (ACQ) were performed. In COPD subjects, sputum induction and exacerbation frequency were collected. In the exhaled breath condensate (EBC) study, similar data were collected in asthmatics and healthy controls. Breath samples were analysed using gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) while EBC was analysed using NMR spectroscopy. Discriminatory compounds or NMR spectral regions were identified by univariate logistic regression, followed by multivariate analysis: 1. principal component analysis (PCA); 2. multivariate logistic regression; 3. receiver operating characteristic (ROC) analysis. The reproducibility was assessed using intraclass correlation coefficient (ICC).Results: In the COPD exhaled breath study, 11 VOCs significantly discriminated the COPD and healthy controls with AUROC of 0.74. The AUROC for phenotype discrimination was 0.83, 0.90, 0.94, 0.96 and 0.97 for inhaled corticosteroid (ICS) use, sputum eosinophilia (1% and 2% cut-off), neutrophilia (median cut-off) and exacerbation frequency respectively. In the asthma study, 15 VOCs significantly discriminated the two groups with AUROC of 0.93. The AUROC for phenotype discrimination was 0.96, 0.98, 0.90 and 0.97 for ICS use, eosinophils (2% cut-off), neutrophils (40% cut-off) and asthma control respectively. In EBC analysis, AUROC for asthmatics vs controls comparison was 0.96. Phenotyping results in this study were less good: only ICS use and sputum neutrophilia (65% cut-off) were clearly classified with AUROC of 0.89 and 0.88 while eosinophilia (3% cut-off) and asthma control had poor discrimination; 0.69 and 0.62 respectively. Breath VOC reproducibility varied greatly depending on the class of compounds studied, while for the EBC analysis, reproducibility was moderate to very good (ICCs in the range of 0.42-0.99).Conclusions: We have demonstrated the ability of breath analysis in discriminating asthmatics and COPD subjects from controls. Exhaled breath analysis was also able to phenotype these patients based on steroid treatment, sputum inflammatory cells, exacerbation frequency and asthma control. This metabolomic approach could provide a novel, non-invasive method of diagnosing and phenotyping obstructive lung diseases in the future.

The nematode Caenorhabditis elegans possesses a relatively simple nervous system of only 302 neurons, but is able to perform an impressive range of complex behaviors. This dissertation aims to understand the neurobiology of behavior by quantifying, at the systems-level, the sensorimotor response to carefully controlled stimuli. Through neuronal or genetic perturbations to the system, we can begin to uncouple the behavior from the underlying circuitry. The behavior studied here is thermal nociception, an escape response designed to protect an organism from potential tissue damage or harm from noxious heat. Vertebrates and invertebrates alike possess sensory neurons called nociceptors that detect noxious stimuli and relay the stimulus information to elicit an appropriate escape response. C. elegans is known to perform a reversal or forward response when presented with noxious stimuli at the head or tail, respectively. In this work, we develop a novel thermal stimulus assay with precise spatiotemporal control of an infrared pulse that targets small regions along the worm to spatially dissect the noxious response. We comprehensively quantify the nociceptive behavior, and identify key metrics that scale with intensity, such as speed in the escape state and the probability of certain behavioral states after the stimulus. Furthermore, we have mapped the behavioral receptive field of the worm along its body, and show a previously unreported probabilistic midbody behavior distinct from the head and tail responses. Surprisingly, the worm is able to differentiate localized stimuli at the midbody that are as close as 80 microns. We identified PVD as the thermal nociceptor for the midbody response using calcium imaging, genetic ablation and laser ablation. This suggests PVD could be used as a model to study spatial discrimination at the level of a single nociceptor. This spatial specificity further extends to pharmacological perturbations of the system. In particular, the application of clinically used painkillers to the worm results in a knockdown of this nociceptive response, but does so in a spatially specific manner. These results are promising for future studies building upon the techniques developed here, as they evidentiate the use of C. elegans as a model organism to study pain.

The nematode Caenorhabditis elegans possesses a relatively simple nervous system of only 302 neurons, but is able to perform an impressive range of complex behaviors. This dissertation aims to understand the neurobiology of behavior by quantifying, at the systems-level, the sensorimotor response to carefully controlled stimuli. Through neuronal or genetic perturbations to the system, we can begin to uncouple the behavior from the underlying circuitry. The behavior studied here is thermal nociception, an escape response designed to protect an organism from potential tissue damage or harm from noxious heat. Vertebrates and invertebrates alike possess sensory neurons called nociceptors that detect noxious stimuli and relay the stimulus information to elicit an appropriate escape response. C. elegans is known to perform a reversal or forward response when presented with noxious stimuli at the head or tail, respectively. In this work, we develop a novel thermal stimulus assay with precise spatiotemporal control of an infrared pulse that targets small regions along the worm to spatially dissect the noxious response. We comprehensively quantify the nociceptive behavior, and identify key metrics that scale with intensity, such as speed in the escape state and the probability of certain behavioral states after the stimulus. Furthermore, we have mapped the behavioral receptive field of the worm along its body, and show a previously unreported probabilistic midbody behavior distinct from the head and tail responses. Surprisingly, the worm is able to differentiate localized stimuli at the midbody that are as close as 80 microns. We identified PVD as the thermal nociceptor for the midbody response using calcium imaging, genetic ablation and laser ablation. This suggests PVD could be used as a model to study spatial discrimination at the level of a single nociceptor. This spatial specificity further extends to pharmacological perturbations of the system. In particular, the application of clinically used painkillers to the worm results in a knockdown of this nociceptive response, but does so in a spatially specific manner. These results are promising for future studies building upon the techniques developed here, as they evidentiate the use of C. elegans as a model organism to study pain.

Doctor of Philosophy / Department of Agronomy / V. P. Vara Prasad / Crop assessment and monitoring is important to crop management both at crop production level and research plot level, such as high-throughput phenotyping in breeding programs. Optical sensors based agricultural applications have been around for decades and have soared over the past ten years because of the potential of some new technologies to be low-cost, accessible, and high resolution for crop remote sensing which can help to improve crop management to maintain producers’ income and diminish environmental degradation. The overall objective of this study was to develop methods and compare the different optical sensors in crop assessment and monitoring at different scales and perspectives. At crop production level, we reviewed the current status of different optical sensors used in precision crop production including satellite-based, manned aerial vehicle (MAV)-based, unmanned aircraft system (UAS)-based, and vehicle-based active or passive optical sensors. These types of sensors were compared thoroughly on their specification, data collection efficiency, data availability, applications and limitation, economics, and adoption. At research plot level, four winter wheat experiments were conducted to compare three optical sensors (a Canon T4i® modified color infrared (CIR) camera, a MicaSense RedEdge® multispectral imager and a Holland Scientific® RapidScan CS-45® hand-held active optical sensor (AOS)) based high-throughput phenotyping for in-season biomass estimation, canopy estimation, and grain yield prediction in winter wheat across eleven Feekes stages from 3 through 11.3. The results showed that the vegetation indices (VIs) derived from the Canon T4i CIR camera and the RedEdge multispectral camera were highly correlated and can equally estimate winter wheat in-season biomass between Feekes 3 and 11.1 with the optimum point at booting stage and can predict grain yield as early as Feekes 7. Compared to passive sensors, the RapidScan AOS was less powerful and less temporally stable for biomass estimation and yield prediction. Precise canopy height maps were generated from a CMOS sensor camera and a multispectral imager although the accuracy could still be improved. Besides, an image processing workflow and a radiometric calibration method were developed for UAS based imagery data as bi-products in this project. At temporal dimension, a wheat phenology model based on weather data and field contextual information was developed to predict the starting date of three key growth stages (Feekes 4, 7, and 9), which are critical for N management. The model could be applied to new data within the state of Kansas to optimize the date for optical sensor (such as UAS) data collection and save random or unnecessary field trips. Sensor data collected at these stages could then be plugged into pre-built biomass estimation models (mentioned in the last paragraph) to estimate the productivity variability within 20% relative error.

Heat stress poses a serious threat to plant survival and productivity, and has a direct influence on crop yield stability. Plants response to high temperature is tightly controlled by complex genetic networks. Plants can be acclimated through gradual pre-exposure to increasing temperatures and that in turn causes higher survival in subsequent and otherwise lethal heat stress conditions. To investigate the physiological and molecular processes underlying heat acclimation and recovery, we examined changes in Arabidopsis thaliana transcriptome throughout the acclimation and the subsequent heat shock treatment. Groups of differentially expressed genes and enriched biological pathways that constitute the heat transcriptional memory were identified. The function of flavonoids in plant heat stress were further explored experimentally. In addition, we observed altered stomata density and aperture responses in heat acclimated plants, and this might be partially controlled by AGAMOUS-LIKE16 (AGL16) transcription factor and its negative regulator microRNA824 (miR824). By utilizing an automated non-invasive phenotyping facility, we have developed a protocol to record plant growth and photosynthetic performance after heat stress in wild type Arabidopsis thaliana and mutant lines at daily intervals. Through an imaging-based analysis of plants growth, we confirmed impaired thermotolerance of hsp101 compared to wild type plants by a time-series growth, morphology and chlorophyll responses. This offers a novel experimental setup for thermotolerance screenings in Arabidopsis, with defined digital markers linking the function of selected genes in heat stress responses to phenotypic traits.

With growing concerns about global food supply and environmental impacts of modern agriculture, we are seeing an increased demand for more horticultural research. While research into plant genetics has seen an increased throughput from recent technological advancements, plant phenotypic research throughput has lagged behind. Improvements in open-source image processing software and image capture hardware have created an opportunity for the development of more competitively-priced, faster data-acquisition tools. These tools could be used to collect measurements of plants' phenotype on a much larger scale without sacrificing data quality. This paper demonstrates the feasibility of creating such a tool. The resulting design utilized stereo vision and image processes in the OpenCV project to measure a representative collection of observable plant traits like leaflet length or plant height. After the stereo camera was assembled and calibrated, visual and stereo images of potato plant canopies and tubers(potatoes) were collected. By processing the visual data, the meaningful regions of the image (the canopy, the leaflets, and the tubers) were identified. The same regions in the stereo images were used to determine plant physical geometry, from which the desired plant measurements were extracted. Using this approach, the tool had an average accuracy of 0.15 inches with respect to distance measurements. Additionally, the tool detected vegetation, tubers, and leaves with average Dice indices of 0.98, 0.84, and 0.75 respectively. To compare the tool's utility to that of traditional implements, a study was conducted on a population of 27 potato plants belonging to 9 separate genotypes. Both newly developed and traditional measurement techniques were used to collect measurements of a variety of the plants' characteristics. A multiple linear regression of the plant characteristics on the plants' genetic data showed that the measurements collected by hand were generally better correlated with genetic characteristics than those collected using the developed tool; the average adjusted coefficient of determination for hand-measurements was 0.77, while that of the tool-measurements was 0.66. Though the aggregation of this platform's results is unsatisfactory, this work has demonstrated that such an alternative to traditional data-collection tools is certainly attainable. / Master of Science