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

Indiana University-Purdue University Indianapolis (IUPUI) / The biological underpinnings that control iris pigmentation and facial morphology are two areas of research that over the last decade are becoming more thoroughly investigated due to the increased affordability of genotyping and advances in technology allowing for more advanced analysis techniques. Despite the ease of access to the data and the tools required to perform iris pigmentation and facial morphological studies, there are still numerous challenges researchers must overcome when exploring the genetics of these complex phenotypes. Some of these challenges include difficulty in working with the bioinformatic programs designed to analyze genetic associations, the inability to define a phenotype that captures the true nature of these traits, and analysis techniques that fail to model complex gene-gene interactions and their effect on a phenotype or phenotypes of interest. In this body of work, I attempted to address these challenges by designing a bioinformatic pipeline, Odyssey, that bridges the communication gaps between various data preparation programs and the programs that analyze genomic data. With this program, genome-wide association studies (GWAS) could be conducted in a quicker, more efficient, and easier manner. I also redefined iris color as a quantitative measurement of pre-defined color classes. In this way it is possible to define and quantify the unique and intricate mixtures of color, which allows for the identification of known and novel variants that affect individual iris color. I also improved upon current prediction models by developing a neural network model capable of predicting a quantitative output to four pre-defined classes; blue/grey, light brown (hazel), perceived green, and dark brown. I examined the effects of defining a simple facial morphology phenotype that more accurately captures the lower face and jaw shape. I then analyzed this phenotype via a GWAS and found several novel variants that may be associated with a square and diamond shaped face. Lastly, I demonstrated that structural equation modeling can be used in combination with traditional GWAS to examine interactions amongst associated variants, which unearths potential biological relationships that impact the multifaceted phenotype of facial morphology.

This thesis presents research associated to phenotyping of plants by applying informatics techniques which includes databases, web technologies, image processing and feature measurements of 2D and 3D images. The thesis presents two enabling bioinformatics tools related by a shared set of research objectives and distinct by the nature of their applications. The first project called ClearedLeavesDB, is a common platform for plant biologists to share data and metadata about cleared leaf images. This project resulted in an online interactive database of cleared leaf images. The second project called Digital Imaging of Root Traits (DIRT), is an application to store, manage, share and process root images as well as analyze root image traits with respect to different experiments. This application is deployed on iPlant's cyber-infrastructure and currently supports management of 2D root images and high-throughput processing and structural descriptor/trait estimation from root images. The application enables storage, management and sharing heterogeneous image data and metadata including dynamic environmental and descriptor data. In the final part of the thesis, I describe ongoing challenges in developing new methods to measure global and local descriptors from reconstructed 3D root images.

Master of Science / Department of Computer Science / Mitchell L. Neilsen / Kansas State University is a world leader in the study of small grain genetics to develop new varieties which tolerate a wide range of environmental conditions. A phenotype is a composite of a plants observable traits. Several mobile applications, called PhenoApps, have been developed for field-based, high-throughput phenotyping (HTP) to advance plant breeding programs around the world. These applications require novel image analysis algorithms to be developed to model and extract plant phenotypes. Some of the first algorithms developed were focused on using static image analysis to count and characterize a wide variety of seeds in a single image with a static colored background. This thesis describes both a static algorithm and development of a hopper system for a dynamic, real-time algorithm to accurately count and characterize seeds using a modest mobile device. The static algorithm analyzes a single image of a particular seed sample, captured on a mobile device; whereas, the dynamic algorithm analyzes multiple frames from the video input of a mobile device in real time. Novel 3D models are designed and printed to set a steady flow rate for the seeds, but the analysis is also completed to consider seeds flowing at variable rates and to determine the range of allowable flow rates and achievable precision for a wide variety of seeds. Both algorithms have been implemented in user-friendly mobile applications for realistic, field-based use. A plant breeder can use the applications to both count and characterize a smaller sample using the static approach or a larger sample using the dynamic approach, with seeds sampled in real time without the need to analyze multiple static images. There are many directions for future research to enhance the algorithms performance and accuracy.

90% de la mortalité par cancer provient de tumeurs disséminées, ou métastases. Ces métastases se forment à partir de cellules tumorales qui s'échappent d'une tumeur primaire, circulent dans le sang, puis quittent les vaisseaux sanguins pour enfin aller nicher dans des organes distants et former des tumeurs secondaires. Les processus par lesquels ces cellules circulantes envahissent les organes distants, remodèlent leur environnement pour créer une «niche micrométastatique», prolifèrent pour produire des métastases macroscopiques, sont mal connus, principalement en raison d'un manque de modèles expérimentaux. En effet ces événements sont rares, se produisent à une échelle microscopique et à des localisations à priori inconnues. La perte d'adhérence cellulaire des cellules tumorales se détachant des tissus tumoraux primaires est associée à un phénomène de transformation connu sous le nom de transition épithéliale-mésenchymateuse (EMT) conduisant à la perte des caractéristiques épithéliales. Dans ce travail, nous avons souhaité aborder la question du processus métastatiques par l'étude de l'influence de l'étape de circulation dans le flux sanguin sur différentes caractéristiques de cellules tumorales. Pour cela, des modèles microfluidiques contenant des constrictions mécaniques afin d'imiter la microcirculation sanguine ont été conçus et fabriqués. Nous avons soumis des cellules provenant de tumeurs primaires du sein dans des situations de confinement périodiques à l'intérieur de ces canaux microfluidiques en utilisant un système de contrôle de flux. Nous avons étudiés l'impact des déformations induites par les constrictions des canaux microfluidiques sur l'expression génétique des marqueurs EMT, la morphologie ainsi que la dynamique des changements morphologiques. Nous montrons que ces paramètres cellulaires sont touchés par la déformation mécanique imposée sous flux, suggérant que l'étape de circulation des cellules tumorales dans le sang a un rôle important dans la capacité de celles-ci à produire des métastases. / 90% of cancer mortality arises from metastases, due to cells that escape from a primary tumor, circulate in the blood as circulating tumor cells (CTCs), leave blood vessels and nest in distant organs. The processes by which CTCs invade distant organs, remodel their environment to create a “micrometastatic niche”, the eventual triggering of a proliferation leading to a macroscopic metastases, are poorly known, mostly because of a lack of experimental models. These events are rare; occur in the body at unknown places and on a microscopic scale. The loss of cell adhesion of tumor cells detaching from the primary tumor tissues will undergo a transformation phenomenon known as epithelial-to mesenchymal transition (EMT) leading to the loss of epithelial characteristics with different expression patterns of EMT markers (E-cadherin, N-cadherin, Vimentin, Snail1/2, Twist1/2, ZEB1/2). The changes in mechanical and physical properties of interacting cells during morphological and malignant transformation are investigated and their quantifications measured. Here, microfluidic models containing mechanical constrictions in order to mimic the blood microcirculation have been designed and fabricated. Metastatic breast cancer cells are subjected and confined to the microfluidic channels using a flow control system. These cells are circulated under optimal culture conditions, and monitored in the channels for the observance of biophysical occurrences from continuous mechanical cellular deformations. The biophysical effects of circulation and confinement on tumor cell morphogenesis will be investigated.

Salt stress severely constrains plant performance and global agricultural productivity. 5% of arable land, 20% of irrigated areas and 98% of water reserves worldwide are saline. Improving the salt tolerance of major crop species could help attenuate yield losses and expand irrigation opportunities and provide in situ relief in areas where poverty, food and water scarcity are prevalent. Increasing the salt tolerance of crops with high commercial and nutritional value, such as tomato (Solanum lycopersicum L.), would provide particularly significant economic and health benefits. However, salt tolerance is a complex trait with a limited genetic repertoire in domesticated crop varieties, including tomato, frustrating attempts to breed and engineer tolerant crop varieties. Here, a genome-wide association study (GWAS) was undertaken, leveraging the rich genetic diversity of the wild, salt tolerant tomato Solanum pimpinellifolium and the latest phenotyping technologies to identify traits that contribute to salt tolerance and the genetic basis for variation in those traits. A panel of 220 S. pimpinellifolium accessions was phenotyped, focusing on image-based high-throughput phenotyping over time in controlled and field conditions in young and mature plants. Results reveal substantial natural variation in salt tolerance over time across many traits. In particular, the use of unmanned aerial vehicle (UAV)-based remote sensing in the field allowed high-resolution RGB, thermal and hyperspectral mapping that offers new insights into plant performance in the field, over time. To empower our GWAS and facilitate the identification of candidate genes, a new S. pimpinellifolium reference genome was generated, 811Mb in size, N50 of ~76kb, containing 25,970 annotated genes. Analysis of this reference genome highlighted potential contributors to salt tolerance, including enrichments in genes with stress response functions and a high copy number of the salt tolerance-associated gene inositol- 3-phosphate synthase (I3PS). A recently completed full genome re-sequencing of the panel, along with a newly available pseudomolecule-level assembly of the S. pimpinellifolium genome with N50 of ~11Mb, will serve to drive a GWAS to identify loci associated with traits that contribute to salt tolerance. Further research including gene validation, breeding, genetic modification and gene editing experiments will drive the development of new salt tolerant tomato cultivars.

BACKGROUND: As smartphone devices have become a ubiquitous part of our modern lives, parents and clinicians have become increasingly concerned about the effects of digital media use on the mental well-being of adolescents and young adults. Smartphone ownership in youth has increased significantly over the last decade, paralleling the rise in mental health disorders. This study seeks to use the digital phenotyping (DP) methodology to elucidate these relationships. Most studies examining these variables use cross-sectional data in healthy adolescents. To our knowledge, no studies have used DP methodology to characterize the relationship between digital media use, depression and anxiety in a population of clinical adolescents. METHODS: 50 adolescent and young adults between the ages of 12-23 receiving outpatient mental health services from a community hospital network in the greater Boston area were enrolled. Participants installed an application on their personal smartphones that collected daily surveys that captured mood symptoms, digital media use (screen time, social media time, and top apps used [active data]), and that also continuously captured sensor data (GPS and accelerometer [passive data]) over six weeks. RESULTS: Using linear regression and multilevel modeling, no significant associations were found between screen time or social media time, and anxiety and depression symptoms. Productivity apps were used significantly more in those with no depression symptoms than in those with moderate to severe levels of depression. CONCLUSION: Our study results challenge the present intuition that the amount of digital media use negatively impacts mental well-being in youth. Total screen time and social media time measures may be insufficient when attempting to assess the impact of digital media engagement on youth. Additionally, the results of our study suggest that the types of apps used by youth may depend on an individual’s mood severity. Although not without limitations, DP studies may be the ideal methodology for capturing with greater granularity digital use behavior and its association with mood symptoms in adolescents. / 2022-11-22T00:00:00Z

The scientific development of innovative molecular techniques has transformed the approach towards human identification. In forensic casework, the emergence of molecular phenotyping, or phenotypic prediction from DNA, has mitigated some challenges involving the unavailability of references samples for traditional forensic DNA analysis. Molecular phenotyping via SNP analysis can be used as a tool in a forensic setting to predict physical traits, such as hair, skin and eye colour, and provide investigative leads. Several ancestry informative markers (AIMs) have previously been associated with human skin colour in mainly the European and North American population groups, while admixed populations are hardly studied. The present study aims to contribute towards this gap by investigating the relationship between two AIMs (SLC45A2, rs16891982 and SLC24A5, rs1426654) that are typically involved in molecular phenotyping, and melanin index (MI) in the South African (SA) metapopulation (n = 389). The self-reported ancestry, ethnicity and relevant biographic information for each participant was documented and MI was recorded using a dermaspectrophotometer. DNA was extracted from saliva samples and PCR amplification of target regions was performed. Thereafter, SNaPshot® PCR was used to genotype the variants. Significant differences (p < 0.0001) were observed between MI readings and ancestral as well as population census groups. A generalised linear model (GLM) was developed which could accurately predicted the MI readings for each genotype combination within the 95 % confidence interval of the recorded MI readings. Our results suggest that these two markers were consistently associated with MI in the admixed SA population and are thus informative to predict MI in a forensic setting. Finally, this was the first study in a SA context to use SNP analysis for objective MI prediction.

Agriculture faces many challenges related to the increasing food demands of a growing global population and the sustainable use of resources in a changing environment. To address them, we need reliable information sources, like exploiting hyperspectral satellite, airborne, and ground-based remote sensing data to observe phenological traits through a crops growth cycle and gather information to precisely diagnose when, why, and where a crop is suffering negative impacts. By combining hyperspectral capabilities with unmanned aerial vehicles (UAVs), there is an increased capacity for providing time-critical monitoring and new insights into patterns of crop development. However, considerable effort is required to effectively utilize UAV-integrated hyperspectral systems in crop-modeling and crop-breeding tasks. Here, a UAV-based hyperspectral solution for mapping crop physiological parameters was explored within a machine learning framework. To do this, a range of complementary measurements were collected from a field-based phenotyping experiment, based on a diversity panel of wild tomato (Solanum pimpinellifolium) that were grown under fresh and saline conditions. From the UAV data, positionally accurate reflectance retrievals were produced using a computationally robust automated georectification and mosaicking methodology. The resulting multitemporal UAV data were then employed to retrieve leaf-chlorophyll (Chl) dynamics via a machine learning framework. Several approaches were evaluated to identify the best-performing regression supervised methods. An investigation of two learning strategies (i.e., sequential and retraining) and the value of using spectral bands and vegetation indices (VIs) as prediction features was also performed. Finally, the utility of UAVbased hyperspectral phenotyping was demonstrated by detecting the effects of salt-stress on the different tomato accessions by estimating the salt-induced senescence index from the retrieved Chl dynamics, facilitating the identification of salt-tolerant candidates for future investigations. This research illustrates the potential of UAV-based hyperspectral imaging for plant phenotyping and precision agriculture. In particular, a) developing systematic imaging calibration and pre-processing workflows; b) exploring machine learning-driven tools for retrieving plant phenological dynamics; c) establishing a plant stress detection approach from hyperspectral-derived metrics; and d) providing new insights into using computer vision, big-data analytics, and modeling strategies to deal effectively with the complexity of the UAV-based hyperspectral data in mapping plant physiological indicators.

Barley is a resilient crop that performs better than other cereal plants under abiotic stress conditions, including salinity stress. The understanding of salinity tolerance in crops is a major milestone to increase yield in areas affected by soil salinity. In barley, some components of salinity tolerance have been elucidated, (e.g. HVP10, which is involved in tissue tolerance), yet little research has explored the discovery of other components contributing to salinity tolerance. In this PhD project, a forward genetics approach was used, whereby two barley populations were phenotyped under controlled and field conditions for salinity tolerance. The first population is a diversity panel of two-row European spring barley, and the second population is a nested association mapping barley population with wild donors from the Fertile Crescent. The use of non-destructive high-throughput experiments conducted under controlled conditions provided insight into the understudied shoot ion-independent component of salinity tolerance. In addition, the previously known association HvHKT1;5 was detected under controlled conditions. In parallel, the field experiments increased our understanding of new components of salinity tolerance, such as the maintenance of yield and yield-related traits under saline conditions. This strategy was successful with the identification of a locus on chromosome 2H (140-145 cM), where the allele from one of the wild donors of the nested association mapping population increased yield under saline conditions in the field. When re-evaluating lines homozygous at the 2H locus, ear length, ear number per plant, yield and harvest index were all significantly higher under saline conditions for the lines carrying the wild allele. Furthermore, another interesting locus on chromosome 7H that was responsive to salt treatment and co-localized with HVP1 was identified using the diversity panel population. Loci with known flowering genes were also shown to be involved in salinity tolerance. To conclude, this PhD project shed more light on the genetic mechanisms of salinity tolerance in barley, a knowledge that can benefit breeding programs and can be extended to other crops such as wheat.