Big data analytics in high-throughput phenotyping

Doctor of PhilosophyDepartment of Computer ScienceMitchell L. NeilsenAs the global population rises, advancements in plant diversity and crop yield is necessary for resource stability and nutritional security. In the next thirty years, the global population will pass 9 billion. Genetic advancements have become inexpensive and widely available to address this issue; however, phenotypic acquisition development has stagnated. Plant breeding programs have begun to support efforts in data mining, computer vision, and graphics to alleviate the gap from genetic advancements. This dissertation creates a bridge between computer vision research and phenotyping by designing and analyzing various deep neural networks for concrete applications while presenting new and novel approaches. The significant contributions are research advancements to the current state-of-the-art in mobile high-throughput phenotyping (HTP), which promotes more efficient plant science workflow tasks. Novel tools and utilities created for automatic code generation, maintenance, and source translation are featured. Promoted tools replace boiler-plate segments and redundant tasks. Finally, this research investigates various state-of-the-art deep neural network architectures to derive methods for object identification and enumeration. Seed kernel counting is a crucial task in the plant research workflow. This dissertation explains techniques and tools for generating data to scale training. New dataset creation methodologies are debuted and aim to replace the classical approach to labeling data. Although HTP is a general topic, this research focuses on various grains and plant-seed phenotypes. Applying deep neural networks to seed kernels for classification and object detection is a relatively new topic. This research uses a novel open-source dataset that supports future architectures for detecting kernels. State-of-the-art pre-trained regional convolutional neural networks (RCNN) perform poorly on seeds. The proposed counting architectures outperform the models above by focusing on learning a labeled integer count rather than anchor points for localization. Concurrently, pre-trained models on the seed dataset, a composition of geometrically primitive-like objects, boasts improvements to evaluation metrics in comparison to the Common Object in Context (COCO) dataset. A widely accepted problem in image processing is the segmentation of foreground objects from the background. This dissertation shows that state-of-the-art regional convolutional neural networks (RCNN) perform poorly in cases where foreground objects are similar to the background. Instead, transfer learning leverages salient features and boosts performance on noisy background datasets. The accumulation of new ideas and evidence of growth for mobile computer vision surmise a bright future for data-acquisition in various fields of HTP. The results obtained provide horizons and a solid foundation for future research to stabilize and continue the growth of phenotypic acquisition and crop yield

Open source application development for phenotypical data acquisition

Master of ScienceDepartment of Computing and Information SciencesMitchell L. NeilsenThe Poland Lab at Kansas State University studies the genetics of wheat ‘to develop a climate-resilient wheat variety that can combat rising heat and drought.’ With populations and food demand rising the need for accelerated food growth is imminent. A previous group of Android applications, Field book has shown that the use of open source app development could create a segue to increasing food development through modernized plant breeding across the world. This is especially useful to various countries that may have a limited budget and have a rising market for Android mobile devices. The applications described in this report include: Verify, a barcode scanning application that can quickly confirm if various seed identifiers are found within a database, Field Mapping, an application that identifies individual plot segments throughout farmland and finally, Survey, an application for manual input of latitude and longitude data. These three applications and services open a new open-source domain for acquisition of data on farmlands to accompany the research of plant breeders

How to Build Reputation in Financial Markets

A company's reputation for accountability and trustworthiness is a critical factor in its ability to attract the financial resources required to support its strategies. However, there has been little research done on how companies build and preserve the trust of financial markets. This research highlights a number of practices and features that seem to positively influence the formation of corporate reputation in financial markets. Collectively, the findings indicate that companies are guided by knowledgeable, respected and committed leaders, that are transparent and comprehensive in their communication of corporate plans, and that display credible and independent control systems are more likely to gather the consensus of the financial community around bold strategic plans

Ectopic G-protein expression in dopamine and serotonin neurons blocks cocaine sensitization in Drosophila melanogaster

Sensitization to repeated doses of psychostimulants is thought to be an important component underlying the addictive process in humans. In all vertebrate animal models, including humans, and even in fruit flies, sensitization is observed after repeated exposure to volatilized crack cocaine. In vertebrates, sensitization is thought to be initiated by processes occurring in brain regions that contain dopamine cell bodies. Here, we show that modulated cell signaling in the Drosophila dopamine and serotonin neurons plays an essential role in cocaine sensitization. Targeted expression of either a stimulatory (Gαs) or inhibitory (Gαi) Gα subunit, or tetanus toxin light chain (TNT) in dopamine and serotonin neurons of living flies blocked behavioral sensitization to repeated cocaine exposures. These flies showed alterations in their initial cocaine responsiveness that correlated with compensatory adaptations of postsynaptic receptor sensitivity. Finally, repeated drug stimulation of a nerve cord preparation that is postsynaptic to the brain amine cells failed to induce sensitization, further showing the importance of presynaptic modulation in sensitization

Filling the silence: Giving voice to gender violence in Una’s graphic novel Becoming Unbecoming

Written in the style of a memoir, Una’s graphic novel, Becoming Unbecoming (2015), takes readers on a poignant journey of a young girl’s experiences of silence, shame and blame after being subjected to male sexual violence. The protagonist’s story is played out against the backdrop of the rapes and murders committed by the notorious Yorkshire Ripper. This paper examines the text’s multilayered narrative, which uses a range of graphic strategies and artistic styles to challenge its readers to make meaning, fill in the gaps, and piece together their own version of events. The text’s fragmented and disconnected sequences mimic the nature of traumatic memory, and the shifting linguistic-visual narration moves between fact, story, experience and emotion

Intergenerational incarceration: risk factors and social exclusion

10.1080/10875549.2013.833161Journal of Poverty174437-45