Characterization of Fsr1-Interacting Complex and Its Downstream Pathogenic Subnetwork Modules in Fusarium verticillioides

Fusarium verticillioides is an ascomycete fungus responsible for stalk and ear rots of maize. Previously, we identified a striatin-like protein Fsr1 that plays a key role in stalk rot pathogenesis. In mammals, striatin interacts with multiple proteins to form a STRIPAK (striatin-interacting phosphatase and kinase) complex that regulates a variety of developmental processes and cellular mechanisms. In this study, we identified the homolog of a key mammalian STRIPAK component STRIP1/2 in F. verticillioides, FvStp1, that interacts with Fsr1 in vivo. Gene deletion analysis showed that FvStp1 is critical for F. verticillioides stalk rot virulence. In addition, we identified three proteins, designated FvCyp1, FvScp1 and FvSel1, that interact with the Fsr1 CC domain by yeast-two-hybrid screen. Importantly, FvCyp1, FvScp1, and FvSel1 co-localize to endomembrane structures, each having preferred localization in the cell, and they are all required for F. verticillioides virulence in stalk rot. Moreover, these proteins are necessary for proper localization of Fsr1 to endoplasmic reticulum (ER) and nuclear envelope. To further characterize genetic networks downstream of Fsr1, we performed RNA-Seq with maize B73 stalks inoculated with wild type and fsr1 mutant. We used a computationally efficient branch-out technique, along with an adopted probabilistic pathway activity inference method, to identify functional subnetwork modules likely involved in F. verticillioides virulence. We identified two putative hub genes, i.e., FvSYN1 and FvEBP1 identified from the potential virulence-associated subnetwork modules for functional validation and network robustness studies, such as gene knockout, virulence assays and qPCR studies. Our results provide evidence that FvSYN1 and FvEBP1 are important virulence genes that can infulence the expression of closely correlated genes, providing evidence that these are important hub genes of their respective subnetworks. Further characterization of FvSYN1 showed that FvSyn1 is important for regulating spore germination and hyphal morphology. Furthermore, FvSyn1 is localized to vacuoles, plasma membranes, and septa, and has been shown to play a role in the response to cell wall stressors. Motif-deletion studies showed that both N-terminal SynN domain and C-terminal SNARE domain of FvSyn1 are required for pathogenicity but dispensable for fumonisin production and sexual mating

Characterization of Fsr1-Interacting Complex and Its Downstream Pathogenic Subnetwork Modules in Fusarium verticillioides

Fusarium verticillioides is an ascomycete fungus responsible for stalk and ear rots of maize. Previously, we identified a striatin-like protein Fsr1 that plays a key role in stalk rot pathogenesis. In mammals, striatin interacts with multiple proteins to form a STRIPAK (striatin-interacting phosphatase and kinase) complex that regulates a variety of developmental processes and cellular mechanisms. In this study, we identified the homolog of a key mammalian STRIPAK component STRIP1/2 in F. verticillioides, FvStp1, that interacts with Fsr1 in vivo. Gene deletion analysis showed that FvStp1 is critical for F. verticillioides stalk rot virulence. In addition, we identified three proteins, designated FvCyp1, FvScp1 and FvSel1, that interact with the Fsr1 CC domain by yeast-two-hybrid screen. Importantly, FvCyp1, FvScp1, and FvSel1 co-localize to endomembrane structures, each having preferred localization in the cell, and they are all required for F. verticillioides virulence in stalk rot. Moreover, these proteins are necessary for proper localization of Fsr1 to endoplasmic reticulum (ER) and nuclear envelope. To further characterize genetic networks downstream of Fsr1, we performed RNA-Seq with maize B73 stalks inoculated with wild type and fsr1 mutant. We used a computationally efficient branch-out technique, along with an adopted probabilistic pathway activity inference method, to identify functional subnetwork modules likely involved in F. verticillioides virulence. We identified two putative hub genes, i.e., FvSYN1 and FvEBP1 identified from the potential virulence-associated subnetwork modules for functional validation and network robustness studies, such as gene knockout, virulence assays and qPCR studies. Our results provide evidence that FvSYN1 and FvEBP1 are important virulence genes that can infulence the expression of closely correlated genes, providing evidence that these are important hub genes of their respective subnetworks. Further characterization of FvSYN1 showed that FvSyn1 is important for regulating spore germination and hyphal morphology. Furthermore, FvSyn1 is localized to vacuoles, plasma membranes, and septa, and has been shown to play a role in the response to cell wall stressors. Motif-deletion studies showed that both N-terminal SynN domain and C-terminal SNARE domain of FvSyn1 are required for pathogenicity but dispensable for fumonisin production and sexual mating

Nondestructive Multivariate Classification of Codling Moth Infested Apples Using Machine Learning and Sensor Fusion

Apple is the number one on the list of the most consumed fruits in the United States. The increasing market demand for high quality apples and the need for fast, and effective quality evaluation techniques have prompted research into the development of nondestructive evaluation methods. Codling moth (CM), Cydia pomonella L. (Lepidoptera: Tortricidae), is the most devastating pest of apples. Therefore, this dissertation is focused on the development of nondestructive methods for the detection and classification of CM-infested apples. The objective one in this study was aimed to identify and characterize the source of detectable vibro-acoustic signals coming from CM-infested apples. A novel approach was developed to correlate the larval activities to low-frequency vibro-acoustic signals, by capturing the larval activities using a digital camera while simultaneously registering the signal patterns observed in the contact piezoelectric sensors on apple surface. While the larva crawling was characterized by the low amplitude and higher frequency (around 4 Hz) signals, the chewing signals had greater amplitude and lower frequency (around 1 Hz). In objective two and three, vibro-acoustic and acoustic impulse methods were developed to classify CM-infested and healthy apples. In the first approach, the identified vibro-acoustic patterns from the infested apples were used for the classification of the CM-infested and healthy signal data. The classification accuracy was as high as 95.94% for 5 s signaling time. For the acoustic impulse method, a knocking test was performed to measure the vibration/acoustic response of the infested apple fruit to a pre-defined impulse in comparison to that of a healthy sample. The classification rate obtained was 99% for a short signaling time of 60-80 ms. In objective four, shortwave near infrared hyperspectral imaging (SWNIR HSI) in the wavelength range of 900-1700 nm was applied to detect CM infestation at the pixel level for the three apple cultivars reaching an accuracy of up to 97.4%. In objective five, the physicochemical characteristics of apples were predicted using HSI method. The results showed the correlation coefficients of prediction (Rp) up to 0.90, 0.93, 0.97, and 0.91 for SSC, firmness, pH and moisture content, respectively. Furthermore, the effect of long-term storage (20 weeks) at three different storage conditions (0 °C, 4 °C, and 10 °C) on CM infestation and the detectability of the infested apples was studied. At a constant storage temperature the detectability of infested samples remained the same for the first three months then improved in the fourth month followed by a decrease until the end of the storage. Finally, a sensor data fusion method was developed which showed an improvement in the classification performance compared to the individual methods. These findings indicated there is a high potential of acoustic and NIR HSI methods for detecting and classifying CM infestation in different apple cultivars

Plant Seed Identification

Plant seed identification is routinely performed for seed certification in seed trade, phytosanitary certification for the import and export of agricultural commodities, and regulatory monitoring, surveillance, and enforcement. Current identification is performed manually by seed analysts with limited aiding tools. Extensive expertise and time is required, especially for small, morphologically similar seeds. Computers are, however, especially good at recognizing subtle differences that humans find difficult to perceive. In this thesis, a 2D, image-based computer-assisted approach is proposed. The size of plant seeds is extremely small compared with daily objects. The microscopic images of plant seeds are usually degraded by defocus blur due to the high magnification of the imaging equipment. It is necessary and beneficial to differentiate the in-focus and blurred regions given that only sharp regions carry distinctive information usually for identification. If the object of interest, the plant seed in this case, is in- focus under a single image frame, the amount of defocus blur can be employed as a cue to separate the object and the cluttered background. If the defocus blur is too strong to obscure the object itself, sharp regions of multiple image frames acquired at different focal distance can be merged together to make an all-in-focus image. This thesis describes a novel non-reference sharpness metric which exploits the distribution difference of uniform LBP patterns in blurred and non-blurred image regions. It runs in realtime on a single core cpu and responses much better on low contrast sharp regions than the competitor metrics. Its benefits are shown both in defocus segmentation and focal stacking. With the obtained all-in-focus seed image, a scale-wise pooling method is proposed to construct its feature representation. Since the imaging settings in lab testing are well constrained, the seed objects in the acquired image can be assumed to have measureable scale and controllable scale variance. The proposed method utilizes real pixel scale information and allows for accurate comparison of seeds across scales. By cross-validation on our high quality seed image dataset, better identification rate (95%) was achieved compared with pre- trained convolutional-neural-network-based models (93.6%). It offers an alternative method for image based identification with all-in-focus object images of limited scale variance. The very first digital seed identification tool of its kind was built and deployed for test in the seed laboratory of Canadian food inspection agency (CFIA). The proposed focal stacking algorithm was employed to create all-in-focus images, whereas scale-wise pooling feature representation was used as the image signature. Throughput, workload, and identification rate were evaluated and seed analysts reported significantly lower mental demand (p = 0.00245) when using the provided tool compared with manual identification. Although the identification rate in practical test is only around 50%, I have demonstrated common mistakes that have been made in the imaging process and possible ways to deploy the tool to improve the recognition rate

High-throughput analysis and advanced search for visually-observed phenotypes

Title from PDF of title page (University of Missouri--Columbia, viewed on May 13, 2013).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Dissertation advisor: Dr. Chi-Ren ShyuIncludes bibliographical references.Vita.Ph. D. University of Missouri--Columbia 2012."May 2012"The trend in many scientific disciplines today, especially in biology and genetics, is towards larger scale experiments in which a tremendous amount of data is generated. As imaging of data becomes increasingly more popular in experiments related to phenotypes, the ability to perform high-throughput big data analyses and to efficiently locate specific information within these data based on increasingly complicated and varying search criteria is of great importance to researchers. This research develops several methods for high-throughput phenotype analysis. This notably includes a registration algorithm called variable object pattern matching for mapping multiple indistinct and dynamic objects across images and detecting the presence of missing, extra, and merging objects. Research accomplishments resulted in a number of unique advanced search mechanisms including a retrieval engine that integrates multiple phenotype text sources and domain ontologies and a search method that retrieves objects based on temporal semantics and behavior. These search mechanisms represent the first of their kind in the phenotype community. While this computational framework is developed primarily for the plant community, it has potential applications in other domains including the medical field.Includes bibliographical references

Advanced Image Acquisition, Processing Techniques and Applications

"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution

Earth Resources: A continuing bibliography with indexes, issue 29, April 1981

This bibliography lists 308 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1, 1981 and March 31, 1981. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Cibola Breadstuff: Foodways and Social Transformation in the Cibola Region A.D. 1150-1400

abstract: Foodways in societies at every social scale are linked in complex ways to processes of social change. This dissertation explores the interrelationship between foodways and processes of rapid social transformation. Drawing on a wide range of archaeological and ethnographic data from the Cibola region, I examine the role of foodways in processes of population aggregation and community formation and address how changes in the scale and diversity of social life interacted with the scale and organization of food production and consumption practices. To address the interrelationships between foodways and social transformations, I employ a conceptual framework focused on two social dimensions of food: cuisine and commensality. This study comparatively examines cuisine and commensality through time by investigating a range of interrelated food activities including: food production, storage, preparation, cooking, consumption and discard. While settlement patterns and other more obvious manifestations of aggregation have been studied frequently, by examining foodways during periods of aggregation and social reorganization this study provides new insights into the micro-scalar processes of social transformation, cuisine change, and economic intensification associated with increases in settlement size, density, and social diversity. I document how food production and preparation intensified in conjunction with increases in the size of settlements and the scale of communal commensal events. I argue that foodways were a critical aspect of the social work of establishing and maintaining large, dense communities in the 13th and 14th centuries. At the same time, widespread changes in commensal practices placed a larger burden on household surplus and labor and women were likely the most affected as maize flatbreads and other foods made with finely ground flour were adopted and became central to cuisine. As such, this study provides insights into how rapid social transformations in the late 13th and 14th centuries were experienced differently by individuals, particularly along gendered lines. Studies of foodways, and specifically the social dimensions of food, offer a promising and often underutilized source of information about past processes of population aggregation, social integration, and transformations in the political economies of small-scale societies the past.Dissertation/ThesisDoctoral Dissertation Anthropology 201

Central and peripheral autonomic influences : analysis of cardio-pulmonary dynamics using novel wavelet statistical methods

The development and implementation of novel signal processing techniques, particularly with regard to applications in the clinical environment, is critical to bringing computer-aided diagnoses of disease to reality. One of the most confounding factors in the field of cardiac autonomic response (CAR) research is the influence of the coupling of respiratory oscillations with cardiac oscillations. This research had three objectives. The first was the assessment of central autonomic influence over heart rate oscillations when the pulmonary system is damaged. The second was to assess the link between peripheral and central autonomic control schema by evaluating the heart rate variability (HRV) of people who were able or unable to adapt to the use of integrated lenses for vision, specifically acconrrmodation, correction (adaptive and non-adaptive presbyopes). The third objective was the development of a wavelet-based toolset by which the first two objectives could be achieved. The first tool is a wavelet based entropy measure that quantifies the level of information by assessing not only the entropy levels, but also the distribution of the entropy across frequency bands. The second tool is a wavelet source separation (WayS) method used to separate the respiratory component from the cardiac component, thereby allowing for analysis of the dynamics of the cardiac signal without the confounding influence of the respiratory signal that occurs when the body is perturbed. With regard to hypothesis one, the entropy method was used to separate the COPD study populations with 93% classification accuracy at rest, and with 100% accuracy during exercise. Changes in COPD and control autonomic markers were evident after respiration is removed. Specifically, the LF/HF ratio slightly decreased on average from pre to post reconstruction for controls, increased on average for COPD. In healthy controls, respiration frequency is distributed across multiple bandwidths, causing large decreases in both LF and HF when removed. With respiration effect removed from COPD population, LE dominates autonomic response, indicating that the frequency is concentrated in the HF autonomic region. Decrease in variance of data set increases probability tat smaller changes can be detected in values. The theory set forth in hypothesis two was validated by the quantification of a correlation between peripheral and central autonomic influences, as evidenced by differences in oculomotor adaptability correlating with differences in HRV. Standard Deviation varies with grouping, not with age. Increasing controlled respiration frequencies resulted in adaptive presbyopes and controls displaying similar sympathetic responses, diverging from non-adaptive group. WayS reduced frequency content in ranges concurrent with breathing rate, indicating a robust analysis. The outcome of hypothesis three was the confirmation that wavelet statistical methods possess significant potential for applications in HRV. Entropy can be used in conjunction with cluster analysis to classify patient populations with high accuracy. Using the WayS analysis, the respiration effect can be removed from HRV data sets, providing new insights into autonomic alterations, both central and peripheral, in disease