Browsing by Subject "detection"
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Item The Development of a Novel Capacitive Water Conductivity Sensor(2018-05) Cantilina, KeiranA novel sensor was developed which measures water conductivity by tracking the capacitance of two insulated probes submerged in the water under test. Unlike commercially available water conductivity sensors based on 4-terminal potentiometric methods, this sensor does not require metal to be in direct contact with water. In addition, this sensor has much higher sensitivity and lower power consumption compared to induction-based conductivity sensors. These differences make this sensor uniquely suited for use in distributed sensor networks, where resistance to corrosion and fouling, low power consumption, and reliability are necessary traits. Lastly, this sensor is a good fit for use by citizen scientists or hobbyists due to the ease of assembly and the low price of components needed to construct the device.Item DNA-based detection and reference genome assembly of Aphanomyces cochlioides(2021-08) Botkin , JacobAphanomyces root rot (ARR) and Aphanomyces damping-off, caused by the soil-borne oomycete A. cochlioides, are common diseases of sugar beet in major production regions. Management techniques are implemented to mitigate losses, but ultimately A. cochlioides is intractable and significantly reduces the sucrose content of the sugar beet taproot throughout the growing season, especially during periods with above average precipitation. Currently, a genome sequence for A. cochlioides is not available, and existing diagnostic assays are time consuming and have limitations. The first objective was to assemble and annotate a reference genome for A. cochlioides. We conducted a de novo genome assembly and annotation of A. cochlioides using 232x coverage of Nanopore long-reads, and error corrected with 77x coverage of Illumina short-reads. The assembled genome was 76.3 Mb, consisted of 97 contigs, and had a contig N50 of 2.6 Mb. The assembly contained 93.2% of complete benchmarking universal single-copy orthologs, a repeat content of 32.1%, and 20,274 gene models. This is the first report of a reference genome for A. cochlioides, which could serve as a platform for future investigations into virulence mechanisms, comparative genomics, and the development of diagnostic assays. The second objective was to develop a rapid, sensitive, and accurate DNA-based detection assay to quantify A. cochlioides inoculum in infested soil and infected sugar beet tissue. We developed a TaqMan qPCR assay that was specific to A. cochlioides. The qPCR assay was validated with 12 naturally infested soil samples, which had Ct (cycle threshold) values of 26.72 to 34.64 and ARR disease severity index (DSI) values of 48 to 100. The qPCR assay was further validated on infected adult sugar beets and seedlings. For 60 adult sugar beet roots, A. cochlioides DNA was detected in 63% of the samples, while a culture-based assay identified A. cochlioides in 15% of the samples. Furthermore, A. cochlioides DNA was detected in infected seedlings as early as 5 days after planting in a naturally infested soil. Finally, when oospore infested potting soil was tested with the qPCR assay and ARR bioassay, a strong correlation was observed between oospore density and Ct value (R2 = 0.96), as well as oospore density and DSI value (R2 = 0.968). The limit of detection (LOD) was 5 oospores per g soil (dry wt.), which had a mean Ct value of 34.58, and a mean DSI value of 23.33. Our DNA-based detection assay could provide growers with the A. cochlioides infestation level of field soils to help them make informed management decisions prior to planting.Item R Code and Output Supporting: Do Capture and Survey Methods Influence Whether Marked Animals are Representative of Unmarked Animals?(2015-03-27) Fieberg, John R; White, Kevin S; jfieberg@umn.edu; Fieberg, John RThese files contain R code (along with associated output from running the code) supporting all results reported in "Do Capture and Survey Methods Influence Whether Marked Animals are Representative of Unmarked Animals?" in Wildlife Society Bulletin. The lead author wrote this code to analyze multi-year re-sighting data collected from moose (Alces alces) in Minnesota, elk (Cervus elaphus) in Washington, and mountain goats (Oreamnos americanus) in Washington and Alaska, to evaluate whether detection probabilities increased or decreased as a function of time since animals were captured.Item Simulating Effects of Imperfect Detectability in Bird Surveys(2016-11) Rigby, ElizabethCounts obtained from point count bird surveys can be treated as an index to bird abundance, but imperfect detectability can complicate inferences about abundance. Adjustment analysis methods, including double-observer, replicated counts, removal, and distance sampling methods, have been developed to estimate detection in addition to abundance. These methods require additional information to estimate detection, which may entail added logistical costs or be additional sources of error. It is not clear when or if adjustment methods outperform index methods, or how the benefits of adjustment methods compare to their costs. I simulated point counts of birds, modeling birds spatially as moving within bivariate normal territories, modeling song production as an autocorrelated process, and modeling perceptibility as a logit function of distance to the observer. In Chapter 1, I simulated counts using a test scenario with parameters reflecting surveys of black-throated blue warblers (BTBW, Setophaga caerulescens), analyzed counts using index and adjustment analysis methods, then evaluated and compared the performance of analysis methods. Estimates from index methods underestimated true density of birds (Dp) for all survey types, but were highly correlated with true density. Adjusted estimates from distance sampling and removal analysis methods showed a reduction in bias as compared to index estimates, but had reduced correlation with true density. Adjusted estimates from double-observer analysis methods were nearly unchanged from index estimates. Adjusted estimates from replicated counts analysis methods were susceptible to highly inflated density estimates, resulting in extremely high bias and low correlation with true density. Index methods, while biased, were better correlated with true density and would provide better information about changes in abundance than an adjustment analysis method for the BTBW scenario. If detection is constant and relative abundance is sufficient to meet survey objectives, using an index method is often preferable. For systems with variable detection probability where inference about absolute abundance is necessary to meet objectives, practitioners should select adjustment methods suited to model the source of imperfect detection in their system. Ill-suited adjustment methods will not improve inference and are no more useful than an index. In Chapter 2, I used the model to simulate counts for scenarios with high or low availability and high or low perceptibility. I also included scenarios where abundance was confounded with perceptibility, and scenarios where they were independent. I then analyzed count data using index methods and adjustment methods. Although index methods were biased and only had a strong correlation with true density when detectability was high, adjustment methods generally did not offer an improvement. As compared to index methods, adjustment method performance ranged from far worse (replicated counts), to no added value (double-observer) to moderate improvement (in bias only, for removal and distance sampling in specific scenarios). Practitioners should carefully consider the sources of variation in detection probability in their system. If detection components are unknown or known to be variable, I advise practitioners to perform a pilot study to estimate detection components. Additionally, practitioners should standardize their methods to increase availability and perceptibility in their surveys and to lower the variation in these detection components. In Chapter 3, I conducted simulated bird surveys using recorded bird songs to assess factors affecting detection probability in grassland bird point counts. I used mixed effects logistic regression models to estimate factors affecting detection probability and to estimate and visualize the variation in the area around the observer where birds can be perceived (the perceptible area). I conducted simulated surveys with 8926 binary opportunities for detection in Minnesota grasslands in 2011 and 2012. Species, distance to the observer, wind speed and direction, observer, and density of vegetation all affected detection of recorded bird songs. Species had a strong effect; the size of the predicted perceptible area around the observer differed by more than 10-fold among species. Wind also had a strong effect on detection. As wind speed increased, probability of detection downwind of the observer was reduced and the perceptible area around the observer became smaller and more asymmetrical. The effective distance at which an observer is more likely to detect a bird than to not detect it may differ among species and angles to the wind, even within the same survey. I recommend using fixed-radius counts for bird surveys in grasslands and reducing the variation in detection probability by standardizing surveys across wind conditions.