To support the growing human population, plant phenotyping technologies must innovate to rapidly interpret hyperspectral (HS) data into genetic inferences for plant breeders and managers. While pigment and nutrient concentrations within canopies are known to be vertically non-uniform, these chemical distributions as sources of HS noise are not universally addressed in scaling leaf information to canopy data nor in detecting spectral plant health traits. <br>In this project, soybeans (Glycine Max, cultivar Williams 82) were imaged with a Spectra Vista Corporation (SVC) HR-1024 spectroradiometer (350-2500 nm) at the highest five node positions. The samples were subjected to nitrogen and drought stress in factorial design (n=12) that was validated via relative water content (RWC) and PLS Regression of photopigments (chlorophyll a, chlorophyll b, lutein, neoxanthin, violaxanthin, and zeaxanthin in mg/g DW) and N concentration (%) for each imaged tissue. Welch’s ANOVA and Tamhane’s T2 post-hoc testing quantified spectral heterogeneity with respect to treatments and node positions through spectral angle measurements (SAMs) and percent NDVI difference. Drought-stressed samples had the lowest SAM between node positions compared to other treatments, and SAM node comparisons were greatest when including the highest sampled tissues. Taking ratios of NDVI between node positions proved more statistically effective at discerning between all factorial treatments than individual leaf NDVI values. Finally, intra-canopy spectral heterogeneity was exploited by training Linear Discriminant Analysis (LDA) classifiers on relative reflectance between node positions, tuning for the F1-Score. A classifier built on Node 1 vs. Node 3 reflectance outperformed in class-specific accuracies compared to analogous models trained on point-view data. Accounting for intra-canopy spectral variability is an opportunity to develop more comprehensive phenotyping tools for plant breeders in a world with rapidly rising agricultural demand.<br><br>

Monitoring of Chemical Parameters of Mining Waters from Hyperspectral Image Data Abstract The thesis deals with utilization of hyperspectral image data for mining water quality monitoring. Sokolov lignite basin, facing many environmental problems caused by brown coal mining activities is the area of interest. Airborne hyperspectral image data acquired by the HyMap sensor in 2009 and 2010 and ground truth data - chemical and physical parameters of water samples are the main data sources for the thesis. Practical part aims at estimating of the amount of the dissolved iron and suspended sediments in selected water bodies. Two approaches were used to achieve this goal - the empirically derived relationship between the ground measurements and reflectance of the water bodies, and spectral unmixing method. Comparison of the two mentioned approaches and evaluation of validity to use the proposed methods for the data acquired by the same sensor one year later is also a part of this thesis.

The selection of a representative sample is a delicate problem when drug seizures comprised of large number of units arrive at the Swedish National Forensic Centre (NFC). If deviating objects in the selected sample size are found, additional analyzes are required to investigate how representative the results are for the entire population. This generates further pressure on operational analysis flow. With the goal to provide a tool which forensic scientists at NFC can base their assessment of the representative nature of the selected sampling of large drug seizures on, this project investigated the possibilities of evaluating the level of homogeneity in drug seizures using near-infrared (NIR) hyperspectral imaging along with principal component analysis (PCA). A total of 27 sample groups (homogeneous, heterogeneous and seized sample groups) were analyzed and different predictive models were developed. The models were either based on quantifying the variation in NIR spectra or in PCA scores plots. It was shown that in the spectral range of 1300-2000 nm, using a pre-processing combination of area normalization, quadratic (second polynomial) detrending and mean centering, promising predictive abilities of the models in their evaluation of the level of homogeneity in drug seizures were achieved. A model where the approximated signal-dependent variation was related to the quotient of significant and noise explained variance given by PCA indicated most promising predictive abilities when quantifying the variation in NIR spectra. Similarly, a model where a rectangular area, defined by the maximum distances along PC1 and PC2, was related to the cumulative explained variance of the two PCs showed most promising predictive abilities when quantifying the variation in PCA scores plots. Different zones for which within sample groups are expected to appear based upon their degree of homogeneity could be established for both models. The two models differed in sensitivity. However, more comprehensive studies are required to evaluate the models applicability from an operational point-of-view.

Alzheimer's disease is a widespread disease that has devastating effects on the human brain and mind. Ultimately, it leads to death and there are currently no treatment methods available that can stop the disease progression. The mechanisms involved behind the disease are not fully understood although it is known that amyloid fibrils play an important role in the disease development. These fibrils are able to form plaques that can trigger neuronal death, by interacting with receptors on the cell surface and the synaptic cleft or by entering the cell and disturb important functions such as metabolic pathways. To study the plaque formation of amyloid proteins, both in vitro and in vivo methods are used to investigate the characteristics of the protein. Luminescent conjugated oligothiophene probes are able to bind in to amyloid beta fibrils and emit light when excited by an external light source. This way fibrillation properties of the protein can be studied. Developing probes that can serve as biomarkers for detection of amyloid fibrils could change the way Alzheimer's is treated. Being able to detect the disease in its early disease course, and start treatments early, is suggested to stop the progression of neural breakdown. In this project a software is developed to analyze fluorescent microscopy images, taken on tissue stained with these probes. The software is able to filter out background noise and capture parts of the picture that are of interest when studying the amyloid plaques. This software generates results similar to if the images were to be analyzed using any software where the regions to analyze are selected manually, suggesting that the software developed produce reliable results unbiased by background noise.

<p>  </p> <p>Modern agriculture must address the massive challenge of providing food for the increasing population. The challenge lies in increasing crop yield and reducing losses caused by abiotic and biotic stresses. In fact, for some crops, such as wheat and maize, over 40% of the production is lost due to environmental conditions (abiotic stresses) or pests and pathogens (biotic stresses). Specialists in the area are suggesting a need for a second green revolution to meet the increasing demand in food production. While in the first green revolution was focused on breeding and genetics to produce crops' genetic lines with a higher yield. The second green revolution will utilize cutting-edge technologies to increase yield and reduce crop losses. The development of remote sensing technologies and their applications is the main driving force of modern agricultural practices. Currently, farmers are relying more on automation, data collection, and data analysis to manage farming operations. The reliance on remote sensor technologies is a game-changer for traditional agricultural practices, and it is contributing tremendously to increasing production and avoiding yield losses. Hyperspectral phenotyping is an emerging remote sensing technology that utilizes the light's reflectance to provide insightful information about plant traits. For several years, research groups have been applying hyperspectral phenotyping techniques to detect plant traits information, such as nitrogen content, photosynthesis rates, pests infestation, and abiotic stress detection. Although this is not a novel approach to plant traits detection, this technology application is not mature yet. Several challenges are associated with using hyperspectral information for phenotyping, such as model transferability, data collection scalability, and the heritability of plant traits retrieved using hyperspectral data. In my thesis dissertation, I addressed some of those challenges contributing to advances in hyperspectral phenotyping. My results demonstrate that using full-range hyperspectral reflectance data (400-2400nm) to retrieve nitrogen in winter wheat increases the model transferability across years and genotypes. Predicting nitrogen content using hyperspectral data can be used as a surrogate to calculate nitrogen use efficiency traits. My research highlights the hurdles associated with spectral detection of stresses interaction, such as drought stress, which can mask western corn rootworm detection in maize. Finally, I explored the correlation among spectral, functional, and field traits in a soybean NAM (Nested Association Mapping) population to understand the relationship among those traits' variability and how that information can be used for soybean breeding programs. The outcomes of my thesis dissertation advance the knowledge in the hyperspectral phenotyping field and its application to modern agriculture. Consequently, my study also contributes to food security programs by providing insightful information about the hyperspectral assessment of plant health status, which is essential to increase yield production and reduce crop losses. </p>

Abstract Purpose: This study evaluated the use of hyperspectral imaging for the determination of the resection margin during colorectal resections instead of clinical macroscopic assessment. Methods: The used hyperspectral camera is able to record light spectra from 500 to 1000 nm and provides information about physiologic parameters of the recorded tissue area intraoperatively (e.g., tissue oxygenation and perfusion). We performed an open-label, single-arm, and non-randomized intervention clinical trial to compare clinical assessment and hyperspectral measurement to define the resection margin in 24 patients before and after separation of the marginal artery over 15 min; HSI was performed each minute to assess the parameters mentioned above. Results: The false color images calculated from the hyperspectral data visualized the margin of perfusion in 20 out of 24 patients precisely. In the other four patients, the perfusion difference could be displayed with additional evaluation software. In all cases, there was a deviation between the transection line planed by the surgeon and the border line visualized by HSI (median 1 mm; range - 13 to 13 mm). Tissue perfusion dropped up to 12% within the first 10 mm distal to the border line. Therefore, the resection area was corrected proximally in five cases due to HSI record. The biggest drop in perfusion took place in less than 2 min after devascularization. Conclusion: Determination of the resection margin by HSI provides the surgeon with an objective decision aid for assessment of the best possible perfusion and ideal anastomotic area in colorectal surgery.:Inhaltsverzeichnis Inhaltsverzeichnis................................................................. I 1 Einführung............................................................................. 1 1.1 Anastomoseninsuffizienz...................................................1 1.2 Methodik Hyperspectral Imaging (HSI)............................. 3 1.3 Einsatzbereiche der Hyperspektral-Kamera..................... 5 1.4 Chirurgische Technik........................................................ 6 1.5 Studienplanung................................................................. 7 1.6 Vergleich der HSI-Technik mit weiteren Messmethoden...8 2 Publikation...............................................................................11 3 Zusammenfassung der Arbeit............................................... 21 4 Literaturverzeichnis............................................................... 26 5 Anhang.................................................................................... 30 Darstellung des eigenen Beitrags.........................................34 Eigenständigkeitserklärung...................................................35 Lebenslauf.............................................................................. 36 Danksagung........................................................................... 38

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第19771号 / 農博第2167号 / 新制||農||1040(附属図書館) / 学位論文||H28||N4987(農学部図書室) / 32807 / 京都大学大学院農学研究科地域環境科学専攻 / (主査)教授 飯田 訓久, 教授 近藤 直, 准教授 中村 公人 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第20054号 / 医博第4162号 / 新制||医||1018(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 湊谷 謙司, 教授 富樫 かおり, 教授 木村 剛 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第20688号 / 工博第4385号 / 新制||工||1681(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 小池 克明, 教授 三ｹ田 均, 准教授 須崎 純一 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

No description available.