Staging is critical in the management of cancer. Sentinel lymph node (SLN) biopsy is one method used in the assessment of cancer spread. SLN procedures are standard practice in the management of some cancers although; these procedures have only recently been developed and refined. SLN procedures are commonly used in the management of melanomas and breast cancers in patients with no evidence of metastatic disease on clinical exam. SLN procedures include detection, localization, and assessment of SLNs. The detection/localization components vary in technique and rates of success. The procedures with the least number of detection/localization techniques generally include the use of blue dye or the use of a radiotracer with intraoperative gamma counting. The most complex procedures involve the use of blue dye, the use of a radiotracer with preoperative gamma imaging and preoperative gamma counting, intraoperative gamma counting, or some combination of these techniques. The ideal procedure for SLN would include all the listed techniques however; all facilities do not incorporate the most complete procedure, for different reasons. An investigation using a small FOV (5 in x 5 in) gamma camera intraoperatively for SLN procedures in melanoma and breast cancer patients was performed. A smaller FOV camera is capable of obtaining some of the same information as a conventional gamma camera. It is possible that centers, which do not or are not able to take advantage of preoperative imaging, may find the use of a smaller FOV gamma camera in the operating room useful. The investigation consisted of a total of 41 patients; it was split into two studies, Study 1: melanoma and study 2: breast cancer. The melanoma study found the added use of a smaller FOV camera under the parameters of this study to be minimal. Study 2 was broken into two branches; branch 1: camera/probe/dye and branch 2: probe/dye, for a comparison study. Comparing the two branches did not show the smaller FOV camera to reduce the time spent in the operating room versus using the probe and blue dye.

The overall objective of this research project was to assess whether fish populations in areas of potato cultivation responded to changes in environmental conditions. An effects-based assessment was conducted in the ‘potato belt’ of northwestern New Brunswick in the Little River catchment. From 1999-2001, the health and performance of slimy sculpin (Cottus cognatus) was monitored in agricultural and forested sections of the river. In the fall of 1999 and 2000, agricultural sites had fewer young-of-the-year (YOY) sculpin than the forested region. Adult sculpin were larger in the agricultural region, but had significantly smaller gonads, and female sculpin had smaller livers, and fewer and smaller eggs than the forested region. By the fall of 2001, only female gonad size showed a difference from the forested region. These results were used to design a follow-up study designed to investigate the relative importance of environmental factors influencing sculpin responses. <br><br> The second study investigated the relative influence of temperature and sediment deposition on slimy sculpin populations across 20 sites on 19 streams in forested and agricultural catchments in northwestern New Brunswick. YOY sculpin were present at all forested sites, but only at 2 of 11 agricultural sites. There were no relationships between body size or density and sediment deposition in either the agricultural or forested regions, but sculpin density decreased and median YOY size increased with increasing temperatures. The variability in density of YOY sculpin at agricultural sites suggested that additional factors beyond temperature might be contributing to responses. <br><br> A secondary overall objective was to evaluate the slimy sculpin as a sentinel and indicator of site-specific conditions. Stable isotopes of muscle tissues showed little variability in isotopic signatures, and significant differences between adjacent sites. Passive integrated transponder (PIT) tags implanted in 112 adult sculpin showed that 75% of sculpin captured over 10 months moved less than 30m. Both isotopes and PIT tags suggested high spatial and temporal residency of slimy sculpin. <br><br> This PhD project showed biological impacts on sculpin populations residing in streams influenced by non-point source agricultural stressors, and provided support for the ability of the slimy sculpin to reflect local environmental conditions.

Thesis (Ph.D.)--Duquesne University, 2006. / Title from document title page. Abstract included in electronic submission form. Includes bibliographical references (p.146-151) and index.

The main goal of this diploma thesis was to find and quantify the connection between coherence, entropy, polarimetric angle alpha obtained from Sentinel-1 radar data and grass cutting/pastures. The research was carried out in the area of the Krkonoše national park. To assemble and validate applied methodology, field data was collected 5 times. Hourly rainfall data from Czech hydrometeorological institute was available, but it did not have to be used - no rainfalls were recorded at the time of data acquisitions. Dependence between mowing and the value of coherence has been confirmed. After mowing, median coherence was higher than before mowing. The results were similar to VH as well as VV polarization. Coherence on polygons remained higher after 12-24 days. In total, two different data acquisition geometries (ascending and descending) were examined. The results in both cases were similar. For polarimetric parameters, no correlation between polarimetric parameters and grass mowing or pasture has been confirmed. Keywords: radar, SAR, Sentinel-1, coherence, polarimetry, grass mowing

Indiana University-Purdue University Indianapolis (IUPUI) / Endometrial cancer is the most common gynecologic cancer in the United States, and patients with early-stage endometrioid adenocarcinoma have a favorable prognosis. Over the past decade, the gynecologic oncology community has debated whether potential harms of systematic lymph node dissection (LND) outweigh potential benefits for these patients. To minimize number of nodes removed, sentinel lymph node dissection (SLND) is under investigation as an alternative. However, ongoing uncertainty of LND/SLND best practices may result in variations in disease management and discrepant outcomes. Methods Three retrospective cohort studies examined LND/SLND use in patients with endometrioid adenocarcinoma. Two examined temporal and geographic variations, respectively, utilizing the Surveillance, Epidemiology, and End Results (SEER) 18 dataset for the years 2004 through 2015. The third used the SEER-Medicare dataset from 2003 through 2016 to quantify and compare the risk of developing 6-month post-surgical lymphedema, lymphocele, hemorrhage, ileus, infection, thrombosis, and all-cause death by number of lymph nodes removed (0, 1-4, 5-9, or 10+). Results Time trend analyses found LND increased from 2004 through 2008, followed by a significant decline through 2015. SLND was rare and did not increase significantly. Significant geographic variation existed for LND use but not SLND. Per 1,000 patients, analyses of 6-month post-surgical complications found 6.5 experienced lymphedema, 3.9 experienced lymphocele, 15.7 experienced hemorrhage, 28.7 experienced ileus, 37.1 experienced infection, 18.6 experienced thrombosis, and 19.8 died. Controlling for size of primary tumor, tumor grade, comorbidities, race/ethnicity, age at diagnosis, adjuvant chemotherapy, and radiotherapy, adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) showed greater risk for ileus (HR: 1.53; 95% CI: 1.24-1.90), infection (HR: 1.52; 95% CI: 1.25-1.83), and thrombosis (HR: 1.41; 95% CI: 1.09-1.82) when comparing removal of 10+ nodes versus 0 nodes. Conclusion Overall, these studies found significant temporal and geographic variation in LND, as well as increasing risk of post-surgical complications associated with increasing numbers of lymph nodes removed. Should continued research into SLND find strong evidence that it effectively detects cancer spread, patients may benefit through decreased risk of post-surgical ileus, infection, and thrombosis.

In den letzten Jahren wurde die Sentinel Node Biopsie in Studien zur Diagnostik und Behandlung des N0-Halses als Stagingverfahren untersucht. Ziel der vorliegenden Studie war der Vergleich der postoperativen Lebensqualität und des funktionellen Status bei Patienten mit oralen und oropharyngealen Karzinomen, die sich einer Sentinelbiopsie, bzw. einer selektiven Neck Dissection bei N0-Hals unterzogen haben. 57 Patienten mit primär operativ behandeltem Karzinom wurden im Rahmen der Studie untersucht, 29 nach Sentinel Node Biopsie und 28 nach selektiver Neck Dissection. Es wurde die postoperative gesundheitsbezogene (EORTC QLQ-C30) und krankheitsspezifische Lebensqualität (EORTC QLQ-H&N35) erfasst. Patientenbezogene psychosoziale Variablen wurden anhand der „Hospital Anxiety and Depression Scale“ (HADS) und der Kurzform des „Progredienzangst-Fragebogens“ (PA-F-KF) ermittelt. Die Erhebung des klinischen Gesundheitszustandes erfolgte unter besonderer Berücksichtigung des funktionellen Status nach zervikaler Lymphknotendissektion. Bezüglich der gesundheitsbezogenen Lebensqualität sowie der Angst und Depression existierten keine signifikanten Unterschiede. Hinsichtlich der krankheitsspezifischen Lebensqualität zeigte sich, dass die Patienten nach Sentinel Node Biopsie weniger Schluckbeschwerden angaben (p=0,037). Ebenso zeigte sich bei der klinischen Untersuchung eine geringere Beeinträchtigung durch die zervikalen Narben. Die Sentinel Node Biopsie bietet, neben dem geringeren operativen Aufwand, der schnelleren Rekonvaleszenz und der geringeren funktionellen Minderung der Patienten, eine tendenzielle Verbesserung der krankheitsspezifischen Lebensqualität. Als Grund für die subjektiv weniger beeinträchtigte Schluckfunktion kann die fehlende Präparation der suprahyoidalen Muskulatur und der entsprechend innervierenden Nerven in Erwägung gezogen werden.

Cette étude a eu pour but d'étudier la possibilité de couplage de modèles dynamiques de croissance de l'herbe avec des données de télédétection, et ce pour deux terrains contrastés : La Réunion et le Kalahari (Afrique du Sud). Deux phases se sont succédé. Une première phase exploratoire, basée sur des images SPOT5 et SPOT5take5 (satellites désorbités en cours d'étude) a permis de tirer plusieurs enseignements. A La Réunion l'ajustement d’un modèle empirique entre indices de végétation et biomasse engendre trop d'erreur. Il est en revanche possible d'estimer le Leaf Area Index (LAI) grâce au NDVI (Normalized Difference Vegetation Index). Les parcours du Kalahari, plus complexes, avec différentes strates de végétation (graminées, arbustes, arbres) n'ont pas permis d'estimer l'état du couvert de graminées. Cette phase a ouvert la voie au travail effectué sur un capteur plus pérenne dans le temps, Sentinel-2. Les données Sentinel-2 ont permis d'estimer le LAI des prairies réunionnaises avec une RMSE (Root Mean Square Error) de 0,63 (r²=0,82). Le LAI ainsi estimé a été utilisé dans le couplage du modèle dynamique permettant une baisse générale de la RMSE de l'ordre de 40% par rapport au modèle sans couplage. Ces résultats ont été obtenus durant l'hiver austral, la saison sèche. Durant la période d'été austral les pluies plus abondantes accélèrent la croissance des plantes et les cycles de pousse se raccourcissent. Les images satellites sans couvert nuageux se font plus rares. La prise en compte de cette combinaison de facteurs pouvant impacter les prédictions de biomasse fourragère fera partie des principale perspectives de ce travail. / The purpose of this study was to explore the possibility of coupling dynamic models of grass growth with remote sensing data for two contrasting countries: Reunion Island and Kalahari (South Africa). Two phases followed one another. A first exploratory phase, based on SPOT5 and SPOT5take5 images (desorbed satellites under study) allowed us to learn from this experience. In Reunion the adjustment of an empirical model between vegetation indices and biomass generates too much error. However it is possible to estimate the Leaf Area Index (LAI) thanks to the NDVI (Normalized Difference Vegetation Index). More complex Kalahari rangelands with different vegetation strata (grasses, shrubs, trees) failed to estimate grass cover conditions. This phase set the stage to work on a more durable sensor over time, Sentinel-2. Sentinel-2 data made it possible to estimate the LAI of Reunion Island grasslands with a RMSE (Root Mean Square Error) of 0.63 (r² = 0.82). The LAI thus estimated was used in the coupling of the dynamic model, allowing a general decrease of the RMSE of the order of 40% compared to the model without coupling. These results were obtained during the austral winter, the dry season. During the austral summer, the more abundant rains speed up the growth of the plants and the growth cycles become shorter. Satellite images without cloud cover are becoming scarce. Taking into account this combination of factors that may impact predictions of forage biomass will be one of the main perspectives of this work.

The Island of Tongatapu, Tonga, was struck by a tsunami on January 15, 2022. Internet was cut off from the island, which made remote sensing a valuable tool for the assessment of damages. Through land cover classification, change vector analysis and log-ratio image differencing, damages caused by the tsunami were assessed remotely in this thesis. Damage assessment is a vital part of both assessing the need for humanitarian aid after a tsunami, but also lays the foundation for preventative measurements and reconstruction. The objective of this thesis was to assess damage in terms of square kilometers and create damage maps. It was also vital to assess the different methods and evaluate their accuracy. Results from this study could theoretically be combined with other damage assessments to evaluate different aspects of damage. It was also important to evaluate which methods would be good to use in a similar event. In this study Sentinel-1, Sentinel-2 and high-resolution Planet Imagery were used to conduct a damage assessment. Evaluating both moderate and high-resolution imagery in combination with SAR yielded plausible, but flawed results. Land cover was computed for moderate and high-resolution imagery using three types of classifiers. It was found that the Random Forest classifier outperforms both CART and Support Vector Machine classification for this study area.  Land cover composite image differencing for pre-and-post tsunami Sentinel-2 images achieved an accuracy of around 85%. Damage was estimated to be about 10.5 km^2. Land cover classification with high-resolution images gave higher accuracy. The total estimated damaged area was about 18 km^2. The high-resolution image classification was deemed to be the better method of urban damage assessment, with moderate-resolution imagery working well for regional damage assessment.  Change vector analysis provided plausible results when using Sentinel-2 with NDVI, NDMI, SAVI and BSI. NDVI was found to be the most comprehensive change indicator when compared to the other tested indices. The total estimated damage using all tested indices was roughly 7.6 km^2. Using the same method for Sentinel-1's VV and VH bands, the total damage was estimated to be 0.4 and 2.6 km^2 respectively. Log ratio for Sentinel-1 did not work well compared to change vector analysis. Issues with false positives occurred. Both log-ratios of VV and VH gave a similar total estimated damage of roughly 5.2 km^2.  Problems were caused by cloud cover and ash deposits. The analysis could have been improved by being consistent with the choice of dates for satellite images. Also, balancing classification samples and using high-resolution land cover classification on specific areas of interest indicated by regional methods. This would circumvent problems with ash, as reducing the study area would make more high-resolution imagery available.

No description available.

Runtom i Sverige och Europa skapar igenvuxna sjöar och vattendrag allt fler problem, vilket bland annat beror på klimatförändringar och mänsklig påverkan. En av de främsta anledningarna till igenväxning av sjöar och vattendrag är övergödning. Studiens syfte är att utvärdera om det är möjligt att på ett automatiskt sätt identifiera utbredning av vattenvegetation i sjöar och vattendrag med hjälp av fjärranalys och GIS. En analys av vegetationsförändringar i sjön Sottern i Uppland genomförs, där utbredd igenväxning skapar problem och där röjningsmaskiner används för att hantera vegetationen, som främst består av bladvass, näckrosor och annan flytande vatten-vegetation. Genom tillämpning av olika klassificeringsalgoritmer, bandkombinationer och vegetationsindex undersöks förändringar i sjöns tillstånd genom att klassa Sottern i två huvudklasser, vatten och vattenvegetation. Studien baseras på fjärranalysdata från den optiska satellitkonstellationen Sentinel-2 och en högupplöst referensbild från Google Earth Pro. Data samlades in under växtsäsongen, maj till oktober, för åren 2021 och 2022 för att analysera om och hur vattenvegetationen förändras över tid. Resultaten visar att Maximum Likelihood Classification (MLC) framträder som den mest effektiva algoritmen för att studera vegetationsförändringar, särskilt om den appliceras på en "False color" bandkombination bestående av banden 8 (NIR), 4 (rött) och 3 (grönt). MLC visar högre (94%) noggrannhet jämfört med Random Trees (RT) och Support Vector Machine (SVM). Genom att tillämpa vegetationsindexet NDVI (Normalized Difference Vegetation Index) ger studien en fördjupad förståelse för hur vegetationen förändras över tid. Genom att kombinera resultaten från dessa metoder går det att dra slutsatser om hur vattenvegetationen breder ut sig över tid i sjön Sottern, där en tydlig ökning av vattenvegetation sker mellan mitten av maj till mitten av juni, medan minskningen av vattenvegetationen inte är lika konsekvent. / In Europe, overgrown lakes and watercourses are creating increasing problems, which are partly due to climate change and human impact. One of the main reasons for the overgrowth of lakes and watercourses is eutrophication. The aim of the study is to evaluate the possibility of automatically identifying overgrown lakes and watercourses using remote sensing and GIS. An analysis of vegetation changes in Lake Sottern in Uppland county, Sweden is conducted, where overgrowth creates problems and where clearing machines are used to manage the vegetation, primarily consisting of reeds, water lilies, and other aquatic vegetation. By applying various classification algorithms, band combinations and vegetation indices, changes in the lake's condition are investigated by classifying Sottern into two main classes: water and aquatic vegetation. The study is based on remote sensing data from the optical satellite constellation Sentinel-2 and a high-resolution reference image from Google Earth Pro. Data were collected during the growing season, from May to October, for the years 2021 and 2022 to analyze if and how aquatic vegetation changes over time. The results show that Maximum Likelihood Classification (MLC) emerges as the most effective algorithm for identifying aquatic vegetation, especially when combined with a "False color" band combination consisting of bands 8 (NIR), 4 (red), and 3 (green). MLC shows higher accuracy compared to Random Trees (RT) and Support Vector Machine (SVM). By applying the Normalized Difference Vegetation Index (NDVI), the study provides a deeper understanding of how vegetation changes over time. By combining the results from these methods, it is possible to draw conclusions about how aquatic vegetation changes over time in lakes like Sottern, where a clear increase in aquatic vegetation occurs between May and June, while the decrease in aquatic vegetation is not as consistent.