17,488 research outputs found

    Computertomographie-basierte Bestimmung von Aortenklappenkalk und seine Assoziation mit Komplikationen nach interventioneller Aortenklappenimplantation (TAVI)

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    Background: Severe aortic valve calcification (AVC) has generally been recognized as a key factor in the occurrence of adverse events after transcatheter aortic valve implantation (TAVI). To date, however, a consensus on a standardized calcium detection threshold for aortic valve calcium quantification in contrast-enhanced computed tomography angiography (CTA) is still lacking. The present thesis aimed at comparing two different approaches for quantifying AVC in CTA scans based on their predictive power for adverse events and survival after a TAVI procedure.   Methods: The extensive dataset of this study included 198 characteristics for each of the 965 prospectively included patients who had undergone TAVI between November 2012 and December 2019 at the German Heart Center Berlin (DHZB). AVC quantification in CTA scans was performed at a fixed Hounsfield Unit (HU) threshold of 850 HU (HU 850 approach) and at a patient-specific threshold, where the HU threshold was set by multiplying the mean luminal attenuation of the ascending aorta by 2 (+100 % HUAorta approach). The primary endpoint of this study consisted of a combination of post-TAVI outcomes (paravalvular leak ≄ mild, implant-related conduction disturbances, 30-day mortality, post-procedural stroke, annulus rupture, and device migration). The Akaike information criterion was used to select variables for the multivariable regression model. Multivariable analysis was carried out to determine the predictive power of the investigated approaches.   Results: Multivariable analyses showed that a fixed threshold of 850 HU (calcium volume cut-off 146 mm3) was unable to predict the composite clinical endpoint post-TAVI (OR=1.13, 95 % CI 0.87 to 1.48, p=0.35). In contrast, the +100 % HUAorta approach (calcium volume cut-off 1421 mm3) enabled independent prediction of the composite clinical endpoint post-TAVI (OR=2, 95 % CI 1.52 to 2.64, p=9.2x10-7). No significant difference in the Kaplan-Meier survival analysis was observed for either of the approaches.   Conclusions: The patient-specific calcium detection threshold +100 % HUAorta is more predictive of post-TAVI adverse events included in the combined clinical endpoint than the fixed HU 850 approach. For the +100 % HUAorta approach, a calcium volume cut-off of 1421 mm3 of the aortic valve had the highest predictive value.Hintergrund: Ein wichtiger Auslöser von Komplikationen nach einer Transkatheter-Aortenklappen-Implantation (TAVI) sind ausgeprĂ€gte Kalkablagerung an der Aortenklappe. Dennoch erfolgte bisher keine Einigung auf ein standardisiertes Messverfahren zur Quantifizierung der Kalklast der Aortenklappe in einer kontrastverstĂ€rkten dynamischen computertomographischen Angiographie (CTA). Die vorliegende Dissertation untersucht, inwieweit die Wahl des Analyseverfahrens zur Quantifizierung von Kalkablagerungen in der Aortenklappe die Prognose von Komplikationen und der Überlebensdauer nach einer TAVI beeinflusst.   Methodik: Der Untersuchung liegt ein umfangreicher Datensatz von 965 Patienten mit 198 Merkmalen pro Patienten zugrunde, welche sich zwischen 2012 und 2019 am Deutschen Herzzentrum Berlin einer TAVI unterzogen haben. Die Quantifizierung der Kalkablagerung an der Aortenklappe mittels CTA wurde einerseits mit einem starren Grenzwert von 850 Hounsfield Einheiten (HU) (HU 850 Verfahren) und andererseits anhand eines individuellen Grenzwertes bemessen. Letzterer ergibt sich aus der HU-DĂ€mpfung in dem Lumen der Aorta ascendens multipliziert mit 2 (+100 % HUAorta Verfahren). Der primĂ€re klinische Endpunkt dieser Dissertation besteht aus einem aus sechs Variablen zusammengesetzten klinischen Endpunkt, welcher ungewĂŒnschte Ereignisse nach einer TAVI abbildet (paravalvulĂ€re Leckage ≄mild, Herzrhythmusstörungen nach einer TAVI, Tod innerhalb von 30 Tagen, post-TAVI Schlaganfall, Ruptur des Annulus und Prothesendislokation). Mögliche Störfaktoren, die auf das Eintreten der Komplikationen nach TAVI Einfluss haben, wurden durch den Einsatz des Akaike Informationskriterium ermittelt. Um die Vorhersagekraft von Komplikationen nach einer TAVI durch beide Verfahren zu ermitteln, wurde eine multivariate Regressionsanalyse durchgefĂŒhrt.   Ergebnisse: Die multivariaten logistischen Regressionen zeigen, dass die Messung der Kalkablagerungen anhand der HU 850 Messung (Kalklast Grenzwert von 146 mm3) die Komplikationen und die Überlebensdauer nicht vorhersagen konnten (OR=1.13, 95 % CI 0.87 bis 1.48, p=0.35). Die nach dem +100 % HUAorta Verfahren (Kalklast Grenzwert von 1421 mm3) individualisierte Kalkmessung erwies sich hingegen als sehr aussagekrĂ€ftig, da hiermit Komplikationen nach einer TAVI signifikant vorhergesagt werden konnten (OR=2, 95 % CI 1.52 bis 2.64, p=9.2x10-7). In Hinblick auf die postoperative Kaplan-Meier Überlebenszeitanalyse kann auch mit dem +100 % HUAorta Verfahren keine Vorhersage getroffen werden.   Fazit: Aus der Dissertation ergibt sich die Empfehlung, die Messung von Kalkablagerungen nach dem +100 % HUAorta Verfahren vorzunehmen, da Komplikationen wesentlich besser und zuverlĂ€ssiger als nach der gĂ€ngigen HU 850 Messmethode vorhergesagt werden können. FĂŒr das +100 % HUAorta Verfahren lag der optimale Kalklast Grenzwert bei 1421 mm3

    Kirchhoff-Love shell representation and analysis using triangle configuration B-splines

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    This paper presents the application of triangle configuration B-splines (TCB-splines) for representing and analyzing the Kirchhoff-Love shell in the context of isogeometric analysis (IGA). The Kirchhoff-Love shell formulation requires global C1C^1-continuous basis functions. The nonuniform rational B-spline (NURBS)-based IGA has been extensively used for developing Kirchhoff-Love shell elements. However, shells with complex geometries inevitably need multiple patches and trimming techniques, where stitching patches with high continuity is a challenge. On the other hand, due to their unstructured nature, TCB-splines can accommodate general polygonal domains, have local refinement, and are flexible to model complex geometries with C1C^1 continuity, which naturally fit into the Kirchhoff-Love shell formulation with complex geometries. Therefore, we propose to use TCB-splines as basis functions for geometric representation and solution approximation. We apply our method to both linear and nonlinear benchmark shell problems, where the accuracy and robustness are validated. The applicability of the proposed approach to shell analysis is further exemplified by performing geometrically nonlinear Kirchhoff-Love shell simulations of a pipe junction and a front bumper represented by a single patch of TCB-splines

    Large-Scale Landslide Susceptibility Mapping Using an Integrated Machine Learning Model: A Case Study in the Lvliang Mountains of China

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    Integration of different models may improve the performance of landslide susceptibility assessment, but few studies have tested it. The present study aims at exploring the way to integrating different models and comparing the results among integrated and individual models. Our objective is to answer this question: Will the integrated model have higher accuracy compared with individual model? The Lvliang mountains area, a landslide-prone area in China, was taken as the study area, and ten factors were considered in the influencing factors system. Three basic machine learning models (the back propagation (BP), support vector machine (SVM), and random forest (RF) models) were integrated by an objective function where the weight coefficients among different models were computed by the gray wolf optimization (GWO) algorithm. 80 and 20% of the landslide data were randomly selected as the training and testing samples, respectively, and different landslide susceptibility maps were generated based on the GIS platform. The results illustrated that the accuracy expressed by the area under the receiver operating characteristic curve (AUC) of the BP-SVM-RF integrated model was the highest (0.7898), which was better than that of the BP (0.6929), SVM (0.6582), RF (0.7258), BP-SVM (0.7360), BP-RF (0.7569), and SVM-RF models (0.7298). The experimental results authenticated the effectiveness of the BP-SVM-RF method, which can be a reliable model for the regional landslide susceptibility assessment of the study area. Moreover, the proposed procedure can be a good option to integrate different models to seek an "optimal" result. Keywords: landslide susceptibility, random forest, integrated model, causal factor, GI

    Palaeoecological data indicates land-use changes across Europe linked to spatial heterogeneity in mortality during the Black Death pandemic

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    Historical accounts of the mortality outcomes of the Black Death plague pandemic are variable across Europe, with much higher death tolls suggested in some areas than others. Here the authors use a 'big data palaeoecology' approach to show that land use change following the pandemic was spatially variable across Europe, confirming heterogeneous responses with empirical data.The Black Death (1347-1352 ce) is the most renowned pandemic in human history, believed by many to have killed half of Europe's population. However, despite advances in ancient DNA research that conclusively identified the pandemic's causative agent (bacterium Yersinia pestis), our knowledge of the Black Death remains limited, based primarily on qualitative remarks in medieval written sources available for some areas of Western Europe. Here, we remedy this situation by applying a pioneering new approach, 'big data palaeoecology', which, starting from palynological data, evaluates the scale of the Black Death's mortality on a regional scale across Europe. We collected pollen data on landscape change from 261 radiocarbon-dated coring sites (lakes and wetlands) located across 19 modern-day European countries. We used two independent methods of analysis to evaluate whether the changes we see in the landscape at the time of the Black Death agree with the hypothesis that a large portion of the population, upwards of half, died within a few years in the 21 historical regions we studied. While we can confirm that the Black Death had a devastating impact in some regions, we found that it had negligible or no impact in others. These inter-regional differences in the Black Death's mortality across Europe demonstrate the significance of cultural, ecological, economic, societal and climatic factors that mediated the dissemination and impact of the disease. The complex interplay of these factors, along with the historical ecology of plague, should be a focus of future research on historical pandemics

    II tĂŒĂŒpi kollageeni neoepitoop C2C uriinis kui pĂ”lve osteoartriidi diagnoosimise ja kulu prognoosimise biomarker

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    VĂ€itekirja elektrooniline versioon ei sisalda publikatsiooneOsteoartriit (OA) on sagedasim liigeshaigus, tabades ligi poolt miljardit inimest maailmas. PĂ”lv on ĂŒks peamisi kahjustuskohti. Haiguse kaasaegse kĂ€sitluse jĂ€rgi arenevad kahjustused molekulaarsetest muutustest kuni kudede (kĂ”hr, luu, sĂŒnoviaalkest, menisk, sidemed) struktuuri muutusteni. OA on aastate jooksul ebaĂŒhtlase kiirusega sĂŒvenev haigus, mille puhul stabiilsemad perioodid vahelduvad kiiremate muutustega, kulgedes varajases jĂ€rgus haigustunnusteta. SeetĂ”ttu pakuvad kudede ainevahetuse muutusi peegeldavad molekulaarsed markerid varajast hoiatust koekahjustuse tekkest, vĂ”imalust hinnata haiguse kulgu ning tulevikus ka ravivastust. Kuna II tĂŒĂŒpi kollageen (Kol2) on kĂ”hre peamine struktuurne komponent, on OA hindamiseks loodud mitmeid Kol2 lammutamist mÔÔtvaid teste. KĂ€esolevas uurimuses hindasime OA uue biomarkeri, uC2C kasutusvĂ”imalusi pĂ”lve OA (pOA) korral. uC2C on Kol2 lĂ”hustumise neoepitoop C2C uriinis. VĂ”rdlesime uC2C vÀÀrtusi röntgenleiu, kĂ”hre otsese vaatlusleiu ja patsiendi kliinilise seisundiga, kasutades erinevaid statistilisi mudeleid. Selgus, et uC2C on sobiv kandidaat pOA varajase diagnostilise testi arendamiseks. C2C sisaldus tĂ”useb juba haiguse varajases jĂ€rgus ja on seotud haiguse mitme pĂ”hiprotsessiga: kĂ”hre lammutamise ja luukasviste tekkega pĂ”lveliigese eri osades. uC2C on hea progressioonimarker naistel: uC2C kĂ”rgem algvÀÀrtus ennustab naistel vĂ€ga hĂ€sti (>90%) pOA teket vĂ”i sĂŒvenemist jĂ€rgneva 3 aasta jooksul. uC2C tase on kĂ”rgem suuremate röntgenmuutuste korral, seega uC2C tase on seotud pOA raskusastmega. uC2C vÀÀrtused on suurimad kOA lĂ”ppjĂ€rgus olevatel haigetel, kes jĂ”uavad liigeseasenduseni suhteliselt noorelt (50–70 a vanuses). PĂ€rast pĂ”lveliigese asendamist vĂ”ib C2C eritumine uriiniga vĂ€heneda, suureneda vĂ”i jÀÀda muutumatuks. Seega ei peata liigeseasendus paljudel juhtudel Kol2 lammutamist organismis ja OA on sĂŒsteemsem haigus, kui on seni arvatud. uC2C nĂ€ib olevat naistel vĂ”rreldes meestega parem pOA biomarker.Osteoarthritis (OA) is the most common joint disease, affecting about half a billion people worldwide. The knee is one of the main sites of impairment. According to the new approach to the disease, the alterations develop from the molecular level to structural changes in tissues (cartilage, bone, synovium, meniscus, ligaments). OA is a disease with an alternating course, with no signs of disease at an early stage. Therefore, molecular markers that reflect changes in tissue metabolism provide an early warning of tissue damage, an opportunity to assess the course of the disease, and a response to future treatment. Because type II collagen (Col2) is a major structural component of cartilage, several tests have been developed to measure Col2 degradation. In the current study, we evaluated the potential use of a new OA biomarker, C2C, in knee OA (kOA). uC2C is a Col2 cleavage neoepitope in urine. We compared uC2C values with X-ray findings, direct visual assessment of cartilage, and clinical status using different statistical models. uC2C is a good candidate for the development of an early diagnostic test for kOA. The level of uC2C is increased in the early stages of kOA and is related to several main processes of kOA: the cartilage lesions and the osteophytes in distinct knee compartments. uC2C is a good marker of progression in women–a higher baseline uC2C is an excellent predictor (> 90%) of the initiation or worsening of kOA over the next 3 years. uC2C is higher in higher X-ray grades, so uC2C levels are associated with the severity of kOA. uC2C values are highest in patients with end-stage kOA who reach joint replacement at a relatively young age (50-70 years). After knee replacement, urinary excretion of C2C may decrease, increase, or remain unchanged. Thus, in many cases, joint replacement does not stop the breakdown of Col2 in the body, and OA is a more systemic disease than previously thought. uC2C appears to be a better biomarker of pOA in women than in men.https://www.ester.ee/record=b550707

    The Relationship Between Climate Change and Food Insecurity In Sub-Saharan Africa

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    Magister Artium (Development Studies) - MA(DVS)According to the research conducted for this thesis, climate change has a potential to be a hazard to food security in not only South Africa, but also to most of Sub-Saharan Africa. The threat is presented in terms of food distribution and consumption, including agricultural productivity. Food security is impacted by global warming, global warming in turn is a direct result of climate change since it affects the supply of food, its accessibility, how it is utilized, and whether or not people can afford it. The only way to mitigate the dangers is through an integrated policy approach that protects fertile land from global warming. The key point presented here is that Sub-Saharan Africa has all of the resources necessary to adapt to climate change and secure food supplies; nevertheless, it is critical that they first recognize the hazards that various agricultural products face because of global warming. However, a lot of emerging countries face significant challenges as a result of a lack of robust institutions, making policy changes difficult. The influence on food security will be significant, and it may be broken down into three categories: availability, access, and use. Systematic peer-reviewed literature reviews of climate change and food security research were undertaken utilizing the realist review approach as the methodology for this study. In order to alleviate the region's acute food insecurity, adaptation approaches were thoroughly investigated. This is related to development challenges, where adaptation is necessary to mitigate negative effects and improve the population's ability to participate in development processes. Finances are also a concern for poor countries, such as South Africa, because there is a disparity between the cost of adaptation and government subsidies. The remedy could come in the form of technology interventions that help to make food systems less vulnerable to dangers


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    Through the second half of the 20th century, the North American Great Plains saw widespread rural out-migration, a continuation of trends that began with the Dust Bowl crisis during the Great Depression of the 1930s. As part of a wider academic focus on the roles climate and environmental changes have on migration, this research project sought to understand the relationship between drought conditions and rural population decline on the Great Plains. In this explorative research, census population data for Canada and the US from 1970-2010 were analyzed along with temperature, precipitation, and Palmer Drought Severity Index data for the same period using a variety of regression to seek out possible association between drought conditions and population loss at local scales. As part of this process, a novel index for identifying drought likelihood was also developed and tested. Results indicate that the significance and direction of the relationship between drought and rural population loss is spatially heterogenous. Geographically weighted regression models are demonstrated to have better predictive power than traditional regression methods, although that predictive power deteriorates through the decades in the study period. Small clusters of counties were detected where the drought-population loss is relatively strong in certain decades, but generally the results suggest that non-climatic factors were the primary drivers of population loss across the Great Plains. The modelling results are discussed in the context of a case study of Lincoln County, Colorado, a dryland county visited as part of field research for this project

    Elasto-plastic deformations within a material point framework on modern GPU architectures

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    Plastic strain localization is an important process on Earth. It strongly influ- ences the mechanical behaviour of natural processes, such as fault mechanics, earthquakes or orogeny. At a smaller scale, a landslide is a fantastic example of elasto-plastic deformations. Such behaviour spans from pre-failure mech- anisms to post-failure propagation of the unstable material. To fully resolve the landslide mechanics, the selected numerical methods should be able to efficiently address a wide range of deformation magnitudes. Accurate and performant numerical modelling requires important compu- tational resources. Mesh-free numerical methods such as the material point method (MPM) or the smoothed-particle hydrodynamics (SPH) are particu- larly computationally expensive, when compared with mesh-based methods, such as the finite element method (FEM) or the finite difference method (FDM). Still, mesh-free methods are particularly well-suited to numerical problems involving large elasto-plastic deformations. But, the computational efficiency of these methods should be first improved in order to tackle complex three-dimensional problems, i.e., landslides. As such, this research work attempts to alleviate the computational cost of the material point method by using the most recent graphics processing unit (GPU) architectures available. GPUs are many-core processors originally designed to refresh screen pixels (e.g., for computer games) independently. This allows GPUs to delivers a massive parallelism when compared to central processing units (CPUs). To do so, this research work first investigates code prototyping in a high- level language, e.g., MATLAB. This allows to implement vectorized algorithms and benchmark numerical results of two-dimensional analysis with analytical solutions and/or experimental results in an affordable amount of time. After- wards, low-level language such as CUDA C is used to efficiently implement a GPU-based solver, i.e., ep2-3De v1.0, can resolve three-dimensional prob- lems in a decent amount of time. This part takes advantages of the massive parallelism of modern GPU architectures. In addition, a first attempt of GPU parallel computing, i.e., multi-GPU codes, is performed to increase even more the performance and to address the on-chip memory limitation. Finally, this GPU-based solver is used to investigate three-dimensional granular collapses and is compared with experimental evidences obtained in the laboratory. This research work demonstrates that the material point method is well suited to resolve small to large elasto-plastic deformations. Moreover, the computational efficiency of the method can be dramatically increased using modern GPU architectures. These allow fast, performant and accurate three- dimensional modelling of landslides, provided that the on-chip memory limi- tation is alleviated with an appropriate parallel strategy