500 Naturwissenschaften und Mathematik
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Structural equation modeling (SEM) has been used and developed for decades across various domains and research fields such as, among others, psychology, sociology, and business research. Although no unique definition exists, SEM is best understood as the entirety of a set of related theories, mathematical models, methods, algorithms, and terminologies related to analyzing the relationships between theoretical entities -- so-called concepts --, their statistical representations -- referred to as constructs --, and observables -- usually called indicators, items or manifest variables.
This thesis is concerned with aspects of a particular strain of research within SEM -- namely, composite-based SEM. Composite-based SEM is defined as SEM involving linear compounds, i.e., linear combinations of observables when estimating parameters of interest.
The content of the thesis is based on a working paper (Chapter 2), a published refereed journal article (Chapter 3), a working paper that is, at the time of submission of this thesis, under review for publication (Chapter 4), and a steadily growing documentation that I am writing for the R package cSEM (Chapter 5). The cSEM package -- written by myself and my former colleague at the University of Wuerzburg, Florian Schuberth -- provides functions to estimate, analyze, assess, and test nonlinear, hierarchical and multigroup structural equation models using composite-based approaches and procedures.
In Chapter 1, I briefly discuss some of the key SEM terminology.
Chapter 2 is based on a working paper to be submitted to the Journal of Business Research titled “Assessing overall model fit of composite models in structural equation modeling”. The article is concerned with the topic of overall model fit assessment of the composite model. Three main contributions to the literature are made. First, we discuss the concept of model fit in SEM in general and composite-based SEM in particular. Second, we review common fit indices and explain if and how they can be applied to assess composite models. Third, we show that, if used for overall model fit assessment, the root mean square outer residual covariance (RMS_theta) is identical to another well-known index called the standardized root mean square residual (SRMR).
Chapter 3 is based on a journal article published in Internet Research called “Measurement error correlation within blocks of indicators in consistent partial least squares: Issues and remedies”. The article enhances consistent partial least squares (PLSc) to yield consistent parameter estimates for population models whose indicator blocks contain a subset of correlated measurement errors. This is achieved by modifying the correction for attenuation as originally applied by PLSc to include a priori assumptions on the structure of the measurement error correlations within blocks of indicators. To assess the efficacy of the modification, a Monte Carlo simulation is conducted. The paper is joint work with Florian Schuberth and Theo Dijkstra.
Chapter 4 is based on a journal article under review for publication in Industrial Management & Data Systems called “Estimating and testing second-order constructs using PLS-PM: the case of composites of composites”. The purpose of this article is threefold: (i) evaluate and compare common approaches to estimate models containing second-order constructs modeled as composites of composites, (ii) provide and statistically assess a two-step testing procedure to test the overall model fit of such models, and (iii) formulate recommendation for practitioners based on our findings. Moreover, a Monte Carlo simulation to compare the approaches in terms of Fisher consistency, estimated bias, and RMSE is conducted. The paper is joint work with Florian Schuberth and Jörg Henseler.
Die Calcineurin/NFAT-Signalkaskade spielt eine wichtige Rolle bei der Entwicklung einer kardialen Hypertrophie. Im Zytoplasma von Kardiomyozyten wird die Phosphatase Calcineurin nach Stimulierung der Zellen, z. B. durch Dehnungsreize, Angiotensin II (Ang II) oder Endothelin I (ET-1), und einen daraus folgenden intrazellulären Ca2+-Strom aktiviert. Dies führt zur Dephosphorylierung von NFAT und zu dessen nukleärer Translokation. In früheren Arbeiten von Ritter et al. wurden sowohl eine nukleäre Lokalisationssequenz (NLS) als auch eine nukleäre Exportsequenz (NES) innerhalb von Calcineurin identifiziert, die den Transport von Calcineurin zwischen dem Zytoplasma und dem Nukleus ermöglichen. Basierend auf diesen Ergebnissen wurde das Import Blocking Peptid (IBP) entwickelt. Dieses Peptid entspricht der NLS von Calcineurin und blockiert die Calcineurin-Bindungsstellen des Shuttleproteins (Karyopherins) Importin β1. So wird die Translokation von Calcineurin in den Nukleus unterbunden und die Signalkaskade zur Aktivierung von Hypertrophie-Genen in Kardiomyozyten unterbrochen. Dabei blieb die Phosphatase-Aktivität von Calcineurin unbeeinflusst. Eines der Ziele dieser Arbeit war, IBP weiter zu optimieren und den „proof of principle“ auch in vivo zu führen. Hierfür wurden u. a. ein geeignetes Lösungsmittel bestimmt (biokompatibel und an die Peptidcharakteristika angepasst), die Peptidstruktur modifiziert (Erhöhung der Spezifität/Wirksamkeit) und die erforderliche Dosis weiter eingegrenzt (Belastungs- und Kostenreduktion). Unter Verwendung einer TAMRA-markierten Wirkstoffvariante konnten der Weg des Peptids in Mäusen nachverfolgt und die Ausscheidung quantifiziert werden.
Aufbauend auf den Ergebnissen von Burkard et al., die die Entstehung einer konstitutiv-aktiven und nukleären Calcineurin-Isoform nach proteolytischer Spaltung durch Calpain nachwiesen, wurde die Rolle von Calcineurin im Zellkern genauer untersucht. Außerdem sollte die Frage beantwortet werden, wie (über Calcineurin?) die Herzmuskelzelle zwischen Calciumschwankungen im Zuge der Exzitations-Kontraktions-Kopplung (ECC) und vergleichsweise schwachen Calciumsignalen zur Transkriptionsteuerung unterscheidet. Mit Hilfe von nukleären Calcineurin-Mutanten, die einen Defekt in der Ca2+-Bindung aufwiesen, konnte die Bedeutung von Calcineurin als Calciumsensor für die NFAT-abhängige Transkription nachgewiesen werden. Im Mausmodell waren unter Hypertrophie-Bedingungen die Ca2+-Transienten in der nukleären Mikrodomäne signifikant stärker als im Zytosol, wodurch die Hypothese, dass die Aktivierung der Calcineurin/NFAT-Signalkaskade unabhängig von zytosolischem Ca2+ erfolgt, gestützt wird. Messungen von nukleären und zytosolischen Ca2+-Transienten in IP3-Sponge-Mäusen zeigten im Vergleich zu Wildtyp-Mäusen keine Erhöhung des Ca2+-Spiegels während der Diastole, was auf eine Rolle von Inositoltrisphosphat (IP3) in der Signalkaskade deutet. Außerdem zeigten isolierte Zellkerne ventrikulärer adulter Kardiomyozyten eine erhöhte Expression des IP3-Rezeptors 2 (IP3R2) nach Ang II-Stimulierung. Diese gesteigerte Expression war abhängig von der Calcineurin/NFAT-Kaskade und bestand sogar
3 Wochen nach Entfernung des Ang II-Stimulus fort. Zusammenfassend lässt sich sagen, dass nukleäres Calcineurin als ein Ca2+-Sensor agiert, dass die lokale Ca2+-Freisetzung im Kern über IP3-Rezeptoren detektiert wird und dass dies im Zusammenspiel mit NFAT die Transkription von Hypertrophiegenen initiiert.
Worldwide, cold regions are undergoing significant alterations due to climate change. Snow, the most widely distributed cold region component, is highly sensitive to climate change. At the same time, snow itself profoundly impacts the Earth’s energy budget, biodiversity, and natural hazards, as well as hydropower management, freshwater management, and winter tourism/sports. Large parts of the cold regions in Europe are mountain areas, which are densely populated because of the various ecosystem services and socioeconomic well-being in mountains. At present, severe consequences caused by climate change have been observed in European mountains and their surrounding areas. Yet, large knowledge gaps hinder the development of effective regional and local adaptation strategies. Long-term and evidence-based regional studies are urgently needed to enhance the comprehension of regional responses to climate change.
Earth Observation (EO) provides long-term consistent records of the Earth’s surface. It is a great alternative and/or supplement to conventional in-situ measurements which are usually time-consuming, cost-intensive and logistically demanding, particularly for the poor accessibility of cold regions. With the assistance of EO, land surface dynamics in cold regions can be observed in an objective, repeated, synoptic and consistent way. Thanks to free and open data policies, long-term archives such as Landsat Archive and Sentinel Archive can be accessed free-of-charge. The high- to medium-resolution remote sensing imagery from these freely accessible archives gives EO-based time series datasets the capability to depict snow dynamics in European mountains from the 1980s to the present. In order to compile such a dataset, it is necessary to investigate the spatiotemporal availability of EO data, and develop a spatiotemporally transferable framework from which one can investigate snow dynamics.
Among the available EO image archives, the Landsat Archive has the longest uninterrupted records of the Earth’s land surface. Furthermore, its 30 m spatial resolution fulfils the requirements for snow monitoring in complex terrains. Landsat data can yield a time series of snow dynamics in mountainous areas from 1984 to the present. However, severe Landsat data gaps have occurred across certain regions of Europe. Moreover, the Landsat Level 1 Precision and Terrain (L1TP) data is scarcer (up to 50% less) in high-latitude mountainous areas than in low-latitude mountainous areas. Given the abovementioned facts, the Regional Snowline Elevation (RSE) is selected to characterize the snow dynamics in mountainous areas, as it can handle cloud obstructions in the optical images. In this thesis, I present a five-step framework to derive and densify RSE time series in European mountains, i.e. (1) pre-processing, (2) snow detection, (3) RSE retrieval, (4) time series densification, and (5) Regional Snowline Retreat Curve (RSRC) production.
The results of the intra-annual RSE variations show a uniquely high variation in the beginning of the ablation seasons in the Alpine catchment Tagliamento, mainly toward higher elevation. As for inter-annual variations of RSE, median RSE increases in all selected catchments, with an average speed of around 4.66 m ∙ a−1 (median) and 5.87 m ∙ a−1 (at the beginning of the ablation season). The fastest significant retreat is observed in the catchment Drac (10.66 m ∙ a−1, at the beginning of the ablation season), and the slowest significant retreat is observed in the catchment Uzh (1.74 m ∙ a−1, at the beginning of the ablation season). The increase of RSEs at the beginning of the ablation season is faster than the median RSEs, whose average difference is nearly 1.21 m ∙ a−1, particularly in the catchment Drac (3.72 m ∙ a−1). The results of the RSRCs show a significant rise in RSEs at the beginning of the ablation season, except for the Alpine catchment Alpenrhein and Var, and the Pyrenean catchment Ariege. It indicates that 11.8 and 3.97 degrees Celsius less per year are needed for the regional snowlines to reach the middle point of the RSRC in the Tagliamento and Tysa, respectively. The variation of air temperature is regarded as an example of a potential climate driver in this thesis. The retrieved monthly mean RSEs are highly correlated (mean correlation coefficient "R" ̅ = 0.7) with the monthly temperature anomalies, which are more significant in months with extremely low/high temperature. Another case study that investigates the correlation between river discharges and RSEs is carried out to demonstrate the potential consequences of the derived snowline dynamics. The correlation analysis shows a good correlation between river discharges and RSEs (correlation coefficient, R=0.52).
In this thesis, the developed framework signifies a better understanding of the snow dynamics in mountain areas, as well as their potential triggers and consequences. Nonetheless, an urgent need persists for: (1) validation data to assess long-term snow-related observations based on high-resolution EO data; (2) further studies to reveal interactions between snow and its ambient environment; and (3) regional and local adaptation-strategies coping with climate change. Further studies exploring the above-mentioned research gaps are urgently needed in the future.