Mineral chemistry and structural relationships of inclusions in diamond samples

Diamant ist das härteste Mineral – und dazu ein Edelstein -, das unter höchstem Druck und hohen Temperaturen in tiefen kontinentalen Regionen der Erde kristallisiert. Die Mineraleinschlüsse in Diamanten werden durch die physikalische Stabilität und chemische Beständigkeit der umgebenden – eigentlich metastabilen -Diamant-Phase geschützt. Aufgrund der koexistierenden Phasenkombination ermöglichen sie, die Mineral-Entwicklung zu studieren, während deren der Einschlüssen und die Diamanten kristallisierten. rnDie Phasenkombinationen von Diamant und Chrom-Pyrop, Chrom-Diopsid, Chromit, Olivin, Graphit und Enstatit nebeneinander (teilweise in Berührungsexistenz) mit Chrom-Pyrop Einschlüssen wurden von neunundzwanzig Diamant-Proben von sechs Standorten in Südafrika (Premier, Koffiefontein, De Beers Pool, Finsch, Venetia und Koingnaas Minen) und Udachnaya (Sibirien/Russland) identifiziert und charakterisiert. Die Mineraleinschlüsse weisen z.T. kubo-oktaedrische Form auf, die unabhängig von ihren eigenen Kristallsystemen ausgebildet werden können. Das bedeutet, dass sie syngenetische Einschlüsse sind, die durch die sehr hohe Formenergie des umgebenden Diamanten morphologisch unter Zwang stehen. Aus zweidiemnsionalen Messungen der ersten Ordnung von charakteristischen Raman-Banden lassen sich relative Restdrucke in Diamanten zwischen Diamant und Einschlussmineral gewinnen; sie haben charakteristische Werte von ca. 0,4 bis 0,9 GPa um Chrom-Pyrop-Einschlüsse, 0,6 bis 2,0 GPa um Chrom-Diopsid-Einschlüsse, 0,3 bis 1,2 GPa um Olivin-Einschlüsse, 0,2 bis 1,0 GPa um Chromit-Einschlüsse, beziehungsweise 0,5 GPa um Graphit Einschlüsse.rnDie kristallstrukturellen Beziehung von Diamanten und ihren monomineralischen Einschlüssen wurden mit Hilfe der Quantifizierung der Winkelkorrelationen zwischen der [111] Richtung von Diamanten und spezifisch ausgewählten Richtungen ihrer mineralischen Einschlüsse untersucht. Die Winkelkorrelationen zwischen Diamant [111] und Chrom-Pyrop [111] oder Chromit [111] zeigen die kleinsten Verzerrungen von 2,2 bis zu 3,4. Die Chrom-Diopsid- und Olivin-Einschlüsse zeigen die Missorientierungswerte mit Diamant [111] bis zu 10,2 und 12,9 von Chrom-Diopsid [010] beziehungsweise Olivin [100].rnDie chemische Zusammensetzung von neun herausgearbeiteten (orientiertes Anschleifen) Einschlüssen (drei Chrom-Pyrop-Einschlüsse von Koffiefontein-, Finsch- und Venetia-Mine (zwei von drei koexistieren nebeneinander mit Enstatit), ein Chromit von Udachnaya (Sibirien/Russland), drei Chrom-Diopside von Koffiefontein, Koingnaas und Udachnaya (Sibirien/Russland) und zwei Olivin Einschlüsse von De Beers Pool und Koingnaas) wurden mit Hilfe EPMA und LA-ICP-MS analysiert. Auf der Grundlage der chemischen Zusammensetzung können die Mineraleinschlüsse in Diamanten in dieser Arbeit der peridotitischen Suite zugeordnet werden.rnDie Geothermobarometrie-Untersuchungen waren aufgrund der berührenden Koexistenz von Chrom-Pyrop- und Enstatit in einzelnen Diamanten möglich. Durchschnittliche Temperaturen und Drücke der Bildung sind mit ca. 1087 (± 15) C, 5,2 (± 0,1) GPa für Diamant DHK6.2 von der Koffiefontein Mine beziehungsweise ca. 1041 (± 5) C, 5,0 (± 0,1) GPa für Diamant DHF10.2 von der Finsch Mine zu interpretieren.rn

Systematics, phylogeny and biogeography of Juncaginaceae

I investigated the systematics, phylogeny and biogeographical history of Juncaginaceae, a small family of the early-diverging monocot order Alismatales which comprises about 30 species of annual and perennial herbs. A wide range of methods from classical taxonomy to molecular systematic and biogeographic approaches was used. rnrnIn Chapter 1, a phylogenetic analysis of the family and members of Alismatales was conducted to clarify the circumscription of Juncaginaceae and intrafamilial relationships. For the first time, all accepted genera and those associated with the family in the past were analysed together. Phylogenetic analysis of three molecular markers (rbcL, matK, and atpA) showed that Juncaginaceae are not monophyletic. As a consequence the family is re-circumscribed to exclude Maundia which is pro-posed to belong to a separate family Maundiaceae, reducing Juncaginaceae to include Tetroncium, Cycnogeton and Triglochin. Tetroncium is weakly supported as sister to the rest of the family. The reinstated Cycnogeton (formerly included in Triglochin) is highly supported as sister to Triglochin s.str. Lilaea is nested within Triglochin s. str. and highly supported as sister to the T. bulbosa complex. The results of the molecular analysis are discussed in combination with morphological characters, a key to the genera of the family is given, and several new combinations are made.rnrnIn Chapter 2, phylogenetic relationships in Triglochin were investigated. A species-level phylogeny was constructed based on molecular data obtained from nuclear (ITS, internal transcribed spacer) and chloroplast sequence data (psbA-trnH, matK). Based on the phylogeny of the group, divergence times were estimated and ancestral distribution areas reconstructed. The monophyly of Triglochin is confirmed and relationships between the major lineages of the genus were resolved. A clade comprising the Mediterranean/African T. bulbosa complex and the American T. scilloides (= Lilaea s.) is sister to the rest of the genus which contains two main clades. In the first, the widespread T. striata is sister to a clade comprising annual Triglochin species from Australia. The second clade comprises T. palustris as sister to the T. maritima complex, of which the latter is further divided into a Eurasian and an American subclade. Diversification in Triglochin began in the Miocene or Oligocene, and most disjunctions in Triglochin were dated to the Miocene. Taxonomic diversity in some clades is strongly linked to habitat shifts and can not be observed in old but ecologically invariable lineages such as the non-monophyletic T. maritima.rnrnChapter 3 is a collaborative revision of the Triglochin bulbosa complex, a monophyletic group from the Mediterranean region and Africa. One new species, Triglochin buchenaui, and two new subspecies, T. bulbosa subsp. calcicola and subsp. quarcicola, from South Africa were described. Furthermore, two taxa were elevated to species rank and two reinstated. Altogether, seven species and four subspecies are recognised. An identification key, detailed descriptions and accounts of the ecology and distribution of the taxa are provided. An IUCN conservation status is proposed for each taxon.rnrnChapter 4 deals with the monotypic Tetroncium from southern South America. Tetroncium magellanicum is the only dioecious species in the family. The taxonomic history of the species is described, type material is traced, and a lectotype for the name is designated. Based on an extensive study of herbarium specimens and literature, a detailed description of the species and notes on its ecology and conservation status are provided. A detailed map showing the known distribution area of T. magellanicum is presented. rnrnIn Chapter 5, the flower structure of the rare Australian endemic Maundia triglochinoides (Maundiaceae, see Chapter 1) was studied in a collaborative project. As the morphology of Maundia is poorly known and some characters were described differently in the literature, inflorescences, flowers and fruits were studied using serial mictrotome sections and scanning electron microscopy. The phylogenetic placement, affinities to other taxa, and the evolution of certain characters are discussed. As Maundia exhibits a mosaic of characters of other families of tepaloid core Alismatales, its segregation as a separate family seems plausible.

Visualization and validation of (Q)SAR models

Analyzing and modeling relationships between the structure of chemical compounds, their physico-chemical properties, and biological or toxic effects in chemical datasets is a challenging task for scientific researchers in the field of cheminformatics. Therefore, (Q)SAR model validation is essential to ensure future model predictivity on unseen compounds. Proper validation is also one of the requirements of regulatory authorities in order to approve its use in real-world scenarios as an alternative testing method. However, at the same time, the question of how to validate a (Q)SAR model is still under discussion. In this work, we empirically compare a k-fold cross-validation with external test set validation. The introduced workflow allows to apply the built and validated models to large amounts of unseen data, and to compare the performance of the different validation approaches. Our experimental results indicate that cross-validation produces (Q)SAR models with higher predictivity than external test set validation and reduces the variance of the results. Statistical validation is important to evaluate the performance of (Q)SAR models, but does not support the user in better understanding the properties of the model or the underlying correlations. We present the 3D molecular viewer CheS-Mapper (Chemical Space Mapper) that arranges compounds in 3D space, such that their spatial proximity reflects their similarity. The user can indirectly determine similarity, by selecting which features to employ in the process. The tool can use and calculate different kinds of features, like structural fragments as well as quantitative chemical descriptors. Comprehensive functionalities including clustering, alignment of compounds according to their 3D structure, and feature highlighting aid the chemist to better understand patterns and regularities and relate the observations to established scientific knowledge. Even though visualization tools for analyzing (Q)SAR information in small molecule datasets exist, integrated visualization methods that allows for the investigation of model validation results are still lacking. We propose visual validation, as an approach for the graphical inspection of (Q)SAR model validation results. New functionalities in CheS-Mapper 2.0 facilitate the analysis of (Q)SAR information and allow the visual validation of (Q)SAR models. The tool enables the comparison of model predictions to the actual activity in feature space. Our approach reveals if the endpoint is modeled too specific or too generic and highlights common properties of misclassified compounds. Moreover, the researcher can use CheS-Mapper to inspect how the (Q)SAR model predicts activity cliffs. The CheS-Mapper software is freely available at http://ches-mapper.org.