DNA methylation-based refinement of sinonasal tumor classification

Due to its small anatomic size, the sinonasal space offers the possibility for the development of an enormous range of tumor entities. An important focus of research is the diagnosis and classification of undifferentiated sinonasal tumors. The aim of this dissertation was to perform a diagnostic reclassification for sinonasal tumors based on DNA methylation. An other focus was on undifferentiated singnasal tumors with an IDH2 mutation.Obwohl anatomisch eher ein begrenzter und kleiner Raum, bietet der sinunasale Raum vielen verschiedenen Tumorentitäten die Möglichkeit zur Entstehung. Ein wesentlicher Fokus der Forschung liegt auf der Diagnostik und Klassifikation von undifferenzierten singnasalen Tumoren. Ziel dieser Dissertation war die Verbesserung der Diagnostik von undifferenzierten singnasalen Tumoren auf Basis der DNA Methylierung. Ein weiterer Fokus lag auf singnasalen Tumoren mit einer IDH2-Mutation

Entwicklung und Implementierung schneller MP2-R12Methoden

Diese Arbeit dokumentiert die Entwicklung und effiziente Implementierung der MP2-R12- und RI-MP2-R12-Methoden in die Programmpakete SORE und TURBOMOLE. Die Geschwindigkeit und Genauigkeit der Implementierungen wird detailliert an Beispielsystemen untersucht. Desweiteren befasst sich die Arbeit mit neu entwickelten Näherungen, welche das Verhältnis von Rechenkosten zu Genauigkeit senken. Es ist nun möglich die Korrelationsenergien quantenchemischer Systeme mit mehr als 50 Atomen zu untersuchen

Antiphase Boundaries Constitute Fast Cation Diffusion Paths in SrTiO3 Memristive Devices

AbstractResistive switching in transition metal oxide‐based metal‐insulator‐metal structures relies on the reversible drift of ions under an applied electric field on the nanoscale. In such structures, the formation of conductive filaments is believed to be induced by the electric‐field driven migration of oxygen anions, while the cation sublattice is often considered to be inactive. This simple mechanistic picture of the switching process is incomplete as both oxygen anions and metal cations have been previously identified as mobile species under device operation. Here, spectromicroscopic techniques combined with atomistic simulations to elucidate the diffusion and drift processes that take place in the resistive switching model material SrTiO3 are used. It is demonstrated that the conductive filament in epitaxial SrTiO3 devices is not homogenous but exhibits a complex microstructure. Specifically, the filament consists of a conductive Ti3+‐rich region and insulating Sr‐rich islands. Transmission electron microscopy shows that the Sr‐rich islands emerge above Ruddlesden–Popper type antiphase boundaries. The role of these extended defects is clarified by molecular static and molecular dynamic simulations, which reveal that the Ruddlesden–Popper antiphase boundaries constitute diffusion fast‐paths for Sr cations in the perovskites structure

Conductive Self-Assembled Monolayers of Paramagnetic {CoIICo4III} and {Co4IICo2III} Coordination Clusters on Gold Surfaces

Two polynuclear cobalt(II,III) complexes, [Co5(N3)4(N-n-bda)4(bza·SMe)2] (1) and [Co6(N3)4(N-n-bda)2(bza·SMe)5(MeOH)4]Cl (2), where Hbza·SMe = 4-(methylthio)benzoic acid and N-n-H2bda = N-n-butyldiethanolamine, were synthesized and fully characterized by various techniques. Compound 1 exhibits an unusual, approximately C2-symmetric {CoII Co4II } core of two isosceles Co3 triangles with perpendicularly oriented planes, sharing a central, high-spin CoII ion residing in a distorted tetrahedral coordination environment. This central CoII ion is connected to four outer, octahedrally coordinated low-spin CoIII ions via oxo bridges. Compound 2 comprises a semi-circular {Co4IICo2III } motif of four non-interacting high-spin CoII and two low-spin CoIII centers in octahedral coordination environments. Self-assembled monolayers (SAMs) of 1 and 2 were physisorbed on template-stripped gold surfaces contacted by an eutectic gallium-indium (EGaIn) tip. The acquired current density-voltage (I-V) data revealed that the cobalt-based SAMs are more electrically robust than those of the previously reported dinuclear {CuIILnIII} complexes with Ln = Gd, Tb, Dy, or Y (Schmitz et al., 2018a). In addition, between 170 and 220°C, the neutral, mixed-valence compound 1 undergoes a redox modification, yielding a {Co5}-based coordination cluster (1-A) with five non-interacting, high-spin octahedral CoII centers as indicated by SQUID magnetometry analysis in combination with X-ray photoelectron spectroscopy and infrared spectroscopy. Solvothermal treatment of 1 results in a high-nuclearity coordination cluster, [Co10(N3)2(N-n-bda)6(bza·SMe)6] (3), containing 10 virtually non-interacting high-spin CoII centers. © Copyright © 2019 Schmitz, Qiu, Glöß, van Leusen, Izarova, Nadeem, Griebel, Chiechi, Kögerler and Monakhov

Conductive Self-Assembled Monolayers of Paramagnetic {CoII Co 4 III } and { Co 4 II Co 2 III } Coordination Clusters on Gold Surfaces

Two polynuclear cobalt(II,III) complexes, [Co5(N3)4(N-n-bda)4(bza·SMe)2] (1) and [Co6(N3)4(N-n-bda)2(bza·SMe)5(MeOH)4]Cl (2), where Hbza·SMe = 4-(methylthio)benzoic acid and N-n-H2bda = N-n-butyldiethanolamine, were synthesized and fully characterized by various techniques. Compound 1 exhibits an unusual, approximately C2-symmetric {CoIICoIII4} core of two isosceles Co3 triangles with perpendicularly oriented planes, sharing a central, high-spin CoII ion residing in a distorted tetrahedral coordination environment. This central CoII ion is connected to four outer, octahedrally coordinated low-spin CoIII ions via oxo bridges. Compound 2 comprises a semi-circular {CoII4CoIII2} motif of four non-interacting high-spin CoII and two low-spin CoIII centers in octahedral coordination environments. Self-assembled monolayers (SAMs) of 1 and 2 were physisorbed on template-stripped gold surfaces contacted by an eutectic gallium-indium (EGaIn) tip. The acquired current density-voltage (I-V) data revealed that the cobalt-based SAMs are more electrically robust than those of the previously reported dinuclear {CuIILnIII} complexes with Ln = Gd, Tb, Dy, or Y (Schmitz et al., 2018a). In addition, between 170 and 220°C, the neutral, mixed-valence compound 1 undergoes a redox modification, yielding a {Co5}-based coordination cluster (1-A) with five non-interacting, high-spin octahedral CoII centers as indicated by SQUID magnetometry analysis in combination with X-ray photoelectron spectroscopy and infrared spectroscopy. Solvothermal treatment of 1 results in a high-nuclearity coordination cluster, [Co10(N3)2(N-n-bda)6(bza·SMe)6] (3), containing 10 virtually non-interacting high-spin CoII centers

CP2K: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations

CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular, and biological systems. It is especially aimed at massively parallel and linear-scaling electronic structure methods and state-of-the-art ab initio molecular dynamics simulations. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern high-performance computing systems. This review revisits the main capabilities of CP2K to perform efficient and accurate electronic structure simulations. The emphasis is put on density functional theory and multiple post–Hartree–Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension

DNA methylation-based classification of sinonasal tumors

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs

DNA methylation-based classification of sinonasal tumors

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs

DNA methylation-based classification of sinonasal tumors

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs