Integrated Calendar

The preservation of the organic matter (OM) occurs as a result of post-depositional abiotic sulfurization, condensation and polymerization processes that convert the OM into stable macromolecular material termed kerogen. Different sulfurization processes, pathways and rates affect the 34S values of organic and inorganic S compounds. These sulfurization processes are affected by the redox conditions and paleo-environmental conditions (e.g. organic matter and Fe availability). Therefore, studying the organic and inorganic S distribution and their associated 34S values could be useful for understanding the paleo-environmental history associated with the deposition of ancient organic rich sediments. Until recently, only bulk phases of S could be measured for their 34S values, usually excluding organic S. A new method was developed that allows for S isotope analysis of specific organic S compounds (OSCs) at the sub- nanogram level. In my talk I will give an overview about the utility of compound specific S isotope analysis (CSSIA) for the study of different geochemical environments (e.g., immature organic rich sediments). Applying CSSIA to immature organic rich sediments from the Monterey and Ghareb formations I will show the combination of biomarkers and their S isotope composition in a single analysis. This provides a more detailed and in-depth understanding of the S and C cycles than bulk measurements of organic and inorganic S species alone.

The macroscopic fluctuation theory gives an efficient hydrodynamic description for classical nonequilibrium diffusive systems. In this talk, we would cover how it can be applied and generalised in three directions:
a. Towards a theory for open quantum diffusive systems, comparable to the macroscopic fluctuation theory.
b. Defect formation as a system is (slowly) driven in time through a continuous phase transition can be described by a scaling theory - the Kibble-Zurek Mechanism. The macroscopic fluctuation theory allows to explore the exact evolution of defects for a large set of cases. Thus, we are in a position to go beyond the scaling arguments of the Kibble-Zurek Mechanism.
c. The recently discovered thermodynamic uncertainty relations define a transport efficiency in thermal systems showing that the mean current, current fluctuations and dissipation are intimately linked. Here we will briefly show how this idea can be extended to (athermal) mesoscopic coherent processes.

Face recognition is a computationally challenging classification task that requires generalization across different views of the same identity as well as discrimination across different identities of a relatively homogenous set of visual stimuli. How does the brain resolve this taxing classification task? It is well-established that faces are processed by specialized neural mechanisms in high-level visual cortex. Nevertheless, it is not clear how this divergence to a face-specific and an object-general system contributes to face recognition. Recent advances in machine face recognition together with our understanding of how humans recognize faces enable us to address this question. In particular, I will show that a deep convolutional neural network (DCNN) that is trained on face recognition, but not a DCNN that is trained on object recognition, is sensitive to the same view-invariant facial features that humans use for face recognition. Similar to the hierarchical architecture of the visual system that diverges to a face and an object system at high-level visual cortex, a human-like, view-invariant face representation emerges only at higher layers of the face-trained but not the object-trained neural network. This view-invariant face representation is specific to the category of faces that the system was trained with both in humans and machines. I will therefore further emphasize the important role of experience and suggest that human face recognition depends on our social experience with familiar faces (“supervised learning”) rather than passive perceptual exposure to unfamiliar faces (“unsupervised learning”), highlighting the important role of social cognition in face recognition.

Additive manufacturing, which is fabrication through printing processes, has gained a lot of interest in the academy and industry, and is considered as the next industrial revolution. The synthesis and formulations of new inks compositions will be presented, along with their applications in various fields. New materials and processes for 2, 3, and 4D printing will be introduced, for fabrication of objects composed of hybrid materials, ceramics, glass, shape memory polymers, elastomers and hydrogels. Examples of applications of these materials will be presented, such as in soft robotics, drug delivery systems, 3D electrical circuits, responsive connectors, and medical devices.

We investigate theoretically a model system of colloids in water. The colloid size is neither very small compared to the Debye length, nor very large. We look at the orientation of the colloid near a surface, and find bistable behavior. This may have implications for flow in microfluidic channels, and for crystallization near surfaces.

Glycans are ubiquitous in nature and participate in a wide variety of biological processes, that span from mediating cell-cell interactions to modulating protein stability and folding. Glycan involvement in diverse biological functions can be rationalized by the equally extensive potential for structural diversity. They vary not only in monosaccharide composition and primary sequence, like proteins and nucleic acids, but also the monosaccharides can vary in ring sizes, linkage types, and functional group modifications. Therefore, their structural complexity has the potential for encoding a myriad of functions. However, it is this “structural richness” that hampers progress in stablishing structure-function relationships, simply because tools and strategies for structure determination are lacking.
We are delineating three-dimensional glycan solution structure to gain insight into how they function, which should facilitate development of glycan-based vaccines, drug delivery systems, and antibiotics of the future. To this end, we use heteronuclear multidimensional NMR to determine conformations and dynamics of 15N, 13C enriched oligo- and polysaccharides. We have detected interresidue hydrogen bonds and used RDCs to delineate the relative orientations of the rigid monosaccharide building blocks. However, RDC measurements are fraught with errors from strong coupling effects. Thus, we have developed methods to accurately measure one-bond 1H-13C splittings and 13C-13C splittings as well as RDCs. I will illustrate the application of these methods for bacterial polysaccharide model systems and show how we applied them to support the two-residue per turn helical structure of 2-8 tetrasialic acid and the smaller conformationally stable dimer in solution at low temperatures.