Rich probabilistic models for semantic labeling

Das Ziel dieser Monographie ist es die Methoden und Anwendungen des semantischen Labelings zu erforschen. Unsere Beiträge zu diesem sich rasch entwickelten Thema sind bestimmte Aspekte der Modellierung und der Inferenz in probabilistischen Modellen und ihre Anwendungen in den interdisziplinären Bereichen der Computer Vision sowie medizinischer Bildverarbeitung und Fernerkundung

DNA-Based Mimics of Membrane Proteins Lipid-DNA Interactions Determine Function

Nucleic acids, particularly DNA, are used as a nanoscale building material, due to their unique controllability via complementarity of base pairing. One of the potential applications of DNA nanotechnology is creating synthetic constructs mimicking function of membrane proteins. These natural molecular machines function embedded in the lipid bilayer. Similar membrane attachment of DNA-based structures is achieved by modifying the nucleic acid with hydrophobic anchors, most commonly cholesterol. Aiming at developing a fully functional and controllable synthetic membrane construct, the first step I undertook was to understand and utilize fundamental interactions between molecules: DNA, cholesterol and lipids. Instead of starting with a complicated DNA-based model mimicking protein architecture, here I have created a set of simple systems that allowed me to examine the major interactions between involved molecules. This work describes four aspects of the DNA-lipid systems that I have built and studied experimentally. Firstly, I have analysed the effects of membrane-spanning DNA duplex on the lipids’ arrangement in the pore and presented how this arrangement can be remodelled depending on the hydrophilicity of the DNA design. Secondly, I have looked at the same system from the opposite perspective - studied and prevented the distortion of the transmembrane DNA construct induced by the surrounding lipids. Thirdly, I have evaluated the importance of ions in mediating DNA-lipid interactions, reporting analysis of two electrostatic phenomena: screening and bridging. Finally, utilizing a nanoengineered four-helix structure, I discussed surfactant’s influence on DNA membrane insertion efficiency, showing that aggregation of the nanostructures is one of the major factors determining their spontaneous membrane-spanning. While the understanding of phenomena in minimalistic systems is crucial for further development of complex pore-forming constructs, here I showed that even simple DNA nanostructures, when rationally designed, can mimic functionality of natural membrane proteins.EPSRC; Winton Programme for the Physics of Sustainabilit

Caveolin-1 dolines form a distinct and rapid caveolae-independent mechanoadaptation system.

In response to different types and intensities of mechanical force, cells modulate their physical properties and adapt their plasma membrane (PM). Caveolae are PM nano-invaginations that contribute to mechanoadaptation, buffering tension changes. However, whether core caveolar proteins contribute to PM tension accommodation independently from the caveolar assembly is unknown. Here we provide experimental and computational evidence supporting that caveolin-1 confers deformability and mechanoprotection independently from caveolae, through modulation of PM curvature. Freeze-fracture electron microscopy reveals that caveolin-1 stabilizes non-caveolar invaginations-dolines-capable of responding to low-medium mechanical forces, impacting downstream mechanotransduction and conferring mechanoprotection to cells devoid of caveolae. Upon cavin-1/PTRF binding, doline size is restricted and membrane buffering is limited to relatively high forces, capable of flattening caveolae. Thus, caveolae and dolines constitute two distinct albeit complementary components of a buffering system that allows cells to adapt efficiently to a broad range of mechanical stimuli.We thank R. Parton (Institute for Molecular Biosciences, Queensland), P. Pilch (Boston University School of Medicine) and L. Liu (Boston University School of Medicine) for kindly providing PTRFKO cells and reagents, S. Casas Tintó for kindly providing SH-Sy5y cells, P. Bassereau (Curie Institute, Paris) for kindly providing OT setup, V. Labrador Cantarero from CNIC microscopy Unit for helping with ImageJ analysis, O. Otto and M. Herbig for providing help with RTDC experiments, S. Berr and K. Gluth for technical assistance in cell culture, F. Steiniger for support in electron tomography, and A. Norczyk Simón for providing pCMV-FLAG-PTRF construct. This project received funding from the European Union Horizon 2020 Research and Innovation Programme through Marie Sklodowska-Curie grant 641639; grants from the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033): SAF2014-51876-R, SAF2017-83130-R co-funded by ‘ERDF A way of making Europe’, PID2020-118658RB-I00, PDC2021-121572-100 co-funded by ‘European Union NextGenerationEU/PRTR’, CSD2009- 0016 and BFU2016-81912-REDC; and the Asociación Española Contra el Cáncer foundation (PROYE20089DELP) all to M.A.d.P. M.A.d.P. is member of the Tec4Bio consortium (ref. S2018/NMT¬4443; Comunidad Autónoma de Madrid/FEDER, Spain), co-recipient with P.R.-C. of grants from Fundació La Marató de TV3 (674/C/2013 and 201936- 30-31), and coordinator of a Health Research consortium grant from Fundación Obra Social La Caixa (AtheroConvergence, HR20-00075). M.S.-A. is recipient of a Ramón y Cajal research contract from MCIN (RYC2020-029690-I). The CNIC Unit of Microscopy and Dynamic Imaging is supported by FEDER ‘Una manera de hacer Europa’ (ReDIB ICTS infrastructure TRIMA@CNIC, MCIN). We acknowledge the support from Deutsche Forschungsgemeinschaft through grants to M.M.K. (KE685/7-1) and B.Q. (QU116/6-2 and QU116/9-1). Work in D.N. laboratory was supported by grants from the European Union Horizon 2020 Research and Innovation Programme through Marie Sklodowska-Curie grant 812772 and MCIN (DPI2017-83721-P). Work in C.L. laboratory was supported by grants from Curie, INSERM, CNRS, Agence Nationale de la Recherche (ANR-17-CE13-0020-01) and Fondation ARC pour la Recherche (PGA1-RF20170205456). Work in P.R.-C. lab is funded by the MCIN (PID2019-110298GB-I00), the EC (H20 20-FETPROACT-01-2016-731957). Work in X.T. lab is funded by the MICIN (PID2021-128635NB-I00), ERC (Adv-883739) and La Caixa Foundation (LCF/PR/HR20/52400004; co-recipient with P.R.-C.). IBEC is recipient of a Severo Ochoa Award of Excellence from the MINECO. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The CNIC is supported by the Instituto de Salud Carlos III (ISCIII), the MCIN and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (grant CEX2020-001041-S funded by MICIN/AEI/10.13039/501100011033).S

Light Microscopy of Proteins in Their Ultrastructural Context

Fluorescence light microscopy is an essential tool in biomedical research. In immunofluorescence, fluorophore-conjugated antibodies are used to detect specific proteins of interest in a fixed biological sample. With recently developed nanoscopy techniques, whole cells can be imaged at an isotropic spatial resolution of ~10 nm, revealing accurate protein distributions on the nanoscale. However, most localized proteins are imaged against a dark background, which forbids seeing the overall subcellular compartments (ultrastructural context) that encompass them. Electron microscopy (EM), on the other hand, offers a complete cellular overview on the scale of a few nanometers. However, EM fails to reliably detect specific molecules of interest. To this end, correlated light and electron microscopy (CLEM) techniques have emerged to combine the high molecular contrast of fluorescence microscopy with the ultrastructural imaging capabilities of EM. Despite the merits of CLEM, sample preparation and image alignment are extremely laborious, limiting this correlative approach to only proof-of-concept biological experiments. This thesis poses this specific question: why is light microscopy alone incapable of resolving the ultrastructural context of cells, despite extraordinary improvements in spatial resolution? We argue that the limitation stems from the physical properties of fluorescent dyes: dyes are ~1 nm in diameter, a size comparable to the distance between proteins in the densely crowded cell. If labeled in bulk, fluorescent dyes would sterically hinder and self-quench via electron transfer and dipole-dipole interactions, which would limit the achievable staining density and thereby the sampling necessary to resolve the crowded cellular interior. This thesis made the conceptual realization that if the sample protein content is isotropically expanded up to 20-fold in all three dimensions, the relative size of fluorescent dyes would shrink by the same factor. Here, the relative radius of a fluorescent dye would approach ~50 pm, which is comparable to the size of an osmium atom (~200 pm) used in heavy metal EM staining. Bulk fluorescence staining of the decrowded cell will therefore no longer be limited by sampling and quenching restrictions, and ultrastructural details, previously accessible with only EM, can now be revealed on a standard light microscope. We call the underlying sample preparation technique pan-Expansion Microscopy (pan-ExM). pan-ExM combines the philosophy of bulk- (pan-) staining of the total protein content with a newly developed Expansion Microscopy (ExM) protocol capable of 20-fold linear sample expansion and protein retention. We first develop pan-ExM in cultured cells as a proof-of-concept demonstration. We then develop the technique in dissociated neuronal cultures and in thick (~70 μm) mouse brain tissue sections to establish its applicability in neurobiological research. Finally, in a method we call panception, we demonstrate that the conceptual advance of pan-staining is also applicable to transmitted light microscopy. Using polymers of varying refractive indices and light-scattering analogs of fluorescent dyes, we show that sample ultrastructure can be imaged with brightfield microscopy, and that sample microstructure can be revealed with the un-aided eye

Electronic Spectra: Topology, Supersymmetry, and Statistics

The description of electronic behavior within solids is a major part of modern Condensed Matter Physics. It is well known that depending on the precise conditions, very diverse phenomena arise from the interacting electrons in the material. To make predictions, it is therefore crucial to understand the electronic structure in a material and to compute its electronic spectrum. This thesis discusses three different aspects of electronic spectra including their numerical solution, each highlighting a distinct approach. In a first part, this thesis presents a numerical solution of many-electron spectra on small clusters of IrO6 octahedra. Such clusters are relevant in the field of strongly coupled matter as they give rise to the elementary building blocks of many topological spin systems, localized j = 1/2 moments. Exact diagonalization of the full many-electron interaction Hamiltonian is utilized to compute multi-particle spectra with respective eigenstates. Subsequently, these eigenstates are further used for numerical calculations of resonant inelastic X-ray scattering (RIXS) amplitudes. The numerical approach is versatile enough to be applied to different examples in this thesis, covering single-site RIXS spectra as in Ba2CeIrO6, materials with local clusters like Ba3InIr2O9 and Ba3Ti3−xIrxO9 and Kitaev materials such as Na2IrO3 and α-RuCl3. In particular, interference effects in the RIXS amplitudes are shown to play a crucial role of determining the nature of delocalized eigenstates in these materials. In a second part, supersymmetry is used to link the spectra of electronic lattice models with bosonic counterparts. To this endeavor, an exact lattice construction is introduced, underlying the supersymmetric identification and providing a visual representation of the supersymmetric matching. As a first instance of the supersymmetric map, it will be shown that models of complex fermions and models of complex bosons are supersymmetrically related if they reside on the two sublattices of a bipartite lattice. Another similar identification is introduced for Majorana fermions on a bipartite lattice which can be related to real boson models on one of the sublattices, allowing for the explicit construction of related mechanical models. As examples of this classical construction, the Kitaev model and a second order topological insulator with floppy corner modes are discussed. In both examples, the supersymmetrically related mechanical model is shown to exhibit the same spectral properties as its quantum mechanical analogue and even inherit topologically protected localized corner modes. In a third part, the electronic spectra of general Moiré materials are investigated at the example of twisted bilayer graphene. This part demonstrates that statistical principles are best suited to describe the vast number of bands originating from the large Moiré unit cells. The statistical description reveals a localization mechanism in momentum space which is investigated and described. The mechanism does not only apply to all parts of the spectrum in twisted bilayer graphene but is also believed to apply to generic Moiré materials. Moreover, exceptions from this general mechanism in twisted bilayer graphene are discussed in detail which turn out to be described by harmonic oscillator states

Scale-Adaptive Video Understanding.

The recent rise of large-scale, diverse video data has urged a new era of high-level video understanding. It is increasingly critical for intelligent systems to extract semantics from videos. In this dissertation, we explore the use of supervoxel hierarchies as a type of video representation for high-level video understanding. The supervoxel hierarchies contain rich multiscale decompositions of video content, where various structures can be found at various levels. However, no single level of scale contains all the desired structures we need. It is essential to adaptively choose the scales for subsequent video analysis. Thus, we present a set of tools to manipulate scales in supervoxel hierarchies including both scale generation and scale selection methods. In our scale generation work, we evaluate a set of seven supervoxel methods in the context of what we consider to be a good supervoxel for video representation. We address a key limitation that has traditionally prevented supervoxel scale generation on long videos. We do so by proposing an approximation framework for streaming hierarchical scale generation that is able to generate multiscale decompositions for arbitrarily-long videos using constant memory. Subsequently, we present two scale selection methods that are able to adaptively choose the scales according to application needs. The first method flattens the entire supervoxel hierarchy into a single segmentation that overcomes the limitation induced by trivial selection of a single scale. We show that the selection can be driven by various post hoc feature criteria. The second scale selection method combines the supervoxel hierarchy with a conditional random field for the task of labeling actors and actions in videos. We formulate the scale selection problem and the video labeling problem in a joint framework. Experiments on a novel large-scale video dataset demonstrate the effectiveness of the explicit consideration of scale selection in video understanding. Aside from the computational methods, we present a visual psychophysical study to quantify how well the actor and action semantics in high-level video understanding are retained in supervoxel hierarchies. The ultimate findings suggest that some semantics are well-retained in the supervoxel hierarchies and can be used for further video analysis.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133202/1/cliangxu_1.pd

Elucidating the Interplay of Structure, Dynamics, and Function in the Brain’s Neural Networks.

Brain’s structure, dynamics, and function are deeply intertwined. To understand how the brain functions, it is crucial to uncover the links between network structure and its dynamics. Here I examine different approaches to exploring the key connecting factors between network structure, dynamics and eventually its function. I predominantly concentrate on emergence and temporal evolution of synchronization, or coincidence of neuronal spike timings, as it has been associated with many brain functions while aberrant synchrony is implicated in many neurological disorders. Specifically, in chapter II, I investigate how the interplay of cellular properties with network coupling characteristics could affect the propensity of neural networks for synchronization. Then, in chapter III, I develop a set of measures that identify hallmarks and potentially predict autonomous network transitions from asynchronous to synchronous dynamics under various conditions. The developed metrics can be calculated in real time and therefore potentially applied in clinical situations. Finally, in chapter IV, I aim to tie the correlates of neural network dynamics to the brain function. More specifically, I elucidate dynamical underpinnings of learning and memory consolidation from in vivo recordings of mice experiencing contextual fear conditioning (CFC) and show, that the introduced notion of network stability may predict future animal performance on memory retrieval. Overall, the results presented within this dissertation underscore the importance of concurrent analysis of networks’ dynamical and structural properties. The developed approaches may prove useful beyond the specific application presented within this thesis.PhDBiophysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120768/1/mofakham_1.pd

Target classification in multimodal video

The presented thesis focuses on enhancing scene segmentation and target recognition methodologies via the mobilisation of contextual information. The algorithms developed to achieve this goal utilise multi-modal sensor information collected across varying scenarios, from controlled indoor sequences to challenging rural locations. Sensors are chiefly colour band and long wave infrared (LWIR), enabling persistent surveillance capabilities across all environments. In the drive to develop effectual algorithms towards the outlined goals, key obstacles are identified and examined: the recovery of background scene structure from foreground object ’clutter’, employing contextual foreground knowledge to circumvent training a classifier when labeled data is not readily available, creating a labeled LWIR dataset to train a convolutional neural network (CNN) based object classifier and the viability of spatial context to address long range target classification when big data solutions are not enough. For an environment displaying frequent foreground clutter, such as a busy train station, we propose an algorithm exploiting foreground object presence to segment underlying scene structure that is not often visible. If such a location is outdoors and surveyed by an infra-red (IR) and visible band camera set-up, scene context and contextual knowledge transfer allows reasonable class predictions for thermal signatures within the scene to be determined. Furthermore, a labeled LWIR image corpus is created to train an infrared object classifier, using a CNN approach. The trained network demonstrates effective classification accuracy of 95% over 6 object classes. However, performance is not sustainable for IR targets acquired at long range due to low signal quality and classification accuracy drops. This is addressed by mobilising spatial context to affect network class scores, restoring robust classification capability

A unified evolutionary origin for the ubiquitous protein transporters SecY and YidC.

BACKGROUND: Protein transporters translocate hydrophilic segments of polypeptide across hydrophobic cell membranes. Two protein transporters are ubiquitous and date back to the last universal common ancestor: SecY and YidC. SecY consists of two pseudosymmetric halves, which together form a membrane-spanning protein-conducting channel. YidC is an asymmetric molecule with a protein-conducting hydrophilic groove that partially spans the membrane. Although both transporters mediate insertion of membrane proteins with short translocated domains, only SecY transports secretory proteins and membrane proteins with long translocated domains. The evolutionary origins of these ancient and essential transporters are not known. RESULTS: The features conserved by the two halves of SecY indicate that their common ancestor was an antiparallel homodimeric channel. Structural searches with SecY's halves detect exceptional similarity with YidC homologs. The SecY halves and YidC share a fold comprising a three-helix bundle interrupted by a helical hairpin. In YidC, this hairpin is cytoplasmic and facilitates substrate delivery, whereas in SecY, it is transmembrane and forms the substrate-binding lateral gate helices. In both transporters, the three-helix bundle forms a protein-conducting hydrophilic groove delimited by a conserved hydrophobic residue. Based on these similarities, we propose that SecY originated as a YidC homolog which formed a channel by juxtaposing two hydrophilic grooves in an antiparallel homodimer. We find that archaeal YidC and its eukaryotic descendants use this same dimerisation interface to heterodimerise with a conserved partner. YidC's sufficiency for the function of simple cells is suggested by the results of reductive evolution in mitochondria and plastids, which tend to retain SecY only if they require translocation of large hydrophilic domains. CONCLUSIONS: SecY and YidC share previously unrecognised similarities in sequence, structure, mechanism, and function. Our delineation of a detailed correspondence between these two essential and ancient transporters enables a deeper mechanistic understanding of how each functions. Furthermore, key differences between them help explain how SecY performs its distinctive function in the recognition and translocation of secretory proteins. The unified theory presented here explains the evolution of these features, and thus reconstructs a key step in the origin of cells