Accelerating the simulation of turbulence to stationarity is a critical challenge in various engineering applications. This study presents an innovative equation-free multiscale approach combined with a machine learning technique to address this challenge in the context of the one-dimensional stochastic Burgers' equation, a widely used toy model for turbulence. We employ an encoder-decoder recurrent neural network to perform super-resolution reconstruction of the velocity field from lower-dimensional energy spectrum data, enabling seamless transitions between fine and coarse levels of description. The proposed multiscale-machine learning framework significantly accelerates the computation of the statistically stationary turbulent Burgers' velocity field, achieving up to 442 times faster wall clock time compared to direct numerical simulation, while maintaining three-digit accuracy in the velocity field. Our findings demonstrate the potential of integrating equation-free multiscale methods with machine learning methods to efficiently simulate stochastic partial differential equations and highlight the possibility of using this approach to simulate stochastic systems in other engineering domains. / Master of Science / In many practical engineering problems, simulating turbulence can be computationally expensive and time-consuming. This research explores an innovative method to accelerate these simulations using a combination of equation-free multiscale techniques and deep learning. Multiscale methods allow researchers to simulate the behavior of a system at a coarser scale, even when the specific equations describing its evolution are only available for a finer scale. This can be particularly helpful when there is a notable difference in the time scales between the coarser and finer scales of a system. The ``equation-free approach multiscale method coarse projective integration" can then be used to speed up the simulations of the system's evolution. Turbulence is an ideal candidate for this approach since it can be argued that it evolves to a statistically steady state on two different time scales. Over the course of evolution, the shape of the energy spectrum (the coarse scale) changes slowly, while the velocity field (the fine scale) fluctuates rapidly. However, applying this multiscale framework to turbulence simulations has been challenging due to the lack of a method for reconstructing the velocity field from the lower-dimensional energy spectrum data. This is necessary for moving between the two levels of description in the multiscale simulation framework. In this study, we tackled this challenge by employing a deep neural network model called an encoder-decoder sequence-to-sequence architecture. The model was used to capture and learn the conversions between the structure of the velocity field and the energy spectrum for the one-dimensional stochastic Burgers' equation, a simplified model of turbulence. By combining multiscale techniques with deep learning, we were able to achieve a much faster and more efficient simulation of the turbulent Burgers' velocity field. The findings of this study demonstrated that this novel approach could recover the final steady-state turbulent Burgers' velocity field up to 442 times faster than the traditional direct numerical simulations, while maintaining a high level of accuracy. This breakthrough has the potential to significantly improve the efficiency of turbulence simulations in a variety of engineering applications, making it easier to study and understand these complex phenomena.

The nuclear pore complex (NPC) is a pivotal subcellular structure governing nucleocytoplasmic transport through a selectively permeable barrier. Comprising approximately 30 distinct proteins, it includes FG-Nups with phenylalanine-glycine (FG) motifs and non-FG Nups forming the pore's scaffold. The selectively permeable barrier formed by FG-Nups enables the passive diffusion of small molecules and facilitates the transport of larger ones recognized by nuclear transport receptors (NTRs). Their roles are critical in regulating mRNA and pre-ribosome nuclear export and the nuclear import of transcription factors, underscoring their significance in cellular processes. However, studying NPCs remains challenging due to their structural complexity, heterogeneity, dynamic interactions, and inaccessibility within live cells. In this dissertation, three core questions were investigated to elucidate the structure and function of the NPC. First, the nuclear export dynamics of pre-ribosomal subunits revealed significantly higher transport efficiency compared to other large cargos. Through inhibition of nuclear transport receptor (NTR), CRM1, by small-molecule inhibitor, leptomycin B, we found a dose-dependent inhibition of CRM1s played a crucial role in pre-ribosome export efficiency. We confirmed these results through a series of controlled environments with both import and export NTRs. Our results suggest that cooperative NTR mechanisms may enhance the nucleocytoplasmic transport of not only pre-ribosomal subunits but other protein complexes as well. Second, we investigated the dynamic properties of the NPC’s selectivity barrier by altering the concentration of O-linked β-N-acetylglucosamine (O-GlcNAc) sites on nuclear pore proteins. Using small-molecule inhibitors of O-GlcNAc transferase (OGT) or O-GlcNAcase (OGA) to decrease or increase NPC O-GlcNAcylation, respectively, we found a significant change in the overall 3D spatial density of NPC O-GlcNAc sites. Then, by applying the same OGT- and OGA-inhibited conditions, we found that NPC O-GlcNAcylation significantly impacted the nuclear export of mRNA, suggesting that NPC O-GlcNAcylation regulates mRNA’s passage through the NPC’s selective permeability barrier. Third, we examined the nuclear transport mechanism for intrinsically disordered proteins (IDPs). Our findings revealed that IDPs, unlike large folded proteins, can passively diffuse through NPCs independent of size, and their diffusion behaviors are differentiated by the content ratio of charged (Ch) and hydrophobic (Hy) amino acids. Thus, we proposed a Ch/Hy-ratio mechanism for IDP nucleocytoplasmic transport. In summary, comprehending the dynamic behavior of the NPC selectivity barrier and its involvement in mediating large transiting complexes and IDPs has provided valuable insights into the fundamental nucleocytoplasmic transport mechanism, emphasizing the NPC's crucial role in cellular health and function. / Biology

Segmentation of the knee cartilage is an important step for surgery planning and manufacturing patient-specific prostheses. What has been a promising technology in recent years is deep learning-based super-resolution methods that are composed of feed-forward models which have been successfully applied on natural and medical images. This thesis aims to test the feasibility to super-resolve thick slice 2D sequence acquisitions and acquire sufficient segmentation accuracy of the articular cartilage in the knee. The investigated approaches are single- and multi-contrast super-resolution, where the contrasts are either based on the 2D sequence, 3D sequence, or both. The deep learning models investigated are based on predicting the residual image between the high- and low-resolution image pairs, finding the hidden latent features connecting the image pairs, and approximating the end-to-end non-linear mapping between the low- and high-resolution image pairs. The results showed a slight improvement in segmentation accuracy with regards to the baseline bilinear interpolation for the single-contrast super-resolution, however, no notable improvements in segmentation accuracy were observed for the multi-contrast case. Although the multi-contrast approach did not result in any notable improvements, there are still unexplored areas not covered in this work that are promising and could potentially be covered as future work. / Segmentering av knäbrosket är ett viktigt steg för planering inför operationer och tillverkning av patientspecifika proteser. Idag segmenterar man knäbrosk med hjälp av MR-bilder tagna med en 3D-sekvens som både tidskrävande och rörelsekänsligt, vilket kan vara obehagligt för patienten. I samband med 3D-bildtagningar brukar även thick slice 2D-sekvenser tas för diagnostiska skäl, däremot är de inte anpassade för segmentering på grund av för tjocka skivor. På senare tid har djupinlärningsbaserade superupplösningsmetoder uppbyggda av så kallade feed-forwardmodeller visat sig vara väldigt framgångsrikt när det applicerats på verkliga- och medicinska bilder. Syftet med den här rapporten är att testa hur väl superupplösta thick slice 2D-sekvensbildtagningar fungerar för segmentering av ledbrosket i knät. De undersökta tillvägagångssätten är superupplösning av enkel- och flerkontrastbilder, där kontrasten är antingen baserade på 2D-sekvensen, 3D-sekvensen eller både och. Resultaten påvisar en liten förbättring av segmenteringnoggrannhet vid segmentering av enkelkontrastbilderna över baslinjen linjär interpolering. Däremot var det inte någon märkvärdig förbättring i superupplösning av flerkontrastbilderna. Även om superupplösning av flerkontrastmetoden inte gav någon märkbar förbättring segmenteringsresultaten så finns det fortfarande outforskade områden som inte tagits upp i det här arbetet som potentiellt skulle kunna utforskas i framtida arbeten.

The primary cilium is an immotile, microtubule-based protrusion on the surface of many eukaryotic cells and contains a unique complement of proteins that function critically in cell motility and signaling. Critically, the transport of membrane and cytosolic proteins into the primary cilium is essential for its role in cellular signaling. Since cilia are incapable of synthesizing their own protein, nearly 200 unique ciliary proteins need to be trafficked between the cytosol and primary cilia. However, it is still a technical challenge to map three-dimensional (3D) locations of transport pathways for these proteins in live primary cilia due to the limitations of currently existing techniques. To conquer the challenge, this work employed a high-speed virtual 3D super-resolution microscopy, termed single-point edge-excitation sub-diffraction (SPEED) microscopy, to determine the 3D spatial location of transport pathways for both cytosolic and membrane proteins in primary cilia of live cells. Using SPEED microscopy and single molecule tracking, we mapped the movement of membrane and soluble proteins at the base of the primary cilium. In addition to the well-known intraflagellar transport (IFT) route, we identified two new pathways within the lumen of the primary cilium - passive diffusional and vesicle transport routes - that are adopted by proteins for cytoplasmic-cilium transport in live cells. Independent of the IFT path, approximately half of IFT motors (KIF3A) and cargo (α-tubulin) take the passive diffusion route and more than half of membrane-embedded G protein coupled receptors (SSTR3 and HTR6) use RAB8A-regulated vesicles to transport into and inside cilia. Furthermore, ciliary lumen transport is the preferred route for membrane proteins in the early stages of ciliogenesis and inhibition of SSTR3 vesicle transport completely blocks ciliogenesis. Furthermore, clathrin-mediated, signal-dependent internalization of SSTR3 also occurs through the ciliary lumen. These transport routes were also observed in Chlamydomonas reinhardtii flagella, suggesting their conserved roles in trafficking of ciliary proteins. While the 3D transport pathways in this work are always replicated multiple times with a high degree of consistency, several experimental parameters directly affect the 3D transport routes’ error, such as single molecule localization precision and the number of single molecule localizations. In fact, if these experimental parameters do not meet a minimum threshold, the resultant 3D transport pathways may not have significant resolution to determine any biological details. To estimate the 3D transport routes’ error, this work will explain in detail the component of SPEED microscopy that estimates 3D sub-diffraction-limited structural or dynamic information in rotationally symmetric bio-structures, such as the primary cilium. This component is a post-localization analysis that transforms 2D super-resolution images or 2D single-molecule localization distributions into their corresponding 3D spatial probability distributions based on prior known structural knowledge. This analysis is ideal in cases where the ultrastructure of a cellular structure is known but the sub-structural localization of a particular protein is not. This work will demonstrate how the 2D-to-3D component of SPEED microscopy can be successfully applied to achieve 3D structural and functional sub-diffraction-limited information for 25-300 nm subcellular organelles that meet the rotational symmetry requirement, such as the primary cilium and microtubules. Furthermore, this work will provide comprehensive analyses of this method by using computational simulations which investigate the role of various experimental parameters on the 3D transport pathway error. Lastly, this work will demonstrate that this method can distinguish different types of 3D transport pathway distributions in addition to their locations. / Biology

The nucleus of eukaryotic cells is a vitally important organelle that sequesters the genetic information of the cell, and protects it with the help of two highly evolved structures, the nuclear envelope (NE) and nuclear pore complexes (NPCs). Together, these two structures mediate the bidirectional trafficking of molecules between the nucleus and cytoplasm by forming a barrier. NE transmembrane proteins (NETs) embedded in either the outer nuclear membrane (ONM) or the inner nuclear membrane (INM) play crucial roles in both nuclear structure and functions, including: genome architecture, epigenetics, transcription, splicing, DNA replication, nuclear structure, organization and positioning. Furthermore, numerous human diseases are associated with mutations and mislocalization of NETs on the NE. There are still many fundamental questions that are unresolved with NETs, but we focused on two major questions: First, the localization and transport rate of NETs, and second, the transport route taken by NETs to reach the INM. Since NETs are involved with many of the mechanisms used to maintain cellular homeostasis, it is important to quantitatively determine the spatial locations of NETs along the NE to fully understand their role in these vital processes. However, there are limited available approaches for this task, and moreover, these methods provide no information about the translocation rates of NETs between the two membranes. Furthermore, while the trafficking of soluble proteins between the cytoplasm and the nucleus has been well studied over the years, the path taken by NETs into the nucleus remains in dispute. At least four distinct models have been proposed to suggest how transmembrane proteins destined for the INM cross the NE through NPC-dependent or NPC-independent mechanisms, based on specific features found on the soluble domains of INM proteins. In order to resolve these two major questions, it is necessary to employ techniques with the capabilities to observe these dynamics at the nanoscale. Current experimental techniques are unable to break the temporal and spatial resolution barriers required to study these phenomena. Therefore, we developed and modified single-molecule techniques to answer these questions. First, to study the distribution of NETs on the NE, we developed a new single-molecule microscopy method called single-point single-molecule fluorescence recovery after photobleaching (smFRAP), which is able to provide spatial resolution <10 nm and, furthermore, provide previously unattainable information about NET translocation rates from the ONM to INM. Secondly, to examine the transport route used by NETs destined for the INM, we used a single-molecule microscopy technique previously developed in our lab called single-point edge-excitation sub-diffraction (SPEED) microscopy, which provides spatio-temporal resolution of <10 nm precision and 0.4 ms detection time. The major findings from my doctoral research work can be classified into two categories: (i) Technical developments to study NETs in vivo, and (ii) biological findings from employing these microscopy techniques. In regards to technical contributions, we created and validated of a new single-molecule microscopy method, smFRAP, to accurately determine the localization and distribution ratios of NETs on both the ONM and INM in live cells. Second, we adapted SPEED microscopy to study transmembrane protein translocation in vivo. My work has also contributed four main biological findings to the field: first, we determined the in vivo translocation rates for lamin-B receptor (LBR), a major INM protein found in the nucleus of cells. Second, we verified the existence of peripheral channels in the scaffolding of NPCs and, for the first time, directly observed the transit of INM proteins through these channels in live cells. Third, our research has elucidated the roles that both the nuclear localization signal (NLS) and intrinsically disordered (ID) domains play in INM protein transport. Finally, my work has elucidated which transport routes are used by NETs destined to localize in the INM. / Biology

<p dir="ltr">This dissertation encompasses three studies in distinct yet impactful domains: B2B marketing, real-time video super-resolution (VSR), and smart office document routing systems. In the B2B marketing sphere, the study addresses the extended buying cycle by developing an algorithm for customer data aggregation and employing a CatBoost model to predict potential purchases with 91% accuracy. This approach enables the identification of high-potential<br>customers for targeted marketing campaigns, crucial for optimizing marketing efforts.<br>Transitioning to multimedia enhancement, the dissertation presents a lightweight recurrent network for real-time VSR. Developed for applications requiring high-quality video with low latency, such as video conferencing and media playback, this model integrates an optical flow estimation network for motion compensation and leverages a hidden space for the propagation of long-term information. The model demonstrates high efficiency in VSR. A<br>comparative analysis of motion estimation techniques underscores the importance of minimizing information loss.<br>The evolution towards smart office environments underscores the importance of an efficient document routing system, conceptualized as an online class-incremental image classification challenge. This research introduces a one-versus-rest parametric classifier, complemented by two updating algorithms based on passive-aggressiveness, and adaptive thresholding methods to manage low-confidence predictions. Tested on 710 labeled real document<br>images, the method reports a cumulative accuracy rate of approximately 97%, showcasing the effectiveness of the chosen aggressiveness parameter through various experiments.</p>

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Correlative light and electron microscopy (CLEM) is a powerful approach to investigate the molecular ultrastructure of labeled cell compartments. However, quantitative CLEM studies are rare, mainly due to small sample sizes and the sensitivity of fluorescent proteins to strong fixatives and contrasting reagents for EM. Here, we show that fusion of a self-labeling protein to insulin allows for the quantification of age-distinct insulin granule pools in pancreatic beta cells by a combination of super resolution and transmission electron microscopy on Tokuyasu cryosections. In contrast to fluorescent proteins like GFP organic dyes covalently bound to self-labeling proteins retain their fluorescence also in epoxy resin following high pressure freezing and freeze substitution, or remarkably even after strong chemical fixation. This enables for the assessment of age-defined granule morphology and degradation. Finally, we demonstrate that this CLEM protocol is highly versatile, being suitable for single and dual fluorescent labeling and detection of different proteins with optimal ultrastructure preservation and contrast.

Le cerveau est composé de neurones et de cellules gliales qui jouent un rôle de soutien et de protection du réseau cellulaire. L’espace extra-cellulaire (ECS) correspond à l’espace qui existe entre ces cellules. Les modifications de sa structure peuvent dépendre de plusieurs paramètres comme l’âge, l’apprentissage ou les maladies neuro-dégénératives. Le volume de l’ECS correspond à environ 20$%$ du volume total du cerveau et les neurotransmetteurs et autres molécules circulent dans cet espace pour assurer une communication neuronale optimale. Cependant, les dimensions et la viscosité locale de cet espace restent encore mal-connues. L’ECS est composé entre autres de protéoglycans, de glycoaminoglycans (acide hyaluronique…) et de fluide cérébrospinal. Nous avons proposé dans cette thèse une stratégie pour mesurer les dimensions et les propriétés rhéologiques de l’espace extra-cellulaire de tranches de cerveaux de rats maintenue en vie à l’aide du suivi de nanotubes de carbone individuels luminescents. Pour ces applications, nous avons étudier la biocompatibilité et le rapport signal sur bruit de nos échantillons de nanotubes afin de les détecter en profondeur dans les tranches de cerveaux et de pouvoir mesurer leurs propriétés de diffusion. / The brain is mainly composed of neurons which ensure neuronal communication and glialcells which play a role in supporting and protecting the neural network. The extracellular space corresponds to the space that exists between all these cells and represents around 20 %of the whole brain volume. In this space, neurotransmitters and other molecules circulate into ensure optimal neuronal functioning and communication. Its complex organization whichis important to ensure proper functioning of the brain changes during aging, learning or neurodegenerative diseases. However, its local dimensions and viscosity are still poorly known.To understand these key parameters, in this thesis, we developed a strategy based on the tracking of single luminescent carbon nanotubes. We applied this strategy to measure the structural and viscous properties of the extracellular space of living rodent brains slices at the nanoscale. The organization of the manuscript is as follows. After an introduction of the photoluminescence properties of carbon nanotubes, we present the study that allowed us to select the optimal nanotube encapsulation protocol to achieve our biological applications. We also present a quantitative study describing the temperature increase of the sample when laser irradiations at different wavelengths are used to detect single nanotubes in a brain slice.Thanks to a fine analysis of the singular diffusion properties of carbon nanotubes in complex environments, we then present the strategy set up to reconstruct super-resolved maps (i.e. with resolution below the diffraction limit) of the brain extracellular space morphology.We also show that two local properties of this space can be extracted : a structural complexity parameter (tortuosity) and the fluid’s in situ viscosity seen by the nanotubes. This led us to propose a methodology allowing to model the viscosity in situ that would be seen, not by the nanotubes,but by any molecule of arbitrary sizes to simulate those intrinsically present or administered in the brain for pharmacological treatments. Finally, we present a strategy to make luminescent ultra-short carbon nanotubes that are not intrinsically luminescent and whose use could be a complementary approach to measure the local viscosity of the extracellular space of the brain.

There are many clinical situations where magnetic resonance imaging (MRI) is preferable over other imaging modalities, while the major disadvantage is the relatively long scan time. Due to limited resources, this means that not all patients can be offered an MRI scan, even though it could provide crucial information. It can even be deemed unsafe for a critically ill patient to undergo the examination. In MRI, there is a trade-off between resolution, signal-to-noise ratio (SNR) and the time spent gathering data. When time is of utmost importance, we seek other methods to increase the resolution while preserving SNR and imaging time. In this work, I have studied one of the most promising methods for this task. Namely, constructing super-resolution algorithms to learn the mapping from a low resolution image to a high resolution image using convolutional neural networks. More specifically, I constructed networks capable of transferring high frequency (HF) content, responsible for details in an image, from one kind of image to another. In this context, contrast or weight is used to describe what kind of image we look at. This work only explores the possibility of transferring HF content from T1-weighted images, which can be obtained quite quickly, to T2-weighted images, which would take much longer for similar quality. By doing so, the hope is to contribute to increased efficacy of MRI, and reduce the problems associated with the long scan times. At first, a relatively simple network was implemented to show that transferring HF content between contrasts is possible, as a proof of concept. Next, a much more complex network was proposed, to successfully increase the resolution of MR images better than the commonly used bicubic interpolation method. This is a conclusion drawn from a test where 12 participants were asked to rate the two methods (p=0.0016) Both visual comparisons and quality measures, such as PSNR and SSIM, indicate that the proposed network outperforms a similar network that only utilizes images of one contrast. This suggests that HF content was successfully transferred between images of different contrasts, which improves the reconstruction process. Thus, it could be argued that the proposed multi-contrast model could decrease scan time even further than what its single-contrast counterpart would. Hence, this way of performing multi-contrast super-resolution has the potential to increase the efficacy of MRI.