@unpublished{18874, abstract = {Despite extensive research since the community learned about adversarial examples 10 years ago, we still do not know how to train high-accuracy classifiers that are guaranteed to be robust to small perturbations of their inputs. Previous works often argued that this might be because no classifier exists that is robust and accurate at the same time. However, in computer vision this assumption does not match reality where humans are usually accurate and robust on most tasks of interest. We offer an alternative explanation and show that in certain settings robust generalization is only possible with unrealistically large amounts of data. More precisely we find a setting where a robust classifier exists, it is easy to learn an accurate classifier, yet it requires an exponential amount of data to learn a robust classifier. Based on this theoretical result, we explore how well robust classifiers generalize on datasets such as CIFAR-10. We come to the conclusion that on this datasets, the limitation of current robust models also lies in the generalization, and that they require a lot of data to do well on the test set. We also show that the problem is not in the expressiveness or generalization capabilities of current architectures, and that there are low magnitude features in the data which are useful for non-robust generalization but are not available for robust classifiers.}, author = {Prach, Bernd and Lampert, Christoph}, booktitle = {arXiv}, title = {{Intriguing properties of robust classification}}, doi = {10.48550/arXiv.2412.04245}, year = {2024}, } @inproceedings{17634, abstract = {System behaviors are traditionally evaluated through binary classifications of correctness, which do not suffice for properties involving quantitative aspects of systems and executions. Quantitative automata offer a more nuanced approach, mapping each execution to a real number by incorporating weighted transitions and value functions generalizing acceptance conditions. In this paper, we introduce QuAK, the first tool designed to automate the analysis of quantitative automata. QuAK currently supports a variety of quantitative automaton types, including Inf, Sup, LimInf, LimSup, LimInfAvg, and LimSupAvg automata, and implements decision procedures for problems such as emptiness, universality, inclusion, equivalence, as well as for checking whether an automaton is safe, live, or constant. Additionally, QuAK is able to compute extremal values when possible, construct safety-liveness decompositions, and monitor system behaviors. We demonstrate the effectiveness of QuAK through experiments focusing on the inclusion, constant-function check, and monitoring problems.}, author = {Chalupa, Marek and Henzinger, Thomas A and Mazzocchi, Nicolas Adrien and Sarac, Naci E}, booktitle = {12th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation}, isbn = {9783031753862}, issn = {1611-3349}, location = {Crete, Greece}, pages = {3--20}, publisher = {Springer Nature}, title = {{QuAK: Quantitative Automata Kit}}, doi = {10.1007/978-3-031-75387-9_1}, volume = {15222}, year = {2024}, } @inproceedings{18121, abstract = {It is known that sparsity can improve interpretability for deep neural networks. However, existing methods in the area either require networks that are pre-trained with sparsity constraints, or impose sparsity after the fact, altering the network’s general behavior. In this paper, we demonstrate, for the first time, that sparsity can instead be incorporated into the interpretation process itself, as a sample-specific preprocessing step. Unlike previous work, this approach, which we call SPADE, does not place constraints on the trained model and does not affect its behavior during inference on the sample. Given a trained model and a target sample, SPADE uses sample-targeted pruning to provide a "trace" of the network’s execution on the sample, reducing the network to the most important connections prior to computing an interpretation. We demonstrate that preprocessing with SPADE significantly increases the accuracy of image saliency maps across several interpretability methods. Additionally, SPADE improves the usefulness of neuron visualizations, aiding humans in reasoning about network behavior. Our code is available at https://github.com/IST-DASLab/SPADE.}, author = {Moakhar, Arshia Soltani and Iofinova, Eugenia B and Frantar, Elias and Alistarh, Dan-Adrian}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, issn = {2640-3498}, location = {Vienna, Austria}, pages = {45955--45987}, publisher = {ML Research Press}, title = {{SPADE: Sparsity-guided debugging for deep neural networks}}, volume = {235}, year = {2024}, } @article{15297, abstract = {Populations evolve by accumulating advantageous mutations. Every population has some spatial structure that can be modeled by an underlying network. The network then influences the probability that new advantageous mutations fixate. Amplifiers of selection are networks that increase the fixation probability of advantageous mutants, as compared to the unstructured fully-connected network. Whether or not a network is an amplifier depends on the choice of the random process that governs the evolutionary dynamics. Two popular choices are Moran process with Birth-death updating and Moran process with death-Birth updating. Interestingly, while some networks are amplifiers under Birth-death updating and other networks are amplifiers under death-Birth updating, so far no spatial structures have been found that function as an amplifier under both types of updating simultaneously. In this work, we identify networks that act as amplifiers of selection under both versions of the Moran process. The amplifiers are robust, modular, and increase fixation probability for any mutant fitness advantage in a range r ∈ (1, 1.2). To complement this positive result, we also prove that for certain quantities closely related to fixation probability, it is impossible to improve them simultaneously for both versions of the Moran process. Together, our results highlight how the two versions of the Moran process differ and what they have in common.}, author = {Svoboda, Jakub and Joshi, Soham Shrikant and Tkadlec, Josef and Chatterjee, Krishnendu}, issn = {1553-7358}, journal = {PLoS Computational Biology}, number = {3}, publisher = {Public Library of Science}, title = {{Amplifiers of selection for the Moran process with both Birth-death and death-Birth updating}}, doi = {10.1371/journal.pcbi.1012008}, volume = {20}, year = {2024}, } @article{18703, abstract = {Spatial games provide a simple and elegant mathematical model to study the evolution of cooperation in networks. In spatial games, individuals reside in vertices, adopt simple strategies, and interact with neighbors to receive a payoff. Depending on their own and neighbors’ payoffs, individuals can change their strategy. The payoff is determined by the Prisoners’ Dilemma, a classical matrix game, where players cooperate or defect. While cooperation is the desired behavior, defection provides a higher payoff for a selfish individual. There are many theoretical and empirical studies related to the role of the network in the evolution of cooperation. However, the fundamental question of whether there exist networks that for low initial cooperation rate ensure a high chance of fixation, i.e., cooperation spreads across the whole population, has remained elusive for spatial games with strong selection. In this work, we answer this fundamental question in the affirmative by presenting network structures that ensure high fixation probability for cooperators in the strong selection regime. Besides, our structures have many desirable properties: (a) they ensure the spread of cooperation even for a low initial density of cooperation and high temptation of defection, (b) they have constant degrees, and (c) the number of steps, until cooperation spreads, is at most quadratic in the size of the network.}, author = {Svoboda, Jakub and Chatterjee, Krishnendu}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, number = {50}, publisher = {National Academy of Sciences}, title = {{Density amplifiers of cooperation for spatial games}}, doi = {10.1073/pnas.2405605121}, volume = {121}, year = {2024}, } @article{14687, abstract = {The short history of research on Li-O2 batteries has seen a remarkable number of mechanistic U-turns over the years. From the initial use of carbonate electrolytes, that were then found to be entirely unsuitable, to the belief that (su)peroxide was solely responsible for degradation, before the more reactive singlet oxygen was found to form, to the hypothesis that capacity depends on a competing surface/solution mechanism before a practically exclusive solution mechanism was identified. Herein, we argue for an ever-fresh look at the reported data without bias towards supposedly established explanations. We explain how the latest findings on rate and capacity limits, as well as the origin of side reactions, are connected via the disproportionation (DISP) step in the (dis)charge mechanism. Therefrom, directions emerge for the design of electrolytes and mediators on how to suppress side reactions and to enable high rate and high reversible capacity.}, author = {Jethwa, Rajesh B and Mondal, Soumyadip and Pant, Bhargavi and Freunberger, Stefan Alexander}, issn = {1521-3773}, journal = {Angewandte Chemie International Edition}, keywords = {General Chemistry, Catalysis}, number = {28}, publisher = {Wiley}, title = {{To DISP or not? The far‐reaching reaction mechanisms underpinning Lithium‐air batteries}}, doi = {10.1002/anie.202316476}, volume = {63}, year = {2024}, } @article{13044, abstract = {Singlet oxygen (1O2) formation is now recognised as a key aspect of non-aqueous oxygen redox chemistry. For identifying 1O2, chemical trapping via 9,10-dimethylanthracene (DMA) to form the endoperoxide (DMA-O2) has become the mainstay method due to its sensitivity, selectivity, and ease of use. While DMA has been shown to be selective for 1O2, rather than forming DMA-O2 with a wide variety of potentially reactive O-containing species, false positives might hypothetically be obtained in the presence of previously overlooked species. Here, we first give unequivocal direct spectroscopic proof by the 1O2-specific near infrared (NIR) emission at 1270 nm for the previously proposed 1O2 formation pathways, which centre around superoxide disproportionation. We then show that peroxocarbonates, common intermediates in metal-O2 and metal carbonate electrochemistry, do not produce false-positive DMA-O2. Moreover, we identify a previously unreported 1O2-forming pathway through the reaction of CO2 with superoxide. Overall, we give unequivocal proof for 1O2 formation in non-aqueous oxygen redox and show that chemical trapping with DMA is a reliable method to assess 1O2 formation.}, author = {Mondal, Soumyadip and Jethwa, Rajesh B and Pant, Bhargavi and Hauschild, Robert and Freunberger, Stefan Alexander}, issn = {1364-5498}, journal = {Faraday Discussions}, keywords = {Physical and Theoretical Chemistry}, pages = {175--189}, publisher = {Royal Society of Chemistry}, title = {{Singlet oxygen in non-aqueous oxygen redox: Direct spectroscopic evidence for formation pathways and reliability of chemical probes}}, doi = {10.1039/d3fd00088e}, volume = {248}, year = {2024}, } @phdthesis{17465, abstract = {In the modern age of machine learning, artificial neural networks have become an integral part of many practical systems. One of the key ingredients of the success of the deep learning approach is recent computational advances which allowed the training of models with billions of parameters on large-scale data. Such over-parameterized and data-hungry regimes pose a challenge for the theoretical analysis of modern models since “classical” statistical wisdom is no longer applicable. In this view, it is paramount to extend or develop new machinery that will allow tackling the neural network analysis under new challenging asymptotic regimes, which is the focus of this thesis. Large neural network systems are usually optimized via “local” search algorithms, such as stochastic gradient descent (SGD). However, given the high-dimensional nature of the parameter space, it is a priori not clear why such a crude “local” approach works so remarkably well in practice. We take a step towards demystifying this phenomenon by showing that the landscape of the SGD training dynamics exhibits a few beneficial properties for the optimization. First, we show that along the SGD trajectory an over-parameterized network is dropout stable. The emergence of dropout stability allows to conclude that the minima found by SGD are connected via a continuous path of small loss. This in turn means that the high-dimensional landscape of the neural network optimization problem is provably not so unfavourable to gradient-based training, due to mode connectivity. Next, we show that SGD for an over-parameterized network tends to find solutions that are functionally more “simple”. This in turn means that the SGD minima are more robust, since a less complicated solution will less likely overfit the data. More formally, for a prototypical example of a wide two-layer ReLU network on a 1d regression task we show that the SGD algorithm is implicitly selective in its choice of an interpolating solution. Namely, at convergence the neural network implements a piece-wise linear function with the number of linear regions depending only on the amount of training data. This is in contrast to a “smooth”-like behaviour which one would expect given such a severe over-parameterization of the model. Diverging from the generic supervised setting of classification and regression problems, we analyze an auto-encoder model that is commonly used for representation learning and data compression. Despite the wide applicability of the auto-encoding paradigm, the theoretical understanding of their behaviour is limited even in the simplistic shallow case. The related work is restricted to extreme asymptotic regimes in which the auto-encoder is either severely over-parameterized or under-parameterized. In contrast, we provide a tight characterization for the 1-bit compression of Gaussian signals in the challenging proportional regime, i.e., the input dimension and the size of the compressed representation obey the same asymptotics. We also show that gradient-based methods are able to find a globally optimal solution and that the predictions made for Gaussian data extrapolate beyond - to the case of compression of natural images. Next, we relax the Gaussian assumption and study more structured input sources. We show that the shallow model is sometimes agnostic to the structure of the data vii which results in a Gaussian-like behaviour. We prove that making the decoding component slightly less shallow is already enough to escape the “curse” of Gaussian performance. }, author = {Shevchenko, Aleksandr}, issn = {2663-337X}, pages = {232}, publisher = {Institute of Science and Technology Austria}, title = {{High-dimensional limits in artificial neural networks}}, doi = {10.15479/at:ista:17465}, year = {2024}, } @phdthesis{17881, abstract = {This work can be broadly classified into the study of critical phenomena in a one dimensional array of Josephson junctions. While we study quantum criticality when the array is in thermal equilibrium at zero bias, the non-equilibrium study involves understanding the bistability of the array at a critical non-zero bias. This work furthers our knowledge in understanding quantum critical behaviour at finite temperatures in a one dimensional Josephson array, while also establishing relaxation behaviour dual to that observed in a single Josephson junction. Chapter 1 briefly introduces the model to understand superconductor-insulator phase transition in a one dimensional Josephson array and points out the state of the field from where we started our zero-bias experiments. In this context it discusses the phase-charge duality observed in a Josephson array and its dual hysteretic behaviour to that of a single junction, setting the ground for our non-equilibrium study of the array. Chapter 2 shows the experimental setup and the chip layout of the device we measured. In chapter 3 we show that, unlike the typical quantum-critical broadening scenario, in one dimensional Josephson arrays temperature dramatically shifts the critical region. This shift leads to a regime of superconductivity at high temperature, arising from the melted zero-temperature insulator. Our results quantitatively explain the low-temperature onset of superconductivity in nominally insulating regimes, and the transition to the strongly insulating phase. We further present, to our knowledge, the first understanding of the onset of anomalous-metallic resistance saturation [30]. This work demonstrates a non-trivial interplay between thermal effects and quantum criticality. A practical consequence is that, counterintuitively, the coherence of high-impedance quantum circuits is expected to be stabilized by thermal fluctuations. In chapter 4, we show relaxation oscillations in a current-biased one dimensional array of Josephson junctions. These oscillations are well described by a circuit model, dual to the ordinary Josephson relaxation oscillations [72]. Injection locking these oscillations results in current plateaux. The relaxation step is found to obey a characteristic self-consistent relation, suggesting that it is governed by overheating effects. Chapter 5 describes the various checks and analysis we performed to support our conclusions made in chapters 3 and 4. Finally, chapter 6 describes the nanofabrication steps and the finite element electromagnetic simulations we performed to fabricate our devices.}, author = {Mukhopadhyay, Soham}, isbn = {978-3-99078-043-5}, issn = {2663-337X}, pages = {82}, publisher = {Institute of Science and Technology Austria}, title = {{Thermal effects in one dimensional Josephson chains}}, doi = {10.15479/at:ista:17881}, year = {2024}, } @inproceedings{17469, abstract = {Autoencoders are a prominent model in many empirical branches of machine learning and lossy data compression. However, basic theoretical questions remain unanswered even in a shallow two-layer setting. In particular, to what degree does a shallow autoencoder capture the structure of the underlying data distribution? For the prototypical case of the 1-bit compression of sparse Gaussian data, we prove that gradient descent converges to a solution that completely disregards the sparse structure of the input. Namely, the performance of the algorithm is the same as if it was compressing a Gaussian source - with no sparsity. For general data distributions, we give evidence of a phase transition phenomenon in the shape of the gradient descent minimizer, as a function of the data sparsity: below the critical sparsity level, the minimizer is a rotation taken uniformly at random (just like in the compression of non-sparse data); above the critical sparsity, the minimizer is the identity (up to a permutation). Finally, by exploiting a connection with approximate message passing algorithms, we show how to improve upon Gaussian performance for the compression of sparse data: adding a denoising function to a shallow architecture already reduces the loss provably, and a suitable multi-layer decoder leads to a further improvement. We validate our findings on image datasets, such as CIFAR-10 and MNIST.}, author = {Kögler, Kevin and Shevchenko, Aleksandr and Hassani, Hamed and Mondelli, Marco}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, location = {Vienna, Austria}, pages = {24964--25015}, publisher = {ML Research Press}, title = {{Compression of structured data with autoencoders: Provable benefit of nonlinearities and depth}}, volume = {235}, year = {2024}, } @unpublished{18057, abstract = {We report relaxation oscillations in a one-dimensional array of Josephson junctions. The oscillations are circuit-dual to those ordinarily observed in single junctions. The dual circuit quantitatively accounts for temporal dynamics of the array, including the dependence on biasing conditions. Injection locking the oscillations results in well-developed current plateaux. A thermal model explains the relaxation step of the oscillations.}, author = {Mukhopadhyay, Soham and Lancheros Naranjo, Diego A and Senior, Jorden L and Higginbotham, Andrew P}, booktitle = {arXiv}, title = {{Dual relaxation oscillations in a Josephson junction array}}, doi = {10.48550/arXiv.2408.07829}, year = {2024}, } @article{17890, abstract = {Our understanding of the molecular pathways that regulate oogenesis and define cellular identity in the Arthropod female reproductive system and the extent of their conservation is currently very limited. This is due to the focus on model systems, including Drosophila and Daphnia, which do not reflect the observed diversity of morphologies, reproductive modes, and sex chromosome systems. We use single-nucleus RNA and ATAC sequencing to produce a comprehensive single nucleus atlas of the adult Artemia franciscana female reproductive system. We map our data to the Fly Cell Atlas single-nucleus dataset of the Drosophila melanogaster ovary, shedding light on the conserved regulatory programs between the two distantly related Arthropod species. We identify the major cell types known to be present in the Artemia ovary, including germ cells, follicle cells, and ovarian muscle cells. Additionally, we use the germ cells to explore gene regulation and expression of the Z chromosome during meiosis, highlighting its unique regulatory dynamics and allowing us to explore the presence of meiotic sex chromosome silencing in this group.}, author = {Elkrewi, Marwan N and Vicoso, Beatriz}, issn = {1553-7404}, journal = {PLoS Genetics}, number = {8}, publisher = {Public Library of Science}, title = {{Single-nucleus atlas of the Artemia female reproductive system suggests germline repression of the Z chromosome}}, doi = {10.1371/journal.pgen.1011376}, volume = {20}, year = {2024}, } @article{15009, abstract = {Since the commercialization of brine shrimp (genus Artemia) in the 1950s, this lineage, and in particular the model species Artemia franciscana, has been the subject of extensive research. However, our understanding of the genetic mechanisms underlying various aspects of their reproductive biology, including sex determination, is still lacking. This is partly due to the scarcity of genomic resources for Artemia species and crustaceans in general. Here, we present a chromosome-level genome assembly of A. franciscana (Kellogg 1906), from the Great Salt Lake, United States. The genome is 1 GB, and the majority of the genome (81%) is scaffolded into 21 linkage groups using a previously published high-density linkage map. We performed coverage and FST analyses using male and female genomic and transcriptomic reads to quantify the extent of differentiation between the Z and W chromosomes. Additionally, we quantified the expression levels in male and female heads and gonads and found further evidence for dosage compensation in this species.}, author = {Bett, Vincent K and Macon, Ariana and Vicoso, Beatriz and Elkrewi, Marwan N}, issn = {1759-6653}, journal = {Genome Biology and Evolution}, number = {1}, publisher = {Oxford University Press}, title = {{Chromosome-level assembly of Artemia franciscana sheds light on sex chromosome differentiation}}, doi = {10.1093/gbe/evae006}, volume = {16}, year = {2024}, } @phdthesis{17119, abstract = {Genomes are shaped by natural selection at the level of the organism, as genomic variants that have a beneficial effect on the viability or fecundity of their carriers are on average expected to be passed on to more offspring than less beneficial alleles. However, selection also favors genomic variants that drive their own transmission to the next generation above the mendelian expectation of 50 percent in heterozygotes, even if these self-promoting variants are less beneficial to the organism than other variants at the same locus. Such variants, called meiotic drivers, are found in diverse taxa, and often impose fitness costs on their host organisms. As meiotic drivers often require multiple genes and sequences for transmission ratio distortion, they are often found in regions of low recombination, such as inversions, which prevent their recombination with the non-driving homologous regions. Reduced recombination rates are expected to lead to the accumulation of deleterious mutations, which may affect hundreds of genes trapped in the inversions of meiotic drivers. Although the observed fitness costs of self-promoting haplotypes are thought to possibly reflect sequence degeneration, no study has systematically investigated the level of degeneration on a meiotic driver. Further, the low rates of recombination between driving and non-driving haplotypes have limited the power of traditional genetic studies in uncovering the gene content of meiotic drivers, and made the the identification of the genes causing transmission ratio distortion difficult. After an introduction to meiotic drivers in Chapter 1, this thesis presents three studies that make use of next generation sequencing data to characterize the sequence and expression evolution of genes on the t-haplotype, a large and ancient meiotic driver in house mice that is transmitted to up to 100% of the offspring in males heterozygous for it. Chapter 2 presents a comprehensive assessment of the t-haplotype’s sequence evolution, which shows signs of sequence degeneration counteracted by occasional recombination with the non-driving homolog over large parts of the meiotic driver, proposing an explanation for its long-term survival. Chapter 3 investigates the sequence and expression evolution of genes on the t-haplotype, and finds widespread expression and copy number changes and signs of less efficient purifying selection compared to the genes on the non-driving homolog. Further, this chapter finds candidates for involvment in drive: two positively selected genes on the t-haplotype, and the discovery of a t-specific gene duplicate, which was gained from another chromosome, and which acquired novel sequence and testis-specific expression on the t-haplotype. Finally, Chapter 4 provides unprecedented insights into the gene expression landscape in testes of t-carrier mice, using single nucleus sequencing. Cell-resolved RNA-sequencing allows the comparison of expression in spermatids carrying or not carrying the t-haplotype as well as the timing of t-haplotype-induced expression changes along spermatogenesis. This study shows the timing of previously found drive-associated genes, and uncovers novel candidate genes and biological processes that may underlie the complex biology of transmission ratio distortion of the t-haplotype. Chapter 5 synthesizes the findings of the three studies, and discusses them in the context of the current state of meiotic drive research.}, author = {Kelemen, Réka K}, isbn = {978-3-99078-039-8}, issn = {2663-337X}, keywords = {meiotic driver, neofunctionalization, single nucleus sequencing}, pages = {105}, publisher = {Institute of Science and Technology Austria}, title = {{Characterizing the sequence and expression evolution of the t-haplotype, a model meiotic driver}}, doi = {10.15479/at:ista:17119}, year = {2024}, } @phdthesis{18477, abstract = {ADAR1 is broadly expressed across various tissues and is vital in regulating pathways associated with innate immune responses. ADAR1 marks double-stranded RNA as "self" through its A-to-I editing activity, effectively repressing autoimmunity and maintaining immune tolerance. This editing process has been detected at millions of sites across the human genome. However, the mechanism underlying ADAR1's substrate selectivity properties remains largely unclear, with much of the current knowledge derived from comparisons to its more extensively studied homolog, ADAR2. By studying ADAR1 in complex with its RNA substrates and applying a combination of biochemical techniques and structural studies using CryoEM, we aim to gain a more comprehensive understanding of the substrate selectivity characteristics of ADAR1. In this thesis, the purification protocol for ADAR1 was successfully optimized, resulting in the first report in the literature to achieve high protein purity and activity. This advancement enabled the investigation of complex formation between ADAR1 and various RNA substrates, leading to the identification of optimal conditions for preparing the cryoEM sample. However, despite comprehensive optimization of the cryo-EM conditions, the resulting data lacked the desired quality, highlighting the need for similar rigorous optimization of the RNA substrates to facilitate structural studies of the ADAR1-RNA complex. The study was complemented by AlphaFold predictions, which provided some insights into this mechanism. Moreover, during this project I established a collaboration with a research group focused on studying ADAR homologs. Notably ADAR homologs were identified in bivalve species, and it was further demonstrated that ADAR and its A-to-I editing activity are upregulated in Pacific oysters during infections with Ostreid herpesvirus-1—a highly infectious virus that leads to significant losses in oyster populations globally. I successfully purified oyster ADAR and prepared in vitro edited RNA for nanopore sequencing—a direct sequencing technology capable of detecting modified nucleotides without the need for reverse transcription. The collaborators initiated optimization of this nanopore-based approach. However, current technological limitations still constrain the reliable detection of modified nucleotides. The project also examined the impact of RNA editing on RNA binding and filament formation by MDA5, a key cytosolic dsRNA sensor that triggers an interferon response. A primary target of ADAR1's editing activity is RNA derived from repetitive elements present in the genome, particularly Alu elements forming double-stranded RNA. When unedited, these RNA sequences are recognized by MDA5. However, the mechanisms by which MDA5 interacts with Alu RNAs, as well as the role of A-to-I editing in influencing this binding, are still not well understood. The interaction between MDA5 and Alu elements, was successfully established. This was achieved through the testing of different RNA variants and the evaluation of filament formation using binding techniques and electron microscopy imaging. This groundwork has set the conditions for further evaluation using CryoEM. Furthermore, the effects of A-to-I editing on the binding properties of MDA5 with Alu RNA were investigated. Given the recent research that has provided new insights into MDA5's interaction with dsRNA, it is essential to revise the experimental setup to integrate these findings before moving forward with the CryoEM sample analysis.}, author = {Kaczmarek, Beata M}, isbn = {978-3-99078-045-9}, issn = {2663-337X}, pages = {124}, publisher = {Institute of Science and Technology Austria}, title = {{Biochemical and structural insights into ADAR1 RNA editing}}, doi = {10.15479/at:ista:18477}, year = {2024}, } @phdthesis{15352, abstract = {Epilepsy affects about 50 to 65 million people globally. It summarizes a spectrum of neurological disorders that have in common a hyperactivity of the neuronal network resulting in seizures. A common assumption is that an imbalance between neuronal excitation and inhibition is a key mechanism in seizure generation and epileptogeneisis. In at least one-third of the patients, current therapies have proven unsuccessful in treating seizure progression. One potential reason could be that the therapies only focus on neurons. Recent studies suggest that neuronal hyperactivity causes a microglial response, which reinstates brain homeostasis. Additionally, interactions between microglia and neurons have been shown to inhibit neuronal firing and dampen seizure activity. However, the exact relationship between microglia and seizure progression in epilepsy is yet to be elucidated. A main bottleneck is that several studies investigate microglia dynamics in ex vivo slice models, which can severely affect the microglia dynamics due to their rapid response to environmental changes. On the other hand, in vivo studies focus mostly on behavior characterization of the epileptic seizure phenotype and their long-term consequences on microglia activity leaving out the direct consequences of acute seizure activity on microglia dynamics. Here, we perform a pilot study to combine electroencephalography (EEG) and in vivo live imaging to directly monitor and correlate the onset of seizure activity with microglia response. To induce seizures, we take advantage of the kainic acid (KA) model, which represents similar neuropathological and electroencephalographic features seen in human patients with temporal lobe epilepsy (TLE). After confirmation of induction of the seizure and microglia activity in the hippocampus as a focal point, we investigated whether these changes also reached the primary visual cortex (V1) as a secondary generalized seizure activity. Indeed, we found that microglia changed their morphology at high doses of KA in the V1. Next, we optimized each of the two methodological components: for the EEG recording, our initial attempts under the microscope suffered from extensive electrical noise, which overlaid the actual signal. Thus, we built a customized Faraday-cage and confirmed that the signal-to-noise ratio was sufficiently reduced to be able to record brain oscillatory activity. For the in vivo live imaging of microglia, we had to optimize the imaging parameters, so that we would be able to detect microglial processes in a sufficient resolution to track their process changes. Finally, we combined both methodologies with the KA model. We confirmed that KA induced seizure activity and found first indication that those correlate with microglia volume changes. Overall, we have developed a first methodological approach, which allows the analysis of the acute effects of seizure onset on microglia. Future studies will have to continue to optimize the drift during imaging recording and the post-image analysis. }, author = {Murmann, Julie Stefanie}, issn = {2791-4585}, pages = {54}, publisher = {Institute of Science and Technology Austria}, title = {{Investigating acute microglia response to seizure activity in vivo: Combining 2-Photon imaging and EEG recording}}, doi = {10.15479/at:ista:15352}, year = {2024}, } @article{14843, abstract = {The coupling between Ca2+ channels and release sensors is a key factor defining the signaling properties of a synapse. However, the coupling nanotopography at many synapses remains unknown, and it is unclear how it changes during development. To address these questions, we examined coupling at the cerebellar inhibitory basket cell (BC)-Purkinje cell (PC) synapse. Biophysical analysis of transmission by paired recording and intracellular pipette perfusion revealed that the effects of exogenous Ca2+ chelators decreased during development, despite constant reliance of release on P/Q-type Ca2+ channels. Structural analysis by freeze-fracture replica labeling (FRL) and transmission electron microscopy (EM) indicated that presynaptic P/Q-type Ca2+ channels formed nanoclusters throughout development, whereas docked vesicles were only clustered at later developmental stages. Modeling suggested a developmental transformation from a more random to a more clustered coupling nanotopography. Thus, presynaptic signaling developmentally approaches a point-to-point configuration, optimizing speed, reliability, and energy efficiency of synaptic transmission.}, author = {Chen, JingJing and Kaufmann, Walter and Chen, Chong and Arai, Itaru and Kim, Olena and Shigemoto, Ryuichi and Jonas, Peter M}, issn = {1097-4199}, journal = {Neuron}, number = {5}, pages = {755--771.e9}, publisher = {Elsevier}, title = {{Developmental transformation of Ca2+ channel-vesicle nanotopography at a central GABAergic synapse}}, doi = {10.1016/j.neuron.2023.12.002}, volume = {112}, year = {2024}, } @phdthesis{14821, abstract = {The hippocampus is central to memory formation, storage and retrieval over many timescales. Neurons in this brain area are highly selective to spatial position as well as to many other variables of the environment. It is believed that the selectivity patterns of hippocampal neurons reflect the structure of tasks an animal performs. However, especially at timescales longer than a few minutes or hours it is not fully known how these representations evolve, nor how they map to behaviour in the process. In this thesis, I monitored the evolution of hippocampal representations in a novel spatial-associative memory task for rats. Reward locations were associated with global sensory cues (i.e. context); animals had to remember the associations and dig for food in those locations only. I used in vivo electrophysiology to record the activity of the hippocampus dorsal CA1 neurons during the learning period of a few days. I report here a novel and simple method to classify behaviour performance to account for individual variability in learning speed and spurious performance unrelated to true task rule learning. Using this classification I was then able to investigate neural responses on different stages of learning matched across animals. On the first day of learning, I observed a fast formation of single-cell selectivity to task variables which remained stable over days. I also observed that reward tuning was not a single process but dependent on task-related cognitive load. At the population level, a linear decoding approach revealed a hierarchy in the representation of task variables that changed with learning. In the high-dimensional space of population activity, the representation of contexts was specific to each position in the maze, and could thus be better decoded if the position was known. The decoding of position did not improve with knowledge of other variables. As learning progressed, the hippocampal code underwent a reorganisation of high-variance directions in population activity, identified by principal component analysis. I found that dominant dimensions started carrying increasing amounts of information about task context specifically at those positions where it mattered for task performance. When I contrasted this with variables less relevant to task performance (e.g. movement direction), I did not observe differences in decoding quality over positions nor a reduction of dimensionality with learning. Overall, the largest changes in CA1 neural response with task learning happened in a matter of a few trials; over days, changes undetectable in single-cell statistics were responsible for re-structuring the hierarchy of neural representations at the population level; these changes were task-specific and reflected different stages of learning. This indicates that complex task learning may involve different magnitudes of response modulation in CA1, which happen at specific time scales linked to behaviour.}, author = {Chiossi, Heloisa}, issn = {2663-337X}, pages = {89}, publisher = {Institute of Science and Technology Austria}, title = {{Adaptive hierarchical representations in the hippocampus}}, doi = {10.15479/at:ista:14821}, year = {2024}, } @phdthesis{15101, abstract = {The coupling between presynaptic Ca2+ channels and release sensors is a key factor that determines speed and efficacy of synapse transmission. At some excitatory synapses, channel–sensor coupling becomes tighter during development, and tightening is often associated with a switch in the reliance on different Ca2+ channel subtypes. However, the coupling topography at many synapses remains unknown, and it is unclear how it changes during development. To address this question, we analyzed the coupling configuration at the cerebellar basket cell (BC) to Purkinje cell (PC) synapse at different developmental stages, combining biophysical analysis, structural analysis, and modeling. Quantal analysis of BC–PC indicated that release probability decreased, while the number of functional sites increased during development. Although transmitter release persistently relied on P/Q-type Ca2+ channels in the time period postnatal day 7–23, effects of the Ca2+ chelator EGTA and BAPTA applied by intracellular pipette perfusion decreased during development, indicative of tightening of source-sensor coupling. Furthermore, presynaptic action potentials became shorter during development, suggesting reduced efficacy of Ca2+ channel activation. Structural analysis by freeze-fracture replica labeling (FRL) and transmission electron microscopy (EM) indicated that presynaptic P/Q-type Ca2+ channels formed nanoclusters throughout development, whereas docked vesicles were only clustered at later developmental stages. The number of functional release sites correlated better with the AZ number early in development, but match better with the Ca2+ channel cluster number at later stages. Modeling suggested a developmental transformation from a more random to a more clustered coupling nanotopography. Thus, presynaptic signaling developmentally approaches a point-to-point configuration, optimizing speed, reliability, and energy efficiency of synaptic transmission.}, author = {Chen, JingJing}, issn = {2663-337X}, pages = {84}, publisher = {Institute of Science and Technology Austria}, title = {{Developmental transformation of nanodomain coupling between Ca2+ channels and release sensors at a central GABAergic synapse}}, doi = {10.15479/at:ista:15101}, year = {2024}, } @phdthesis{18471, abstract = {Spatial omics technologies are enriching our understanding of complex biological samples, by allowing us to study their molecular composition while preserving the spatial relationships between molecules in their native context. As the field continues to advance, there are technical challenges that need to be addressed in order to take full advantage of the spatial capabilities of these methods. In this work, I present two technical developments that I established for multiplexed error robust FISH (MERFISH) throughout my PhD: (1) pushing the spatial resolution limits to the nanoscale, and (2) adding rich tissue context to the mouse brain transcriptome. To achieve nanoscale resolution with MERFISH in cultured cells, I combined it with stimulated emission depletion (STED) and expansion microscopy (ExM) to achieve a spatial resolution as low as ~20 nm, and explored the compatibility of MERFISH with singlemolecule localization microscopy (SMLM) techniques. To visualize targeted mRNAs in mouse brain tissue, I applied the comprehensive analysis of tissues across scales (CATS) toolbox, which provides an unbiased morphological readout by labeling the extracellular domain. I successfully established this method, which we call CATS-MERFISH-ExM, to work with thick mouse brain slices, being able to extract transcriptomics information with 3D tissue context. CATS-MERFISH-ExM enabled us to identify cell types and further visualize the subcellular distribution of transcripts in mouse brain tissue, shedding light on the neuropil-specific transcriptome. This method provides integrated information on cellular structure and transcriptomes in situ, and could potentially be applied with other modalities, opening new avenues for scientific discovery. }, author = {Agudelo Duenas, Nathalie}, isbn = {978-3-99078-044-2}, issn = {2663-337X}, pages = {97}, publisher = {Institute of Science and Technology Austria}, title = {{Visualizing the neuronal transcriptional landscape with tissue context}}, doi = {10.15479/at:ista:18471}, year = {2024}, }