944 research outputs found

    Neuromodulatory effects on early visual signal processing

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    Understanding how the brain processes information and generates simple to complex behavior constitutes one of the core objectives in systems neuroscience. However, when studying different neural circuits, their dynamics and interactions researchers often assume fixed connectivity, overlooking a crucial factor - the effect of neuromodulators. Neuromodulators can modulate circuit activity depending on several aspects, such as different brain states or sensory contexts. Therefore, considering the modulatory effects of neuromodulators on the functionality of neural circuits is an indispensable step towards a more complete picture of the brain’s ability to process information. Generally, this issue affects all neural systems; hence this thesis tries to address this with an experimental and computational approach to resolve neuromodulatory effects on cell type-level in a well-define system, the mouse retina. In the first study, we established and applied a machine-learning-based classification algorithm to identify individual functional retinal ganglion cell types, which enabled detailed cell type-resolved analyses. We applied the classifier to newly acquired data of light-evoked retinal ganglion cell responses and successfully identified their functional types. Here, the cell type-resolved analysis revealed that a particular principle of efficient coding applies to all types in a similar way. In a second study, we focused on the issue of inter-experimental variability that can occur during the process of pooling datasets. As a result, further downstream analyses may be complicated by the subtle variations between the individual datasets. To tackle this, we proposed a theoretical framework based on an adversarial autoencoder with the objective to remove inter-experimental variability from the pooled dataset, while preserving the underlying biological signal of interest. In the last study of this thesis, we investigated the functional effects of the neuromodulator nitric oxide on the retinal output signal. To this end, we used our previously developed retinal ganglion cell type classifier to unravel type-specific effects and established a paired recording protocol to account for type-specific time-dependent effects. We found that certain retinal ganglion cell types showed adaptational type-specific changes and that nitric oxide had a distinct modulation of a particular group of retinal ganglion cells. In summary, I first present several experimental and computational methods that allow to study functional neuromodulatory effects on the retinal output signal in a cell type-resolved manner and, second, use these tools to demonstrate their feasibility to study the neuromodulator nitric oxide

    Convergence of resistance and evolutionary responses in Escherichia coli and Salmonella enterica co-inhabiting chicken farms in China

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    Sharing of genetic elements among different pathogens and commensals inhabiting same hosts and environments has significant implications for antimicrobial resistance (AMR), especially in settings with high antimicrobial exposure. We analysed 661 Escherichia coli and Salmonella enterica isolates collected within and across hosts and environments, in 10 Chinese chicken farms over 2.5 years using novel data-mining methods. Most isolates within same hosts possessed same clinically relevant AMR-carrying mobile genetic elements (plasmids: 70.6%, transposons: 78%), which also showed recent common evolution. Machine learning revealed known and novel AMR-associated mutations and genes underlying resistance to 28 antimicrobials and primarily associated with resistance in E. coli and susceptibility in S. enterica. Many were essential and affected same metabolic processes in both species, albeit with varying degrees of phylogenetic penetration. Multi-modal strategies are crucial to investigate the interplay of mobilome, resistance and metabolism in cohabiting bacteria, especially in ecological settings where community-driven resistance selection occurs

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Social interactions in bacteria mediated by bacteriocins and horizontal gene transfer

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    Bacteria are highly social organisms that frequently engage in cooperative and competitive interactions to successfully survive and reproduce. Examples include cell-to-cell communication, nutrient scavenging, and chemical warfare. However, the vast majority of our understanding of bacterial sociality has come from the laboratory strains of a small number of gram-negative social evolution model organisms, such as Pseudomonas spp. and Escherichia coli. In my thesis, I aim to expand our understanding of bacterial sociality in natural populations and further across the bacterial tree of life. I do this using two different approaches. Firstly, I use laboratory experiments and sequence analysis to study the evolution and ecology of bacteriocin-mediated competition in natural S. aureus populations, sampled as part of a carriage study on human nasal passages. Theory and laboratory experiments to date have provided extensive evidence that bacteriocin production plays a key role in determining the competitive dynamics of bacterial strains, however evidence from natural populations to support this hypothesis is lacking. I find that inhibitory strains were associated with the propensity to displace competing strains from the nasal cavity, which occurs despite inhibitory activity not being displayed by the majority of strains and targeting interspecific over intraspecific competitors. I also provide evidence for the genetic underpinnings of bacteriocin activity, by identifying five bacteriocin gene clusters associated with inhibition. Secondly, I use a comparative approach to study the role of horizontal gene transfer in stabilising cooperation across bacteria. Bacterial cooperation is typically mediated by the secretion of extracellular public goods, which are costly molecules that provide a fitness benefit to neighbouring cells. Cooperation can be destabilised by the invasion of selfish ‘cheats’ that reap the benefit of public good production without paying a cost. It is widely accepted that horizontal gene transfer, especially via plasmids, can allow cooperators to ‘re-infect’ cheats with the gene for a cooperative trait, thus stabilising cooperation. Although theoretical and experimental studies have provided evidence to support this hypothesis, a comprehensive genomic study that controls for phylogenetic non-independence across species remains to be conducted. The results from our analysis of plasmid genes from 51 diverse bacterial species do not support the cooperation hypothesis across bacteria and are instead supportive of environmental variability as a determining factor in the relationship between horizontal gene transfer and extracellular proteins. Taken together, this thesis provides a body of work that emphasises the importance of testing predictions from theoretical and laboratory experiments in natural populations, and across diverse species

    Relatively Absolute : Relative and Absolute Chronologies in the Neolithic of Southeast Europe

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    Зборник радова на тему апсолутне и релативне хронологије неолитског периода у југоисточној Европи. Географски покрива области од Грчке до Хрватске, а хронолошки период између 7000 и 4500 године пре нове ере. У зборнику су приказани најновији приступи и резултати радиокарбонских анализа и статистички и типолошки модели који побољшавају прецизност резултата

    Online Machine Learning for Inference from Multivariate Time-series

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    Inference and data analysis over networks have become significant areas of research due to the increasing prevalence of interconnected systems and the growing volume of data they produce. Many of these systems generate data in the form of multivariate time series, which are collections of time series data that are observed simultaneously across multiple variables. For example, EEG measurements of the brain produce multivariate time series data that record the electrical activity of different brain regions over time. Cyber-physical systems generate multivariate time series that capture the behaviour of physical systems in response to cybernetic inputs. Similarly, financial time series reflect the dynamics of multiple financial instruments or market indices over time. Through the analysis of these time series, one can uncover important details about the behavior of the system, detect patterns, and make predictions. Therefore, designing effective methods for data analysis and inference over networks of multivariate time series is a crucial area of research with numerous applications across various fields. In this Ph.D. Thesis, our focus is on identifying the directed relationships between time series and leveraging this information to design algorithms for data prediction as well as missing data imputation. This Ph.D. thesis is organized as a compendium of papers, which consists of seven chapters and appendices. The first chapter is dedicated to motivation and literature survey, whereas in the second chapter, we present the fundamental concepts that readers should understand to grasp the material presented in the dissertation with ease. In the third chapter, we present three online nonlinear topology identification algorithms, namely NL-TISO, RFNL-TISO, and RFNL-TIRSO. In this chapter, we assume the data is generated from a sparse nonlinear vector autoregressive model (VAR), and propose online data-driven solutions for identifying nonlinear VAR topology. We also provide convergence guarantees in terms of dynamic regret for the proposed algorithm RFNL-TIRSO. Chapters four and five of the dissertation delve into the issue of missing data and explore how the learned topology can be leveraged to address this challenge. Chapter five is distinct from other chapters in its exclusive focus on edge flow data and introduces an online imputation strategy based on a simplicial complex framework that leverages the known network structure in addition to the learned topology. Chapter six of the dissertation takes a different approach, assuming that the data is generated from nonlinear structural equation models. In this chapter, we propose an online topology identification algorithm using a time-structured approach, incorporating information from both the data and the model evolution. The algorithm is shown to have convergence guarantees achieved by bounding the dynamic regret. Finally, chapter seven of the dissertation provides concluding remarks and outlines potential future research directions.publishedVersio

    Measuring the impact of COVID-19 on hospital care pathways

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    Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted

    Discovering Causal Relations and Equations from Data

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    Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries. Discoveries emerge from observing the world and, when possible, performing interventional studies in the system under study. With the advent of big data and the use of data-driven methods, causal and equation discovery fields have grown and made progress in computer science, physics, statistics, philosophy, and many applied fields. All these domains are intertwined and can be used to discover causal relations, physical laws, and equations from observational data. This paper reviews the concepts, methods, and relevant works on causal and equation discovery in the broad field of Physics and outlines the most important challenges and promising future lines of research. We also provide a taxonomy for observational causal and equation discovery, point out connections, and showcase a complete set of case studies in Earth and climate sciences, fluid dynamics and mechanics, and the neurosciences. This review demonstrates that discovering fundamental laws and causal relations by observing natural phenomena is being revolutionised with the efficient exploitation of observational data, modern machine learning algorithms and the interaction with domain knowledge. Exciting times are ahead with many challenges and opportunities to improve our understanding of complex systems.Comment: 137 page

    Multi-modal and multi-model interrogation of large-scale functional brain networks

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    Existing whole-brain models are generally tailored to the modelling of a particular data modality (e.g., fMRI or MEG/EEG). We propose that despite the differing aspects of neural activity each modality captures, they originate from shared network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct features of brain activity - captured across modalities - to the dynamics unfolding on a macroscopic structural connectome. To jointly predict connectivity, spatiotemporal and transient features of distinct signal modalities, we consider two large-scale models - the Stuart Landau and Wilson and Cowan models - which generate short-lived 40 Hz oscillations with varying levels of realism. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data. We show that both models can represent MEG functional connectivity (FC), functional connectivity dynamics (FCD) and generate MOMs to a comparable degree. This is achieved by adjusting the global coupling and mean conduction time delay and, in the WC model, through the inclusion of balance between excitation and inhibition. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD and MOMs, highlighting the importance of balanced dynamics for the emergence of spatiotemporal and transient patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.4 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns that extended beyond the constraints of the anatomical structure. Finally, we show that both models can generate MOMs with empirical-like properties such as size (number of brain regions engaging in a mode) and duration (continuous time interval during which a mode appears). Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours

    Building better than we know: The residential built environment, trust, social behaviour, biology, and health

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    Over the last decade there has been a renewed interest in identifying exactly how aspects of the residential built environment “get under the skin” and affect the physical health of not only of those who dwell within, but reside and commute among, disorderly and deteriorating neighbourhoods. This thesis is focused on better understanding how aspects of the social environment are crystallised in the residential built environment, and in particular the proximate environmental, behavioural, and perceptual mechanisms that account for how our interaction with the residential built environment modulates both our social behaviour and physical health. Building on Wilson and O’Brien’s evolutionary construct of Community Perception, Chapter 1 reviews the relevant literature from across the evolutionary human sciences, social psychology, applied social epidemiology, and social neuroscience to propose a biologically plausible pathway from the residential built environment to physical health. The empirical chapters (Chapters 2 to 4), then test this framework through both experimental and observational studies. Employing an eye tracking paradigm, in Chapter 2 we learn about the perceptual mechanisms that account for how residential maintenance has a significant impact on our assessment of the social environment. In Chapter 3 we find no significant difference in social behaviour, assayed through a behavioural economics paradigm, following affective priming via different levels of residential maintenance. A result which could be a consequence of methodological factors, or a finding due to the absence of task-specific relevance of the maintenance cue in a socially neutral experimental framing. In Chapter 4, through an analysis of the UK Household Longitudinal Study biomarker data asset, we find that residential maintenance is significantly associated with poor physical health. Chapter 5 then assesses the validity of the thesis’s proposed framework, the thesis’s contribution to the burgeoning field of inquiry, and considers future work towards generating impactful evidence-based public policy proposals
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