463 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

    Understanding the role of CaMKIIa in Angelman Syndrome by looking at its potential interactors through proximity labelling

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    280 p.El Síndrome de Angelman es una enfermedad rara que se caracteriza por la ausencia de la E3 ligasa de ubicuitina UBE3A en las neuronas. Numerosos datos sugieren una relación entre CAMKII y UBE3A. En esta tesis se emplea el etiquetado por proximidad con BioID2 y TurboID, tanto en cultivos celulares como en Drosophila melanogaster, con el fin de identificar posibles interactores. Mediante esta estrategia hemos sido capaces de identificar la E3 ligasa de ubicuitina ITCH como la responsable de monoubicuitinar a CAMKIIa y a la deubiquitinasa MYSM1 como un mediador indirecto de la ubicuitinación de CAMKIIa. Además, cuando CaMKIIa se encuentra más ubicuitinada, su fosforilación en la T286 y, por tanto, activación se ve reducida. Los experimentos en mosca permitieron identificar varias proteínas relacionadas con enfermedades neurodegenerativas y la proteína Nbea, también identificada como posible sustrato de UBE3A, involucrada en el espectro autista. Por otro lado, nuestro grupo recientemente ha identificado la proteína Neurocondrina (NCDN) como un sustrato de UBE3A en el cerebro de ratón. NCDN regula de forma negativa la fosforilación de T286 de CaMKII, reduciendo así su actividad. Observamos que las cadenas a través de la lisina 48 son las que se forman en NCDN por UBE3A y la envían a su degradación. Finalmente, realizamos un estudio sobre cuáles eran las lisinas de NCDN que tendían a ser ubicuitinadas por UBE3A. Los resultados, aunque prometedores, no identificaban de manera significativa ninguna lisina dentro de la secuencia de NCDN

    WEIGH-IN-MOTION DATA-DRIVEN PAVEMENT PERFORMANCE PREDICTION MODELS

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    The effective functioning of pavements as a critical component of the transportation system necessitates the implementation of ongoing maintenance programs to safeguard this significant and valuable infrastructure and guarantee its optimal performance. The maintenance, rehabilitation, and reconstruction (MRR) program of the pavement structure is dependent on a multidimensional decision-making process, which considers the existing pavement structural condition and the anticipated future performance. Pavement Performance Prediction Models (PPPMs) have become indispensable tools for the efficient implementation of the MRR program and the minimization of associated costs by providing precise predictions of distress and roughness based on inventory and monitoring data concerning the pavement structure\u27s state, traffic load, and climatic conditions. The integration of PPPMs has become a vital component of Pavement Management Systems (PMSs), facilitating the optimization, prioritization, scheduling, and selection of maintenance strategies. Researchers have developed several PPPMs with differing objectives, and each PPPM has demonstrated distinct strengths and weaknesses regarding its applicability, implementation process, and data requirements for development. Traditional statistical models, such as linear regression, are inadequate in handling complex nonlinear relationships between variables and often generate less precise results. Machine Learning (ML)-based models have become increasingly popular due to their ability to manage vast amounts of data and identify meaningful relationships between them to generate informative insights for better predictions. To create ML models for pavement performance prediction, it is necessary to gather a significant amount of historical data on pavement and traffic loading conditions. The Long-Term Pavement Performance Program (LTPP) initiated by the Federal Highway Administration (FHWA) offers a comprehensive repository of data on the environment, traffic, inventory, monitoring, maintenance, and rehabilitation works that can be utilized to develop PPPMs. The LTPP also includes Weigh-In-Motion (WIM) data that provides information on traffic, such as truck traffic, total traffic, directional distribution, and the number of different axle types of vehicles. High-quality traffic loading data can play an essential role in improving the performance of PPPMs, as the Mechanistic-Empirical Pavement Design Guide (MEPDG) considers vehicle types and axle load characteristics to be critical inputs for pavement design. The collection of high-quality traffic loading data has been a challenge in developing Pavement Performance Prediction Models (PPPMs). The Weigh-In-Motion (WIM) system, which comprises WIM scales, has emerged as an innovative solution to address this issue. By leveraging computer vision and machine learning techniques, WIM systems can collect accurate data on vehicle type and axle load characteristics, which are critical factors affecting the performance of flexible pavements. Excessive dynamic loading caused by heavy vehicles can result in the early disintegration of the pavement structure. The Long-Term Pavement Performance Program (LTPP) provides an extensive repository of WIM data that can be utilized to develop accurate PPPMs for predicting pavement future behavior and tolerance. The incorporation of comprehensive WIM data collected from LTPP has the potential to significantly improve the accuracy and effectiveness of PPPMs. To develop artificial neural network (ANN) based pavement performance prediction models (PPPMs) for seven distinct performance indicators, including IRI, longitudinal crack, transverse crack, fatigue crack, potholes, polished aggregate, and patch failure, a total of 300 pavement sections with WIM data were selected from the United States of America. Data collection spanned 20 years, from 2001 to 2020, and included information on pavement age, material properties, climatic properties, structural properties, and traffic-related characteristics. The primary dataset was then divided into two distinct subsets: one which included WIMgenerated traffic data and another which excluded WIM-generated traffic data. Data cleaning and normalization were meticulously performed using the Z-score normalization method. Each subset was further divided into two separate groups: the first containing 15 years of data for model training and the latter containing 5 years of data for testing purposes. Principal Component Analysis (PCA) was then employed to reduce the number of input variables for the model. Based on a cumulative Proportion of Variation (PoV) of 96%, 12 input variables were selected. Subsequently, a single hidden layer ANN model with 12 neurons was generated for each performance indicator. The study\u27s results indicate that incorporating Weigh-In-Motion (WIM)-generated traffic loading data can significantly enhance the accuracy and efficacy of pavement performance prediction models (PPPMs). This improvement further supports the suitability of optimized pavement maintenance scheduling with minimal costs, while also ensuring timely repairs to promote acceptable serviceability and structural stability of the pavement. The contributions of this research are twofold: first, it provides an enhanced understanding of the positive impacts that high-quality traffic loading data has on pavement conditions; and second, it explores potential applications of WIM data within the Pavement Management System (PMS)

    Exploring the pharmacodynamics of multidrug combinations and using the advances in technology to individualise anaesthetic drug titration

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    In current practice, pharmacokinetic-dynamic (PK/PD) models are frequently used to describe the combined relationship between the time course of drug plasma concentrations (PK) and the time independent relationship between the drug concentration at the receptor site and the clinical effect (PD). This thesis contributes to the knowledge in anaesthetic pharmacology and explores the dose-response relationships of propofol and sevoflurane (with and without the coadministration of remifentanil) in greater detail using PK/PD models. Our studies show that PK/PD models are useful in clinical practice. The concept of neural inertia could have an influence on these models, but is still controversial in humans and it does not break down the essence and applicability of these PK/PD models. Subsequently, we used these models to compare the pharmacodynamics of propofol and sevoflurane (with and without remifentanil) at both a population level as well as at an individual level. This comparison let us describe potency ratios between both hypnotics which is very helpful for anaesthetist when switching between these drugs for any reason during a case. We applied the same PK/PD models and similar potency ratios in clinical practice using the SmartPilot® View, a drug advisory system, to guide anaesthetic drug titration, and we assessed its clinical utility. Finally, we evaluated a novel method to analyse the cerebral drug effect on the EEG using Artificial Intelligence in order to explore the feasibility of whether a single index can quantify the hypnotic effect in a drug-independent way

    Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)

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    This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21–22 September 2023

    2023 SOARS Conference Program

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    Program for the 2023 Showcase of Osprey Advancements in Research and Scholarship (SOARS

    Brain Computations and Connectivity [2nd edition]

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    This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

    From Mouse Models to Patients: A Comparative Bioinformatic Analysis of HFpEF and HFrEF

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    Heart failure (HF) represents an immense health burden with currently no curative therapeutic strategies. Study of HF patient heterogeneity has led to the recognition of HF with preserved (HFpEF) and reduced ejection fraction (HFrEF) as distinct syndromes regarding molecular characteristics and clinical presentation. Until the recent past, HFrEF represented the focus of research, reflected in the development of a number of therapeutic strategies. However, the pathophysiological concepts applicable to HFrEF may not be necessarily applicable to HFpEF. HF induces a series of ventricular modeling processes that involve, among others, hallmarks of hypertrophy, fibrosis, inflammation, all of which can be observed to some extent in HFpEF and HFrEF. Thus, by direct comparative analysis between HFpEF and HFrEF, distinctive features can be uncovered, possibly leading to improved pathophysiological understanding and opportunities for therapeutic intervention. Moreover, recent advances in biotechnologies, animal models, and digital infrastructure have enabled large-scale collection of molecular and clinical data, making it possible to conduct a bioinformatic comparative analysis of HFpEF and HFrEF. Here, I first evaluated the field of HF transcriptome research by revisiting published studies and data sets to provide a consensus gene expression reference. I discussed the patient clientele that was captured, revealing that HFpEF patients were not represented. Thus, I applied alternative approaches to study HFpEF. I utilized a mouse surrogate model of HFpEF and analyzed single cell transcriptomics to gain insights into the interstitial tissue remodeling. I contrasted this analysis by comparison of fibroblast activation patterns found in mouse models resembling HFrEF. The human reference was used to further demonstrate similarities between models and patients and a novel possible biomarker for HFpEF was introduced. Mouse models only capture selected aspects of HFpEF but largely fail to imitate the complex multi-factor and multi-organ syndrome present in humans. To account for this complexity, I performed a top-down analysis in HF patients by analyzing phenome-wide comorbidity patterns. I derived clinical insights by contrasting HFpEF and HFrEF patients and their comorbidity profiles. These profiles were then used to predict associated genetic profiles, which could be also recovered in the HFpEF mouse model, providing hypotheses about the molecular links of comorbidity profiles. My work provided novel insights into HFpEF and HFrEF syndromes and exemplified an interdisciplinary bioinformatic approach for a comparative analysis of both syndromes using different data modalities
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