475 research outputs found
Precision mapping of gene expression and proteins in the brain using gene editing and barcoded viral vectors
The human brain is a masterpiece of intricate design and impeccable functionality. It serves as the ultimate command center for our thoughts, sensations, and actions, which define our very existence. This organ operates flawlessly, with billions of neurons working in perfect harmony to process information, create memories, and regulate our emotions. The brain's neural network is composed of trillions of connections, consisting of interconnected cells that communicate through electrical impulses and chemical signals at remarkable speeds. These connections, also known as synapses, serve as the means of communication that allow for information to travel uninterrupted throughout the brain. This intricate network enables us to think, learn, reason, and react to our surroundings. However, neurological disorders have the potential to disrupt this delicate balance, leading to a range of manifestations. These can include gradual memory erosion in Alzheimer's disease to the slow progression of motor and cognitive impairment in Parkinson's disease. Each condition presents a unique puzzle for scientists and researchers to decipher. The intricate interactions of genes, proteins, and neural circuits create a complex landscape that holds the key to understanding these disorders' origins and potential treatments.In this thesis, we worked on understanding a new type of neuronal communication based on the retrotransposon protein of Arc. The investigation was conducted using a gene editing technique based on the CRISPR/Cas9 system, next-generation sequencing technologies, and refined immunohistochemistry protocol. Using a mouse animal model, our findings reinforced the hypothesis that Arc has the capacity for inter-neuronal transport, as previously proposed in vitro studies. An additional objective of the thesis has been the investigation of molecular changes occurring within the Substantia Nigra throughout the progression of Parkinson's disease. At the core of this disorder's pathophysiology lies the alpha-synuclein protein. With this objective in mind, we developed a single- cell methodology to effectively investigate modifications in gene expression provoked by an overload of alpha- synuclein in animal models of rodents. From this data set, the overarching goal is to train a machine learning able to predict the disease course and to establish possible therapeutic interventions
Undergraduate and Graduate Course Descriptions, 2023 Spring
Wright State University undergraduate and graduate course descriptions from Spring 2023
Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends
Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks
Synthesizing FDIR Recovery Strategies for Space Systems
Dynamic Fault Trees (DFTs) are powerful tools to drive the design of fault tolerant systems. However, semantic pitfalls limit their practical utility for interconnected systems that require complex recovery strategies to maximize their reliability. This thesis discusses the shortcomings of DFTs in the context of analyzing Fault Detection, Isolation and Recovery (FDIR) concepts with a particular focus on the needs of space systems. To tackle these shortcomings, we introduce an inherently non-deterministic model for DFTs. Deterministic recovery strategies are synthesized by transforming these non-deterministic DFTs into Markov automata that represent all possible choices between recovery actions. From the corresponding scheduler, optimized to maximize a given RAMS (Reliability, Availability, Maintainability and Safety) metric, an optimal recovery strategy can then be derived and represented by a model we call recovery automaton. We discuss dedicated techniques for reducing the state space of this recovery automaton and analyze their soundness and completeness. Moreover, modularized approaches to handle the complexity added by the state-based transformation approach are discussed. Furthermore, we consider the non-deterministic approach in a partially observable setting and propose an approach to lift the model for the fully observable case. We give an implementation of our approach within the Model-Based Systems Engineering (MBSE) framework Virtual Satellite. Finally, the implementation is evaluated based on the FFORT benchmark. The results show that basic non-deterministic DFTs generally scale well. However, we also found that semantically enriched non-deterministic DFTs employing repair or delayed observability mechanisms pose a challenge
Maximal Hardy Fields
We show that all maximal Hardy fields are elementarily equivalent as
differential fields, and give various applications of this result and its
proof. We also answer some questions on Hardy fields posed by Boshernitzan.Comment: 470 pp. This document is not intended for publication in its current
for
Large population limit for a multilayer SIR model including households and workplaces
We study a multilayer SIR model with two levels of mixing, namely a global
level which is uniformly mixing, and a local level with two layers
distinguishing household and workplace contacts, respectively. We establish the
large population convergence of the corresponding stochastic process. For this
purpose, we use an individual-based model whose state space explicitly takes
into account the duration of infectious periods. This allows to deal with the
natural correlation of the epidemic states of individuals whose household and
workplace share a common infected. In a general setting where a non-exponential
distribution of infectious periods may be considered, convergence to the unique
deterministic solution of a measurevalued equation is obtained. In the
particular case of exponentially distributed infectious periods, we show that
it is possible to further reduce the obtained deterministic limit, leading to a
closed, finite dimensional dynamical system capturing the epidemic dynamics.
This model reduction subsequently is studied from a numerical point of view. We
illustrate that the dynamical system derived from the large population
approximation is a pertinent model reduction when compared to simulations of
the stochastic process or to an alternative edgebased compartmental model, both
in terms of accuracy and computational cost.Comment: Modification of proofs for the identification of the deterministic
limit. 50 pages, 6 figure
Risk analysis and decision making for autonomous underwater vehicles
Risk analysis for autonomous underwater vehicles (AUVs) is essential to enable AUVs to
explore extreme and dynamic environments. This research aims to augment existing risk
analysis methods for AUVs, and it proposes a suite of methods to quantify mission risks and to
support the implementation of safety-based decision making strategies for AUVs in harsh
marine environments. This research firstly provides a systematic review of past progress of risk
analysis research for AUV operations. The review answers key questions including fundamental
concepts and evolving methods in the domain of risk analysis for AUVs, and it highlights future
research trends to bridge existing gaps. Based on the state-of-the-art research, a copula-based
approach is proposed for predicting the risk of AUV loss in underwater environments. The
developed copula Bayesian network (CBN) aims to handle non-linear dependencies among
environmental variables and inherent technical failures for AUVs, and therefore achieve
accurate risk estimation for vehicle loss given various environmental observations. Furthermore,
path planning for AUVs is an effective decision making strategy for mitigating risks and
ensuring safer routing. A further study presents an offboard risk-based path planning approach
for AUVs, considering a challenging environment with oil spill scenarios incorporated. The
proposed global Risk-A* planner combines a Bayesian-based risk model for probabilistic risk
reasoning and an A*-based algorithm for path searching. However, global path planning
designed for static environments cannot handle the unpredictable situations that may emerge,
and real-time replanned solutions are required to account for dynamic environmental
observations. Therefore, a hybrid risk-aware decision making strategy is investigated for AUVs
to combine static global planning with dynamic local re-planning. A dynamic risk analysis
model based on the system theoretic process analysis (STPA) and BN is applied for generating
a real-time risk map in target mission areas. The dynamic window algorithm (DWA) serves for
local path planning to avoid moving obstacles. The proposed hybrid risk-aware decisionmaking
architecture is essential for the real-life implementation of AUVs, leading eventually to
a real-time adaptive path planning process onboard the AUV
25th Annual Computational Neuroscience Meeting: CNS-2016
Abstracts of the 25th Annual Computational Neuroscience
Meeting: CNS-2016
Seogwipo City, Jeju-do, South Korea. 2–7 July 201
Artificial Intelligence and Cognitive Computing
Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that
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