1,063 research outputs found

    Human-machine diversity in the use of computerised advisory systems: a case study

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    Computer-based advisory systems form with their users composite, human-machine systems. Redundancy and diversity between the human and the machine are often important for the dependability of such systems. We discuss the modelling approach we applied in a case study. The goal is to assess failure probabilities for the analysis of X-ray films for detecting cancer, performed by a person assisted by a computer-based tool. Differently from most approaches to human reliability assessment, we focus on the effects of failure diversity — or correlation — between humans and machines. We illustrate some of the modelling and prediction problems, especially those caused by the presence of the human component. We show two alternative models, with their pros and cons, and illustrate, via numerical examples and analytically, some interesting and non-intuitive answers to questions about reliability assessment and design choices for human-computer systems

    Design of Loss Functions for Solving Inverse Problems using Deep Learning

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    Solving inverse problems is a crucial task in several applications that strongly a ffect our daily lives, including multiple engineering fields, military operations, and/or energy production. There exist different methods for solving inverse problems, including gradient based methods, statistics based methods, and Deep Learning (DL) methods. In this work, we focus on the latest. Speci fically, we study the design of proper loss functions for dealing with inverse problems using DL. To do this, we introduce a simple benchmark problem with known analytical solution. Then, we propose multiple loss functions and compare their performance when applied to our benchmark example problem. In addition, we analyze how to improve the approximation of the forward function by: (a) considering a Hermite-type interpolation loss function, and (b) reducing the number of samples for the forward training in the Encoder-Decoder method. Results indicate that a correct desig

    Intracellular Ca2+ release through ryanodine receptors contributes to AMPA receptor-mediated mitochondrial dysfunction and ER stress in oligodendrocytes

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    Overactivation of ionotropic glutamate receptors in oligodendrocytes induces cytosolic Ca2+ overload and excitotoxic death, a process that contributes to demyelination and multiple sclerosis. Excitotoxic insults cause well-characterized mitochondrial alterations and endoplasmic reticulum (ER) dysfunction, which is not fully understood. In this study, we analyzed the contribution of ER-Ca2+ release through ryanodine receptors (RyRs) and inositol triphosphate receptors (IP3Rs) to excitotoxicity in oligodendrocytes in vitro. First, we observed that oligodendrocytes express all previously characterized RyRs and IP3Rs. Blockade of Ca2+-induced Ca2+ release by TMB-8 following α-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) receptor-mediated insults attenuated both oligodendrocyte death and cytosolic Ca2+ overload. In turn, RyR inhibition by ryanodine reduced as well the Ca2+ overload whereas IP3R inhibition was ineffective. Furthermore, AMPA-triggered mitochondrial membrane depolarization, oxidative stress and activation of caspase-3, which in all instances was diminished by RyR inhibition. In addition, we observed that AMPA induced an ER stress response as revealed by α subunit of the eukaryotic initiation factor 2α phosphorylation, overexpression of GRP chaperones and RyR-dependent cleavage of caspase-12. Finally, attenuating ER stress with salubrinal protected oligodendrocytes from AMPA excitotoxicity. Together, these results show that Ca2+ release through RyRs contributes to cytosolic Ca2+ overload, mitochondrial dysfunction, ER stress and cell death following AMPA receptor-mediated excitotoxicity in oligodendrocytes