Data-based fault detection in chemical processes: Managing records with operator intervention and uncertain labels

Developing data-driven fault detection systems for chemical plants requires managing uncertain data labels and dynamic attributes due to operator-process interactions. Mislabeled data is a known problem in computer science that has received scarce attention from the process systems community. This work introduces and examines the effects of operator actions in records and labels, and the consequences in the development of detection models. Using a state space model, this work proposes an iterative relabeling scheme for retraining classifiers that continuously refines dynamic attributes and labels. Three case studies are presented: a reactor as a motivating example, flooding in a simulated de-Butanizer column, as a complex case, and foaming in an absorber as an industrial challenge. For the first case, detection accuracy is shown to increase by 14% while operating costs are reduced by 20%. Moreover, regarding the de-Butanizer column, the performance of the proposed strategy is shown to be 10% higher than the filtering strategy. Promising results are finally reported in regard of efficient strategies to deal with the presented problemPeer ReviewedPostprint (author's final draft

Detection of abnormal cardiac response patterns in cardiac tissue using deep learning

This study reports a method for the detection of mechanical signaling anomalies in cardiac tissue through the use of deep learning and the design of two anomaly detectors. In contrast to anomaly classifiers, anomaly detectors allow accurate identification of the time position of the anomaly. The first detector used a recurrent neural network (RNN) of long short-term memory (LSTM) type, while the second used an autoencoder. Mechanical contraction data present several challanges, including high presence of noise due to the biological variability in the contraction response, noise introduced by the data acquisition chain and a wide variety of anomalies. Therefore, we present a robust deep-learning-based anomaly detection framework that addresses these main issues, which are difficult to address with standard unsupervised learning techniques. For the time series recording, an experimental model was designed in which signals of cardiac mechanical contraction (right and left atria) of a CD-1 mouse could be acquired in an automatic organ bath, reproducing the physiological conditions. In order to train the anomaly detection models and validate their performance, a database of synthetic signals was designed (n = 800 signals), including a wide range of anomalous events observed in the experimental recordings. The detector based on the LSTM neural network was the most accurate. The performance of this detector was assessed by means of experimental mechanical recordings of cardiac tissue of the right and left atria.Peer ReviewedPostprint (author's final draft

Multiscale image analysis of calcium dynamics in cardiac myocytes

Cardiac myocytes are the muscle cells that build up heart tissue and provide the mechanical action to pump blood by synchronously contracting at every heartbeat. Heart muscle contraction is regulated by intracellular calcium concentration which exhibits a complex spatio-temporal dynamical behavior at the molecular, cellular and tissue levels. Details of such dynamical patterns are closely related to the mechanisms responsible for cardiovascular diseases , the single largest cause of death in the developed countries. The emerging field of translational cardiology focuses on the study of how such mechanisms connect and influence each other across spatial and temporal scales, eventually yielding to a certain clinical condition. To study such calcium dynamics in cardiac myocytes, we benefit from the recent advances in the field of experimental cell physiology. Fluorescence microscopy allows us to observe the distribution of calcium in the cell with a spatial resolution below one micron and a frame rate around one millisecond, thus providing a very accurate monitoring of calcium fluxes in the cell. The aim of the thesis summarized in this paper, was to develop image processing computational techniques for extracting quantitative data of physiological relevance from fluorescence confocal microscopy images at different scales. The two main subjects covered in the thesis were image segmentation and classification methods applied to fluorescence microscopy imaging of cardiac myocytes and calcium imaging. These methods were applied to a variety of problems involving different space and time scales, such as the localization of molecular receptors, the detection and characterization of spontaneous calcium-release events, and the propagation of calcium waves across a culture of cardiac cells. The following is a summary of the thesis as a consequence of having been awarded the 7th Justiniano Casas Award accessit by the Sociedad Española de Óptica.Peer ReviewedPostprint (published version

Ante-hoc generation of task-agnostic interpretation maps

Existing explainability approaches for convolutional neural networks (CNNs) are mainly applied after training (post-hoc) which is generally unreliable. Ante-hoc explainers trained simultaneously with the CNN are more reliable. However, current ante-hoc explanation methods mainly generate inexplicit concept-based explanations tailored to specific tasks. To address these limitations, we propose a task-agnostic ante-hoc framework that can generate interpretation maps to visually explain any CNN. Our framework simultaneously trains the CNN and a weighting network - an explanation generation module. The generated maps are self-explanatory, eliminating the need for manual identification of concepts. We demonstrate that our method can interpret tasks such as classification, facial landmark detection, and image captioning. We show that our framework is explicit, faithful, and stable through experiments. To the best of our knowledge, this is the first ante-hoc CNN explanation strategy that produces visual explanations generic to CNNs for different tasks.This research was funded by the Spanish Ministry of Science and Innovation, grant number PID2020-116927RBC22 (R.B.).Peer ReviewedPostprint (published version

Impact of R-carvedilol on ß2-adrenergic receptor-mediated spontaneous calcium release in human atrial myocytes

A hallmark of atrial fibrillation is an excess of spontaneous calcium release events, which can be mimicked by ß1- or ß2-adrenergic stimulation. Because ß1-adrenergic receptor blockers (ß1-blockers) are primarily used in clinical practice, we here examined the impact of ß2-adrenergic stimulation on spontaneous calcium release and assessed whether the R- and S-enantiomers of the non-selective ß- blocker carvedilol could reverse these effects. For this purpose, human atrial myocytes were isolated from patients undergoing cardiovascular surgery and subjected to confocal calcium imaging or immunofluorescent labeling of the ryanodine receptor (RyR2). Interestingly, the ß2-adrenergic agonist fenoterol increased the incidence of calcium sparks and waves to levels observed with the non-specific ß-adrenergic agonist isoproterenol. Moreover, fenoterol increased both the amplitude and duration of the sparks, facilitating their fusion into calcium waves. Subsequent application of the non ß-blocking R-Carvedilol enantiomer reversed these effects of fenoterol in a dose-dependent manner. R-Carvedilol also reversed the fenoterol-induced phosphorylation of the RyR2 at Ser-2808 dose-dependently, and 1 µM of either R- or S-Carvedilol fully reversed the effect of fenoterol. Together, these findings demonstrate that ß2-adrenergic stimulation alone stimulates RyR2 phosphorylation at Ser-2808 and spontaneous calcium release maximally, and points to carvedilol as a tool to attenuate the pathological activation of ß2-receptors.Peer ReviewedPostprint (published version

Reduced expression of cardiac ryanodine receptor protects against stress-induced ventricular tachyarrhythmia, but increases the susceptibility to cardiac alternans

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The cardiac ryanodine receptor, but not sarcoplasmic reticulum Ca2-ATPase, is a major determinant of Ca2 alternans in intact mouse hearts

Sarcoplasmic reticulum (SR) Ca2+ cycling is governed by the cardiac ryanodine receptor (RyR2) and SR Ca2+-ATPase (SERCA2a). Abnormal SR Ca2+ cycling is thought to be the primary cause of Ca2+ alternans that can elicit ventricular arrhythmias and sudden cardiac arrest. Although alterations in either RyR2 or SERCA2a function are expected to affect SR Ca2+ cycling, whether and to what extent altered RyR2 or SERCA2a function affects Ca2+ alternans is unclear. Here we employed a gain-of-function RyR2 variant (R4496C) and the phospholamban-knockout (PLB-KO) mouse model to assess the effect of genetically enhanced RyR2 or SERCA2a function on Ca2+ alternans. Confocal Ca2+ imaging revealed that RyR2-R4496C shortened SR Ca2+ release refractoriness and markedly suppressed rapid pacing-induced Ca2+ alternans. Interestingly, despite enhancing RyR2 function, intact RyR2-R4496C hearts exhibited no detectable spontaneous SR Ca2+ release events during pacing. Unlike for RyR2, enhancing SERCA2a function by ablating PLB exerted a relatively minor effect on Ca2+ alternans in intact hearts expressing RyR2 wildtype or a loss-of-function RyR2 variant, E4872Q, that promotes Ca2+ alternans. Furthermore, partial SERCA2a inhibition with 3 µM 2,5-di-tert-butylhydroquinone (tBHQ) also had little impact on Ca2+ alternans, while strong SERCA2a inhibition with 10 µM tBHQ markedly reduced the amplitude of Ca2+ transients and suppressed Ca2+ alternans in intact hearts. Our results demonstrate that enhanced RyR2 function suppresses Ca2+ alternans in the absence of spontaneous Ca2+ release and that RyR2, but not SERCA2a, is a key determinant of Ca2+ alternans in intact working hearts, making RyR2 an important therapeutic target for cardiac alternans.Peer ReviewedPostprint (published version

MedicalSeg: a medical GUI application for image segmentation management

In the field of medical imaging, the division of an image into meaningful structures using image segmentation is an essential step for pre-processing analysis. Many studies have been carried out to solve the general problem of the evaluation of image segmentation results. One of the main focuses in the computer vision field is based on artificial intelligence algorithms for segmentation and classification, including machine learning and deep learning approaches. The main drawback of supervised segmentation approaches is that a large dataset of ground truth validated by medical experts is required. In this sense, many research groups have developed their segmentation approaches according to their specific needs. However, a generalised application aimed at visualizing, assessing and comparing the results of different methods facilitating the generation of a ground-truth repository is not found in recent literature. In this paper, a new graphical user interface application (MedicalSeg) for the management of medical imaging based on pre-processing and segmentation is presented. The objective is twofold, first to create a test platform for comparing segmentation approaches, and secondly to generate segmented images to create ground truths that can then be used for future purposes as artificial intelligence tools. An experimental demonstration and performance analysis discussion are presented in this paper.Peer ReviewedPostprint (published version

Semi-automatic GUI platform to characterize brain development in preterm children using ultrasound images

The third trimester of pregnancy is the most critical period for human brain development, during which significant changes occur in the morphology of the brain. The development of sulci and gyri allows for a considerable increase in the brain surface. In preterm newborns, these changes occur in an extrauterine environment that may cause a disruption of the normal brain maturation process. We hypothesize that a normalized atlas of brain maturation with cerebral ultrasound images from birth to term equivalent age will help clinicians assess these changes. This work proposes a semi-automatic Graphical User Interface (GUI) platform for segmenting the main cerebral sulci in the clinical setting from ultrasound images. This platform has been obtained from images of a cerebral ultrasound neonatal database images provided by two clinical researchers from the Hospital Sant Joan de Déu in Barcelona, Spain. The primary objective is to provide a user-friendly design platform for clinicians for running and visualizing an atlas of images validated by medical experts. This GUI offers different segmentation approaches and pre-processing tools and is user-friendly and designed for running, visualizing images, and segmenting the principal sulci. The presented results are discussed in detail in this paper, providing an exhaustive analysis of the proposed approach’s effectiveness.Peer ReviewedPostprint (published version

Phase-field models in interfacial pattern formation out of equilibrium

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