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Article: Editorial: Predictive modeling of cognition and behavior on quantum principles.

Surov, Ilya A / Haven, Emmanuel E / Beim Graben, Peter / Sozzo, Sandro / Moreira, Catarina

2023 Volume 13, Page(s) 1107745

Language	English
Publishing date	2023-01-09
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2563826-9
ISSN	1664-1078
ISSN	1664-1078
DOI	10.3389/fpsyg.2022.1107745
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Article ; Online: Evolutionary history and functional characterization of duplicated G protein-coupled estrogen receptors in European sea bass.

Zapater, Cinta / Moreira, Catarina / Knigge, Thomas / Monsinjon, Tiphaine / Gómez, Ana / Pinto, Patrícia I S

The Journal of steroid biochemistry and molecular biology

2023 Volume 236, Page(s) 106423

Abstract: Across vertebrates, the numerous estrogenic functions are mainly mediated by nuclear and membrane receptors, including the G protein-coupled estrogen receptor (GPER) that has been mostly associated with rapid non-genomic responses. Although Gper-mediated ...

Abstract	Across vertebrates, the numerous estrogenic functions are mainly mediated by nuclear and membrane receptors, including the G protein-coupled estrogen receptor (GPER) that has been mostly associated with rapid non-genomic responses. Although Gper-mediated signalling has been characterized in only few fish species, Gpers in fish appear to present more mechanistic functionalities as those of mammals due to additional gene duplicates. In this study, we ran a thorough investigation of the fish Gper evolutionary history in light of available genomes, we carried out the functional characterization of the two gper gene duplicates of European sea bass (Dicentrarchus labrax) using luciferase reporter gene transactivation assays, validated it with natural and synthetic estrogen agonists/antagonists and applied it to other chemicals of aquaculture and ecotoxicological interest. Phylogenetic and synteny analyses of fish gper1 and gper1-like genes suggest their duplication may have not resulted from the teleost-specific whole genome duplication. We confirmed that both sbsGper isoforms activate the cAMP signalling pathway and respond differentially to distinct estrogenic compounds. Therefore, as observed for nuclear estrogen receptors, both sbsGpers duplicates retain estrogenic activity although they differ in their specificity and potency (Gper1 being more potent and more specific than Gper1-like), suggesting a more conserved role for Gper1 than for Gper1-like. In addition, Gpers were able to respond to estrogenic environmental pollutants known to interfere with estrogen signalling, such as the phytoestrogen genistein and the anti-depressant fluoxetine, a point that can be taken into account in aquatic environment pollution screenings and chemical risk assessment, complementing previous assays for sea bass nuclear estrogen receptors.
MeSH term(s)	Animals ; Bass/genetics ; Bass/metabolism ; Phylogeny ; Estrogens/metabolism ; Receptors, Estrogen/genetics ; Receptors, Estrogen/metabolism ; Receptors, G-Protein-Coupled/genetics ; Receptors, G-Protein-Coupled/metabolism ; GTP-Binding Proteins/genetics ; GTP-Binding Proteins/metabolism ; Mammals/metabolism
Chemical Substances	Estrogens ; Receptors, Estrogen ; Receptors, G-Protein-Coupled ; GTP-Binding Proteins (EC 3.6.1.-)
Language	English
Publishing date	2023-11-07
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1049188-0
ISSN	1879-1220 ; 0960-0760
ISSN (online)	1879-1220
ISSN	0960-0760
DOI	10.1016/j.jsbmb.2023.106423
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Article ; Online: Shedding light on ai in radiology: A systematic review and taxonomy of eye gaze-driven interpretability in deep learning.

Neves, José / Hsieh, Chihcheng / Nobre, Isabel Blanco / Sousa, Sandra Costa / Ouyang, Chun / Maciel, Anderson / Duchowski, Andrew / Jorge, Joaquim / Moreira, Catarina

European journal of radiology

2024 Volume 172, Page(s) 111341

Abstract: X-ray imaging plays a crucial role in diagnostic medicine. Yet, a significant portion of the global population lacks access to this essential technology due to a shortage of trained radiologists. Eye-tracking data and deep learning models can enhance X- ... ...

Abstract	X-ray imaging plays a crucial role in diagnostic medicine. Yet, a significant portion of the global population lacks access to this essential technology due to a shortage of trained radiologists. Eye-tracking data and deep learning models can enhance X-ray analysis by mapping expert focus areas, guiding automated anomaly detection, optimizing workflow efficiency, and bolstering training methods for novice radiologists. However, the literature shows contradictory results regarding the usefulness of eye-tracking data in deep-learning architectures for abnormality detection. We argue that these discrepancies between studies in the literature are due to (a) the way eye-tracking data is (or is not) processed, (b) the types of deep learning architectures chosen, and (c) the type of application that these architectures will have. We conducted a systematic literature review using PRISMA to address these contradicting results. We analyzed 60 studies that incorporated eye-tracking data in a deep-learning approach for different application goals in radiology. We performed a comparative analysis to understand if eye gaze data contains feature maps that can be useful under a deep learning approach and whether they can promote more interpretable predictions. To the best of our knowledge, this is the first survey in the area that performs a thorough investigation of eye gaze data processing techniques and their impacts in different deep learning architectures for applications such as error detection, classification, object detection, expertise level analysis, fatigue estimation and human attention prediction in medical imaging data. Our analysis resulted in two main contributions: (1) taxonomy that first divides the literature by task, enabling us to analyze the value eye movement can bring for each case and build guidelines regarding architectures and gaze processing techniques adequate for each application, and (2) an overall analysis of how eye gaze data can promote explainability in radiology.
MeSH term(s)	Humans ; Fixation, Ocular ; Deep Learning ; Radiography ; Radiology/education ; Eye Movements
Language	English
Publishing date	2024-02-01
Publishing country	Ireland
Document type	Systematic Review ; Journal Article ; Review
ZDB-ID	138815-0
ISSN	1872-7727 ; 0720-048X
ISSN (online)	1872-7727
ISSN	0720-048X
DOI	10.1016/j.ejrad.2024.111341
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Article ; Online: Tracheobronchopathia Osteochondroplastica: Two Different Facets of a Rare Entity.

Barata, Margarida / Gomes, Ricardo / Moreira, Catarina / Soares, Jorge

European journal of case reports in internal medicine

2020 Volume 7, Issue 11, Page(s) 1925

Abstract: Tracheobronchopathia osteochondroplastica (TBPO) is an uncommon benign disease, characterized by osseous or metaplastic cartilaginous nodules in the submucosa of the tracheobronchial tree. TBPO is easy to misdiagnose due to its non-specific clinical ... ...

Abstract	Tracheobronchopathia osteochondroplastica (TBPO) is an uncommon benign disease, characterized by osseous or metaplastic cartilaginous nodules in the submucosa of the tracheobronchial tree. TBPO is easy to misdiagnose due to its non-specific clinical manifestation. We describe two cases of TBPO. The first patient was a 57-year-old woman with nocturnal dry cough and wheezing, in whom bronchoscopy revealed small diffuse mucosal irregularities involving the airway until the segmental bronchi. The other patient was a 69-year-old man with progressive worsening dyspnoea and productive cough presenting with severe stenosis of the trachea. Histological examination of both cases was consistent with TBPO. These cases highlight distinct forms of presentation of this rare entity. Learning points: Tracheobronchopathia osteochondroplastica (TBPO) can present as a diffuse large airway disease with mild obstructive symptoms or as severe tracheal obstruction.Direct observation by bronchial fibroscopy of lumen protrusions sparing the posterior wall is diagnostic.
Language	English
Publishing date	2020-08-28
Publishing country	Italy
Document type	Journal Article
ISSN	2284-2594
ISSN (online)	2284-2594
DOI	10.12890/2020_001925
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Article ; Online: Balanced Quantum-Like Bayesian Networks.

Wichert, Andreas / Moreira, Catarina / Bruza, Peter

Entropy (Basel, Switzerland)

2020 Volume 22, Issue 2

Abstract: Empirical findings from cognitive psychology indicate that, in scenarios under high levels of uncertainty, many people tend to make irrational decisions. To address this problem, models based on quantum probability theory, such as the quantum-like ... ...

Abstract	Empirical findings from cognitive psychology indicate that, in scenarios under high levels of uncertainty, many people tend to make irrational decisions. To address this problem, models based on quantum probability theory, such as the quantum-like Bayesian networks, have been proposed. However, this model makes use of a Bayes normalisation factor during probabilistic inference to convert the likelihoods that result from quantum interference effects into probability values. The interpretation of this operation is not clear and leads to extremely skewed intensity waves that make the task of prediction of these irrational decisions challenging. This article proposes the law of balance, a novel mathematical formalism for probabilistic inferences in quantum-like Bayesian networks, based on the notion of balanced intensity waves. The general idea is to balance the intensity waves resulting from quantum interference in such a way that, during Bayes normalisation, they cancel each other. With this representation, we also propose the law of maximum uncertainty, which is a method to predict these paradoxes by selecting the amplitudes of the wave with the highest entropy. Empirical results show that the law of balance together with the law of maximum uncertainty were able to accurately predict different experiments from cognitive psychology showing paradoxical or irrational decisions, namely in the Prisoner's Dilemma game and the Two-Stage Gambling Game.
Language	English
Publishing date	2020-02-02
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2014734-X
ISSN	1099-4300 ; 1099-4300
ISSN (online)	1099-4300
ISSN	1099-4300
DOI	10.3390/e22020170
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Article ; Online: Sarcoidosis related to checkpoint and BRAF/MEK inhibitors in melanoma.

Rubio-Rivas, Manuel / Moreira, Catarina / Marcoval, J

Autoimmunity reviews

2020 Volume 19, Issue 8, Page(s) 102587

Abstract: Therapy for advanced melanoma has deeply changed in the last decade with the introduction of checkpoint and BRAF/MEK inhibitors. Granulomatous reactions have been reported related to these drugs. We performed a systematic review of all the cases ... ...

Abstract	Therapy for advanced melanoma has deeply changed in the last decade with the introduction of checkpoint and BRAF/MEK inhibitors. Granulomatous reactions have been reported related to these drugs. We performed a systematic review of all the cases described in the medical literature by the search (("Melanoma"[Mesh]) AND ("Sarcoidosis"[Mesh] OR "Granuloma"[Mesh])). Ninety-one patients under immunotherapy were included in the analyses. The time from the initiation of the immunotherapy until the onset of sarcoidosis or sarcoid-like reaction (SLR) was 7.1 months (SD 9). Peripheral lymph nodes as the mode of onset were seen more frequently in patients under CTLA-4 inhibitors (p = .016) whereas in patients under BRAF/MEK inhibitors used to be in the form of specific skin lesions (p = .006). Chest X-ray stage I-II was the rule in the CTLA-4 and PD-1 groups. On the contrary, stage 0 accounted for 80% of the patients in the BRAF/MEK group examined for pulmonary involvement. Specific skin involvement was the most common manifestation apart from pulmonary involvement. It was more frequent in patients under BRAF/MEK inhibitors and especially in the form of papules. Splenic involvement was found also more frequently in patients under CTLA-4 inhibitors. Specific treatment for sarcoidosis/SLR was prescribed in 50 patients (58.8%), without differences among groups. Almost all patients presented a good prognosis independently of the decision made regarding their previous immunotherapy. CONCLUSION: Physicians should bear in mind the possibility of sarcoidosis/SLR after the initiation of checkpoint or BRAF/MEK inhibitors in patients diagnosed with advanced melanoma, especially in the form of skin involvement and mediastinal and peripheral lymph nodes. It is important to achieve an accurate diagnosis to rule out the possibility of cancer involvement. What to do with these drugs is yet to be clarified. It seems reasonable to prioritize cancer treatment so it is not mandatory to stop these drugs.
MeSH term(s)	Antineoplastic Agents/adverse effects ; Antineoplastic Agents/therapeutic use ; Humans ; Immunotherapy/adverse effects ; MAP Kinase Kinase 1/antagonists & inhibitors ; Melanoma/drug therapy ; Proto-Oncogene Proteins B-raf/antagonists & inhibitors ; Sarcoidosis/chemically induced
Chemical Substances	Antineoplastic Agents ; BRAF protein, human (EC 2.7.11.1) ; Proto-Oncogene Proteins B-raf (EC 2.7.11.1) ; MAP Kinase Kinase 1 (EC 2.7.12.2)
Language	English
Publishing date	2020-06-14
Publishing country	Netherlands
Document type	Journal Article ; Systematic Review
ZDB-ID	2144145-5
ISSN	1873-0183 ; 1568-9972
ISSN (online)	1873-0183
ISSN	1568-9972
DOI	10.1016/j.autrev.2020.102587
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Article: An Extension of Combinatorial Contextuality for Cognitive Protocols.

Obeid, Abdul Karim / Bruza, Peter / Moreira, Catarina / Bruns, Axel / Angus, Daniel

Frontiers in psychology

2022 Volume 13, Page(s) 871028

Abstract: This article extends the combinatorial approach to support the determination of contextuality amidst causal influences. Contextuality is an active field of study in Quantum Cognition, in systems relating to mental phenomena, such as concepts in human ... ...

Abstract	This article extends the combinatorial approach to support the determination of contextuality amidst causal influences. Contextuality is an active field of study in Quantum Cognition, in systems relating to mental phenomena, such as concepts in human memory. In the cognitive field of study, a contemporary challenge facing the determination of whether a phenomenon is contextual has been the identification and management of disturbances. Whether or not said disturbances are identified through the modeling approach, constitute causal influences, or are disregardableas as noise is important, as contextuality cannot be adequately determined in the presence of causal influences. To address this challenge, we first provide a formalization of necessary elements of the combinatorial approach within the language of canonical causal models. Through this formalization, we extend the combinatorial approach to support a measurement and treatment of disturbance, and offer techniques to separately distinguish noise and causal influences. Thereafter, we develop a protocol through which these elements may be represented within a cognitive experiment. As human cognition seems rife with causal influences, cognitive modelers may apply the extended combinatorial approach to practically determine the contextuality of cognitive phenomena.
Language	English
Publishing date	2022-05-20
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2563826-9
ISSN	1664-1078
ISSN	1664-1078
DOI	10.3389/fpsyg.2022.871028
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Article ; Online: MDF-Net for abnormality detection by fusing X-rays with clinical data.

Hsieh, Chihcheng / Nobre, Isabel Blanco / Sousa, Sandra Costa / Ouyang, Chun / Brereton, Margot / Nascimento, Jacinto C / Jorge, Joaquim / Moreira, Catarina

Scientific reports

2023 Volume 13, Issue 1, Page(s) 15873

Abstract: This study investigates the effects of including patients' clinical information on the performance of deep learning (DL) classifiers for disease location in chest X-ray images. Although current classifiers achieve high performance using chest X-ray ... ...

Abstract	This study investigates the effects of including patients' clinical information on the performance of deep learning (DL) classifiers for disease location in chest X-ray images. Although current classifiers achieve high performance using chest X-ray images alone, consultations with practicing radiologists indicate that clinical data is highly informative and essential for interpreting medical images and making proper diagnoses. In this work, we propose a novel architecture consisting of two fusion methods that enable the model to simultaneously process patients' clinical data (structured data) and chest X-rays (image data). Since these data modalities are in different dimensional spaces, we propose a spatial arrangement strategy, spatialization, to facilitate the multimodal learning process in a Mask R-CNN model. We performed an extensive experimental evaluation using MIMIC-Eye, a dataset comprising different modalities: MIMIC-CXR (chest X-ray images), MIMIC IV-ED (patients' clinical data), and REFLACX (annotations of disease locations in chest X-rays). Results show that incorporating patients' clinical data in a DL model together with the proposed fusion methods improves the disease localization in chest X-rays by 12% in terms of Average Precision compared to a standard Mask R-CNN using chest X-rays alone. Further ablation studies also emphasize the importance of multimodal DL architectures and the incorporation of patients' clinical data in disease localization. In the interest of fostering scientific reproducibility, the architecture proposed within this investigation has been made publicly accessible( https://github.com/ChihchengHsieh/multimodal-abnormalities-detection ).
MeSH term(s)	Humans ; X-Rays ; Reproducibility of Results ; Radiography ; Radiologists
Chemical Substances	milk-derived factor
Language	English
Publishing date	2023-09-23
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-41463-0
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Book ; Online: AMORETTO

Wei, Jia / Ouyang, Chun / ter Hofstede, Arthur H. M. / Moreira, Catarina

A Method for Deriving IoT-enriched Event Logs

2022

Abstract: Process analytics aims to gain insights into the behaviour and performance of business processes through the analysis of event logs, which record the execution of processes. With the widespread use of the Internet of Things (IoT), IoT data has become ... ...

Abstract	Process analytics aims to gain insights into the behaviour and performance of business processes through the analysis of event logs, which record the execution of processes. With the widespread use of the Internet of Things (IoT), IoT data has become readily available and can provide valuable context information about business processes. As such, process analytics can benefit from incorporating IoT data into event logs to support more comprehensive, context-aware analyses. However, most existing studies focus on enhancing business process models with IoT data, whereas little attention has been paid to incorporating IoT data into event logs for process analytics. Hence, this paper aims to systematically integrate IoT data into event logs to support context-aware process analytics. To this end, we propose AMORETTO - a method for deriving IoT-enriched event logs. Firstly, we provide a classification of context data, referred to as the IoT-Pro context classification, which encompasses two context dimensions: IoT context and process context. Next, we present a method for integrating IoT data with event logs, guided by IoT-Pro, to yield IoT-enriched event logs. To demonstrate the applicability of AMORETTO, we applied it to a real-life use case and examined whether the derived IoT-enriched event log sufficed to address certain specific analytical questions.
Keywords	Computer Science - Databases
Subject code	005
Publishing date	2022-12-05
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: DiCE4EL

Hsieh, Chihcheng / Moreira, Catarina / Ouyang, Chun

Interpreting Process Predictions using a Milestone-Aware Counterfactual Approach

2021

Abstract: Predictive process analytics often apply machine learning to predict the future states of a running business~process. However, the internal mechanisms of many existing predictive algorithms are opaque and a human decision-maker is unable to understand \ ... ...

Abstract	Predictive process analytics often apply machine learning to predict the future states of a running business~process. However, the internal mechanisms of many existing predictive algorithms are opaque and a human decision-maker is unable to understand \emph{why} a certain activity was predicted. Recently, counterfactuals have been proposed in the literature to derive human-understandable explanations from predictive models. Current counterfactual approaches consist of finding the minimum feature change that can make a certain prediction flip its outcome. Although many algorithms have been proposed, their application to multi-dimensional sequence data like event logs has not been explored in the literature. In this paper, we explore the use of a recent, popular model-agnostic counterfactual algorithm, DiCE, in the context of predictive process analytics. The analysis reveals that DiCE is unable to derive explanations for process predictions, due to (1) process domain knowledge not being taken into account, (2) long traces of process execution that often tend to be less understandable, and (3) difficulties in optimising the counterfactual search with categorical variables. We design an extension of DiCE, namely DiCE4EL (DiCE for Event Logs), that can generate counterfactual explanations for process prediction, and propose an approach that supports deriving milestone-aware counterfactual explanations at key intermediate stages along process execution to promote interpretability. We apply our approach to a publicly available real-life event log and the analysis results demonstrate the effectiveness of the proposed approach.
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2021-07-19
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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