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Systemic autoinflammatory diseases (SAIDs) are a group of inflammatory disorders caused by dysregulation in the innate immune system that leads to enhanced immune responses. The clinical diagnosis of SAIDs can be difficult since individually these are rare diseases with considerable phenotypic overlap. Most SAIDs have a strong genetic background, but environmental and epigenetic influences can modulate the clinical phenotype. Molecular diagnosis has become essential for confirmation of clinical diagnosis. To date there are over 30 genes and a variety of modes of inheritance that have been associated with monogenic SAIDs. Mutations in the same gene can lead to very distinct phenotypes and can have different inheritance patterns. In addition, somatic mutations have been reported in several of these conditions. New genetic testing methods and databases are being developed to facilitate the molecular diagnosis of SAIDs, which is of major importance for treatment, prognosis and genetic counselling. The aim of this review is to summarize the latest advances in genetic testing for SAIDs and discuss potential obstacles that might arise during the molecular diagnosis of SAIDs.
The pyrin inflammasome has evolved as an innate immune sensor to detect bacterial toxin-induced Rho guanosine triphosphatase (Rho GTPase)-inactivation, a process that is similar to the "guard" mechanism in plants. Rho GTPases act as molecular switches to regulate a variety of signal transduction pathways including cytoskeletal organization. Pathogens can modulate Rho GTPase activity to suppress host immune responses such as phagocytosis. Pyrin is encoded by MEFV, the gene that is mutated in patients with familial Mediterranean fever (FMF). FMF is the prototypic autoinflammatory disease characterized by recurring short episodes of systemic inflammation and is a common disorder in many populations in the Mediterranean basin. Pyrin specifically senses modifications in the activity of the small GTPase RhoA, which binds to many effector proteins including the serine/threonine-protein kinases PKN1 and PKN2 and actin-binding proteins. RhoA activation leads to PKN-mediated phosphorylation-dependent pyrin inhibition. Conversely, pathogen virulence factors downregulate RhoA activity in a variety of ways, and these changes are detected by the pyrin inflammasome irrespective of the type of modifications. MEFV pathogenic variants favor the active state of pyrin and elicit proinflammatory cytokine release and pyroptosis. They can be inherited either as a dominant or recessive trait depending on the variant's location and effect on the protein function. Mutations in the C-terminal B30.2 domain are usually considered recessive, although heterozygotes may manifest a biochemical or even a clinical phenotype. These variants are hypomorphic in regard to their effect on intramolecular interactions, but ultimately accentuate pyrin activity. Heterozygous mutations in other domains of pyrin affect residues critical for inhibition or protein oligomerization, and lead to constitutively active inflammasome. In healthy carriers of FMF mutations who have the subclinical inflammatory phenotype, the increased activity of pyrin might have been protective against endemic infections over human history. This finding is supported by the observation of high carrier frequencies of FMF-mutations in multiple populations. The pyrin inflammasome also plays a role in mediating inflammation in other autoinflammatory diseases linked to dysregulation in the actin polymerization pathway. Therefore, the assembly of the pyrin inflammasome is initiated in response to fluctuations in cytoplasmic homeostasis and perturbations in cytoskeletal dynamics.
The generation and maintenance of intricate spatiotemporal patterns of gene expression in multicellular organisms requires the establishment of complex mechanisms of transcriptional regulation. Estimations that up to one million enhancers exist in the human genome accentuates the utmost importance of this type of cis-regulatory element for gene regulation. However, surprisingly little is known about the mechanisms used to temporarily or permanently activate or inactivate enhancers during cellular differentiation. The current work addresses the question how enhancer regulation can be achieved.
Using the chemokine (C-C motif) ligand gene Ccl22 as a model, the first example is based on the question how the activation of an enhancer can be prevented in a physiological context. Ccl22 is expressed by myeloid cells, such as dendritic cells, upon exposure to inflammatory stimuli. The expression in other cell types, such as fibroblasts, is prevented by the strong accumulation of H3K9me3 at the enhancer's proximal region. This accumulation is attenuated in myeloid cells through activity of the stimulus-induced demethylase Jmjd2d. To tease out which genomic fragment or fragments in the Ccl22 locus could be responsible for the maintenance of enhancer inactivity, potentially through the recruitment of H3K9 methyltransferases, the enhancer repressing capacity of 1 kb fragments of the gene locus was analysed in retroviral reporter assays. It was found that a fragment adjacent to the Ccl22 enhancer that overlaps with a member of a subfamily of long interspersed nuclear elements (LINEs) showed strong repressive potential on a model enhancer. Subsequent retroviral reporter assays with LINEs from loci of other stimulus-dependent genes identified additional LINE fragments that exhibit strong enhancer repressive capacity. These findings suggest a mechanism for enhancer silencing involving LINEs.
The second example concentrates on the inactivation of an enhancer during colorectal cancer (CRC) progression. The adenoma to carcinoma transition during CRC progression often is accompanied by a downregulation of the tumour suppressor gene EPHB2. The EMT inducing factor SNAIL1 strongly downregulated EPHB2 expression in a CRC cell model. To gain insights into the transcriptional regulation of EPHB2, potential cis-regulatory elements in the EPHB2 upstream region were analysed using reporter assays. A cell-type-specific enhancer was identified and subsequent chromatin analyses revealed a correlation between enhancer chromatin conformation and EPHB2 expression in different CRC cell lines. Additionally, the overexpression of the murine Snail1 induced chromatin changes at the EPHB2 enhancer towards a poised, transcriptionally silent chromatin conformation. Mutational analyses of the minimal enhancer region pinpointed three transcription factor binding motifs to be essential for full enhancer activity. Different binding patterns between CRC cell lines at the TCF/LEF motif were subsequently identified. Furthermore, a switch from TCF7L2 to LEF1 occupancy was found upon overexpression of Snail1 in vitro and in vivo. The generation of LS174T CRC cells overexpressing LEF1 confirmed the involvement of LEF1 in the downregulation of EPHB2 and the competitive displacement of TCF7L2. This part of the work demonstrated that the SNAIL1 induced downregulation of EPHB2 is dependent on the decommissioning of a transcriptional enhancer and led to a hypothetical model involving LEF1 and ZEB1.
In summary, this work highlighted two distinct mechanisms for enhancer regulation. One mechanism is based on enhancer repressive LINE fragments that might prevent stimulus-dependent enhancer activation. In the second, enhancer silencing was shown to be based on a competitive transcription factor binding mechanism.
Introduction: Antimicrobial resistance (AMR) has emerged as one of the leading threats to public health. AMR possesses a multidimensional challenge that has social, economic, and environmental dimensions that encompass the food production system, influencing human and animal health. The One Health approach highlights the inextricable linkage and interdependence between the health of people, animals, agriculture, and the environment. Antibiotic use in any of these One Health areas can potentially impact the health of other areas. There is a dearth of evidence on AMR from the natural environment, such as the plant-based agriculture sector. Antibiotics, antibiotic-resistant bacteria (ARB), and related AMR genes (ARGs) are assumed to present in the natural environment and disseminate resistance to fresh produce/vegetables and thus to human health upon consumption. Therefore, this study aims to investigate the role of vegetables in the spread of AMR through an agroecosystem exploration from a One Health perspective in Ahmedabad, India.
Protocol: The present study will be executed in Ahmedabad, located in Gujarat state in the Western part of India, by adopting a mixed-method approach. First, a systematic review will be conducted to document the prevalence of ARB and ARGs on fresh produce in South Asia. Second, agriculture farmland surveys will be used to collect the general farming practices and the data on common vegetables consumed raw by the households in Ahmedabad. Third, vegetable and soil samples will be collected from the selected agriculture farms and analyzed for the presence or absence of ARB and ARGs using standard microbiological and molecular methods.
Discussion: The analysis will help to understand the spread of ARB/ARGs through the agroecosystem. This is anticipated to provide an insight into the current state of ARB/ARGs contamination of fresh produce/vegetables and will assist in identifying the relevant strategies for effectively controlling and preventing the spread of AMR.
In recent years, the ability of intelligent systems to be understood by developers and users has received growing attention. This holds in particular for social robots, which are supposed to act autonomously in the vicinity of human users and are known to raise peculiar, often unrealistic attributions and expectations. However, explainable models that, on the one hand, allow a robot to generate lively and autonomous behavior and, on the other, enable it to provide human-compatible explanations for this behavior are missing. In order to develop such a self-explaining autonomous social robot, we have equipped a robot with own needs that autonomously trigger intentions and proactive behavior, and form the basis for understandable self-explanations. Previous research has shown that undesirable robot behavior is rated more positively after receiving an explanation. We thus aim to equip a social robot with the capability to automatically generate verbal explanations of its own behavior, by tracing its internal decision-making routes. The goal is to generate social robot behavior in a way that is generally interpretable, and therefore explainable on a socio-behavioral level increasing users' understanding of the robot's behavior. In this article, we present a social robot interaction architecture, designed to autonomously generate social behavior and self-explanations. We set out requirements for explainable behavior generation architectures and propose a socio-interactive framework for behavior explanations in social human-robot interactions that enables explaining and elaborating according to users' needs for explanation that emerge within an interaction. Consequently, we introduce an interactive explanation dialog flow concept that incorporates empirically validated explanation types. These concepts are realized within the interaction architecture of a social robot, and integrated with its dialog processing modules. We present the components of this interaction architecture and explain their integration to autonomously generate social behaviors as well as verbal self-explanations. Lastly, we report results from a qualitative evaluation of a working prototype in a laboratory setting, showing that (1) the robot is able to autonomously generate naturalistic social behavior, and (2) the robot is able to verbally self-explain its behavior to the user in line with users' requests.
Introduction. The experience of pain is regularly accompanied by facial expressions. The gold standard for analyzing these facial expressions is the Facial Action Coding System (FACS), which provides so-called action units (AUs) as parametrical indicators of facial muscular activity. Particular combinations of AUs have appeared to be pain-indicative. The manual coding of AUs is, however, too time- and labor-intensive in clinical practice. New developments in automatic facial expression analysis have promised to enable automatic detection of AUs, which might be used for pain detection. Objective. Our aim is to compare manual with automatic AU coding of facial expressions of pain. Methods. FaceReader7 was used for automatic AU detection. We compared the performance of FaceReader7 using videos of 40 participants (20 younger with a mean age of 25.7 years and 20 older with a mean age of 52.1 years) undergoing experimentally induced heat pain to manually coded AUs as gold standard labeling. Percentages of correctly and falsely classified AUs were calculated, and we computed as indicators of congruency, "sensitivity/recall," "precision," and "overall agreement (F1)." Results. The automatic coding of AUs only showed poor to moderate outcomes regarding sensitivity/recall, precision, and F1. The congruency was better for younger compared to older faces and was better for pain-indicative AUs compared to other AUs. Conclusion. At the moment, automatic analyses of genuine facial expressions of pain may qualify at best as semiautomatic systems, which require further validation by human observers before they can be used to validly assess facial expressions of pain.
Dieses Dokument präsentiert eine Zusammenfassung der Dissertation der Autorin. In dieser Dissertation [Ha20] wurde ein neuartiger und hybrider Ansatz für die Scha ̈tzung der Intensität von Gesichtsmuskelbewegungen (Action Unit (AU)) vorgeschlagen und validiert. Dieser Ansatz basiert auf einer Gauß’schen Zustandsschätzung und kombiniert ein verformbares, AU-basiertes Gesichtsformmodell, ein viskoelastisches Modell der Gesichtsmuskelbewegung, mehrere erscheinungsbasierten AU-Klassifikatoren und eine Methode zur Erkennung von Gesichtspunkten. Es wurden mehrere Erweiterungen vorgeschlagen und in das Zustandsschätzungs-Framework integriert, um mit den personenspezifischen Eigenschaften sowie technischen und praktischen Herausforderungen umzugehen.Die mit der vorgeschlagenen Methode erzeugten AU-Intensitätsschätzungen wurden für die automatische Erkennung von Schmerzen und für die Analyse von Fahrerablenkung eingesetzt.
This dissertation presents a probabilistic state estimation framework for integrating data-driven machine learning models and a deformable facial shape model in order to estimate continuous-valued intensities of 22 different facial muscle movements, known as Action Units (AU), defined in the Facial Action Coding System (FACS). A practical approach is proposed and validated for integrating class-wise probability scores from machine learning models within a Gaussian state estimation framework. Furthermore, driven mass-spring-damper models are applied for modelling the dynamics of facial muscle movements. Both facial shape and appearance information are used for estimating AU intensities, making it a hybrid approach. Several features are designed and explored to help the probabilistic framework to deal with multiple challenges involved in automatic AU detection. The proposed AU intensity estimation method and its features are evaluated quantitatively and qualitatively using three different datasets containing either spontaneous or acted facial expressions with AU annotations. The proposed method produced temporally smoother estimates that facilitate a fine-grained analysis of facial expressions. It also performed reasonably well, even though it simultaneously estimates intensities of 22 AUs, some of which are subtle in expression or resemble each other closely. The estimated AU intensities tended to the lower range of values, and were often accompanied by a small delay in onset. This shows that the proposed method is conservative. In order to further improve performance, state-of-the-art machine learning approaches for AU detection could be integrated within the proposed probabilistic AU intensity estimation framework.
It is only a matter of time until autonomous vehicles become ubiquitous; however, human driving supervision will remain a necessity for decades. To assess the drive's ability to take control over the vehicle in critical scenarios, driver distractions can be monitored using wearable sensors or sensors that are embedded in the vehicle, such as video cameras. The types of driving distractions that can be sensed with various sensors is an open research question that this study attempts to answer. This study compared data from physiological sensors (palm electrodermal activity (pEDA), heart rate and breathing rate) and visual sensors (eye tracking, pupil diameter, nasal EDA (nEDA), emotional activation and facial action units (AUs)) for the detection of four types of distractions. The dataset was collected in a previous driving simulation study. The statistical tests showed that the most informative feature/modality for detecting driver distraction depends on the type of distraction, with emotional activation and AUs being the most promising. The experimental comparison of seven classical machine learning (ML) and seven end-to-end deep learning (DL) methods, which were evaluated on a separate test set of 10 subjects, showed that when classifying windows into distracted or not distracted, the highest F1-score of 79%; was realized by the extreme gradient boosting (XGB) classifier using 60-second windows of AUs as input. When classifying complete driving sessions, XGB's F1-score was 94%. The best-performing DL model was a spectro-temporal ResNet, which realized an F1-score of 75%; when classifying segments and an F1-score of 87%; when classifying complete driving sessions. Finally, this study identified and discussed problems, such as label jitter, scenario overfitting and unsatisfactory generalization performance, that may adversely affect related ML approaches.
Towards an Interaction-Centered and Dynamically Constructed Episodic Memory for Social Robots
(2020)
Towards self-explaining social robots. Verbal explanation strategies for a needs-based architecture
(2019)
In order to establish long-term relationships with users, social companion robots and their behaviors need to be comprehensible. Purely reactive behavior such as answering questions or following commands can be readily interpreted by users. However, the robot's proactive behaviors, included in order to increase liveliness and improve the user experience, often raise a need for explanation. In this paper, we provide a concept to produce accessible “why-explanations” for the goal-directed behavior an autonomous, lively robot might produce. To this end we present an architecture that provides reasons for behaviors in terms of comprehensible needs and strategies of the robot, and we propose a model for generating different kinds of explanations.
Towards explaining deep learning networks to distinguish facial expressions of pain and emotions
(2018)
Deep learning networks are successfully used for object and face recognition in images and videos. In order to be able to apply such networks in practice, for example in hospitals as a pain recognition tool, the current procedures are only suitable to a limited extent. The advantage of deep learning methods is that they can learn complex non-linear relationships between raw data and target classes without limiting themselves to a set of hand-crafted features provided by humans. However, the disadvantage is that due to the complexity of these networks, it is not possible to interpret the knowledge that is stored inside the network. It is a black-box learning procedure. Explainable Artificial Intelligence (AI) approaches mitigate this problem by extracting explanations for decisions and representing them in a human-interpretable form. The aim of this paper is to investigate the explainable AI method Layer-wise Relevance Propagation (LRP) and apply it to explain how a deep learning network distinguishes facial expressions of pain from facial expressions of emotions such as happiness and disgust.