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Humans utilize their gaze to concentrate on essential information while perceiving and interpreting intentions in videos. Incorporating human gaze into computational algorithms can significantly enhance model performance in video understanding tasks. In this work, we address a challenging and innovative task in video understanding: predicting the actions of an agent in a video based on a partial video. We introduce the Gaze-guided Action Anticipation algorithm, which establishes a visual-semantic graph from the video input. Our method utilizes a Graph Neural Network to recognize the agent's intention and predict the action sequence to fulfill this intention. To assess the efficiency of our approach, we collect a dataset containing household activities generated in the VirtualHome environment, accompanied by human gaze data of viewing videos. Our method outperforms state-of-the-art techniques, achieving a 7\% improvement in accuracy for 18-class intention recognition. This highlights the efficiency of our method in learning important features from human gaze data., Comment: 2024 Symposium on Eye Tracking Research and Applications (ETRA24), Glasgow, United Kingdom

Eye-tracking applications that utilize the human gaze in video understanding tasks have become increasingly important. To effectively automate the process of video analysis based on eye-tracking data, it is important to accurately replicate human gaze behavior. However, this task presents significant challenges due to the inherent complexity and ambiguity of human gaze patterns. In this work, we introduce a novel method for simulating human gaze behavior. Our approach uses a transformer-based reinforcement learning algorithm to train an agent that acts as a human observer, with the primary role of watching videos and simulating human gaze behavior. We employed an eye-tracking dataset gathered from videos generated by the VirtualHome simulator, with a primary focus on activity recognition. Our experimental results demonstrate the effectiveness of our gaze prediction method by highlighting its capability to replicate human gaze behavior and its applicability for downstream tasks where real human-gaze is used as input., Comment: 2024 Symposium on Eye Tracking Research and Applications (ETRA24), Glasgow, United Kingdom

Adaptive user interfaces (UIs) automatically change an interface to better support users' tasks. Recently, machine learning techniques have enabled the transition to more powerful and complex adaptive UIs. However, a core challenge for adaptive user interfaces is the reliance on high-quality user data that has to be collected offline for each task. We formulate UI adaptation as a multi-agent reinforcement learning problem to overcome this challenge. In our formulation, a user agent mimics a real user and learns to interact with a UI. Simultaneously, an interface agent learns UI adaptations to maximize the user agent's performance. The interface agent learns the task structure from the user agent's behavior and, based on that, can support the user agent in completing its task. Our method produces adaptation policies that are learned in simulation only and, therefore, does not need real user data. Our experiments show that learned policies generalize to real users and achieve on par performance with data-driven supervised learning baselines.

Contains fulltext : 245024.pdf (Publisher’s version ) (Open Access), Witteveen-Kolk syndrome (OMIM 613406) is a recently defined neurodevelopmental syndrome caused by heterozygous loss-of-function variants in SIN3A. We define the clinical and neurodevelopmental phenotypes related to SIN3A-haploinsufficiency in 28 unreported patients. Patients with SIN3A variants adversely affecting protein function have mild intellectual disability, growth and feeding difficulties. Involvement of a multidisciplinary team including a geneticist, paediatrician and neurologist should be considered in managing these patients. Patients described here were identified through a combination of clinical evaluation and gene matching strategies (GeneMatcher and Decipher). All patients consented to participate in this study. Mean age of this cohort was 8.2 years (17 males, 11 females). Out of 16 patients ≥ 8 years old assessed, eight (50%) had mild intellectual disability (ID), four had moderate ID (22%), and one had severe ID (6%). Four (25%) did not have any cognitive impairment. Other neurological symptoms such as seizures (4/28) and hypotonia (12/28) were common. Behaviour problems were reported in a minority. In patients ≥2 years, three were diagnosed with Autism Spectrum Disorder (ASD) and four with Attention Deficit Hyperactivity Disorder (ADHD). We report 27 novel variants and one previously reported variant. 24 were truncating variants; three were missense variants and one large in-frame gain including exons 10-12.

Item does not contain fulltext, The study aimed at widening the clinical and genetic spectrum of ASXL3-related syndrome, a neurodevelopmental disorder, caused by truncating variants in the ASXL3 gene. In this international collaborative study, we have undertaken a detailed clinical and molecular analysis of 45 previously unpublished individuals with ASXL3-related syndrome, as well as a review of all previously published individuals. We have reviewed the rather limited functional characterization of pathogenic variants in ASXL3 and discuss current understanding of the consequences of the different ASXL3 variants. In this comprehensive analysis of ASXL3-related syndrome, we define its natural history and clinical evolution occurring with age. We report familial ASXL3 pathogenic variants, characterize the phenotype in mildly affected individuals and discuss nonpenetrance. We also discuss the role of missense variants in ASXL3. We delineate a variable but consistent phenotype. The most characteristic features are neurodevelopmental delay with consistently limited speech, significant neuro-behavioral issues, hypotonia, and feeding difficulties. Distinctive features include downslanting palpebral fissures, hypertelorism, tubular nose with a prominent nasal bridge, and low-hanging columella. The presented data will inform clinical management of individuals with ASXL3-related syndrome and improve interpretation of new ASXL3 sequence variants.

Witteveen-Kolk syndrome (OMIM 613406) is a recently defined neurodevelopmental syndrome caused by heterozygous loss-of-function variants in SIN3A. We define the clinical and neurodevelopmental phenotypes related to SIN3A-haploinsufficiency in 28 unreported patients. Patients with SIN3A variants adversely affecting protein function have mild intellectual disability, growth and feeding difficulties. Involvement of a multidisciplinary team including a geneticist, paediatrician and neurologist should be considered in managing these patients. Patients described here were identified through a combination of clinical evaluation and gene matching strategies (GeneMatcher and Decipher). All patients consented to participate in this study. Mean age of this cohort was 8.2 years (17 males, 11 females). Out of 16 patients ≥ 8 years old assessed, eight (50%) had mild intellectual disability (ID), four had moderate ID (22%), and one had severe ID (6%). Four (25%) did not have any cognitive impairment. Other neurological symptoms such as seizures (4/28) and hypotonia (12/28) were common. Behaviour problems were reported in a minority. In patients ≥2 years, three were diagnosed with Autism Spectrum Disorder (ASD) and four with Attention Deficit Hyperactivity Disorder (ADHD). We report 27 novel variants and one previously reported variant. 24 were truncating variants; three were missense variants and one large in-frame gain including exons 10–12.

Background and objective: Long-term electrocardiogram monitoring comes at the expense of signal quality. During unconstrained movements, the electrocardiogram is often corrupted by motion artefacts, which can lead to inaccurate physiological information. In this situation, automated quality assessment methods are useful to increase the reliability of the measurements. A generic machine learning pipeline that generates classification models for electrocardiogram quality assessment is presented in this article. The presented pipeline is tested on signals from varied acquisition sources, towards selecting segments that can be used for heart rate analysis in lifestyle applications. Methods: Electrocardiogram recordings from traditional, wearable and ubiquitous devices, are segmented in 10 s windows and manually labeled by experienced researchers into two quality classes. To capture the electrocardiogram dynamics, a comprehensive set of 43 features is extracted from each segment, based on the time-domain signal, its Fast Fourier Transform, the Autocorrelation function and the Stationary Wavelet Transform. To select the most relevant features for each acquisition source we employ both a customized hybrid approach and the state-of-the-art Neighborhood Component Analysis method and compare them. Support Vector Machines (SVM), Decision Trees, K-Nearest-Neighbors and supervised ensemble methods are tested as possible binary classifiers. Results: The results for the best performing models on traditional, wearable and ubiquitous electrocardiogram datasets are, respectively: balanced-accuracy: 89%, F1-score: 93% with the Fine Gaussian SVM model and 10 features; balanced-accuracy: 93%, F1-score: 93% with the Fine Gaussian SVM model and 11 features; balanced-accuracy: 95%, F1-score: 86%, with the Fine Gaussian SVM model and 8 features. Conclusions: According to the results, our generic pipeline can generate classification models tailored to individual acquisition sources, provi, Accepted author manuscript, Circuits and Systems

Witteveen-Kolk syndrome (OMIM 613406) is a recently defined neurodevelopmental syndrome caused by heterozygous loss-of-function variants in SIN3A. We define the clinical and neurodevelopmental phenotypes related to SIN3A-haploinsufficiency in 28 unreported patients. Patients with SIN3A variants adversely affecting protein function have mild intellectual disability, growth and feeding difficulties. Involvement of a multidisciplinary team including a geneticist, paediatrician and neurologist should be considered in managing these patients. Patients described here were identified through a combination of clinical evaluation and gene matching strategies (GeneMatcher and Decipher). All patients consented to participate in this study. Mean age of this cohort was 8.2 years (17 males, 11 females). Out of 16 patients ≥ 8 years old assessed, eight (50%) had mild intellectual disability (ID), four had moderate ID (22%), and one had severe ID (6%). Four (25%) did not have any cognitive impairment. Other neurological symptoms such as seizures (4/28) and hypotonia (12/28) were common. Behaviour problems were reported in a minority. In patients ≥2 years, three were diagnosed with Autism Spectrum Disorder (ASD) and four with Attention Deficit Hyperactivity Disorder (ADHD). We report 27 novel variants and one previously reported variant. 24 were truncating variants; three were missense variants and one large in-frame gain including exons 10-12.

Contains fulltext : 245024.pdf (Publisher’s version ) (Open Access), Witteveen-Kolk syndrome (OMIM 613406) is a recently defined neurodevelopmental syndrome caused by heterozygous loss-of-function variants in SIN3A. We define the clinical and neurodevelopmental phenotypes related to SIN3A-haploinsufficiency in 28 unreported patients. Patients with SIN3A variants adversely affecting protein function have mild intellectual disability, growth and feeding difficulties. Involvement of a multidisciplinary team including a geneticist, paediatrician and neurologist should be considered in managing these patients. Patients described here were identified through a combination of clinical evaluation and gene matching strategies (GeneMatcher and Decipher). All patients consented to participate in this study. Mean age of this cohort was 8.2 years (17 males, 11 females). Out of 16 patients ≥ 8 years old assessed, eight (50%) had mild intellectual disability (ID), four had moderate ID (22%), and one had severe ID (6%). Four (25%) did not have any cognitive impairment. Other neurological symptoms such as seizures (4/28) and hypotonia (12/28) were common. Behaviour problems were reported in a minority. In patients ≥2 years, three were diagnosed with Autism Spectrum Disorder (ASD) and four with Attention Deficit Hyperactivity Disorder (ADHD). We report 27 novel variants and one previously reported variant. 24 were truncating variants; three were missense variants and one large in-frame gain including exons 10-12.

Item does not contain fulltext, The study aimed at widening the clinical and genetic spectrum of ASXL3-related syndrome, a neurodevelopmental disorder, caused by truncating variants in the ASXL3 gene. In this international collaborative study, we have undertaken a detailed clinical and molecular analysis of 45 previously unpublished individuals with ASXL3-related syndrome, as well as a review of all previously published individuals. We have reviewed the rather limited functional characterization of pathogenic variants in ASXL3 and discuss current understanding of the consequences of the different ASXL3 variants. In this comprehensive analysis of ASXL3-related syndrome, we define its natural history and clinical evolution occurring with age. We report familial ASXL3 pathogenic variants, characterize the phenotype in mildly affected individuals and discuss nonpenetrance. We also discuss the role of missense variants in ASXL3. We delineate a variable but consistent phenotype. The most characteristic features are neurodevelopmental delay with consistently limited speech, significant neuro-behavioral issues, hypotonia, and feeding difficulties. Distinctive features include downslanting palpebral fissures, hypertelorism, tubular nose with a prominent nasal bridge, and low-hanging columella. The presented data will inform clinical management of individuals with ASXL3-related syndrome and improve interpretation of new ASXL3 sequence variants.

Item does not contain fulltext, The study aimed at widening the clinical and genetic spectrum of ASXL3-related syndrome, a neurodevelopmental disorder, caused by truncating variants in the ASXL3 gene. In this international collaborative study, we have undertaken a detailed clinical and molecular analysis of 45 previously unpublished individuals with ASXL3-related syndrome, as well as a review of all previously published individuals. We have reviewed the rather limited functional characterization of pathogenic variants in ASXL3 and discuss current understanding of the consequences of the different ASXL3 variants. In this comprehensive analysis of ASXL3-related syndrome, we define its natural history and clinical evolution occurring with age. We report familial ASXL3 pathogenic variants, characterize the phenotype in mildly affected individuals and discuss nonpenetrance. We also discuss the role of missense variants in ASXL3. We delineate a variable but consistent phenotype. The most characteristic features are neurodevelopmental delay with consistently limited speech, significant neuro-behavioral issues, hypotonia, and feeding difficulties. Distinctive features include downslanting palpebral fissures, hypertelorism, tubular nose with a prominent nasal bridge, and low-hanging columella. The presented data will inform clinical management of individuals with ASXL3-related syndrome and improve interpretation of new ASXL3 sequence variants.

Contains fulltext : 245024.pdf (Publisher’s version ) (Open Access), Witteveen-Kolk syndrome (OMIM 613406) is a recently defined neurodevelopmental syndrome caused by heterozygous loss-of-function variants in SIN3A. We define the clinical and neurodevelopmental phenotypes related to SIN3A-haploinsufficiency in 28 unreported patients. Patients with SIN3A variants adversely affecting protein function have mild intellectual disability, growth and feeding difficulties. Involvement of a multidisciplinary team including a geneticist, paediatrician and neurologist should be considered in managing these patients. Patients described here were identified through a combination of clinical evaluation and gene matching strategies (GeneMatcher and Decipher). All patients consented to participate in this study. Mean age of this cohort was 8.2 years (17 males, 11 females). Out of 16 patients ≥ 8 years old assessed, eight (50%) had mild intellectual disability (ID), four had moderate ID (22%), and one had severe ID (6%). Four (25%) did not have any cognitive impairment. Other neurological symptoms such as seizures (4/28) and hypotonia (12/28) were common. Behaviour problems were reported in a minority. In patients ≥2 years, three were diagnosed with Autism Spectrum Disorder (ASD) and four with Attention Deficit Hyperactivity Disorder (ADHD). We report 27 novel variants and one previously reported variant. 24 were truncating variants; three were missense variants and one large in-frame gain including exons 10-12.

Background and objective: Long-term electrocardiogram monitoring comes at the expense of signal quality. During unconstrained movements, the electrocardiogram is often corrupted by motion artefacts, which can lead to inaccurate physiological information. In this situation, automated quality assessment methods are useful to increase the reliability of the measurements. A generic machine learning pipeline that generates classification models for electrocardiogram quality assessment is presented in this article. The presented pipeline is tested on signals from varied acquisition sources, towards selecting segments that can be used for heart rate analysis in lifestyle applications. Methods: Electrocardiogram recordings from traditional, wearable and ubiquitous devices, are segmented in 10 s windows and manually labeled by experienced researchers into two quality classes. To capture the electrocardiogram dynamics, a comprehensive set of 43 features is extracted from each segment, based on the time-domain signal, its Fast Fourier Transform, the Autocorrelation function and the Stationary Wavelet Transform. To select the most relevant features for each acquisition source we employ both a customized hybrid approach and the state-of-the-art Neighborhood Component Analysis method and compare them. Support Vector Machines (SVM), Decision Trees, K-Nearest-Neighbors and supervised ensemble methods are tested as possible binary classifiers. Results: The results for the best performing models on traditional, wearable and ubiquitous electrocardiogram datasets are, respectively: balanced-accuracy: 89%, F1-score: 93% with the Fine Gaussian SVM model and 10 features; balanced-accuracy: 93%, F1-score: 93% with the Fine Gaussian SVM model and 11 features; balanced-accuracy: 95%, F1-score: 86%, with the Fine Gaussian SVM model and 8 features. Conclusions: According to the results, our generic pipeline can generate classification models tailored to individual acquisition sources, provi, Accepted author manuscript, Signal Processing Systems

Witteveen-Kolk syndrome (OMIM 613406) is a recently defined neurodevelopmental syndrome caused by heterozygous loss-of-function variants in SIN3A. We define the clinical and neurodevelopmental phenotypes related to SIN3A-haploinsufficiency in 28 unreported patients. Patients with SIN3A variants adversely affecting protein function have mild intellectual disability, growth and feeding difficulties. Involvement of a multidisciplinary team including a geneticist, paediatrician and neurologist should be considered in managing these patients. Patients described here were identified through a combination of clinical evaluation and gene matching strategies (GeneMatcher and Decipher). All patients consented to participate in this study. Mean age of this cohort was 8.2 years (17 males, 11 females). Out of 16 patients ≥ 8 years old assessed, eight (50%) had mild intellectual disability (ID), four had moderate ID (22%), and one had severe ID (6%). Four (25%) did not have any cognitive impairment. Other neurological symptoms such as seizures (4/28) and hypotonia (12/28) were common. Behaviour problems were reported in a minority. In patients ≥2 years, three were diagnosed with Autism Spectrum Disorder (ASD) and four with Attention Deficit Hyperactivity Disorder (ADHD). We report 27 novel variants and one previously reported variant. 24 were truncating variants; three were missense variants and one large in-frame gain including exons 10–12.

In this thesis, model-based analysis approach is considered as a possible solution towards a patient-specific point-of-care device for the purpose of electrocardiogram monitoring. Two novel methods are proposed, tested, and quantitatively evaluated. First, a method for estimating the instantaneous heart rate using the morphologicalfeatures of one electrocardiogram beat at a time is proposed. This work is not aimed at introducing an alternative way for heart rate estimation, but rather illustrates the utility of model-basedelectrocardiogram analysis in online individualized monitoring ofthe heart function. The heart rate estimation problem is reduced to fitting one parameter, whose value is related to the nine parameters of a realistic nonlinear model of the electrocardiogram and estimated from data by nonlinear least-squares optimization. The method feasibility is evaluated on synthetic electrocardiogram signals as well as signals acquired from MIT-BIH databases at Physionet website. Moreover, the performance of the method was tested under realistic free-moving conditions using a wearable electrocardiogram and heart monitor with encouraging results. Second, a model-based method for patient-specific detection of deformed electrocardiogram beats is proposed. Five parameters of a patient-specific nonlinear electrocardiogram model are estimated from data by nonlinear least-squares optimization. The normal variability of the model parameters is captured by estimated probability density functions. A binary classifier, based on stochastic anomaly detection methods, along with a pre-tuned classification threshold, is employed for detecting the abnormal electrocardiogram beats. The utility of the proposed approach is tested by validating it on annotated arrhythmia data recorded underclinical conditions.

A method for estimating the instantaneous heart rate (HR) using the morphological features of one electrocardiogram (ECG) cycle (beat) at a time is proposed. This work is not aimed at introducing an alternative way for HR estimation, but rather illustrates the utility of model-based ECG analysis in online individualized monitoring of the heart function. The HR estimation problem is reduced to fitting one parameter, whose value is related to the nine parameters of a realistic nonlinear model of the ECG and estimated from data by nonlinear least-squares optimization. The method feasibility is evaluated on synthetic ECG signals as well as signals acquired from MIT-BIH databases at Physionet website. Moreover, the performance of the method was tested under realistic free-moving conditions using a wearable ECG and HR monitor with encouraging results.

A novel model-based method for patient-specific detection of deformed electrocardiogram (ECG) beats is proposed and tested. Five parameters of a patient-specific nonlinear ECG model are estimated from data by nonlinear least-squares optimization. The normal variability of the model parameters is captured by estimated probability density functions. A binary classifier, based on stochastic anomaly detection methods, along with a pre-tuned classification threshold, is employed for detecting the abnormal ECG beats. We demonstrate the utility of the proposed approach by validating it on annotated arrhythmia data recorded under clinical conditions.

In this thesis, model-based analysis approach is considered as a possible solution towards a patient-specific point-of-care device for the purpose of electrocardiogram monitoring. Two novel methods are proposed, tested, and quantitatively evaluated. First, a method for estimating the instantaneous heart rate using the morphologicalfeatures of one electrocardiogram beat at a time is proposed. This work is not aimed at introducing an alternative way for heart rate estimation, but rather illustrates the utility of model-basedelectrocardiogram analysis in online individualized monitoring ofthe heart function. The heart rate estimation problem is reduced to fitting one parameter, whose value is related to the nine parameters of a realistic nonlinear model of the electrocardiogram and estimated from data by nonlinear least-squares optimization. The method feasibility is evaluated on synthetic electrocardiogram signals as well as signals acquired from MIT-BIH databases at Physionet website. Moreover, the performance of the method was tested under realistic free-moving conditions using a wearable electrocardiogram and heart monitor with encouraging results. Second, a model-based method for patient-specific detection of deformed electrocardiogram beats is proposed. Five parameters of a patient-specific nonlinear electrocardiogram model are estimated from data by nonlinear least-squares optimization. The normal variability of the model parameters is captured by estimated probability density functions. A binary classifier, based on stochastic anomaly detection methods, along with a pre-tuned classification threshold, is employed for detecting the abnormal electrocardiogram beats. The utility of the proposed approach is tested by validating it on annotated arrhythmia data recorded underclinical conditions.

In this paper, a synergistic combination of deep reinforcement learning and hierarchical game theory is proposed as a modeling framework for behavioral predictions of drivers in highway driving scenarios. The need for a modeling framework that can address multiple human-human and human-automation interactions, where all the agents can be modeled as decision makers simultaneously, is the main motivation behind this work. Such a modeling framework may be utilized for the validation and verification of autonomous vehicles: It is estimated that for an autonomous vehicle to reach the same safety level of cars with drivers, millions of miles of driving tests are required. The modeling framework presented in this paper may be used in a high-fidelity traffic simulator consisting of multiple human decision makers to reduce the time and effort spent for testing by allowing safe and quick assessment of self-driving algorithms. To demonstrate the fidelity of the proposed modeling framework, game theoretical driver models are compared with real human driver behavior patterns extracted from traffic data., Comment: 22 pages, 19 figures

Recent advances in camera equipped drone applications and their widespread use increased the demand on vision based object detection algorithms for aerial images. Object detection process is inherently a challenging task as a generic computer vision problem, however, since the use of object detection algorithms on UAVs (or on drones) is relatively a new area, it remains as a more challenging problem to detect objects in aerial images. There are several reasons for that including: (i) the lack of large drone datasets including large object variance, (ii) the large orientation and scale variance in drone images when compared to the ground images, and (iii) the difference in texture and shape features between the ground and the aerial images. Deep learning based object detection algorithms can be classified under two main categories: (a) single-stage detectors and (b) multi-stage detectors. Both single-stage and multi-stage solutions have their advantages and disadvantages over each other. However, a technique to combine the good sides of each of those solutions could yield even a stronger solution than each of those solutions individually. In this paper, we propose an ensemble network, SyNet, that combines a multi-stage method with a single-stage one with the motivation of decreasing the high false negative rate of multi-stage detectors and increasing the quality of the single-stage detector proposals. As building blocks, CenterNet and Cascade R-CNN with pretrained feature extractors are utilized along with an ensembling strategy. We report the state of the art results obtained by our proposed solution on two different datasets: namely MS-COCO and visDrone with \%52.1 $mAP_{IoU = 0.75}$ is obtained on MS-COCO $val2017$ dataset and \%26.2 $mAP_{IoU = 0.75}$ is obtained on VisDrone $test-set$., Comment: accepted for publication at ICPR 2020

Predicting the outcomes of cyber-physical systems with multiple human interactions is a challenging problem. This article reviews a game theoretical approach to address this issue, where reinforcement learning is employed to predict the time-extended interaction dynamics. We explain that the most attractive feature of the method is proposing a computationally feasible approach to simultaneously model multiple humans as decision makers, instead of determining the decision dynamics of the intelligent agent of interest and forcing the others to obey certain kinematic and dynamic constraints imposed by the environment. We present two recent exploitations of the method to model 1) unmanned aircraft integration into the National Airspace System and 2) highway traffic. We conclude the article by providing ongoing and future work about employing, improving and validating the method. We also provide related open problems and research opportunities.

In this research, a computer simulation was carried out using the famous electronic systems simulation program PROTEUS 8 ISIS to design the circuit to collect the heart and chest sounds from the surface of the human body (electronic stethoscope). The audio signal received from the output of the analog Multiplexer was processed using ATmega8 microcontroller. The experimental results were performed on the first cardiac signal S1 using the ADC and DAC circuits used in analog to digital conversion to signal the taking of 20 random samples from the signal. Keywords: stethoscope, acoustic, Heart sounds, controller, analog to digital convertor, digital to analog convertor.

In this research, a computer simulation was carried out using the famous electronic systems simulation program PROTEUS 8 ISIS to design the circuit to collect the heart and chest sounds from the surface of the human body (electronic stethoscope). The audio signal received from the output of the analog Multiplexer was processed using ATmega8 microcontroller. The experimental results were performed on the first cardiac signal S1 using the ADC and DAC circuits used in analog to digital conversion to signal the taking of 20 random samples from the signal. Keywords: stethoscope, acoustic, Heart sounds, controller, analog to digital convertor, digital to analog convertor.

Purpose. The study aimed at widening the clinical and genetic spectrum of ASXL3-related syndrome, a neurodevelopmental disorder, caused by truncating variants in the ASXL3 gene. Methods. In this international collaborative study, we have undertaken a detailed clinical and molecular analysis of 45 previously unpublished individuals with ASXL3-related syndrome, as well as a review of all previously published individuals. We have reviewed the rather limited functional characterisation of pathogenic variants in ASXL3 and discuss current understanding of the consequences of the different ASXL3 variants. Results. In this comprehensive analysis of ASXL3-related syndrome, we define its natural history and clinical evolution occurring with age. We report familial ASXL3 pathogenic variants, characterise the phenotype in mildly affected individuals and discuss non-penetrance. We also discuss the role of missense variants in ASXL3. We delineate a variable but consistent phenotype. The most characteristic features are neurodevelopmental delay with consistently limited speech, significant neuro-behavioural issues, hypotonia and feeding difficulties. Distinctive features include down-slanting palpebral fissures, hypertelorism, tubular nose with a prominent nasal bridge and low-hanging columella. Conclusion. The presented data will inform clinical management of individuals with ASXL3-related syndrome and improve interpretation of new ASXL3 sequence variants.

Purpose. The study aimed at widening the clinical and genetic spectrum of ASXL3-related syndrome, a neurodevelopmental disorder, caused by truncating variants in the ASXL3 gene. Methods. In this international collaborative study, we have undertaken a detailed clinical and molecular analysis of 45 previously unpublished individuals with ASXL3-related syndrome, as well as a review of all previously published individuals. We have reviewed the rather limited functional characterisation of pathogenic variants in ASXL3 and discuss current understanding of the consequences of the different ASXL3 variants. Results. In this comprehensive analysis of ASXL3-related syndrome, we define its natural history and clinical evolution occurring with age. We report familial ASXL3 pathogenic variants, characterise the phenotype in mildly affected individuals and discuss non-penetrance. We also discuss the role of missense variants in ASXL3. We delineate a variable but consistent phenotype. The most characteristic features are neurodevelopmental delay with consistently limited speech, significant neuro-behavioural issues, hypotonia and feeding difficulties. Distinctive features include down-slanting palpebral fissures, hypertelorism, tubular nose with a prominent nasal bridge and low-hanging columella. Conclusion. The presented data will inform clinical management of individuals with ASXL3-related syndrome and improve interpretation of new ASXL3 sequence variants.