Hypoparathyroidism, sensorineural deafness, and renal dysgenesis syndrome with a mutation

Hypoparathyroidism, sensorineural deafness, and renal dysgenesis syndrome is an autosomal dominant disease caused by mutations in the GATA3 gene on chromosome 10p15. We identified a patient diagnosed with hypoparathyroidism who also had a family history of hypoparathyroidism and sensorineural deafness, present in the father. The patient was subsequently diagnosed and found to be a heterozygote for an insertion mutation c.255_256ins4 (GTGC) in exon 2 of GATA3. His father was also confirmed to have the same mutation in GATA3

Deep Convolutional Gated Recurrent Unit Combined with Attention Mechanism to Classify Pre-Ictal from Interictal EEG with Minimized Number of Channels

The early prediction of epileptic seizures is important to provide appropriate treatment because it can notify clinicians in advance. Various EEG-based machine learning techniques have been used for automatic seizure classification based on subject-specific paradigms. However, because subject-specific models tend to perform poorly on new patient data, a generalized model with a cross-patient paradigm is necessary for building a robust seizure diagnosis system. In this study, we proposed a generalized model that combines one-dimensional convolutional layers (1D CNN), gated recurrent unit (GRU) layers, and attention mechanisms to classify preictal and interictal phases. When we trained this model with ten minutes of preictal data, the average accuracy over eight patients was 82.86%, with 80% sensitivity and 85.5% precision, outperforming other state-of-the-art models. In addition, we proposed a novel application of attention mechanisms for channel selection. The personalized model using three channels with the highest attention score from the generalized model performed better than when using the smallest attention score. Based on these results, we proposed a model for generalized seizure predictors and a seizure-monitoring system with a minimized number of EEG channels

The effects of layer-wise relevance propagation-based feature selection for EEG classification: a comparative study on multiple datasets

IntroductionThe brain-computer interface (BCI) allows individuals to control external devices using their neural signals. One popular BCI paradigm is motor imagery (MI), which involves imagining movements to induce neural signals that can be decoded to control devices according to the user's intention. Electroencephalography (EEG) is frequently used for acquiring neural signals from the brain in the fields of MI-BCI due to its non-invasiveness and high temporal resolution. However, EEG signals can be affected by noise and artifacts, and patterns of EEG signals vary across different subjects. Therefore, selecting the most informative features is one of the essential processes to enhance classification performance in MI-BCI.MethodsIn this study, we design a layer-wise relevance propagation (LRP)-based feature selection method which can be easily integrated into deep learning (DL)-based models. We assess its effectiveness for reliable class-discriminative EEG feature selection on two different publicly available EEG datasets with various DL-based backbone models in the subject-dependent scenario.Results and discussionThe results show that LRP-based feature selection enhances the performance for MI classification on both datasets for all DL-based backbone models. Based on our analysis, we believe that it can broad its capability to different research domains

HCI in the Pool: A Case for Swimming

Swimming is one of the popular fitness activities. HCI related research includes topics such as assisting swimmers (e.g., record keeping and coaching) and gamifying swimming activities. In this position paper, we envision a wide range of swimming-based exertion games and explore various system design issues such as wearable sensor design, user interaction methods, platform support, and content design.1

MobyDick: an interactive multi-swimmer exergame

The unique aquatic nature of swimming makes it very difficult to use social or technical strategies to mitigate the tediousness of monotonous exercises. In this study, we propose MobyDick, a smartphone-based multi-player exergame designed to be used while swimming, in which a team of swimmers collaborate to hunt down a virtual monster. In this paper, we present a novel, holistic game design that takes into account both human factors and technical challenges. Firstly, we perform a comparative analysis of a variety of wireless networking technologies in the aquatic environment and identify various technical constraints on wireless networking. Secondly, we develop a single phone-based inertial and barometric stroke activity recognition system to enable precise, real-time game inputs. Thirdly, we carefully devise a multi-player interaction mode viable in the underwater environment highly limiting the abilities of human communication. Finally, we prototype MobyDick on waterproof off-the-shelf Android phones, and deploy it to real swimming pool environments (n = 8). Our qualitative analysis of user interview data reveals certain unique aspects of multi-player swimming games.2

Designing Interactive Multiswimmer Exergames: A Case Study

The unique aquatic nature of swimming makes it difficult to use social or technical strategies to mitigate the tediousness of monotonous exercises. In this study, we propose the use of a smartphone-based multiplayer exergame named MobyDick. MobyDick is designed to be played while swimming, where a team of swimmers collaborate to hunt down a virtual monster. To this end, we take into account both human factors and technical challenges under swimming contexts. First, we perform a comparative analysis of a variety of wireless networking technologies in the aquatic environment and identify various technical constraints on wireless networking. Second, we develop a swimming activity recognition system to enable precise and real-time game inputs. Third, we devise a multiplayer game design by employing the unique interaction mode viable in an underwater environment, where the abilities of human communication are highly limited. Finally, we prototype MobyDick on waterproof off-the-shelf Android phones, and we deploy it in real swimming pool environments (n = 8). Our qualitative analysis of user interview data reveals certain unique aspects of multiplayer swimming games.11Nsciescopu

Additional file 1 of Delay in psychiatric hospitalization from the diagnosis of first-episode schizophrenia and its association with clinical outcomes and direct medical costs: a nationwide, health insurance data-based study

Additional file 1: Supplementary Table 1.List of typical and atypical antipsychotics