Learning Representations from Heart Sound: A Comparative Study on Shallow and Deep Models.

Leveraging the power of artificial intelligence to facilitate an automatic analysis and monitoring of heart sounds has increasingly attracted tremendous efforts in the past decade. Nevertheless, lacking on standard open-access database made it difficult to maintain a sustainable and comparable research before the first release of the PhysioNet CinC Challenge Dataset. However, inconsistent standards on data collection, annotation, and partition are still restraining a fair and efficient comparison between different works. To this line, we introduced and benchmarked a first version of the Heart Sounds Shenzhen (HSS) corpus. Motivated and inspired by the previous works based on HSS, we redefined the tasks and make a comprehensive investigation on shallow and deep models in this study. First, we segmented the heart sound recording into shorter recordings (10 s), which makes it more similar to the human auscultation case. Second, we redefined the classification tasks. Besides using the 3 class categories (normal, moderate, and mild/severe) adopted in HSS, we added a binary classification task in this study, i.e., normal and abnormal. In this work, we provided detailed benchmarks based on both the classic machine learning and the state-of-the-art deep learning technologies, which are reproducible by using open-source toolkits. Last but not least, we analyzed the feature contributions of best performance achieved by the benchmark to make the results more convincing and interpretable.

Progressively shifting patterns of co-modulation among premotor cortex neurons carry dynamically similar signals during action execution and observation.

Many neurons in the premotor cortex show firing rate modulation whether the subject performs an action or observes another individual performing a similar action. Although such mirror neurons have been thought to have highly congruent discharge during execution and observation, many if not most show non-congruent activity. Studies of such neuronal populations have shown that the most prevalent patterns of co-modulation, captured as neural trajectories, pass through subspaces which are shared in part, but in part are visited exclusively during either execution or observation. These studies focused on reaching movements for which low-dimensional neural trajectories exhibit comparatively simple dynamical motifs. But the neural dynamics of hand movements are more complex. We developed a novel approach to examine prevalent patterns of co-modulation during execution and observation of a task that involved reaching, grasping, and manipulation. Rather than following neural trajectories in subspaces that contain their entire time course, we identified time series of instantaneous subspaces, calculated principal angles among them, sampled trajectory segments at the times of selected behavioral events, and projected those segments into the series of instantaneous subspaces. We found that instantaneous neural subspaces generally remained distinct during execution versus observation. Nevertheless, execution and observation could be partially aligned with canonical correlation, indicating some dynamical similarity of the neural representations of different movements relative to one another during execution and observation which may enable the nervous system to recognize corresponding actions performed by the subject or by another individual and/or may reflect social interaction between the two. During action execution, mirror neurons showed consistent patterns of co-modulation both within and between sessions, but other neurons that were modulated only during action execution and not during observation showed considerable variability of co-modulation. We speculate that during execution, mirror neurons carry a consistent forward model of the intended movement, while action-execution only neurons process more variable feedback.

Somatisation Disorder Detection via Speech: Introducing a Self-Supervised Learning Model.

With the depressive psychiatric disorders becoming more common, people are gradually starting to take it seriously. Somatisation disorders, as a general mental disorder, are rarely accurately identified in clinical diagnosis for its specific nature. In the previous work, speech recognition technology has been successfully applied to the task of identifying somatisation disorders on the Shenzhen Somatisation Speech Corpus. Nevertheless, there is still a scarcity of labels for somatisation disorder speech database. The current mainstream approaches in the speech recognition heavily rely on the well labelled data. Compared to supervised learning, self-supervised learning is able to achieve the same or even better recognition results while reducing the reliance on labelled samples. Moreover, self-supervised learning can generate general representations without the need for human hand-crafted features depending on the different recognition tasks. To this end, we apply self-supervised learning pre-trained models to solve few-labelled somatisation disorder speech recognition. In this study, we compare and analyse the results of three self-supervised learning models (contrastive predictive coding, wav2vec and wav2vec 2.0). The best result of wav2vec 2.0 model achieves 77.0 % unweighted average recall and is significantly better than CPC (p < .005), performing better than the benchmark of the supervised learning model.Clinical relevance- This work proposed a self-supervised learning model to resolve the few-labelled SD speech data, which can be well used for helping psychiatrists with clinical assistant to diagnosis. With this model, psychiatrists no longer need to spend a lot of time labelling SD speech data.

Less is More: A Novel Feature Extraction Method for Heart Sound Classification via Fractal Transformation.

Cardiovascular diseases (CVDs) are the leading cause of death globally. Heart sound signal analysis plays an important role in clinical detection and physical examination of CVDs. In recent years, auxiliary diagnosis technology of CVDs based on the detection of heart sound signals has become a research hotspot. The detection of abnormal heart sounds can provide important clinical information to help doctors diagnose and treat heart disease. We propose a new set of fractal features - fractal dimension (FD) - as the representation for classification and a Support Vector Machine (SVM) as the classification model. The whole process of the method includes cutting heart sounds, feature extraction, and classification of abnormal heart sounds. We compare the classification results of the heart sound waveform (time domain) and the spectrum (frequency domain) based on fractal features. Finally, according to the better classification results, we choose the fractal features that are most conducive for classification to obtain better classification performance. The features we propose outperform the widely used features significantly (p < .05 by one-tailed z-test) with a much lower dimension.Clinical relevance-The heart sound classification model based on fractal provides a new time-frequency analysis method for heart sound signals. A new effective mechanism is proposed to explore the relationship between the heart sound acoustic properties and the pathology of CVDs. As a non-invasive diagnostic method, this work could supply an idea for the preliminary screening of cardiac abnormalities through heart sounds.

O-GlcNAcylation: cellular physiology and therapeutic target for human diseases.

O-linked-ß-N-acetylglucosamine (O-GlcNAcylation) is a distinctive posttranslational protein modification involving the coordinated action of O-GlcNAc transferase and O-GlcNAcase, primarily targeting serine or threonine residues in various proteins. This modification impacts protein functionality, influencing stability, protein-protein interactions, and localization. Its interaction with other modifications such as phosphorylation and ubiquitination is becoming increasingly evident. Dysregulation of O-GlcNAcylation is associated with numerous human diseases, including diabetes, nervous system degeneration, and cancers. This review extensively explores the regulatory mechanisms of O-GlcNAcylation, its effects on cellular physiology, and its role in the pathogenesis of diseases. It examines the implications of aberrant O-GlcNAcylation in diabetes and tumorigenesis, highlighting novel insights into its potential role in cardiovascular diseases. The review also discusses the interplay of O-GlcNAcylation with other protein modifications and its impact on cell growth and metabolism. By synthesizing current research, this review elucidates the multifaceted roles of O-GlcNAcylation, providing a comprehensive reference for future studies. It underscores the potential of targeting the O-GlcNAcylation cycle in developing novel therapeutic strategies for various pathologies.

Non-coding RNAs in Kawasaki disease: Molecular mechanisms and clinical implications.

Kawasaki disease (KD) is an acute self-limiting vasculitis with coronary complications, usually occurring in children. The incidence of KD in children is increasing year by year, mainly in East Asian countries, but relatively stably in Europe and America. Although studies on KD have been reported, the pathogenesis of KD is unknown. With the development of high-throughput sequencing technology, growing number of regulatory noncoding RNAs (ncRNAs) including microRNA (miRNA), long noncoding RNA (lncRNA), and circular RNA (circRNA) have been identified to involved in KD. However, the role of ncRNAs in KD has not been comprehensively elucidated. Therefore, it is significative to study the regulatory role of ncRNA in KD, which might help to uncover new and effective therapeutic strategies for KD. In this review, we summarize recent studies on ncRNA in KD from the perspectives of immune disorders, inflammatory disorders, and endothelial dysfunction, and highlight the potential of ncRNAs as therapeutic targets for KD.

Research on the Correlation between Breeding Environment and Activity of Yellow Feather Broilers Based on the Multichromatic Aberration Model.

Broiler behavior is closely related to the breeding environment. Therefore, studying broiler behavior helps breeding farm workers to better carry out welfare breeding. In the breeding environment of yellow feather broilers, temperature, humidity, and ammonia concentration are the main factors that affect the behavior of the broilers. This study used a multichromatic aberration model to process the color images of yellow feather broilers to extract the activity feature of the broilers at different periods, utilized the Cb component of YCbCr color model and the b component of Lab color model to remove background litter in images, and employed the Q component of YIQ color model to remove the feeders and the drinkers from the image. The segmented images were constructed into an accumulator to generate a heat map of yellow feather broilers' activity. Then, the correlation between the activity and the temperature and humidity index (THI) and the correlation between the activity and ammonia concentration were explored. The experiment found that the activity of the broilers was significantly positively correlated with ammonia concentration (P < 0.05), indicating that the activity of yellow feather broilers increased with ammonia concentration ascending. Besides, the THI in the broiler chamber had a significant positive correlation with the ammonia data (P < 0.01), indicating that when the THI in the broiler chamber increases, the ammonia concentration rises. The research provides a direction for exploring the impact of THI and ammonia concentration on the performance of yellow feather broilers. At the same time, it provides a theoretical basis for the early warning and judgment of broiler breeding by farm workers in the future.

Impact of Initial Feeding and Molting on Tachypleus tridentatus Gut Microbiota.

Tri-spine horseshoe crabs (HSCs) Tachypleus tridentatus have been facing the threat of population depletion for decades, and the physiology and microbiology of their early life stages are lacking. To explore what directs the change of juvenile T. tridentatus gut microbiota and how gut microbiota change, by using 16S rRNA sequencing of gut samples we detected the intestinal microbiome of juvenile HSCs and compared the impact of initial molting and initial feeding, as well as the effect of environment. Results showed that the predominant phyla in the gut microbial community of juvenile HSCs are Proteobacteria and Bacteroidetes. The richness and diversity of intestinal microbes greatly decreased after initial molting. Microbial-mediated functions predicted by PICRUSt showed that "Signal Transduction", "Cellular Processes and Signaling", "Infective Diseases" and "Digestive System" pathways significantly increased in 2nd instars. As for the effect of environment, the connection between living environment and the intestinal microbiome started to manifest after initial molting. Unexpectedly, initial feeding treatment slightly affected the intestinal microbiome of T. tridentatus in the early life stage, whereas the effect of initial molting was significant. The present study provided the first insight into the gut microbiota of T. tridentatus, and the findings led a new sight to explain what guide the change of gut microbiota.

The genome sequence of Brassica campestris chrysovirus 1, a novel putative plant-infecting tripartite chrysovirus.

A putative chrysovirus recovered from Brassica campestris var. purpurea and provisionally named "Brassica campestris chrysovirus 1" (BrcCV1) was sequenced. The genome of the putative BrcCV1 consists of three double-stranded RNAs (dsRNAs) comprising 3,639 (dsRNA 1), 3,567 (dsRNA 2) and 3,337 (dsRNA 3) base pairs, respectively, each containing a single open reading frame (ORF 1-3). The putative proteins encoded by ORF 1-3 show homologies to RdRp, CP and chryso-P3 of approved or tentative chrysoviruses. In addition, the three dsRNAs of BrcCV1 contain highly conserved 5' and 3' untranslated regions (UTRs) in a way similar to known chrysoviruses. In a phylogenetic tree based on the conserved amino acid sequences of the RdRps of chrysoviruses, totiviruses and partitiviruses, the putative BrcCV1 formed a separate clade with Raphanus sativus chrysovirus 1 (RasCV1), a putative trisegmented, plant-infecting chrysovirus, in the family Chrysoviridae.