A Spatio-Temporal Graph Convolutional Network for Gesture Recognition from High-Density Electromyography

Accurate hand gesture prediction is crucial for effective upper-limb prosthetic limbs control. As the high flexibility and multiple degrees of freedom exhibited by human hands, there has been a growing interest in integrating deep networks with high-density surface electromyography (HD-sEMG) grids to enhance gesture recognition capabilities. However, many existing methods fall short in fully exploit the specific spatial topology and temporal dependencies present in HD-sEMG data. Additionally, these studies are often limited number of gestures and lack generality. Hence, this study introduces a novel gesture recognition method, named STGCN-GR, which leverages spatio-temporal graph convolution networks for HD-sEMG-based human-machine interfaces. Firstly, we construct muscle networks based on functional connectivity between channels, creating a graph representation of HD-sEMG recordings. Subsequently, a temporal convolution module is applied to capture the temporal dependences in the HD-sEMG series and a spatial graph convolution module is employed to effectively learn the intrinsic spatial topology information among distinct HD-sEMG channels. We evaluate our proposed model on a public HD-sEMG dataset comprising a substantial number of gestures (i.e., 65). Our results demonstrate the remarkable capability of the STGCN-GR method, achieving an impressive accuracy of 91.07% in predicting gestures, which surpasses state-of-the-art deep learning methods applied to the same dataset

Predicting Continuous Locomotion Modes via Multidimensional Feature Learning from sEMG

Walking-assistive devices require adaptive control methods to ensure smooth transitions between various modes of locomotion. For this purpose, detecting human locomotion modes (e.g., level walking or stair ascent) in advance is crucial for improving the intelligence and transparency of such robotic systems. This study proposes Deep-STF, a unified end-to-end deep learning model designed for integrated feature extraction in spatial, temporal, and frequency dimensions from surface electromyography (sEMG) signals. Our model enables accurate and robust continuous prediction of nine locomotion modes and 15 transitions at varying prediction time intervals, ranging from 100 to 500 ms. In addition, we introduced the concept of 'stable prediction time' as a distinct metric to quantify prediction efficiency. This term refers to the duration during which consistent and accurate predictions of mode transitions are made, measured from the time of the fifth correct prediction to the occurrence of the critical event leading to the task transition. This distinction between stable prediction time and prediction time is vital as it underscores our focus on the precision and reliability of mode transition predictions. Experimental results showcased Deep-STP's cutting-edge prediction performance across diverse locomotion modes and transitions, relying solely on sEMG data. When forecasting 100 ms ahead, Deep-STF surpassed CNN and other machine learning techniques, achieving an outstanding average prediction accuracy of 96.48%. Even with an extended 500 ms prediction horizon, accuracy only marginally decreased to 93.00%. The averaged stable prediction times for detecting next upcoming transitions spanned from 28.15 to 372.21 ms across the 100-500 ms time advances.Comment: 10 pages,7 figure

Conservation and novelty in the microRNA genomic landscape of hyperdiverse cichlid fishes

MicroRNAs (miRNAs) play crucial roles in the post-transcriptional control of messenger RNA (mRNA). These miRNA-mRNA regulatory networks are present in nearly all organisms and contribute to development, phenotypic divergence, and speciation. To examine the miRNA landscape of cichlid fishes, one of the most species-rich families of vertebrates, we profiled the expression of both miRNA and mRNA in a diverse set of cichlid lineages. Among these, we found that conserved miRNAs differ from recently arisen miRNAs (i.e. lineage specific) in average expression levels, number of target sites, sequence variability, and physical clustering patterns in the genome. Furthermore, conserved miRNA target sites tend to be enriched at the 5′ end of protein-coding gene 3′ UTRs. Consistent with the presumed regulatory role of miRNAs, we detected more negative correlations between the expression of miRNA-mRNA functional pairs than in random pairings. Finally, we provide evidence that novel miRNA targets sites are enriched in genes involved in protein synthesis pathways. Our results show how conserved and evolutionarily novel miRNAs differ in their contribution to the genomic landscape and highlight their particular evolutionary roles in the adaptive diversification of cichlids

Comparative genomic approaches to unravel the evolutionary mechanisms of cichlid fishes

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ANALYSIS OF THE THERMOPHYSICAL PROPERTIES AND INFLUENCING FACTORS OF VARIOUS ROCK TYPES FROM THE GUIZHOU PROVINCE

A series of analyses have been carried out on a number of rock types from the Guizhou Province to investigate their thermophysical properties. A total of 433 samples from 14 types of rock were collected, tested and analyzed. It was found that in this province, the average thermal conductivity of the samples ranged between 1.516±0.264 and 5.066±0.521 W/(m·K), the average specific heat capacity varied from 0.272±0.042 to 0.603±0.096 kJ/(kg·°C), and the average thermal diffusion coefficients were from 0.752±0.331 to 2.854±0.368 MJ/(m3·K). The older rocks always had higher thermal conductivity and thermal diffusion. Thermal conductivity and thermal diffusion of rocks are positively correlated with the mineral content of high thermal conductivity species, but the situation for the specific heat capacity is the opposite. With increasing mineral particle size, the thermal conductivity and thermal diffusion coefficient also increase, but the relationship with specific heat capacity is not obvious. The thermal conductivity and thermal diffusion coefficient of rocks increases under water saturated conditions compared to dry conditions, but the specific heat capacity decreases

The role of microRNAs in the repeated parallel diversification of lineages of Midas cichlid fish from Nicaragua

Cichlid fishes are an ideal model system for studying biological diversification because they provide textbook examples of rapid speciation. To date, there has been little focus on the role of gene regulation during cichlid speciation. However, in recent years, gene regulation has been recognized as a powerful force linking diversification in gene function to speciation. Here, we investigated the potential role of miRNA regulation in the diversification of six cichlid species of the Midas cichlid lineage (Amphilophus spp.) inhabiting the Nicaraguan crater lakes. Using several genomic resources, we inferred 236 Midas miRNA genes that were used to predict the miRNA target sites on 8,232 Midas 3′-UTRs. Using population genomic calculations of SNP diversity, we found the miRNA genes to be more conserved than protein coding genes. In contrast to what has been observed in other cichlid fish, but similar to what has been typically found in other groups, we observed genomic signatures of purifying selection on the miRNA targets by comparing these sites with the less conserved nontarget portion of the 3′-UTRs. However, in one species pair that has putatively speciated sympatrically in crater Lake Apoyo, we recovered a different pattern of relaxed purifying selection and high genetic divergence at miRNA targets. Our results suggest that sequence evolution at miRNA binding sites could be a critical genomic mechanism contributing to the rapid phenotypic evolution of Midas cichlids

Oryzias latipes updated annotation

The PASA v2.0.2 annotation pipeline was employed to incorporate gene structures, including UTRs, into the existing Oryzias latipes gene annotation, based on RNA-Seq data

Evolutionary divergence of 3’ UTRs in cichlid fishes

Abstract Background Post-transcriptional regulation is crucial for the control of eukaryotic gene expression and might contribute to adaptive divergence. The three prime untranslated regions (3’ UTRs), that are located downstream of protein-coding sequences, play important roles in post-transcriptional regulation. These regions contain functional elements that influence the fate of mRNAs and could be exceptionally important in groups such as rapidly evolving cichlid fishes. Results To examine cichlid 3’ UTR evolution, we 1) identified gene features in nine teleost genomes and 2) performed comparative analyses to assess evolutionary variation in length, functional motifs, and evolutionary rates of 3’ UTRs. In all nine teleost genomes, we found a smaller proportion of repetitive elements in 3’ UTRs than in the whole genome. We found that the 3’ UTRs in cichlids tend to be longer than those in non-cichlids, and this was associated, on average, with one more miRNA target per gene in cichlids. Moreover, we provided evidence that 3’ UTRs on average have evolved faster in cichlids than in non-cichlids. Finally, analyses of gene function suggested that both the top 5% longest and 5% most rapidly evolving 3’ UTRs in cichlids tended to be involved in ribosome-associated pathways and translation. Conclusions Our results reveal novel patterns of evolution in the 3’ UTRs of teleosts in general and cichlids in particular. The data suggest that 3’ UTRs might serve as important meta-regulators, regulators of other mechanisms governing post-transcriptional regulation, especially in groups like cichlids that have undergone extremely fast rates of phenotypic diversification and speciation

Astyanax mexicanus updated annotation

The PASA v2.0.2 annotation pipeline was employed to incorporate gene structures, including UTRs, into the existing Astyanax mexicanus gene annotation, based on RNA-Seq data