TraHGR: Transformer for Hand Gesture Recognition via Electromyography.

Deep learning-based Hand Gesture Recognition (HGR) via surface Electromyogram (sEMG) signals have recently shown considerable potential for development of advanced myoelectric-controlled prosthesis. Although deep learning techniques can improve HGR accuracy compared to their classical counterparts, classifying hand movements based on sparse multichannel sEMG signals is still a challenging task. Furthermore, existing deep learning approaches, typically, include only one model as such can hardly extract representative features. In this paper, we aim to address this challenge by capitalizing on the recent advances in hybrid models and transformers. In other words, we propose a hybrid framework based on the transformer architecture, which is a relatively new and revolutionizing deep learning model. The proposed hybrid architecture, referred to as the Transformer for Hand Gesture Recognition (TraHGR), consists of two parallel paths followed by a linear layer that acts as a fusion center to integrate the advantage of each module. We evaluated the proposed architecture TraHGR based on the commonly used second Ninapro dataset, referred to as the DB2. The sEMG signals in the DB2 dataset are measured in real-life conditions from 40 healthy users, each performing 49 gestures. We have conducted an extensive set of experiments to test and validate the proposed TraHGR architecture, and compare its achievable accuracy with several recently proposed HGR classification algorithms over the same dataset. We have also compared the results of the proposed TraHGR architecture with each individual path and demonstrated the distinguishing power of the proposed hybrid architecture. The recognition accuracies of the proposed TraHGR architecture for the window of size 200ms and step size of 100ms are 86.00%, 88.72%, 81.27%, and 93.74%, which are 2.30%, 4.93%, 8.65%, and 4.20% higher than the state-of-the-art performance for DB2 (49 gestures), DB2-B (17 gestures), DB2-C (23 gestures), and DB2-D (9 gestures), respectively.

Signaling-induced systematic repression of miRNAs uncovers cancer vulnerabilities and targeted therapy sensitivity.

Targeted therapies are effective in treating cancer, but success depends on identifying cancer vulnerabilities. In our study, we utilize small RNA sequencing to examine the impact of pathway activation on microRNA (miRNA) expression patterns. Interestingly, we discover that miRNAs capable of inhibiting key members of activated pathways are frequently diminished. Building on this observation, we develop an approach that integrates a low-miRNA-expression signature to identify druggable target genes in cancer. We train and validate our approach in colorectal cancer cells and extend it to diverse cancer models using patient-derived in vitro and in vivo systems. Finally, we demonstrate its additional value to support genomic and transcriptomic-based drug prediction strategies in a pan-cancer patient cohort from the National Center for Tumor Diseases (NCT)/German Cancer Consortium (DKTK) Molecularly Aided Stratification for Tumor Eradication (MASTER) precision oncology trial. In conclusion, our strategy can predict cancer vulnerabilities with high sensitivity and accuracy and might be suitable for future therapy recommendations in a variety of cancer subtypes.

Transformer-based hand gesture recognition from instantaneous to fused neural decomposition of high-density EMG signals.

Designing efficient and labor-saving prosthetic hands requires powerful hand gesture recognition algorithms that can achieve high accuracy with limited complexity and latency. In this context, the paper proposes a Compact Transformer-based Hand Gesture Recognition framework referred to as [Formula: see text], which employs a vision transformer network to conduct hand gesture recognition using high-density surface EMG (HD-sEMG) signals. Taking advantage of the attention mechanism, which is incorporated into the transformer architectures, our proposed [Formula: see text] framework overcomes major constraints associated with most of the existing deep learning models such as model complexity; requiring feature engineering; inability to consider both temporal and spatial information of HD-sEMG signals, and requiring a large number of training samples. The attention mechanism in the proposed model identifies similarities among different data segments with a greater capacity for parallel computations and addresses the memory limitation problems while dealing with inputs of large sequence lengths. [Formula: see text] can be trained from scratch without any need for transfer learning and can simultaneously extract both temporal and spatial features of HD-sEMG data. Additionally, the [Formula: see text] framework can perform instantaneous recognition using sEMG image spatially composed from HD-sEMG signals. A variant of the [Formula: see text] is also designed to incorporate microscopic neural drive information in the form of Motor Unit Spike Trains (MUSTs) extracted from HD-sEMG signals using Blind Source Separation (BSS). This variant is combined with its baseline version via a hybrid architecture to evaluate potentials of fusing macroscopic and microscopic neural drive information. The utilized HD-sEMG dataset involves 128 electrodes that collect the signals related to 65 isometric hand gestures of 20 subjects. The proposed [Formula: see text] framework is applied to 31.25, 62.5, 125, 250 ms window sizes of the above-mentioned dataset utilizing 32, 64, 128 electrode channels. Our results are obtained via 5-fold cross-validation by first applying the proposed framework on the dataset of each subject separately and then, averaging the accuracies among all the subjects. The average accuracy over all the participants using 32 electrodes and a window size of 31.25 ms is 86.23%, which gradually increases till reaching 91.98% for 128 electrodes and a window size of 250 ms. The [Formula: see text] achieves accuracy of 89.13% for instantaneous recognition based on a single frame of HD-sEMG image. The proposed model is statistically compared with a 3D Convolutional Neural Network (CNN) and two different variants of Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) models. The accuracy results for each of the above-mentioned models are paired with their precision, recall, F1 score, required memory, and train/test times. The results corroborate effectiveness of the proposed [Formula: see text] framework compared to its counterparts.

ViT-HGR: Vision Transformer-based Hand Gesture Recognition from High Density Surface EMG Signals.

Recently, there has been a surge of significant interest on application of Deep Learning (DL) models to autonomously perform hand gesture recognition using surface Electromyogram (sEMG) signals. Many of the existing DL models are, however, designed to be applied on sparse sEMG signals. Furthermore, due to the complex structure of these models, typically, we are faced with memory constraint issues, require large training times and a large number of training samples, and; there is the need to resort to data augmentation and/or transfer learning. In this paper, for the first time (to the best of our knowledge), we investigate and design a Vision Transformer (ViT) based architecture to perform hand gesture recognition from High Density (HD-sEMG) signals. Intuitively speaking, we capitalize on the recent breakthrough role of the transformer architecture in tackling different com-plex problems together with its potential for employing more input parallelization via its attention mechanism. The proposed Vision Transformer-based Hand Gesture Recognition (ViT-HGR) framework can overcome the aforementioned training time problems and can accurately classify a large number of hand gestures from scratch without any need for data augmentation and/or transfer learning. The efficiency of the proposed ViT-HGR framework is evaluated using a recently-released HD-sEMG dataset consisting of 65 isometric hand gestures. Our experiments with 64-sample (31.25 ms) window size yield average test accuracy of 84.62 ± 3.07%, where only 78,210 learnable parameters are utilized in the model. The compact structure of the proposed ViT-based ViT-HGR framework (i.e., having significantly reduced number of trainable parameters) shows great potentials for its practical application for prosthetic control.

Involvement of classic and alternative non-homologous end joining pathways in hematologic malignancies: targeting strategies for treatment.

Chromosomal translocations are the main etiological factor of hematologic malignancies. These translocations are generally the consequence of aberrant DNA double-strand break (DSB) repair. DSBs arise either exogenously or endogenously in cells and are repaired by major pathways, including non-homologous end-joining (NHEJ), homologous recombination (HR), and other minor pathways such as alternative end-joining (A-EJ). Therefore, defective NHEJ, HR, or A-EJ pathways force hematopoietic cells toward tumorigenesis. As some components of these repair pathways are overactivated in various tumor entities, targeting these pathways in cancer cells can sensitize them, especially resistant clones, to radiation or chemotherapy agents. However, targeted therapy-based studies are currently underway in this area, and furtherly there are some biological pitfalls, clinical issues, and limitations related to these targeted therapies, which need to be considered. This review aimed to investigate the alteration of DNA repair elements of C-NHEJ and A-EJ in hematologic malignancies and evaluate the potential targeted therapies against these pathways.

FS-HGR: Few-Shot Learning for Hand Gesture Recognition via Electromyography.

This work is motivated by the recent advances in Deep Neural Networks (DNNs) and their widespread applications in human-machine interfaces. DNNs have been recently used for detecting the intended hand gesture through the processing of surface electromyogram (sEMG) signals. Objective: Although DNNs have shown superior accuracy compared to conventional methods when large amounts of data are available for training, their performance substantially decreases when data are limited. Collecting large datasets for training may be feasible in research laboratories, but it is not a practical approach for real-life applications. The main objective of this work is to design a modern DNN-based gesture detection model that relies on minimal training data while providing high accuracy. Methods: We propose the novel Few-Shot learning- Hand Gesture Recognition (FS-HGR) architecture. Few-shot learning is a variant of domain adaptation with the goal of inferring the required output based on just one or a few training observations. The proposed FS-HGR generalizes after seeing very few observations from each class by combining temporal convolutions with attention mechanisms. This allows the meta-learner to aggregate contextual information from experience and to pinpoint specific pieces of information within its available set of inputs. Data Source & Summary of Results: The performance of FS-HGR was tested on the second and fifth Ninapro databases, referred to as the DB2 and DB5, respectively. The DB2 consists of 50 gestures (rest included) from 40 healthy subjects. The Ninapro DB5 contains data from 10 healthy participants performing a total of 53 different gestures (rest included). The proposed approach for the Ninapro DB2 led to 85.94% classification accuracy on new repetitions with few-shot observation (5-way 5-shot), 81.29% accuracy on new subjects with few-shot observation (5-way 5-shot), and 73.36% accuracy on new gestures with few-shot observation (5-way 5-shot). Moreover, the proposed approach for the Ninapro DB5 led to 64.65% classification accuracy on new subjects with few-shot observation (5-way 5-shot).

GDF-15 negatively regulates excess erythropoiesis and its overexpression is involved in erythroid hyperplasia.

Growth differentiation factor-15 (GDF-15) is a member of TGF-ß superfamily. Among hematopoietic cells, this factor is mainly produced by erythroid series and is recently considered a biomarker of ineffective erythropoiesis (IE). Whether IE induces enhanced GDF-15 expression or is prompted by it, has remained elusive. In this study we investigated how high levels of GDF-15 contribute to IE-associated erythroid dysplasia. We assessed mRNA levels of GDF-15 during erythroid maturation as well as in patients with IE using qRT-PCR. Later, the erythroid colony-forming capacity of GDF-15-treated hematopoietic stem cells (HSCs) was evaluated by CFC assay. Any effect of elevated levels of GDF-15 on erythroid maturation was ultimately examined by expression analysis of erythroid-associated transcription factors and flow cytometry analysis of CD235a expression. GDF-15 mRNA expression increased during erythroid differentiation and also in ß-thalassemia and MDS patients which was directly correlated with erythropoiesis severity. Treating the cells with high GDF-15 concentration (50 ng/ml) resulted in an approximate 30% decline in the capacity of erythroid colony formation of HSCs and CD235a positive cells. Additionally, erythroid-specific transcription factors showed significant down-regulation in the early stages of erythroid differentiation. According to the expression level of GDF-15 and the role it plays in the erythroid system, high-levels of this factor could be an auto-modulatory mechanism to control the excessive production of erythroid cells.

DNA repair pathways as guardians of the genome: Therapeutic potential and possible prognostic role in hematologic neoplasms.

DNA repair pathways, which are also identified as guardians of the genome, protect cells from frequent damage that can lead to DNA breaks. The most deleterious types of damage are double-strand breaks (DSBs), which are repaired by homologous recombination (HR) and non-homologous end joining (NHEJ). Single strand breaks (SSBs) can be corrected through base excision repair (BER), nucleotide excision repair (NER), and mismatch repair (MMR). Failure to restore DNA lesions or inappropriately repaired DNA damage culminates in genomic instability and changes in the regulation of cellular functions. Intriguingly, particular mutations and translocations are accompanied by special types of leukemia. Besides, expression patterns of certain repair genes are altered in different hematologic malignancies. Moreover, analysis of mutations in key mediators of DNA damage repair (DDR) pathways, as well as investigation of their expression and function, may provide us with emerging biomarkers of response/resistance to treatment. Therefore, defective DDR pathways can offer a rational starting point for developing DNA repair-targeted drugs. In this review, we address genetic alterations and gene/protein expression changes, as well as provide an overview of DNA repair pathways.

Digital Implementation of the Two-Compartmental Pinsky-Rinzel Pyramidal Neuron Model.

It is believed that brain-like computing system can be achieved by the fusion of electronics and neuroscience. In this way, the optimized digital hardware implementation of neurons, primary units of nervous system, play a vital role in neuromorphic applications. Moreover, one of the main features of pyramidal neurons in cortical areas is bursting activities that has a critical role in synaptic plasticity. The Pinsky-Rinzel model is a nonlinear two-compartmental model for CA3 pyramidal cell that is widely used in neuroscience. In this paper, a modified Pinsky-Rinzel pyramidal model is proposed by replacing its complex nonlinear equations with piecewise linear approximation. Next, a digital circuit is designed for the simplified model to be able to implement on a low-cost digital hardware, such as field-programmable gate array (FPGA). Both original and proposed models are simulated in MATLAB and next digital circuit simulated in Vivado is compared to show that obtained results are in good agreement. Finally, the results of physical implementation on FPGA are also illustrated. The presented circuit advances preceding designs with regards to the ability to replicate essential characteristics of different firing responses including bursting and spiking in the compartmental model. This new circuit has various applications in neuromorphic engineering, such as developing new neuroinspired chips.