40 research outputs found
HYDRA-HGR: A Hybrid Transformer-based Architecture for Fusion of Macroscopic and Microscopic Neural Drive Information
Development of advance surface Electromyogram (sEMG)-based Human-Machine
Interface (HMI) systems is of paramount importance to pave the way towards
emergence of futuristic Cyber-Physical-Human (CPH) worlds. In this context, the
main focus of recent literature was on development of different Deep Neural
Network (DNN)-based architectures that perform Hand Gesture Recognition (HGR)
at a macroscopic level (i.e., directly from sEMG signals). At the same time,
advancements in acquisition of High-Density sEMG signals (HD-sEMG) have
resulted in a surge of significant interest on sEMG decomposition techniques to
extract microscopic neural drive information. However, due to complexities of
sEMG decomposition and added computational overhead, HGR at microscopic level
is less explored than its aforementioned DNN-based counterparts. In this
regard, we propose the HYDRA-HGR framework, which is a hybrid model that
simultaneously extracts a set of temporal and spatial features through its two
independent Vision Transformer (ViT)-based parallel architectures (the so
called Macro and Micro paths). The Macro Path is trained directly on the
pre-processed HD-sEMG signals, while the Micro path is fed with the p-to-p
values of the extracted Motor Unit Action Potentials (MUAPs) of each source.
Extracted features at macroscopic and microscopic levels are then coupled via a
Fully Connected (FC) fusion layer. We evaluate the proposed hybrid HYDRA-HGR
framework through a recently released HD-sEMG dataset, and show that it
significantly outperforms its stand-alone counterparts. The proposed HYDRA-HGR
framework achieves average accuracy of 94.86% for the 250 ms window size, which
is 5.52% and 8.22% higher than that of the Macro and Micro paths, respectively
The Low Abundance of CpG in the SARS-CoV-2 Genome Is Not an Evolutionarily Signature of ZAP
The zinc finger antiviral protein (ZAP) is known to restrict viral replication by binding to the CpG rich regions of viral RNA, and subsequently inducing viral RNA degradation. This enzyme has recently been shown to be capable of restricting SARS-CoV-2. These data have led to the hypothesis that the low abundance of CpG in the SARS-CoV-2 genome is due to an evolutionary pressure exerted by the host ZAP. To investigate this hypothesis, we performed a detailed analysis of many coronavirus sequences and ZAP RNA binding preference data. Our analyses showed neither evidence for an evolutionary pressure acting specifically on CpG dinucleotides, nor a link between the activity of ZAP and the low CpG abundance of the SARS-CoV-2 genome
Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence
Myocarditis is a significant cardiovascular disease (CVD) that poses a threat
to the health of many individuals by causing damage to the myocardium. The
occurrence of microbes and viruses, including the likes of HIV, plays a crucial
role in the development of myocarditis disease (MCD). The images produced
during cardiac magnetic resonance imaging (CMRI) scans are low contrast, which
can make it challenging to diagnose cardiovascular diseases. In other hand,
checking numerous CMRI slices for each CVD patient can be a challenging task
for medical doctors. To overcome the existing challenges, researchers have
suggested the use of artificial intelligence (AI)-based computer-aided
diagnosis systems (CADS). The presented paper outlines a CADS for the detection
of MCD from CMR images, utilizing deep learning (DL) methods. The proposed CADS
consists of several steps, including dataset, preprocessing, feature
extraction, classification, and post-processing. First, the Z-Alizadeh dataset
was selected for the experiments. Subsequently, the CMR images underwent
various preprocessing steps, including denoising, resizing, as well as data
augmentation (DA) via CutMix and MixUp techniques. In the following, the most
current deep pre-trained and transformer models are used for feature extraction
and classification on the CMR images. The findings of our study reveal that
transformer models exhibit superior performance in detecting MCD as opposed to
pre-trained architectures. In terms of DL architectures, the Turbulence Neural
Transformer (TNT) model exhibited impressive accuracy, reaching 99.73%
utilizing a 10-fold cross-validation approach. Additionally, to pinpoint areas
of suspicion for MCD in CMRI images, the Explainable-based Grad Cam method was
employed
Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review
In recent years, cardiovascular diseases (CVDs) have become one of the
leading causes of mortality globally. CVDs appear with minor symptoms and
progressively get worse. The majority of people experience symptoms such as
exhaustion, shortness of breath, ankle swelling, fluid retention, and other
symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia,
cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina
are the most common CVDs. Clinical methods such as blood tests,
electrocardiography (ECG) signals, and medical imaging are the most effective
methods used for the detection of CVDs. Among the diagnostic methods, cardiac
magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the
disease, plan treatment and predict CVDs. Coupled with all the advantages of
CMR data, CVDs diagnosis is challenging for physicians due to many slices of
data, low contrast, etc. To address these issues, deep learning (DL) techniques
have been employed to the diagnosis of CVDs using CMR data, and much research
is currently being conducted in this field. This review provides an overview of
the studies performed in CVDs detection using CMR images and DL techniques. The
introduction section examined CVDs types, diagnostic methods, and the most
important medical imaging techniques. In the following, investigations to
detect CVDs using CMR images and the most significant DL methods are presented.
Another section discussed the challenges in diagnosing CVDs from CMR data.
Next, the discussion section discusses the results of this review, and future
work in CVDs diagnosis from CMR images and DL techniques are outlined. The most
important findings of this study are presented in the conclusion section
Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network
Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism
Performance Assessment of Feasible Scheduling Multiprocessor Tasks Solutions by using DEA FDH method
In this paper, an attempt has been made to investigate how DEA FDH method based on linear programming can select one or more efficient scheduling solutions on multiprocessor tasks obtained by any heuristic algorithms through some feasible solutions for NP-complete problems. This article will consider the problem of scheduling multiprocessor tasks with multi–criteria, namely, minimizing total completion time (makespan) and minimizing the number of tardy tasks and shows that most efficient schedule(s) will be determined