Predictors of COVID-19 vaccine acceptability among refugees and other migrant populations: A systematic scoping review.

OBJECTIVE: This study aimed to map the existing literature to identify predictors of COVID-19 vaccine acceptability among refugees, immigrants, and other migrant populations. METHODS: A systematic search of Medline, Embase, Scopus, APA PsycInfo and Cumulative Index of Nursing and Allied Health Literature (CINAHL) was conducted up to 31 January 2023 to identify the relevant English peer-reviewed observational studies. Two independent reviewers screened abstracts, selected studies, and extracted data. RESULTS: We identified 34 cross-sectional studies, primarily conducted in high income countries (76%). Lower vaccine acceptance was associated with mistrust in the host countries' government and healthcare system, concerns about the safety and effectiveness of COVID-19 vaccines, limited knowledge of COVID-19 infection and vaccines, lower COVID-19 risk perception, and lower integration level in the host country. Female gender, younger age, lower education level, and being single were associated with lower vaccine acceptance in most studies. Additionally, sources of information about COVID-19 and vaccines and previous history of COVID-19 infection, also influence vaccine acceptance. Vaccine acceptability towards COVID-19 booster doses and various vaccine brands were not adequately studied. CONCLUSIONS: Vaccine hesitancy and a lack of trust in COVID-19 vaccines have become significant public health concerns within migrant populations. These findings may help in providing information for current and future vaccine outreach strategies among migrant populations.

Wireless Body Area Nanonetworks via Vascular Molecular Communication.

Advancements in biotechnology and molecular communication have enabled the utilization of nanomachines in Wireless Body Area Networks (WBAN2) for applications such as drug delivery, cancer detection, and emergency rescue services. To study these networks effectively, it is essential to develop an ideal propagation model that includes the channel response between each pair of in-range nanomachines and accounts for the interference received at each receiver node. In this paper, we employ an advection-diffusion equation to obtain a deterministic channel matrix through a vascular WBAN2. Additionally, the closed forms of inter-symbol interference (ISI) and co-channel interference (CCI) are derived for both full duplex (FDX) and half duplex transmission (HDX) modes. By applying these deterministic formulations, we then present the stochastic equivalents of the ideal channel model and interference to provide an innovative communication model by simultaneously incorporating CCI, ISI, and background noise. Finally, we evaluate the results with numerous experiments and use signal-to-interference-plus-noise ratio (SINR) and capacity as metrics.

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.

The Healthy Eating Index and oral health among adults: a cross-sectional study from an Iranian RaNCD cohort data.

This study was conducted to investigate the association between decayed, missing, and filled teeth (DMFT) index and nutritional status measured by Healthy Eating Index 2015 (HEI-2015), in Iranian adults. In this cross-sectional study, data from the Ravansar non-communicable diseases cohort study were analyzed. DMFT index was employed as a measurement of oral health. The HEI-2015 score was calculated based on data obtained from Food Frequency Questionnaire and categorized into quartiles. Linear regression models were used to assess the association between HEI-2015 and DMFT. From total of 7549 participants with the mean age of 45.65 ± 7.70, 3741 of them were female (49.56%). The mean of DMFT in the highest quartile of HEI-2015 was lower than the lowest quartile (12.64 ± 7.04 vs. 14.29 ± 7.54, P < 0.001). The mean of DMFT in subject who had higher socioeconomic status (SES (was significantly lower than those with low SES (P < 0.001). The mean of DMFT in the lowest quartile of HEI-2015 was significantly lower than in the highest quartile, after adjusting for confounding variables (ß = - 0.11, 95% CI - 0.54, - 0.30). The increasing dairy intake (ß = - 0.08, 95% CI - 0.13, - 0.03) was associated with decreasing DMFT score and increasing refined grains (ß = 0.20, 95% CI 0.02, 0.35) and sodium (ß = 0.07, 95% CI 0.02, 0.12) intake was significantly associated with increasing DMFT score. A healthy diet was associated with a decrease in DMFT score in the studied population. Following a healthy diet is recommended for oral health.

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.

Robust framework for COVID-19 identication from a multicenter dataset of chest CT scans.

The main objective of this study is to develop a robust deep learning-based framework to distinguish COVID-19, Community-Acquired Pneumonia (CAP), and Normal cases based on volumetric chest CT scans, which are acquired in different imaging centers using different scanners and technical settings. We demonstrated that while our proposed model is trained on a relatively small dataset acquired from only one imaging center using a specific scanning protocol, it performs well on heterogeneous test sets obtained by multiple scanners using different technical parameters. We also showed that the model can be updated via an unsupervised approach to cope with the data shift between the train and test sets and enhance the robustness of the model upon receiving a new external dataset from a different center. More specifically, we extracted the subset of the test images for which the model generated a confident prediction and used the extracted subset along with the training set to retrain and update the benchmark model (the model trained on the initial train set). Finally, we adopted an ensemble architecture to aggregate the predictions from multiple versions of the model. For initial training and development purposes, an in-house dataset of 171 COVID-19, 60 CAP, and 76 Normal cases was used, which contained volumetric CT scans acquired from one imaging center using a single scanning protocol and standard radiation dose. To evaluate the model, we collected four different test sets retrospectively to investigate the effects of the shifts in the data characteristics on the model's performance. Among the test cases, there were CT scans with similar characteristics as the train set as well as noisy low-dose and ultra-low-dose CT scans. In addition, some test CT scans were obtained from patients with a history of cardiovascular diseases or surgeries. This dataset is referred to as the "SPGC-COVID" dataset. The entire test dataset used in this study contains 51 COVID-19, 28 CAP, and 51 Normal cases. Experimental results indicate that our proposed framework performs well on all test sets achieving total accuracy of 96.15% (95%CI: [91.25-98.74]), COVID-19 sensitivity of 96.08% (95%CI: [86.54-99.5]), CAP sensitivity of 92.86% (95%CI: [76.50-99.19]), Normal sensitivity of 98.04% (95%CI: [89.55-99.95]) while the confidence intervals are obtained using the significance level of 0.05. The obtained AUC values (One class vs Others) are 0.993 (95%CI: [0.977-1]), 0.989 (95%CI: [0.962-1]), and 0.990 (95%CI: [0.971-1]) for COVID-19, CAP, and Normal classes, respectively. The experimental results also demonstrate the capability of the proposed unsupervised enhancement approach in improving the performance and robustness of the model when being evaluated on varied external test sets.

The Effect of Oral Administration of Silymarin on Serum Levels of Tumor Necrosis Factor-α and Interleukin-1ß in Patients with Rheumatoid Arthritis.

BACKGROUND: Rheumatoid Arthritis (RA) is a systemic chronic autoimmune disease. Several inflammatory agents play key roles in RA pathogenesis, among which tumor necrosis factor-alpha (TNF-α) and interleukin 1 beta (IL-1ß) are of great importance. Silymarin is a potent anti-oxidant extracted from Silybummarianum L. seeds. OBJECTIVE: To study the effect of silymarin on serum levels of TNF-α and IL-1ß in patients with RA. METHODS: Patients with stable RA received 140 mg of silymarin, 3 times a day, for 3 months. Serum samples were collected before and after the treatment. Both TNF-α and IL-1ß serum levels were measured by ELISA. RESULTS: 42 patients (14.3% male, and 85.7% female, with a mean age of 47.59±12.8 years old) completed the treatment course. There was no significant difference in the overall mean concentration of either TNF-α (p=0.14) or IL-1ß (p=0.27) in all 42 patients after the treatment with silymarin. CONCLUSION: The addition of silymarin to the treatment regimen of patients with stable RA has no significant effect on the serum levels of TNF-α and IL-1ß, however, this study needs further evaluation with a larger sample size.

The effect of 3 % hydrogen peroxide irrigation on postoperative complications of rhinoplasty: A double-blinded, placebo-controlled Randomized Clinical Trial.

The aim of this study was to evaluate the effect of irrigation with 3% Hydrogen Peroxide solution on early complications of rhinoplasty. Volunteers who had physical status of ASA I were included in the study after informed written consent. This was a randomized, double-blinded, placebo-controlled clinical trial. Standard open rhinoplasty technique was performed for all patients. All patients underwent the same general anesthesia and post-operative analgesic protocol and the only difference between the two studied groups was the use of hydrogen peroxide for irrigation at the end of surgery. Finally, patients were examined for edema, inflammation, ecchymosis, and infections during 8 weeks after surgery. 50 volunteers (43 female and 7 male) with the mean age of 27.84 ± 7.64 were included in this study. Edema scores were constantly lower in the intervention group (p < 0.001), so was the median inflammation score (p < 0.001). However, there was no significant difference in ecchymosis between the two groups (p > 0.05). No cases of post-operative infection were reported in any of the study groups. In conclusion, within the limitations of the study, it seems that irrigation with H2O2 should be applied whenever appropriate, because of its potential positive effects.

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.

DF-SSmVEP: Dual Frequency Aggregated Steady-State Motion Visual Evoked Potential Design with Bifold Canonical Correlation Analysis.

Recent advancements in Electroencephalographic (EEG) sensor technologies and signal processing algorithms have paved the way for further evolution of Brain Computer Interfaces (BCI) in several practical applications, ranging from rehabilitation systems to smart consumer technologies. When it comes to Signal Processing (SP) for BCI, there has been a surge of interest on Steady-State motion Visual Evoked Potentials (SSmVEP), where motion stimulation is used to address key issues associated with conventional light flashing/flickering. Such benefits, however, come with the price of being less accurate and having a lower Information Transfer Rate (ITR). From this perspective, this paper focuses on the design of a novel SSmVEP paradigm without using resources such as trial time, phase, and/or number of targets to enhance the ITR. The proposed design is based on the intuitively pleasing idea of integrating more than one motion within a single SSmVEP target stimuli, simultaneously. To elicit SSmVEP, we designed a novel and innovative dual frequency aggregated modulation paradigm, called the Dual Frequency Aggregated Steady-State motion Visual Evoked Potential (DF-SSmVEP), by concurrently integrating "Radial Zoom" and "Rotation" motions in a single target without increasing the trial length. Compared to conventional SSmVEPs, the proposed DF-SSmVEP framework consists of two motion modes integrated and shown simultaneously each modulated by a specific target frequency. The paper also develops a specific unsupervised classification model, referred to as the Bifold Canonical Correlation Analysis (BCCA), based on two motion frequencies per target. The corresponding covariance coefficients are used as extra features improving the classification accuracy. The proposed DF-SSmVEP is evaluated based on a real EEG dataset and the results corroborate its superiority. The proposed DF-SSmVEP outperforms its counterparts and achieved an average ITR of 30.7 ± 1.97 and an average accuracy of 92.5 ± 2.04, while the Radial Zoom and Rotation result in average ITRs of 18.35 ± 1 and 20.52 ± 2.5, and average accuracies of 68.12 ± 3.5 and 77.5 ± 3.5, respectively.

Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network.

Reverse transcription-polymerase chain reaction is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of this disease. Nevertheless, it is shown that standard dose CT scan gives significant radiation burden to patients, especially those in need of multiple scans. In this study, we consider low-dose and ultra-low-dose (LDCT and ULDCT) scan protocols that reduce the radiation exposure close to that of a single X-ray, while maintaining an acceptable resolution for diagnosis purposes. Since thoracic radiology expertise may not be widely available during the pandemic, we develop an Artificial Intelligence (AI)-based framework using a collected dataset of LDCT/ULDCT scans, to study the hypothesis that the AI model can provide human-level performance. The AI model uses a two stage capsule network architecture and can rapidly classify COVID-19, community acquired pneumonia (CAP), and normal cases, using LDCT/ULDCT scans. Based on a cross validation, the AI model achieves COVID-19 sensitivity of [Formula: see text], CAP sensitivity of [Formula: see text], normal cases sensitivity (specificity) of [Formula: see text], and accuracy of [Formula: see text]. By incorporating clinical data (demographic and symptoms), the performance further improves to COVID-19 sensitivity of [Formula: see text], CAP sensitivity of [Formula: see text], normal cases sensitivity (specificity) of [Formula: see text] , and accuracy of [Formula: see text]. The proposed AI model achieves human-level diagnosis based on the LDCT/ULDCT scans with reduced radiation exposure. We believe that the proposed AI model has the potential to assist the radiologists to accurately and promptly diagnose COVID-19 infection and help control the transmission chain during the pandemic.

Multi-Agent Reinforcement Learning via Adaptive Kalman Temporal Difference and Successor Representation.

Development of distributed Multi-Agent Reinforcement Learning (MARL) algorithms has attracted an increasing surge of interest lately. Generally speaking, conventional Model-Based (MB) or Model-Free (MF) RL algorithms are not directly applicable to the MARL problems due to utilization of a fixed reward model for learning the underlying value function. While Deep Neural Network (DNN)-based solutions perform well, they are still prone to overfitting, high sensitivity to parameter selection, and sample inefficiency. In this paper, an adaptive Kalman Filter (KF)-based framework is introduced as an efficient alternative to address the aforementioned problems by capitalizing on unique characteristics of KF such as uncertainty modeling and online second order learning. More specifically, the paper proposes the Multi-Agent Adaptive Kalman Temporal Difference (MAK-TD) framework and its Successor Representation-based variant, referred to as the MAK-SR. The proposed MAK-TD/SR frameworks consider the continuous nature of the action-space that is associated with high dimensional multi-agent environments and exploit Kalman Temporal Difference (KTD) to address the parameter uncertainty. The proposed MAK-TD/SR frameworks are evaluated via several experiments, which are implemented through the OpenAI Gym MARL benchmarks. In these experiments, different number of agents in cooperative, competitive, and mixed (cooperative-competitive) scenarios are utilized. The experimental results illustrate superior performance of the proposed MAK-TD/SR frameworks compared to their state-of-the-art counterparts.

COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images.

Novel Coronavirus disease (COVID-19) is a highly contagious respiratory infection that has had devastating effects on the world. Recently, new COVID-19 variants are emerging making the situation more challenging and threatening. Evaluation and quantification of COVID-19 lung abnormalities based on chest Computed Tomography (CT) images can help determining the disease stage, efficiently allocating limited healthcare resources, and making informed treatment decisions. During pandemic era, however, visual assessment and quantification of COVID-19 lung lesions by expert radiologists become expensive and prone to error, which raises an urgent quest to develop practical autonomous solutions. In this context, first, the paper introduces an open-access COVID-19 CT segmentation dataset containing 433 CT images from 82 patients that have been annotated by an expert radiologist. Second, a Deep Neural Network (DNN)-based framework is proposed, referred to as the [Formula: see text], that autonomously segments lung abnormalities associated with COVID-19 from chest CT images. Performance of the proposed [Formula: see text] framework is evaluated through several experiments based on the introduced and external datasets. Third, an unsupervised enhancement approach is introduced that can reduce the gap between the training set and test set and improve the model generalization. The enhanced results show a dice score of 0.8069 and specificity and sensitivity of 0.9969 and 0.8354, respectively. Furthermore, the results indicate that the [Formula: see text] model can efficiently segment COVID-19 lesions in both 2D CT images and whole lung volumes. Results on the external dataset illustrate generalization capabilities of the [Formula: see text] model to CT images obtained from a different scanner.

A Unified Optimization for Resilient Dynamic Event-Triggering Consensus Under Denial of Service.

This article proposes a resilient framework for optimized consensus using a dynamic event-triggering (DET) scheme, where the multiagent system (MAS) is subject to denial-of-service (DoS) attacks. When initiated by an adversary, DoS blocks the local and neighboring communication channels in the network. A distributed DET scheme is utilized to limit transmissions between the neighboring agents. A novel convex optimization approach is proposed that simultaneously co-designs all unknown control and DET parameters. The optimization is based on the weighted sum approach and increases the interevent interval for a predefined consensus convergence rate. In the presence of DoS, the proposed co-design framework is beneficial in two ways: 1) the desired level of resilience to DoS is included as a given (desired) input and 2) the upper bound for guaranteed resilience associated with the proposed co-design approach is less conservative (larger) compared to those obtained from other analytical solutions. A structured tradeoff between relevant features of the MAS, namely, the consensus convergence rate, frequency of event triggerings, and level of resilience to DoS attacks, is established. Simulations based on nonholonomic mobile robots quantify the effectiveness of the proposed implementation.

COVID19-HPSMP : COVID-19 adopted Hybrid and Parallel deep information fusion framework for stock price movement prediction.

The novel of coronavirus (COVID-19) has suddenly and abruptly changed the world as we knew at the start of the 3rd decade of the 21st century. Particularly, COVID-19 pandemic has negatively affected financial econometrics and stock markets across the globe. Artificial Intelligence (AI) and Machine Learning (ML)-based prediction models, especially Deep Neural Network (DNN) architectures, have the potential to act as a key enabling factor to reduce the adverse effects of the COVID-19 pandemic and future possible ones on financial markets. In this regard, first, a unique COVID-19 related PRIce MOvement prediction ( COVID19 PRIMO ) dataset is introduced in this paper, which incorporates effects of social media trends related to COVID-19 on stock market price movements. Afterwards, a novel hybrid and parallel DNN-based framework is proposed that integrates different and diversified learning architectures. Referred to as the COVID-19 adopted Hybrid and Parallel deep fusion framework for Stock price Movement Prediction ( COVID19-HPSMP ), innovative fusion strategies are used to combine scattered social media news related to COVID-19 with historical mark data. The proposed COVID19-HPSMP consists of two parallel paths (hence hybrid), one based on Convolutional Neural Network (CNN) with Local/Global Attention modules, and one integrated CNN and Bi-directional Long Short term Memory (BLSTM) path. The two parallel paths are followed by a multilayer fusion layer acting as a fusion center that combines localized features. Performance evaluations are performed based on the introduced COVID19 PRIMO dataset illustrating superior performance of the proposed framework.

Effect of Exercise Training on Spexin Level, Appetite, Lipid Accumulation Product, Visceral Adiposity Index, and Body Composition in Adults With Type 2 Diabetes.

ObjectiveTo compare the effects of resistance and aerobic training (RT and AT) on spexin (SPX), appetite, lipid accumulation product (LAP), visceral adiposity index (VAI), and body composition in type 2 diabetes mellitus (T2DM) patients. Materials and Methods: Thirty-six T2DM men were randomized to receive RT (n = 12), AT (n = 12), or to act as a non-exercise control (CON, n = 12) 3 days a week for 12 weeks. Results: SPX was increased after both RT and AT (66.2% and 46.5%, respectively). VAI, LAP, and homeostasis model assessment-insulin resistance (HOMA-IR) were reduced in both groups, while quantitative insulin sensitivity check index (Quicki) and McAuley's indexes were increased following both interventions. However, the increases of both hunger and PFC in the RT group were greater than those of the AT. Moreover, the improvement of upper-body strength (41% vs. 10.3%) and lower-body strength (42.2% vs. 20.5%) in the RT group was greater than those of the AT. Conclusion: Our investigation shows that regardless of the modes of the regimen, a 12-week exercise intervention with RT and AT can effectively induce a significant improvement in SPX levels, appetite, LAP, VAI, and body composition in adults with T2DM.

COVID-FACT: A Fully-Automated Capsule Network-Based Framework for Identification of COVID-19 Cases from Chest CT Scans.

The newly discovered Coronavirus Disease 2019 (COVID-19) has been globally spreading and causing hundreds of thousands of deaths around the world as of its first emergence in late 2019. The rapid outbreak of this disease has overwhelmed health care infrastructures and arises the need to allocate medical equipment and resources more efficiently. The early diagnosis of this disease will lead to the rapid separation of COVID-19 and non-COVID cases, which will be helpful for health care authorities to optimize resource allocation plans and early prevention of the disease. In this regard, a growing number of studies are investigating the capability of deep learning for early diagnosis of COVID-19. Computed tomography (CT) scans have shown distinctive features and higher sensitivity compared to other diagnostic tests, in particular the current gold standard, i.e., the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Current deep learning-based algorithms are mainly developed based on Convolutional Neural Networks (CNNs) to identify COVID-19 pneumonia cases. CNNs, however, require extensive data augmentation and large datasets to identify detailed spatial relations between image instances. Furthermore, existing algorithms utilizing CT scans, either extend slice-level predictions to patient-level ones using a simple thresholding mechanism or rely on a sophisticated infection segmentation to identify the disease. In this paper, we propose a two-stage fully automated CT-based framework for identification of COVID-19 positive cases referred to as the "COVID-FACT". COVID-FACT utilizes Capsule Networks, as its main building blocks and is, therefore, capable of capturing spatial information. In particular, to make the proposed COVID-FACT independent from sophisticated segmentations of the area of infection, slices demonstrating infection are detected at the first stage and the second stage is responsible for classifying patients into COVID and non-COVID cases. COVID-FACT detects slices with infection, and identifies positive COVID-19 cases using an in-house CT scan dataset, containing COVID-19, community acquired pneumonia, and normal cases. Based on our experiments, COVID-FACT achieves an accuracy of 90.82 % , a sensitivity of 94.55 % , a specificity of 86.04 % , and an Area Under the Curve (AUC) of 0.98, while depending on far less supervision and annotation, in comparison to its counterparts.

A deep explainable artificial intelligent framework for neurological disorders discrimination.

Pathological hand tremor (PHT) is a common symptom of Parkinson's disease (PD) and essential tremor (ET), which affects manual targeting, motor coordination, and movement kinetics. Effective treatment and management of the symptoms relies on the correct and in-time diagnosis of the affected individuals, where the characteristics of PHT serve as an imperative metric for this purpose. Due to the overlapping features of the corresponding symptoms, however, a high level of expertise and specialized diagnostic methodologies are required to correctly distinguish PD from ET. In this work, we propose the data-driven [Formula: see text] model, which processes the kinematics of the hand in the affected individuals and classifies the patients into PD or ET. [Formula: see text] is trained over 90 hours of hand motion signals consisting of 250 tremor assessments from 81 patients, recorded at the London Movement Disorders Centre, ON, Canada. The [Formula: see text] outperforms its state-of-the-art counterparts achieving exceptional differential diagnosis accuracy of [Formula: see text]. In addition, using the explainability and interpretability measures for machine learning models, clinically viable and statistically significant insights on how the data-driven model discriminates between the two groups of patients are achieved.

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).

COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning.

Novel Coronavirus (COVID-19) has drastically overwhelmed more than 200 countries affecting millions and claiming almost 2 million lives, since its emergence in late 2019. This highly contagious disease can easily spread, and if not controlled in a timely fashion, can rapidly incapacitate healthcare systems. The current standard diagnosis method, the Reverse Transcription Polymerase Chain Reaction (RT- PCR), is time consuming, and subject to low sensitivity. Chest Radiograph (CXR), the first imaging modality to be used, is readily available and gives immediate results. However, it has notoriously lower sensitivity than Computed Tomography (CT), which can be used efficiently to complement other diagnostic methods. This paper introduces a new COVID-19 CT scan dataset, referred to as COVID-CT-MD, consisting of not only COVID-19 cases, but also healthy and participants infected by Community Acquired Pneumonia (CAP). COVID-CT-MD dataset, which is accompanied with lobe-level, slice-level and patient-level labels, has the potential to facilitate the COVID-19 research, in particular COVID-CT-MD can assist in development of advanced Machine Learning (ML) and Deep Neural Network (DNN) based solutions.