Quantification of sEMG signals for automated muscle fatigue detection using nonlinear SVM

Fatigue is a multidimensional and subjective concept and is a complex phenomenon including various causes, mechanisms and forms of manifestation. Thus, it is crucial to delineate the different levels and to quantify selfperceived fatigue. The aim of this study was to introduce a method for automatic quantification and detection of muscle fatigue using surface EMG signals. Thus, sEMG signals from right sternocleidomastoid muscle of 9 healthy female subjects were recorded during neck flexion endurance test in Quaem hospital. Then six features in time, frequency and time- scale domains were extracted from signals. After dimensionality estimation and reduction, the SVM classifier was applied to the resulted feature vector. Then, the performance of linear SVM and nonlinear SVM with RBF kernel and the effect of show that the best accuracy is achieved using RBF kernel SVM with features using LLE criterion, were RMS, ZC and AIF. These results suggest that the selected features contained some information that could be used by nonlinear SVM with RBF kernel to best discriminate between fatigue and nonfatigue stages.    </p

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging

Risk factors prediction, clinical outcomes, and mortality in COVID-19 patients

Preventing communicable diseases requires understanding the spread, epidemiology, clinical features, progression, and prognosis of the disease. Early identification of risk factors and clinical outcomes might help in identifying critically ill patients, providing appropriate treatment, and preventing mortality. We conducted a prospective study in patients with flu-like symptoms referred to the imaging department of a tertiary hospital in Iran between March 3, 2020, and April 8, 2020. Patients with COVID-19 were followed up after two months to check their health condition. The categorical data between groups were analyzed by Fisher's exact test and continuous data by Wilcoxon rank-sum test. Three hundred and nineteen patients (mean age 45.48 ± 18.50 years, 177 women) were enrolled. Fever, dyspnea, weakness, shivering, C-reactive protein, fatigue, dry cough, anorexia, anosmia, ageusia, dizziness, sweating, and age were the most important symptoms of COVID-19 infection. Traveling in the past 3 months, asthma, taking corticosteroids, liver disease, rheumatological disease, cough with sputum, eczema, conjunctivitis, tobacco use, and chest pain did not show any relationship with COVID-19. To the best of our knowledge, a number of factors associated with mortality due to COVID-19 have been investigated for the first time in this study. Our results might be helpful in early prediction and risk reduction of mortality in patients infected with COVID-19. © 2020 Wiley Periodicals LL

An insight to HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) pathogenesis; evidence from high-throughput data integration and meta-analysis

Background Human T-lymphotropic virus 1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a progressive disease of the central nervous system that significantly affected spinal cord, nevertheless, the pathogenesis pathway and reliable biomarkers have not been well determined. This study aimed to employ high throughput meta-analysis to find major genes that are possibly involved in the pathogenesis of HAM/TSP. Results High-throughput statistical analyses identified 832, 49, and 22 differentially expressed genes for normal vs. ACs, normal vs. HAM/TSP, and ACs vs. HAM/TSP groups, respectively. The protein-protein interactions between DEGs were identified in STRING and further network analyses highlighted 24 and 6 hub genes for normal vs. HAM/TSP and ACs vs. HAM/TSP groups, respectively. Moreover, four biologically meaningful modules including 251 genes were identified for normal vs. ACs. Biological network analyses indicated the involvement of hub genes in many vital pathways like JAK-STAT signaling pathway, interferon, Interleukins, and immune pathways in the normal vs. HAM/TSP group and Metabolism of RNA, Viral mRNA Translation, Human T cell leukemia virus 1 infection, and Cell cycle in the normal vs. ACs group. Moreover, three major genes including STAT1, TAP1, and PSMB8 were identified by network analysis. Real-time PCR revealed the meaningful down-regulation of STAT1 in HAM/TSP samples than AC and normal samples (P = 0.01 and P = 0.02, respectively), up-regulation of PSMB8 in HAM/TSP samples than AC and normal samples (P = 0.04 and P = 0.01, respectively), and down-regulation of TAP1 in HAM/TSP samples than those in AC and normal samples (P = 0.008 and P = 0.02, respectively). No significant difference was found among three groups in terms of the percentage of T helper and cytotoxic T lymphocytes (P = 0.55 and P = 0.12). Conclusions High-throughput data integration disclosed novel hub genes involved in important pathways in virus infection and immune systems. The comprehensive studies are needed to improve our knowledge about the pathogenesis pathways and also biomarkers of complex diseases.Peer reviewe

Diabetic retinopathy clinical practice guidelines: Customized for Iranian population

Purpose: To customize clinical practice guidelines (CPGs) for management of diabetic retinopathy (DR) in the Iranian population. Methods: Three DR CPGs (The Royal College of Ophthalmologists 2013, American Academy of Ophthalmology Preferred Practice Pattern 2012, and Australian Diabetes Society 2008) were selected from the literature using the AGREE tool. Clinical questions were designed and summarized into four tables by the customization team. The components of the clinical questions along with pertinent recommendations extracted from the above-mentioned CPGs; details of the supporting articles and their levels of evidence; clinical recommendations considering clinical benefts, cost and side effects; and revised recommendations based on customization capability (applicability, acceptability, external validity) were recorded in 4 tables, respectively. Customized recommendations were sent to the faculty members of all universities across the country to score the recommendations from 1 to 9. Results: Agreed recommendations were accepted as the fnal recommendations while the non-agreed ones were approved after revision. Eventually, 29 customized recommendations under three major categories consisting of screening, diagnosis and treatment of DR were developed along with their sources and levels of evidence. Conclusion: This customized CPGs for management of DR can be used to standardize the referral pathway, diagnosis and treatment of patients with diabetic retinopathy. © 2016 Journal of Ophthalmic and Vision Research

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Computational approaches to Explainable Artificial Intelligence:Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.</p

Identification and Introduction to Thysanoptera from Bean Fields in some Regions of Markazi Province

Introduction: Beans, Phaseolus vulgaris L., are one of the important cultivated crops in Iran. Markazi province with production, 17569 tons, on approximately 7837 ha, is one of most important center of the bean production in the country. The bean plants are attacked by various pests including Thysanoptera species. Thysanoptera are very small, slender with fringed wings insects which are widespread. Thrips reduce crop yield or its marketable value indirectly by vectoring viral plant diseases and directly Different studies were performed to investigate thrips fauna in different crop fields in Iran. But no study was done about identification of thrips species in the bean farms of Markazi province. This study as supplementary was established to investigate thrips faun in the bean fields of Markazi province and secondly to determine which species are more common than others. Materials and Methods: The sampling were conducted on commercial bean farms (none chemical treatment) of five principle regions in Markazi (Central) province including Arak, Shazand, Farahan, Khomein and Aman-abad. The Markazi province with an area of 29,406 square kilometers with the climate is semi-arid, moderate and cold mountainous type. The diversity of climate leads to a varied rate of moisture and rain in different regions of the province. The specimens of Thysanoptera were collected from two bean farms (10 farms, each about 0.5 ha) in each five sampling sites every week. The sampling was performed during crop season from May to August, 2012-2013. In each farm, 10 bush bean spaced by at least 10 m from each other were randomly selected. The bush beans were shacked into the white tray and the fallen specimens immediately were collected by thin brush dipped in alcohol. The specimens of each species were counted and stored in Ethanol (80%). The specimens were classified based on body and antennae shape and color and shape of the end of the body and color design of wings., The microscopic slides were prepared and identification were confirmed by Mirab-balu, Assistant professor of Ilam University. All slides were preserved in Entomology laboratory, Department of Plant Protection, Sari University. According the data from 2012, percentage of frequency were calculated by the formula where n is number of the given species and N is total number of all collected species. Results and Discussion: The results of this study indicate that the bean fields in different regions of Markazi province could harbor in sum 12 different species belonging to eight genera and three families. The only species belonged to suborder of Tubulifera was Haplothrips reuteri Karny, 1907 (Phlaeothripidae). The other collected species were belonged to suborder Terebrantia. These thrips species are including, Aeolothrips intermedius* Bagnall, 1934 from Aeolothripidae, Thrips atratus Haliday, 1836، T. tabaci Lindeman, 1889، T. trehernei Priesner, 1927, Microcephalothrips abdominalis* Crawford, 1910، Odontothrips confusus* Amyot & Serville, 1843 ، Scolothrips longicornis* Priesner 1926، Frankliniella intonsa Trybom, 1895، F. pallida* Uzel, 1895 ، F. occidentalis* Pergande, 1895 Tenothrips frici Uzel, 1895, all from Thripidae family. Among the collected species, six records are new for Markazi fauna which indicated by star (*).The species, onion thrips, Thrips tabaci has highest frequency (65.75 % of all collected species) and is widespread species in the bean farms of Markazi province. The thrips species belong to genus of Frankliniella comprising, F. pallida, F. occidentalis, with frequency, 10.9% and 9.49%, respectively are relatively other pest thrips in the bean fields. H. reuteri has very low frequency (4 %) in the bean farms. The rest species, M. abdominalis (0.07%), F. intonsa (0.22%), T. frici (0.54%), T. trehernei (0.54%) and T. atratus (0.049%) are occasional species. The all thrips species existed in bean fields during two crop season years (2012-2013) but the only, exception was Odontothrips onfusus that was collected during crop season in 2013 just from Shazand region. Two species, A. intermedius (Aeolothripidae) and S. longicornis (Thripidae) which reported for the first time for Markazi fauna are predator and they could feed on other herbivore thrips as well as T. tabaci. However, A. intermedius (8.08% frequency) is relatively more common in compare to the occasional species, S. longicornis (0.32% frequency). Conclusion: The existence of 12 thrips species showed the relatively high species diversity in the bean field of Markazi province. Different reasons could explain the relatively high diversity. According the hypothesis that high plant diversity lead to high animal diversity, one reason could be because of the well diversity of other crop plants cultivated near to bean fields. Lack of chemical treatment also could be considered as the other reason. The onion thrips, Thrips tabaci, confirmed as most common thrips species in the bean fields of Markazi province. This species is very cosmopolitan and polyphage. It is already reported as a serious pest in other crops such as onion. Therefore, in pest management program, farmers should focus on this pest. Additionally, two thrips species, A. intermedius and S. longicornis reported for the first time as predator and also these have to consider in pests management in these area

Study of the effect of fatty acids profile on overall migration from PET into different types of oil

Overall migration (OM) of polyethylene terephthalate (PET) pieces into three types of commercial oils, namely sunflower oil, canola oil and blended oil (which included sunflower oil, soybean oil, and cottonseed oil) has been investigated by the determination of the weight variation of plastic pieces before and after 20, 60 days contact with oil at 25, 45 degrees C and also determination the amount of absorbed oil. Also Fatty acid profiles of each type of oil were determined by using a Gas Chromatography (GC) system before and after experiments to find the correlation between the amount of overall migration and fatty acid profile. The result shows that the highest migration level was noticed with PET pieces contacted with blended oil. Also the effect of temperature, storage time, kind of oils and amount of unsaturated fatty acids and degree of unsaturation in amount of migration were observed. The amount of migration has correlation with fatty acid profile, especially the amount of unsaturated fatty acids and also, the degree of unsaturation. The reasons of these subjects can be investigated in future trends. Previous investigations have been performed on food stimulant such as olive oil and synthetic triglyceric mixture HB307, the present study has the advantage of working on real food samples so obtaining more accurate results were possible

Uncertainty quantification of heavy gas release over a barrier

In this study a procedure for input uncertainty quantification (UQ) in computational fluid dynamics (CFD) simulations is proposed. The suggested procedure has been applied to a test case. The test case concerns the modeling of a heavy gas release into an atmospheric boundary layer over a barrier. The following uncertain parameters are investigated in their respective intervals: release velocity (18 m/s, 22 m/s), release temperature (270 K, 310 K) and the atmospheric boundary layer velocity (3 m/s, 7 m/s). The Stochastic Collocation (SC) method is used to perform the probabilistic propagation of the uncertain parameters. The uncertainty analysis was performed with two sets of sampling grids (full and sparse grids) for the uncertain parameters. The results show which of the selected uncertain parameters have the largest impact on the dispersed gas plume and the local concentrations in the gas cloud. Additionally, using sparse grids shows potential to reduce the computational effort of the uncertainty analysis.Non UBCUnreviewedThis collection contains the proceedings of ICASP12, the 12th International Conference on Applications of Statistics and Probability in Civil Engineering held in Vancouver, Canada on July 12-15, 2015. Abstracts were peer-reviewed and authors of accepted abstracts were invited to submit full papers. Also full papers were peer reviewed. The editor for this collection is Professor Terje Haukaas, Department of Civil Engineering, UBC Vancouver.ResearcherOthe