ABSTRACT
Owing to the impact of COVID-19, the venues for dancers to perform have shifted from the stage to the media. In this study, we focus on the creation of dance videos that allow audiences to feel a sense of excitement without disturbing their awareness of the dance subject and propose a video generation method that links the dance and the scene by utilizing a sound detection method and an object detection algorithm. The generated video was evaluated using the Semantic Differential method, and it was confirmed that the proposed method could transform the original video into an uplifting video without any sense of discomfort. © 2023 SPIE.
ABSTRACT
The emergence of SARS-CoV-2 has seriously affected the lives of people worldwide. Clarifying the attenuation rule of SARS-CoV-2 neutralizing antibody (NAb) in vivo is the key to prevent reinfection and recurrence of virus. Currently, the commonly used methods for detecting NAb include virus neutralization tests, pseudovirus neutralization assays, lateral flow immunochromatography and enzyme-linked immunosorbent assays. The detection of NAb not only can be used to evaluate the level of immunity after vaccination or infection but also can provide important theoretical support for virus reinfection, recurrence and vaccine iteration. In this research, the related technologies of SARS-CoV-2 NAb detection were reviewed, aiming to provide better research ideas for SARS-CoV-2 epidemic prevention and control.
Subject(s)
Antibodies, Neutralizing , COVID-19 , Humans , COVID-19/diagnosis , Reinfection , SARS-CoV-2 , Antibodies, ViralABSTRACT
The outbreak of corona virus disease 2019 (COVID-19) has aroused great attention around the world. SARS-CoV-2 possesses characteristics of faster transmission, immune escape, and occult transmission by many mutation, which caused still grim situation of prevention and control. Early detection and isolation of patients are still the most effective measures at present. So, there is an urgent need for new rapid and highly sensitive testing tools to quickly identify infected patients as soon as possible. This review briefly introduces general characteristics of SARS-CoV-2, and provides recentl overview and analysis based on different detection methods for nucleic acids, antibodies, antigens as detection target. Novel nano-biosensors for SARS-CoV-2 detection are analyzed based on optics, electricity, magnetism, and visualization. In view of the advantages of nanotechnology in improving detection sensitivity, specificity and accuracy, the research progress of new nano-biosensors is introduced in detail, including SERS-based biosensors, electrochemical biosensors, magnetic nano-biosensors and colorimetric biosensors. Functions and challenges of nano-materials in construction of new nano-biosensors are discussed, which provides ideas for the development of various coronavirus biosensing technologies for nanomaterial researchers.
ABSTRACT
The contagious illness known as COVID-19 made wearing a mask an essential part of daily life. Mask-covered faces cannot be detected by the current eye detection methods. Many biometric identification systems, like iris recognition, depend on accurate eye detection. Thus, in this study, an efficient method using machine learning for detecting eyes of people wearing mask is presented. Haar-cascade classifier is used to implement real-time eye detection from a live stream via webcam. From the live stream, frames are extracted and saved as images. Dataset was prepared by collecting face images of people wearing mask under various background. Haar-cascade classifier which was trained using 2000 positive and 4000 negative images is used to detect the position of eyes. According to the results on dataset, the system could attain an average accuracy of 96.72%. © 2022 IEEE.
ABSTRACT
Infection with the tick-borne encephalitis virus (TBEV) can cause meningitis, meningoencephalitis and myelitis in humans. TBEV is an enveloped RNA virus of the family Flaviviridae, which is mostly transmitted via tick bites. However, transmission by consumption of virus-contaminated goat raw milk and goat raw milk products has also been described. Only a few methods have been reported for the detection of TBEV in food so far. Here, we compare different virus extraction methods for goat raw milk and goat raw milk cream cheese and subsequent detection of TBEV-RNA by RT-qPCR. Langat virus (LGTV), a naturally attenuated TBEV strain, was used for artificial contamination experiments. Mengovirus and the human coronavirus 229E were compared to assess their suitability to serve as internal process controls. Out of three tested extraction protocols for raw milk, sample centrifugation followed by direct RNA extraction from the aqueous interphase yielded the best results, with a recovery rate (RR) of 31.8 ± 4.9% for LGTV and a detection limit of 6.7 × 103 LGTV genome copies/ml. Out of two methods for cream cheese, treatment of the samples with TRI Reagent® and chloroform prior to RNA extraction showed the best RR of 4.7 ± 1.6% for LGTV and a detection limit of 9.4 × 104 LGTV genome copies/g. RRs of Mengovirus and LGTV were similar for both methods; therefore, Mengovirus is suggested as internal process control virus. The developed methods may be useful for screening or surveillance studies, as well as in outbreak investigations.
ABSTRACT
In recent decades, the accumulation and fragmentation of plastics on the surface of the planet have caused several long-term climatic and health risks. Plastic materials, specifically microplastics (MPs;sizes < 5 mm), have gained significant interest in the global scientific fraternity due to their bioaccumulation, non-biodegradability, and ecotoxicological effects on living organisms. This study explains how microplastics are generated, transported, and disposed of in the environment based on their sources and physicochemical properties. Additionally, the study also examines the impact of COVID-19 on global plastic waste production. The physical and chemical techniques such as SEM-EDX, PLM, FTIR, Raman, TG-DSC, and GC-MS that are employed for the quantification and identification of MPs are discussed. This paper provides insight into conventional and advanced methods applied for microplastic removal from aquatic systems. The finding of this review helps to gain a deeper understanding of research on the toxicity of microplastics on humans, aquatic organisms, and soil ecosystems. Further, the efforts and measures that have been enforced globally to combat MP waste have been highlighted and need to be explored to reduce its potential risk in the future. © 2022 by the authors.
ABSTRACT
The outbreak of corona virus disease 2019 (COVID-19) has aroused great attention around the world. SARS-CoV-2 possesses characteristics of faster transmission, immune escape, and occult transmission by many mutation, which caused still grim situation of prevention and control. Early detection and isolation of patients are still the most effective measures at present. So, there is an urgent need for new rapid and highly sensitive testing tools to quickly identify infected patients as soon as possible. This review briefly introduces general characteristics of SARS-CoV-2, and provides recentl overview and analysis based on different detection methods for nucleic acids, antibodies, antigens as detection target. Novel nano-biosensors for SARS-CoV-2 detection are analyzed based on optics, electricity, magnetism, and visualization. In view of the advantages of nanotechnology in improving detection sensitivity, specificity and accuracy, the research progress of new nano-biosensors is introduced in detail, including SERS-based biosensors, electrochemical biosensors, magnetic nano-biosensors and colorimetric biosensors. Functions and challenges of nano-materials in construction of new nano-biosensors are discussed, which provides ideas for the development of various coronavirus biosensing technologies for nanomaterial researchers.
ABSTRACT
One-stage object detection methods have proven their advantage in terms of both speed and accuracy for addressing vision tasks in real-time scenarios, including Recyclable Waste detection, which has become a prevalent topic during the COVID-19 pandemic. Previous research into this subject has faced many obstacles, mainly due to the requirement of detecting highly deformable and often translucent objects in cluttered scenes without the context information usually present in human-centric datasets. In this paper, we aim to explore the performance of state-of-the-art one-stage object detectors on ZeroWaste dataset, the first in-the-wild industrial-grade waste detection benchmark. Our experiments showed that recent one-stage detectors, namely the YOLO-based detectors, can obtain very competitive results on the benchmark. YOLOv7, thanks to its many improvements, is the current best performer at 33.2% mAP on the ZeroWaste benchmark, to the best of our knowledge. Implementation details are available at our GitHub repository. © 2022 IEEE.
ABSTRACT
Because of the pandemic of COVID-19 since 2020, it seriously affects people's daily life and causes huge economic loss. Recently, the international community has mostly adopted an attitude of coexisting with Covid-19. We cannot ignore the harm the virus can bring to us. In order to effectively protect everyone from the virus, the most basic and effective way is to wear a mask to keep you away from exposure to the virus when going to public areas. Vision intelligence can play an important role in public health issues. In this paper, we utilize the object detection method to implement an actual mask wearing recognition system which can detect if people have a face mask on their face, and send a warning message if not wearing a mask. YOLOv3 is the basic framework for our implementation. After training and fine-tuning processes, the implemented model can perform effectively and correctly. © 2022 IEEE.
ABSTRACT
Since the outbreak of the coronavirus disease 2019 (COVID-19), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been found in wastewater frequently worldwide. Based on the wastewater-based epidemiology (WBE), wastewater surveillance of SARS-CoV-2 can complement population surveillance for COVID-19. Quantification of viral load and genome sequencing of SARS-CoV-2 can help early warning of COVID-19 outbreaks, early identification of asymptomatic cases, assessment of infection scale, prediction of pandemic trend status, and identification of virus sources to provide scientific basis for polices for the prevention and control. Accordingly, here, the sources of SARS-CoV-2 in wastewater at home and abroad and the major factors affecting the survival of virus were reviewed. Common methods to concentrate, detect and quantify SARS-CoV-2 were reviewed, with an overview of global surveillance projects, progresses, and remaining scientific issues. Some shortcomings of the current procedures, including the lack of sufficient information on distribution characteristics and infectivity of SARS-CoV-2 in wastewater and limited development and application of prediction models were also discussed. WBE can provide insight into the scientific prevention and control of COVID-19 in the face of current or future pandemics in China, and enhance China′s ability to deal with the surveillance and early warning, epidemic scale assessment, and accurate policy-making for the infectious and non-infectious diseases. © 2022 Editorial Board, Research of Environmental Sciences. All rights reserved.
ABSTRACT
During the COVID-19 scenario, due to partial lockdown, people did not have permission to go out and buy items freely. Instead, they were given a specific time for purchasing goods. As a result, people were found in multitudes during these hours, without maintaining social distance. Managing this crowd to maintain social distance is a huge task for the government, and hence a system that will assist them in controlling the people is required. The You Only Look Once (YOLO) approach was used to detect the objects. Compared to other object detection methods, this technique has a lot of advantages. YOLO finds objects by applying convolutional networks to forecast bounding boxes and class probabilities for these boxes, and it does it much faster than the existing works. This paper develops a device using a Raspberry Pi-4 board that detects people who are in the frame of the camera, and if they are closer than the distance allocated in the device, an alarm will sound, informing them that they are breaking the rules, and the alert message will be sent to the nearby police station. In this way, the crowd can be managed in a pandemic situation. © 2022 IEEE.
ABSTRACT
Porcine epidemic diarrhea virus (PEDV), transmissible gastroenteritis virus (TGEV), porcine deltacoronavirus (PDCoV), and swine acute diarrhea syndrome coronavirus (SADS-CoV) are four identified porcine enteric coronaviruses. Pigs infected with these viruses show similar manifestations of diarrhea, vomiting, and dehydration. Here, a quadruplex real-time quantitative PCR (qRT-PCR) assay was established for the differential detection of PEDV, TGEV, PDCoV, and SADS-CoV from swine fecal samples. The assay showed extreme specificity, high sensitivity, and excellent reproducibility, with the limit of detection (LOD) of 121 copies/µL (final reaction concentration of 12.1 copies/µL) for each virus. The 3236 clinical fecal samples from Guangxi province in China collected between October 2020 and October 2022 were evaluated by the quadruplex qRT-PCR, and the positive rates of PEDV, TGEV, PDCoV, and SADS-CoV were 18.26% (591/3236), 0.46% (15/3236), 13.16% (426/3236), and 0.15% (5/3236), respectively. The samples were also evaluated by the multiplex qRT-PCR reported previously by other scientists, and the compliance rate between the two methods was more than 99%. This illustrated that the developed quadruplex qRT-PCR assay can provide an accurate method for the differential detection of four porcine enteric coronaviruses.
ABSTRACT
Online teaching has been encouraged for many years but the COVID-19 pandemic has promoted it to an even greater extent. Teachers had to quickly shift to online teaching methods and processes and conduct all the classroom activities online. The global pandemic has accelerated the transition from chalk and board learning to mouse and click - digital learning. Even though there are online whiteboards available for teaching, teachers often find it difficult to draw using a mouse. A solution for this would be to get an external digital board and stylus but not everyone would be able to afford it. The Hand-Gesture Controlled Presentation Viewer With AI Virtual Painter is a project where one can navigate through the presentation slides and draw anything on them just like how one would on a normal board, just by using their fingers. This project aims to digitalise the traditional blackboard-chalk system and eliminate the need for using a mouse or keyboard while taking classes. Hand-Gesture controlled devices especially laptops and computers have recently gained a lot of attraction. This system works by detecting landmarks on one’s hand to recognise the gestures. The project recognises five hand gestures. It uses a thumb finger for moving to the next slide, a little finger for moving to the previous slide, two fingers for displaying the pointer, one finger for drawing on the screen and three fingers for erasing whatever has been drawn. The infrastructure is provided between the user and the system using only a camera. The camera’s output will be presented on the system’s screen so that the user can further calibrate it. © 2022 University of Stockholm. All rights reserved.
ABSTRACT
The aim of this study was to investigate the ecotoxicity of polyvinylidene difluoride (PVDF) and polylactic acid (PLA) microplastics (MPs) in two marine zooplankton: the crustacean Artemia franciscana and the cnidarian Aurelia sp. (common jellyfish). To achieve this goal, (i) MP uptake, (ii) immobility, and (iii) behavior (swimming speed, pulsation mode) of crustacean larval stages and jellyfish ephyrae exposed to MPs concentrations (1, 10, 100 mg/L) were assessed for 24 h. Using traditional and novel techniques, i.e., epifluorescence microscopy and 3D holotomography (HT), PVDF and PLA MPs were found in the digestive systems of the crustaceans and in the gelatinous tissue of jellyfish. Immobility was not affected in either organism, while a significant behavioral alteration in terms of pulsation mode was found in jellyfish after exposure to both PVDF and PLA MPs. Moreover, PLA MPs exposure in jellyfish induced a toxic effect (EC50: 77.43 mg/L) on the behavioral response. This study provides new insights into PLA and PVDF toxicity with the potential for a large impact on the marine ecosystem, since jellyfish play a key role in the marine food chain. However, further investigations incorporating additional species belonging to other trophic levels are paramount to better understand and clarify the impact of such polymers at micro scale in the marine environment. These findings suggest that although PVDF and PLA have been recently proposed as innovative and, in the case of PLA, biodegradable polymers, their effects on marine biota should not be underestimated.
ABSTRACT
This paper presents the design and implementation of the Machine Vision Surveillance System Artificial Intelligence (MaViSS-AI) for Covid-19 Norms using jetson nano. This system is designed to be cost-effective, accurate, efficient, and secure. The proposed system tracks and counts humans for monitoring social distancing and detects face masks using object detection methods. We used YOLO as an object detection method and neural network to detect a person and count them. And for social distancing monitoring the concept of the centroid is based on calculating the distance between pairs of centroids, and thus checking whether there are any violations of the threshold or not. To detect the face mask, a YOLO V4 deep learning model is used as the mask detection algorithm. The system also raises alerts when any suspicious event occurs. Given this alert, security personnel can take relevant actions. This research aims to provide a holistic approach to overcoming the real-time challenges encountered during the monitoring of Covid-19 norms. © 2022 IEEE.
ABSTRACT
In the present work, highly multiplexed diagnostic KITs based on an Interferometric Optical Detection Method (IODM) were developed to evaluate six Coronavirus Disease 2019 (COVID-19)-related biomarkers. These biomarkers of COVID-19 were evaluated in 74 serum samples from severe, moderate, and mild patients with positive polymerase chain reaction (PCR), collected at the end of March 2020 in the Hospital Clínico San Carlos, in Madrid (Spain). The developed multiplexed diagnostic KITs were biofunctionalized to simultaneously measure different types of specific biomarkers involved in COVID-19. Thus, the serum samples were investigated by measuring the total specific Immunoglobulins (sIgT), specific Immunoglobulins G (sIgG), specific Immunoglobulins M (sIgM), specific Immunoglobulins A (sIgA), all of them against SARS-CoV-2, together with two biomarkers involved in inflammatory disorders, Ferritin (FER) and C Reactive Protein (CRP). To assess the results, a Multiple Linear Regression Model (MLRM) was carried out to study the influence of IgGs, IgMs, IgAs, FER, and CRP against the total sIgTs in these serum samples with a goodness of fit of 73.01% (Adjusted R-Squared).
Subject(s)
COVID-19 Testing , COVID-19 , Biomarkers , C-Reactive Protein , COVID-19/diagnosis , COVID-19 Testing/instrumentation , Ferritins , Humans , Immunoglobulin A, Secretory , Reagent Kits, Diagnostic , SARS-CoV-2ABSTRACT
BACKGROUND: This study attempts to explore the influencing factors and solutions of the colloidal gold method for novel coronavirus (2019-nCoV)-specific IgM/IgG antibody detection, summarize the clinical experience and perfect the examination process, improving the application value of antibody detection in COVID-19 diagnosis. METHODS: A total of 13,329 peripheral whole blood/plasma/serum samples were obtained for COVID-19 screening from children who visited the Children's Hospital of the Capital Institute of Pediatrics outpatient clinic from April 22, 2020, to November 30, 2020. The colloidal gold method was adopted for 2019-nCoV-specific IgM/IgG antibody detection. The virus nucleic acid test results, clinical records, and serum protein fingerprint results of antibody-positive patients were collected. RESULTS: All samples were examined using the colloidal gold method with two 2019-nCoV-specific IgM/IgG antibody detection kits. Four patients were tested single antibody-positive using both kits. The details were as follows: two cases of IgM ( +) and IgG (-) using plasma and serum separately, two cases of IgM (-) and IgG ( +) using serum and whole blood. The protein fingerprinting results and nucleic acid tests of 2019-nCoV antibodies were negative in the 4 cases. Considering the epidemiological history, clinical manifestations, and test results, these 4 children were ruled out for 2019-nCoV infection. CONCLUSIONS: When the colloidal gold method was used to detect 2019-nCoV-specific IgM/IgG antibodies, it was important to ascertain the test results as precisely as possible. Specimen type and patient history may interfere with the diagnosis.
Subject(s)
COVID-19 , Nucleic Acids , COVID-19/diagnosis , COVID-19 Testing , Child , Gold Colloid , Humans , Immunoglobulin G , Immunoglobulin M , SARS-CoV-2ABSTRACT
The identification of seriously infected areas across a city, region, or country can inform policies and assist in resources allocation. Concentration of coronavirus infection can be identified through applying cluster detection methods to coronavirus cases over space. To enhance the identification of seriously infected areas by relevant studies, this study focused on coronavirus infection by small area across a city during the second wave. Specifically, we firstly explored spatiotemporal patterns of new coronavirus cases. Subsequently, we detected spatial clusters of new coronavirus cases by small area. Empirically, we used the London-wide small-area coronavirus infection data aggregately collected. Methodologically, we applied a fast Bayesian model-based detection method newly developed to new coronavirus cases by small area. As empirical evidence on the association of socioeconomic factors and coronavirus spread have been found, spatial patterns of coronavirus infection are arguably associated with socioeconomic and built environmental characteristics. Therefore, we further investigated the socioeconomic and built environmental characteristics of the clusters detected. As a result, the most significant clusters of new cases during the second wave are likely to occur around the airports. And, lower income or lower healthcare accessibility is associated with concentration of coronavirus infection across London.
ABSTRACT
Coronavirus disease (COVID-19), caused by the SARS-CoV-2 virus, is a potentially fatal disease of global public health concern. Fever has been reported to be a common clinical symptom in COVID-19 and current CDC recommendations for mitigation of community COVID-19 transmission include temperature screening, so prompting widespread temperature screening across multiple sectors, including hospitals, office buildings and airports. The need for no-contact and rapid measurement of body temperature during the COVID-19 pandemic emergency has led to the widespread use of thermal imaging cameras. However, the body temperature measurement is also disturbed by the environment factors, including ambient temperature, background light etc. When the ambient temperature is low, the temperature of the patient will also be low. It was difficult to screen the fever patients by using the absolute temperature criteria, and it often result in missing detection. In order to solve this problem, this paper proposed a method of screening COVID-19 symptom fever patients by the body temperature difference detection. The temperature difference detection method combined the temperature measurement of the infrared imaging camera and the visible camera face recognition. This method will eliminate environmental interference and equipment errors, to reduce the probability of the fever missed detection. © Published under licence by IOP Publishing Ltd.
ABSTRACT
Nowadays, the Corona Virus outbreak in 2019 (COVID-19) has become a global pandemic. The public must implement health protocols to reduce the spread of COVID-19. Trends show that the number of COVID-19 is increasing over time. This study proposes and develops a smart model to detect COVID-19 Health protocol violators in vehicles. This model can detect violations of the use of masks and social distancing in vehicles. The proposed model is a combination of the YOLO object detection method and the Hourglass architecture. The experimental results of the proposed model can detect violations with a high success rate. Here, the standard YOLOv4 detection model as baseline yields an mAP of 0.87 for validation and 0.74 for test data. On the other hand, the proposed method produces an mAP of 0.92 on the validation data, 0.78 on the test data. From these results, this smart model is quite promising to help reduce the spread of COVID-19. © 2021 ACM.