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1.
Physiol Rep ; 10(14): e15369, 2022 Jul.
Article in English | MEDLINE | ID: covidwho-1957609

ABSTRACT

An interaction between mitochondrial dynamics, physical activity levels, and COVID-19 severity has been previously hypothesized. However, this has not been tested. We aimed to compare mitochondrial morphology and cristae density of PBMCs between subjects with non-severe COVID-19, subjects with severe COVID-19, and healthy controls. Additionally, we compared the level of moderate-vigorous physical activity (MVPA) and sitting time between groups. Blood samples were taken to obtain PBMCs. Mitochondrial dynamics were assessed by electron microscopy images and western blot of protein that regulate mitochondrial dynamics. The International Physical Activity Questionnaire (IPAQ; short version) was used to estimate the level of MVPA and the sitting time The patients who develop severe COVID-19 (COVID-19++) not present alterations of mitochondrial size neither mitochondrial density in comparison to non-severe patients COVID-19 (COVID-19) and control subjects (CTRL). However, compared to CTRL, COVID-19 and COVID-19++ groups have lower mitochondrial cristae length, a higher proportion of abnormal mitochondrial cristae. The COVID-19++ group has lower number (trend) and length of mitochondrial cristae in comparison to COVID-19 group. COVID-19, but not COVID-19++ group had lower Opa 1, Mfn 2 and SDHB (Complex II) proteins than CTRL group. Besides, COVID-19++ group has a higher time sitting. Our results show that low mitochondrial cristae density, potentially due to physical inactivity, is associated with COVID-19 severity.


Subject(s)
COVID-19 , Sitting Position , Humans , Mitochondria/metabolism , Mitochondrial Dynamics , Sedentary Behavior
2.
Am J Transplant ; 22(7): 1884-1892, 2022 07.
Article in English | MEDLINE | ID: covidwho-1956680

ABSTRACT

The development of donor-specific antibodies (DSA) after lung transplantation is common and results in adverse outcomes. In kidney transplantation, Belatacept has been associated with a lower incidence of DSA, but experience with Belatacept in lung transplantation is limited. We conducted a two-center pilot randomized controlled trial of de novo immunosuppression with Belatacept after lung transplantation to assess the feasibility of conducting a pivotal trial. Twenty-seven participants were randomized to Control (Tacrolimus, Mycophenolate Mofetil, and prednisone, n = 14) or Belatacept-based immunosuppression (Tacrolimus, Belatacept, and prednisone until day 89 followed by Belatacept, Mycophenolate Mofetil, and prednisone, n = 13). All participants were treated with rabbit anti-thymocyte globulin for induction immunosuppression. We permanently stopped randomization and treatment with Belatacept after three participants in the Belatacept arm died compared to none in the Control arm. Subsequently, two additional participants in the Belatacept arm died for a total of five deaths compared to none in the Control arm (log rank p = .016). We did not detect a significant difference in DSA development, acute cellular rejection, or infection between the two groups. We conclude that the investigational regimen used in this study is associated with increased mortality after lung transplantation.


Subject(s)
Lung Transplantation , Tacrolimus , Abatacept/therapeutic use , Antilymphocyte Serum/therapeutic use , Graft Rejection/drug therapy , Graft Rejection/etiology , Graft Rejection/prevention & control , Graft Survival , Humans , Immunosuppression Therapy , Immunosuppressive Agents/therapeutic use , Lung Transplantation/adverse effects , Mycophenolic Acid/therapeutic use , Pilot Projects , Prednisone
3.
J Mol Biol ; 434(19): 167759, 2022 Jul 21.
Article in English | MEDLINE | ID: covidwho-1956230

ABSTRACT

The interferon-induced transmembrane (IFITM) proteins broadly inhibit the entry of diverse pathogenic viruses, including Influenza A virus (IAV), Zika virus, HIV-1, and SARS coronaviruses by inhibiting virus-cell membrane fusion. IFITM3 was previously shown to disrupt cholesterol trafficking, but the functional relationship between IFITM3 and cholesterol remains unclear. We previously showed that inhibition of IAV entry by IFITM3 is associated with its ability to promote cellular membrane rigidity, and these activities are functionally linked by a shared requirement for the amphipathic helix (AH) found in the intramembrane domain (IMD) of IFITM3. Furthermore, it has been shown that the AH of IFITM3 alters lipid membranes in vitro in a cholesterol-dependent manner. Therefore, we aimed to elucidate the relationship between IFITM3 and cholesterol in more detail. Using a fluorescence-based in vitro binding assay, we found that a peptide derived from the AH of IFITM3 directly interacted with the cholesterol analog, NBD-cholesterol, while other regions of the IFITM3 IMD did not, and native cholesterol competed with this interaction. In addition, recombinant full-length IFITM3 protein also exhibited NBD-cholesterol binding activity. Importantly, previously characterized mutations within the AH of IFITM3 that strongly inhibit antiviral function (F63Q and F67Q) disrupted AH structure in solution, inhibited cholesterol binding in vitro, and restricted bilayer insertion in silico. Our data suggest that direct interactions with cholesterol may contribute to the inhibition of membrane fusion pore formation by IFITM3. These findings may facilitate the design of therapeutic peptides for use in broad-spectrum antiviral therapy.

4.
EMBO Rep ; 23(8): e55393, 2022 Aug 03.
Article in English | MEDLINE | ID: covidwho-1955118

ABSTRACT

In 1977, the world witnessed both the eradication of smallpox and the beginning of the modern age of genomics. Over the following half-century, 7 epidemic viruses of international concern galvanized virologists across the globe and led to increasingly extensive virus genome sequencing. These sequencing efforts exerted over periods of rapid adaptation of viruses to new hosts, in particular, humans provide insight into the molecular mechanisms underpinning virus evolution. Investment in virus genome sequencing was dramatically increased by the unprecedented support for phylogenomic analyses during the COVID-19 pandemic. In this review, we attempt to piece together comprehensive molecular histories of the adaptation of variola virus, HIV-1 M, SARS, H1N1-SIV, MERS, Ebola, Zika, and SARS-CoV-2 to the human host. Disruption of genes involved in virus-host interaction in animal hosts, recombination including genome segment reassortment, and adaptive mutations leading to amino acid replacements in virus proteins involved in host receptor binding and membrane fusion are identified as the key factors in the evolution of epidemic viruses.


Subject(s)
COVID-19 , Influenza A Virus, H1N1 Subtype , Zika Virus Infection , Zika Virus , Animals , COVID-19/epidemiology , COVID-19/genetics , Evolution, Molecular , Genome, Viral , Humans , Influenza A Virus, H1N1 Subtype/genetics , Pandemics , SARS-CoV-2/genetics , Zika Virus/genetics
5.
Sensors (Basel) ; 22(11)2022 May 25.
Article in English | MEDLINE | ID: covidwho-1953881

ABSTRACT

The extreme rise of the Internet of Things and the increasing access of people to web applications have led to the expanding use of diverse e-commerce solutions, which was even more obvious during the COVID-19 pandemic. Large amounts of heterogeneous data from multiple sources reside in e-commerce environments and are often characterized by data source inaccuracy and unreliability. In this regard, various fusion techniques can play a crucial role in addressing such challenges and are extensively used in numerous e-commerce applications. This paper's goal is to conduct an academic literature review of prominent fusion-based solutions that can assist in tackling the everyday challenges the e-commerce environments face as well as in their needs to make more accurate and better business decisions. For categorizing the solutions, a novel 4-fold categorization approach is introduced including product-related, economy-related, business-related, and consumer-related solutions, followed by relevant subcategorizations, based on the wide variety of challenges faced by e-commerce. Results from the 65 fusion-related solutions included in the paper show a great variety of different fusion applications, focusing on the fusion of already existing models and algorithms as well as the existence of a large number of different machine learning techniques focusing on the same e-commerce-related challenge.


Subject(s)
COVID-19 , Pandemics , Algorithms , Commerce , Humans
6.
Cell Rep ; 39(13): 111009, 2022 Jun 28.
Article in English | MEDLINE | ID: covidwho-1944463

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BA.2 sub-lineage has gained in proportion relative to BA.1. Because spike (S) protein variations may underlie differences in their pathobiology, here we determine cryoelectron microscopy (cryo-EM) structures of the BA.2 S ectodomain and compare these with previously determined BA.1 S structures. BA.2 receptor-binding domain (RBD) mutations induce remodeling of the RBD structure, resulting in tighter packing and improved thermostability. Interprotomer RBD interactions are enhanced in the closed (or 3-RBD-down) BA.2 S, while the fusion peptide is less accessible to antibodies than in BA.1. Binding and pseudovirus neutralization assays reveal extensive immune evasion while defining epitopes of two outer RBD face-binding antibodies, DH1044 and DH1193, that neutralize both BA.1 and BA.2. Taken together, our results indicate that stabilization of the closed state through interprotomer RBD-RBD packing is a hallmark of the Omicron variant and show differences in key functional regions in the BA.1 and BA.2 S proteins.


Subject(s)
COVID-19 , SARS-CoV-2 , Antibodies, Viral , Cryoelectron Microscopy , Humans , Receptors, Virus/metabolism , Spike Glycoprotein, Coronavirus
7.
Appl Soft Comput ; 125: 109111, 2022 Aug.
Article in English | MEDLINE | ID: covidwho-1944285

ABSTRACT

COVID-19 spreads and contracts people rapidly, to diagnose this disease accurately and timely is essential for quarantine and medical treatment. RT-PCR plays a crucial role in diagnosing the COVID-19, whereas computed tomography (CT) delivers a faster result when combining artificial assistance. Developing a Deep Learning classification model for detecting the COVID-19 through CT images is conducive to assisting doctors in consultation. We proposed a feature complement fusion network (FCF) for detecting COVID-19 through lung CT scan images. This framework can extract both local features and global features by CNN extractor and ViT extractor severally, which successfully complement the deficiency problem of the receptive field of the other. Due to the attention mechanism in our designed feature complement Transformer (FCT), extracted local and global feature embeddings achieve a better representation. We combined a supervised with a weakly supervised strategy to train our model, which can promote CNN to guide the VIT to converge faster. Finally, we got a 99.34% accuracy on our test set, which surpasses the current state-of-art popular classification model. Moreover, this proposed structure can easily extend to other classification tasks when changing other proper extractors.

8.
EPMA J ; 13(2): 177-193, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1943338

ABSTRACT

Mitochondria are the "gatekeeper" in a wide range of cellular functions, signaling events, cell homeostasis, proliferation, and apoptosis. Consequently, mitochondrial injury is linked to systemic effects compromising multi-organ functionality. Although mitochondrial stress is common for many pathomechanisms, individual outcomes differ significantly comprising a spectrum of associated pathologies and their severity grade. Consequently, a highly ambitious task in the paradigm shift from reactive to predictive, preventive, and personalized medicine (PPPM/3PM) is to distinguish between individual disease predisposition and progression under circumstances, resulting in compromised mitochondrial health followed by mitigating measures tailored to the individualized patient profile. For the successful implementation of PPPM concepts, robust parameters are essential to quantify mitochondrial health sustainability. The current article analyses added value of Mitochondrial Health Index (MHI) and Bioenergetic Health Index (BHI) as potential systems to quantify mitochondrial health relevant for the disease development and its severity grade. Based on the pathomechanisms related to the compromised mitochondrial health and in the context of primary, secondary, and tertiary care, a broad spectrum of conditions can significantly benefit from robust quantification systems using MHI/BHI as a prototype to be further improved. Following health conditions can benefit from that: planned pregnancies (improved outcomes for mother and offspring health), suboptimal health conditions with reversible health damage, suboptimal life-style patterns and metabolic syndrome(s) predisposition, multi-factorial stress conditions, genotoxic environment, ischemic stroke of unclear aetiology, phenotypic predisposition to aggressive cancer subtypes, pathologies associated with premature aging and neuro/degeneration, acute infectious diseases such as COVID-19 pandemics, among others.

9.
Signal Image Video Process ; : 1-8, 2022 Jan 24.
Article in English | MEDLINE | ID: covidwho-1942887

ABSTRACT

The year 2020 will certainly be remembered in human history as the year in which humans faced a global pandemic that drastically affected every living soul on planet earth. The COVID-19 pandemic certainly had a massive impact on human's social and daily lives. The economy and relations of all countries were also radically impacted. Due to such unexpected situations, healthcare systems either collapsed or failed under colossal pressure to cope with the overwhelming numbers of patients arriving at emergency rooms and intensive care units. The COVID -19 tests used for diagnosis were expensive, slow, and gave indecisive results. Unfortunately, such a hindered diagnosis of the infection prevented abrupt isolation of the infected people which, in turn, caused the rapid spread of the virus. In this paper, we proposed the use of cost-effective X-ray images in diagnosing COVID-19 patients. Compared to other imaging modalities, X-ray imaging is available in most healthcare units. Deep learning was used for feature extraction and classification by implementing a multi-stream convolutional neural network model. The model extracts and concatenates features from its three inputs, namely; grayscale, local binary patterns, and histograms of oriented gradients images. Extensive experiments using fivefold cross-validation were carried out on a publicly available X-ray database with 3886 images of three classes. Obtained results outperform the results of other algorithms with an accuracy of 97.76%. The results also show that the proposed model can make a significant contribution to the rapidly increasing workload in health systems with an artificial intelligence-based automatic diagnosis tool.

10.
Appl Intell (Dordr) ; : 1-17, 2022 May 09.
Article in English | MEDLINE | ID: covidwho-1942030

ABSTRACT

In wake of COVID-19, the world has adapted to a new order. People have started wearing mask on their faces to prevent getting infected. The present face recognition models are no longer proving to be efficient in the current circumstances. This is because, most of the informative part of the face is covered by mask. The periocular recognition therefore holds the key to future of face recognition. However, the periocular region proves to be insufficiently enough to generate highly discriminative features. Also, most of the pre-COVID-19 algorithms fail to work in cases, where the number of training images available is very less. We propose a lightweight periocular recognition framework that uses thermo-visible features and ensemble subspace network classifier to improve upon the existing periocular recognition systems named as Masked Mobile Lightweight Thermo-visible Face Recognition (MmLwThV). The framework successfully improves the accuracy over a single visible modality by mitigating the effect of noise present in the thermo-visible features. The experiments on WHU-IIP dataset and an in-house collected dataset named, CVBL masked dataset, successfully validate the efficacy of our proposed framework. The MmLwFR framework is lightweight and can be easily deployed on mobile phones with a visible and an infrared camera.

11.
Circuits Syst Signal Process ; 41(6): 3397-3414, 2022.
Article in English | MEDLINE | ID: covidwho-1941442

ABSTRACT

The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. COVID-19 is found to be the most infectious disease in last few decades. This disease has infected millions of people worldwide. The inadequate availability and the limited sensitivity of the testing kits have motivated the clinicians and the scientist to use Computer Tomography (CT) scans to screen COVID-19. Recent advances in technology and the availability of deep learning approaches have proved to be very promising in detecting COVID-19 with increased accuracy. However, deep learning approaches require a huge labeled training dataset, and the current availability of benchmark COVID-19 data is still small. For the limited training data scenario, the CNN usually overfits after several iterations. Hence, in this work, we have investigated different pre-trained network architectures with transfer learning for COVID-19 detection that can work even on a small medical imaging dataset. Various variants of the pre-trained ResNet model, namely ResNet18, ResNet50, and ResNet101, are investigated in the current paper for the detection of COVID-19. The experimental results reveal that transfer learned ResNet50 model outperformed other models by achieving a recall of 98.80% and an F1-score of 98.41%. To further improvise the results, the activations from different layers of best performing model are also explored for the detection using the support vector machine, logistic regression and K-nearest neighbor classifiers. Moreover, a classifier fusion strategy is also proposed that fuses the predictions from the different classifiers via majority voting. Experimental results reveal that via using learned image features and classification fusion strategy, the recall, and F1-score have improvised to 99.20% and 99.40%.

12.
Biomed Signal Process Control ; 78: 104000, 2022 Sep.
Article in English | MEDLINE | ID: covidwho-1936112

ABSTRACT

The novel COVID-19 pandemic, has effectively turned out to be one of the deadliest events in modern history, with unprecedented loss of human life, major economic and financial setbacks and has set the entire world back quite a few decades. However, detection of the COVID-19 virus has become increasingly difficult due to the mutating nature of the virus, and the rise in asymptomatic cases. To counteract this and contribute to the research efforts for a more accurate screening of COVID-19, we have planned this work. Here, we have proposed an ensemble methodology for deep learning models to solve the task of COVID-19 detection from chest X-rays (CXRs) to assist Computer-Aided Detection (CADe) for medical practitioners. We leverage the strategy of transfer learning for Convolutional Neural Networks (CNNs), widely adopted in recent literature, and further propose an efficient ensemble network for their combination. The DenseNet-201 architecture has been trained only once to generate multiple snapshots, offering diverse information about the extracted features from CXRs. We follow the strategy of decision-level fusion to combine the decision scores using the blending algorithm through a Random Forest (RF) meta-learner. Experimental results confirm the efficacy of the proposed ensemble method, as shown through impressive results upon two open access COVID-19 CXR datasets - the largest COVID-X dataset, as well as a smaller scale dataset. On the large COVID-X dataset, the proposed model has achieved an accuracy score of 94.55% and on the smaller dataset by Chowdhury et al., the proposed model has achieved a 98.13% accuracy score.

13.
Curr Psychol ; : 1-9, 2022 Jul 15.
Article in English | MEDLINE | ID: covidwho-1935872

ABSTRACT

COVID-19 is a major public health event affecting the people worldwide. Nurses are still under immense psychological pressure. This study aimed to explore the relationship between mental fatigue and negative emotions among frontline medical staff during the COVID-19 pandemic. The study was conducted in August 2020, which included 419 medical staff between 17 to 28 years. The Fatigue Scale, Multidimensional Mental Flexibility Questionnaire, Cognitive Fusion Scale, and Depression-Anxiety-Stress Brief Version Scale were used. During the data collection period, the pandemic was under control in China and continued worldwide. The results indicated that 27.7% of the medical staff experienced depression, and 32.3% of them feel stressed. Specifically, first, correlation analyses showed significant positive pairwise correlations between mental fatigue, psychological inflexibility, cognitive fusion, and negative emotions among nurses. Second, mediation model tests showed statistically significant mediating effects of psychological inflexibility and cognitive fusion between mental fatigue on nurses' negative emotions, and statistically, significant chain mediating effects of psychological inflexibility and cognitive fusion. Mental fatigue indirectly affects nurses' negative effects through the mediating effects of psychological inflexibility, cognitive fusion, and the chain mediating effects of psychological inflexibility and cognitive fusion, respectively. the negative effects of mental fatigue come from impairment of cognitive functioning, and interventions using acceptance and commitment therapy for mental fatigue and negative emotions are more effective since both psychological inflexibility and cognitive fusion are important components of the therapy.

14.
J Membr Biol ; 255(2-3): 211-224, 2022 06.
Article in English | MEDLINE | ID: covidwho-1935761

ABSTRACT

Membrane fusion is an essential process for the survival of eukaryotes and the entry of enveloped viruses into host cells. A proper understanding of the mechanism of membrane fusion would provide us a handle to manipulate several biological pathways, and design efficient vaccines against emerging and re-emerging viral infections. Although fusion proteins take the central stage in catalyzing the process, role of lipid composition is also of paramount importance. Lipid composition modulates membrane organization and dynamics and impacts the lipid-protein (peptide) interaction. Moreover, the intrinsic curvature of lipids has strong impact on the formation of stalk and hemifusion diaphragm. Detection of transiently stable intermediates remains the bottleneck in the understanding of fusion mechanism. In order to circumvent this challenge, analytical methods can be employed to determine the kinetic parameters from ensemble average measurements of observables, such as lipid mixing, content mixing, and content leakage. The current review aims to present an analytical method that would aid our understanding of the fusion mechanism, provides a better insight into the role of lipid shape, and discusses the interplay of lipid and peptide in membrane fusion.


Subject(s)
Membrane Fusion , Peptides , Kinetics , Lipids/chemistry
15.
Appl Intell (Dordr) ; : 1-21, 2021 Oct 30.
Article in English | MEDLINE | ID: covidwho-1942028

ABSTRACT

The Coronavirus disease (COVID-19), which is an infectious pulmonary disorder, has affected millions of people and has been declared as a global pandemic by the WHO. Due to highly contagious nature of COVID-19 and its high possibility of causing severe conditions in the patients, the development of rapid and accurate diagnostic tools have gained importance. The real-time reverse transcription-polymerize chain reaction (RT-PCR) is used to detect the presence of Coronavirus RNA by using the mucus and saliva mixture samples taken by the nasopharyngeal swab technique. But, RT-PCR suffers from having low-sensitivity especially in the early stage. Therefore, the usage of chest radiography has been increasing in the early diagnosis of COVID-19 due to its fast imaging speed, significantly low cost and low dosage exposure of radiation. In our study, a computer-aided diagnosis system for X-ray images based on convolutional neural networks (CNNs) and ensemble learning idea, which can be used by radiologists as a supporting tool in COVID-19 detection, has been proposed. Deep feature sets extracted by using seven CNN architectures were concatenated for feature level fusion and fed to multiple classifiers in terms of decision level fusion idea with the aim of discriminating COVID-19, pneumonia and no-finding classes. In the decision level fusion idea, a majority voting scheme was applied to the resultant decisions of classifiers. The obtained accuracy values and confusion matrix based evaluation criteria were presented for three progressively created data-sets. The aspects of the proposed method that are superior to existing COVID-19 detection studies have been discussed and the fusion performance of proposed approach was validated visually by using Class Activation Mapping technique. The experimental results show that the proposed approach has attained high COVID-19 detection performance that was proven by its comparable accuracy and superior precision/recall values with the existing studies.

16.
Multimed Syst ; : 1-15, 2021 Jul 06.
Article in English | MEDLINE | ID: covidwho-1941743

ABSTRACT

Literature survey shows that convolutional neural network (CNN)-based pretrained models have been largely used for CoronaVirus Disease 2019 (COVID-19) classification using chest X-ray (CXR) and computed tomography (CT) datasets. However, most of the methods have used a smaller number of data samples for both CT and CXR datasets for training, validation, and testing. As a result, the model might have shown good performance during testing, but this type of model will not be more effective on unseen COVID-19 data samples. Generalization is an important term to be considered while designing a classifier that can perform well on completely unseen datasets. Here, this work proposes a large-scale learning with stacked ensemble meta-classifier and deep learning-based feature fusion approach for COVID-19 classification. The features from the penultimate layer (global average pooling) of EfficientNet-based pretrained models were extracted and the dimensionality of the extracted features reduced using kernel principal component analysis (PCA). Next, a feature fusion approach was employed to merge the features of various extracted features. Finally, a stacked ensemble meta-classifier-based approach was used for classification. It is a two-stage approach. In the first stage, random forest and support vector machine (SVM) were applied for prediction, then aggregated and fed into the second stage. The second stage includes logistic regression classifier that classifies the data sample of CT and CXR into either COVID-19 or Non-COVID-19. The proposed model was tested using large CT and CXR datasets, which are publicly available. The performance of the proposed model was compared with various existing CNN-based pretrained models. The proposed model outperformed the existing methods and can be used as a tool for point-of-care diagnosis by healthcare professionals.

17.
ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS ; 13(1), 2022.
Article in English | Web of Science | ID: covidwho-1938081

ABSTRACT

(Aim) COVID-19 has caused more than 2.28 million deaths till 4/Feb/2021 while it is still spreading across the world. This study proposed a novel artificial intelligence model to diagnose COVID-19 based on chest CT images. (Methods) First, the two-dimensional fractional Fourier entropy was used to extract features. Second, a custom deep stacked sparse autoencoder (DSSAE) model was created to serve as the classifier. Third, an improved multiple-way data augmentation was proposed to resist overfitting. (Results) Our DSSAE model obtains a micro-averaged F1 score of 92.32% in handling a four-class problem (COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis, and healthy control). (Conclusion) Our method outperforms 10 state-of-the-art approaches.

18.
Cytopathology ; 33(4):426-429, 2022.
Article in English | EMBASE | ID: covidwho-1937919
19.
Sensors (Basel) ; 22(14)2022 Jul 19.
Article in English | MEDLINE | ID: covidwho-1938962

ABSTRACT

We present a multi-sensor data fusion model based on a reconfigurable module (RM) with three fusion layers. In the data layer, raw data are refined with respect to the sensor characteristics and then converted into logical values. In the feature layer, a fusion tree is configured, and the values of the intermediate nodes are calculated by applying predefined logical operations, which are adjustable. In the decision layer, a final decision is made by computing the value of the root according to predetermined equations. In this way, with given threshold values or sensor characteristics for data refinement and logic expressions for feature extraction and decision making, we reconstruct an RM that performs multi-sensor fusion and is adaptable for a dedicated application. We attempted to verify its feasibility by applying the proposed RM to an actual application. Considering the spread of the COVID-19 pandemic, an unmanned storage box was selected as our application target. Four types of sensors were used to determine the state of the door and the status of the existence of an item inside it. We implemented a prototype system that monitored the unmanned storage boxes by configuring the RM according to the proposed method. It was confirmed that a system built with only low-cost sensors can identify the states more reliably through multi-sensor data fusion.


Subject(s)
COVID-19 , Pandemics , Humans
20.
Healthcare (Basel) ; 10(7)2022 Jul 19.
Article in English | MEDLINE | ID: covidwho-1938767

ABSTRACT

Effective screening provides efficient and quick diagnoses of COVID-19 and could alleviate related problems in the health care system. A prediction model that combines multiple features to assess contamination risks was established in the hope of supporting healthcare workers worldwide in triaging patients, particularly in situations with limited health care resources. Furthermore, a lack of diagnosis kits and asymptomatic cases can lead to missed or delayed diagnoses, exposing visitors, medical staff, and patients to 2019-nCoV contamination. Non-clinical techniques including data mining, expert systems, machine learning, and other artificial intelligence technologies have a crucial role to play in containment and diagnosis in the COVID-19 outbreak. This study developed Enhanced Gravitational Search Optimization with a Hybrid Deep Learning Model (EGSO-HDLM) for COVID-19 diagnoses using epidemiology data. The major aim of designing the EGSO-HDLM model was the identification and classification of COVID-19 using epidemiology data. In order to examine the epidemiology data, the EGSO-HDLM model employed a hybrid convolutional neural network with a gated recurrent unit based fusion (HCNN-GRUF) model. In addition, the hyperparameter optimization of the HCNN-GRUF model was improved by the use of the EGSO algorithm, which was derived by including the concepts of cat map and the traditional GSO algorithm. The design of the EGSO algorithm helps in reducing the ergodic problem, avoiding premature convergence, and enhancing algorithm efficiency. To demonstrate the better performance of the EGSO-HDLM model, experimental validation on a benchmark dataset was performed. The simulation results ensured the enhanced performance of the EGSO-HDLM model over recent approaches.

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