Novel social distancing detector using local binary pattern in comparison with principal component analysis to improve accuracy

The objective is to detect Novel Social Distancing using Local Binary Pattern (LBP) in comparison with Principal Component Analysis (PCA). Social Distance deduction is performed using Local Binary Pattern(N=20) and Principal Component Analysis(N=20) algorithms. Google AI open Images dataset is used for image detection. Dataset contains more than 10,000 images. Accuracy of Principal Component Analysis is 89.8% and Local Binary Pattern is 93.9%. There exists a statistical significant difference between LBP and PCA with (p<0.05). Local Binary Pattern appears to perform significantly better than Principal Component Analysis for Social Distancing Detection. © 2023 Author(s).

Detection of Multiple Structural Breaks in Large Covariance Matrices

This article studies multiple structural breaks in large contemporaneous covariance matrices of high-dimensional time series satisfying an approximate factor model. The breaks in the second-order moment structure of the common components are due to sudden changes in either factor loadings or covariance of latent factors, requiring appropriate transformation of the factor models to facilitate estimation of the (transformed) common factors and factor loadings via the classical principal component analysis. With the estimated factors and idiosyncratic errors, an easy-to-implement CUSUM-based detection technique is introduced to consistently estimate the location and number of breaks and correctly identify whether they originate in the common or idiosyncratic error components. The algorithms of Wild Binary Segmentation for Covariance (WBS-Cov) and Wild Sparsified Binary Segmentation for Covariance (WSBS-Cov) are used to estimate breaks in the common and idiosyncratic error components, respectively. Under some technical conditions, the asymptotic properties of the proposed methodology are derived with near-optimal rates (up to a logarithmic factor) achieved for the estimated breaks. Monte Carlo simulation studies are conducted to examine the finite-sample performance of the developed method and its comparison with other existing approaches. We finally apply our method to study the contemporaneous covariance structure of daily returns of S&P 500 constituents and identify a few breaks including those occurring during the 2007–2008 financial crisis and the recent coronavirus (COVID-19) outbreak. An package "” is provided to implement the proposed algorithms.

Assessment of changes in air quality in Bangalore, India, during the Covid-19 pandemic lockdown: Statistical modelling

COVID-19 pandemic has resulted in a halt to the daily lifestyle of people around the world and bound them to abide by the lockdown measures enforced to prevent the disease from further spreading. In India also, lockdown has been enforced from March 2020. As a result, the level of air pollutants in the atmosphere goes on decreasing. To know the air quality pattern of Bangalore city, ten stations around the city were selected. Air quality data of these stations has been availed from the Central Pollution Control Board (CPCB) of India website. Box chart concept of graphical representation has been applied to show the range of temporal variation of the air pollutants selected (CO, NO2, Ozone, PM2.5, PM10 and SO2) for the study area over two distinct periods (pre-lockdown and post-lockdown). It has been observed that all the pollutants level were drastically or significantly reduced except for SO2 which showed mixed behavior during the entire study period probably due to no restriction on the operation of power plants. GIS based contour mapping is done for each pollutant over the entire study area and separately for two distinct periods (pre-lockdown and post-lockdown). It was found that, change in CO level over the entire study area was significant and the reason behind it was complete restriction on vehicular movement which is the primary reason for CO emission in atmosphere. Reduction in PMs and ozone was also noticeable, but change in SO2 over the entire study area was almost insignificant. To find out the probable sources of pollution during the lockdown and before the lockdown period and the most significant parameters statistical approach has been adopted. The whole data set has been grouped based on similarity and divided into three distinct clusters for both pre-lockdown and post-lockdown period separately using Hierarchical Agglomerative Cluster Analysis (HACA) concept. Principal Component Analysis (PCA) was done for each of the clusters and each time period considered. From the results of PCA it can be confirmed that the most significant parameters were PM10, PM2.5, ozone and SO2. Results suggest that the probable sources of pollution during pre-lockdown period were vehicular emissions, power plants, industrial activities etc. In contrast, during post-lockdown period the sources of pollution were power plants, construction sites and household pollution only. MLR (Multiple Linear Regression) models were developed to predict Air Quality Index (AQI). Most of the models showed good fit with adjusted R2 value more than 0.9. Regression coefficient (R2) values for PM10 followed PM2.5 were highest in each cluster. © 2023 Author(s).

Rapid and Low-Cost Detection of Human Corona Virus Using MEMS ATR-FTIR Spectroscopy

The conventional methods used for the diagnostics of viral infection are either expensive and time-consuming or not accurate enough and dependent on consumable reagents. In the presence of pandemics, a fast and reagent-free solution is needed for mass screening. Recently, the diagnosis of viral infections using infrared spectroscopy has been reported as a fast and low-cost method. In this work a fast and low-cost solution for corona viral detection using infrared spectroscopy based on a compact micro-electro-mechanical systems (MEMS) device and artificial intelligence (AI) suitable for mass deployment is presented. Among the different variants of the corona virus that can infect people, 229E is used in this study due to its low pathogeny. The MEMS ATR-FTIR device employs a 6 reflections ZnSe crystal interface working in the spectral range of 2200-7000 cm-1. The virus was propagated and maintained in a medium for long enough time then cell supernatant was collected and centrifuged. The supernatant was then transferred and titrated using plaque titration assay. Positive virus samples were prepared with a concentration of 105 PFU/mL. Positive and negative control samples were applied on the crystal surface, dried using a heating lamp and the spectrum was captured. Principal component analysis and logistic regression were used as simple AI techniques. A sensitivity of about 90 % and a specificity of about 80 % were obtained demonstrating the potential detection of the virus based on the MEMS FTIR device. © 2023 SPIE.

A survival genome-wide association study to identify variants associated with COVID-19 risk of death

Background/Objectives: SARS-CoV-2 infection clinical manifestations hugely vary among patients, ranging from no symptoms, to life-threatening conditions. This variability is also due to host genetics: COVID-19 Host Genetics Initiative identified six loci associated with COVID-19 severity in a previous case-control genome-wide association study. A different approach to investigate the genetics of COVID-19 severity is looking for variants associated with mortality, e.g. by analyzing the association between genotypes and time-to-event data. Method(s): Here we perform a case-only genome-wide survival analysis, of 1,777 COVID-19 patients from the GEN-COVID cohort, 60 days after infection/hospitalization. Case-only studies has the advantage of eliminating selection biases and confounding related to control subjects. Patients were genotyped using Illumina Infinium Global Screening Arrays. PLINK software was used for data quality check and principal component analysis. GeneAbel R package was used for survival analysis and age, sex and the first four principal components were used as covariates in the Cox proportional hazard model. Result(s): We found four variants associated with COVID-19 patient survival at a nominal P < 1.0 x 10-6. Their minor alleles were associated with a higher mortality risk (i.e. hazard ratios (HR)>1). In detail, we observed: HR=1.03 for rs28416079 on chromosome 19 (P=1.34 x 10-7), HR=1.15 for rs72815354 on chromosome 10 (P=1.66 x 10-7), HR=2.12 for rs2785631 on chromosome 1 (P=5.14 x 10-7), and HR=2.27 for rs2785631 on chromosome 5 (P=6.65 x 10-7). Conclusion(s): The present results suggest that germline variants are COVID-19 prognostic factors. Replication in the remaining HGI COVID-19 patient cohort (EGAS00001005304) is ongoing at the time of submission.

Enhancing Students' Entrepreneurial Competencies through Extracurricular Activities—A Pragmatic Approach to Sustainability-Oriented Higher Education

Entrepreneurship can provide a creative, disruptive, problem-solving-oriented approach to the current economic, environmental, and social challenges of the world. This article aims to provide an analysis about the way universities can have an impact on developing entrepreneurial competence in students through extracurricular activities. The research relies on a questionnaire survey of students at the University of Petrosani, who participated in a range of entrepreneurial activities both online during the COVID-19 pandemic and face-to-face afterwards. The methodology consisted of applying principal component analysis to reduce the dimensionality of the indicators, followed by classification of the respondents through cluster analysis and training of a feedforward neural network. After finishing the network-training process, the error was minimized, resulting in three classes of respondents. Furthermore, based on the three classes, follow-up conclusions, policies, and decisions can be issued regarding the perception of entrepreneurship at the societal level, which is beneficial for academia and entrepreneurs, as well as for future research undertaken in this field. The key conclusion of our research is that entrepreneurship education is a real facilitator of the transition to sustainable entrepreneurship. Students perceived meeting successful entrepreneurs as being among the most effective extracurricular activities, assessing online activities as useful, and the field of study proved to be an important factor in their entrepreneurial intention.

An effective stacked autoencoder based depth separable convolutional neural network model for face mask detection.

The COVID-19 pandemic has been infecting the entire world over the past years. To prevent the spread of COVID-19, people have acclimatised to the new normal, which includes working from home, communicating online, and maintaining personal cleanliness. There are numerous tools required to prepare to compact transmissions in the future. One of these elements for protecting individuals from fatal virus transmission is the mask. Studies have indicated that wearing a mask may help to reduce the risk of viral transmission of all kinds. It causes many public places to take efforts to ensure that its guests wear adequate face masks and keep a safe distance from one another. Screening systems need to be installed at the doors of businesses, schools, government buildings, private offices, and/or other important areas. A variety of face detection models have been designed using various algorithms and techniques. Most of the articles in the previously published research have not worked on dimensionality reduction in conjunction with depth-wise separable neural networks. The necessity of determining the identities of people who do not cover their faces when they are in public is the driving factor for the development of this methodology. This research work proposes a deep learning technique to determine if a person is wearing mask or not and identifies whether it is properly worn or not. Stacked Auto Encoder (SAE) technique is implemented by stacking the following components: Principal Component Analysis (PCA) and Depth-wise Separable Convolutional Neural Network (DWSC-NN). PCA is used to reduce the irrelevant features in the images and resulted high true positive rate in the detection of mask. We achieved an accuracy score of 94.16% and an F1 score of 96.009% by the application of the method described in this research.

FCMCPS-COVID: AI propelled fog-cloud inspired scalable medical cyber-physical system, specific to coronavirus disease.

Medical cyber-physical systems (MCPS) firmly integrate a network of medical objects. These systems are highly efficacious and have been progressively used in the Healthcare 4.0 to achieve continuous high-quality services. Healthcare 4.0 encompasses numerous emerging technologies and their applications have been realized in the monitoring of a variety of virus outbreaks. As a growing healthcare trend, coronavirus disease (COVID-19) can be cured and its spread can be prevented using MCPS. This virus spreads from human to human and can have devastating consequences. Moreover, with the alarmingly rising death rate and new cases across the world, there is an urgent need for continuous identification and screening of infected patients to mitigate their spread. Motivated by the facts, we propose a framework for early detection, prevention, and control of the COVID-19 outbreak by using novel Industry 5.0 technologies. The proposed framework uses a dimensionality reduction technique in the fog layer, allowing high-quality data to be used for classification purposes. The fog layer also uses the ensemble learning-based data classification technique for the detection of COVID-19 patients based on the symptomatic dataset. In addition, in the cloud layer, social network analysis (SNA) has been performed to control the spread of COVID-19. The experimental results reveal that compared with state-of-the-art methods, the proposed framework achieves better results in terms of accuracy (82.28 %), specificity (91.42 %), sensitivity (90 %) and stability with effective response time. Furthermore, the utilization of CVI-based alert generation at the fog layer improves the novelty aspects of the proposed system.

Exploring the authentication of COVID-19 vaccines using Surface-enhanced handheld Raman spectroscopy (SERS) equipped with orbital Raster scattering and machine learning

COVID-19 is a novel coronavirus first emerging in Wuhan, China in December 2019 and has since spread rapidly across the globe escalating into a worldwide pandemic causing millions of fatalities. Emergency response to the pandemic included social distancing and isolation measures as well as the escalation of vaccination programmes. The most popular COVID-19 vaccines are nucleic acid-based. The vast spread and struggles in containment of the virus has allowed a gap in the market to emerge for counterfeit vaccines. This study investigates the use of handheld Raman spectroscopy as a method for nucleic acid-based vaccine authentication and utilises machine learning analytics to assess the efficacy of the method. Conventional Raman spectroscopy requires a large workspace, is cumbersome and energy consuming, and handheld Raman systems show limitations with regards to sensitivity and sample detection. Surface Enhanced Raman spectroscopy (SERS) however, shows potential as an authentication technique for vaccines, allowing identification of characteristic nucleic acid bands in spectra. SERS showed strong identification potential through Correlation in Wavelength Space (CWS) with all vaccine samples obtaining an r value of approximately 1 when plotted against themselves. Variance was observed between some excipients and a selected number of DNA-based vaccines, possibly attributed to the stability of the SERS colloid where the colloid-vaccine complex had been measured over different time intervals. Further development of the technique would include optimisation of the SERS method, stability studies and more comprehensive analysis and interpretation of a greater sample size. © 2023 IEEE.

Introducing virtual classrooms for undergraduate physiology teaching during the COVID-19 pandemic: acceptance by students and subjective impact on learning.

The COVID-19 pandemic and worldwide lockdowns brought major changes in education systems. There was a sudden obligatory shift toward utilization of digital resources for teaching and learning purposes. Medical education, specifically physiology teaching, comprises hands-on training in the laboratory. It is challenging to offer a course like physiology in a virtual format. The objective of this study was to assess the effectiveness and influence of virtual classroom technology on online physiology education in a sample size of 83 first-year MBBS undergraduates. A questionnaire comprising questions related to technology accessibility and utilization, comprehensibility and effectiveness of instructions, faculty proficiency, and learning outcomes was administered to the group. The responses were collected and analyzed. Validation through principal components and factor analysis showed that online teaching is not very effective and has a limited application in the physiology education of undergraduate MBBS students. Our study also revealed that virtual physiology teaching of undergraduate medical students during the COVID-19 pandemic had a moderate level of effectiveness.NEW & NOTEWORTHY In the present qualitative study, we have conducted and validated an online physiology teaching platform at a medical college to continue medical education during the peak times of the COVID-19 pandemic and prolonged lockdowns. Furthermore, we have evaluated the effectiveness of online physiology teaching through multidimensional feedback from undergraduate MBBS students. It is experimental evidence of inadequate sustainability, moderate efficacy, limited application, and poor first-hand experience gained by the students in virtual physiology teaching in a preclinical and clinical setting.

Automatic detection of novel corona virus (SARS-CoV-2) infection in computed tomography scan based on local adaptive thresholding and kernel-support vectors

The recent studies have indicated the requisite of computed tomography scan analysis by radiologists extensively to find out the suspected patients of SARS-CoV-2 (COVID-19). The existing deep learning methods distribute one or more of the subsequent bottlenecks. Therefore, a straight forward method for detecting COVID-19 infection using real-world computed tomography scans is presented. The detection process consists of image processing techniques such as segmentation of lung parenchyma and extraction of effective texture features. The kernel-based support vector machine is employed over feature vectors for classification. The performance parameters of the proposed method are calculated and compared with the existing methodology on the same dataset. The classification results are found outperforming and the method is less probabilistic which can be further exploited for developing more realistic detection system.Copyright © 2023 Inderscience Enterprises Ltd.

Comprehensive evaluation of otorhinolaryngological symptoms in COVID-19 patients

Introduction: The aim of this study is to comprehensively evaluate the incidence and natural course of otorhinolaryngological symptoms of COVID-19 infection and its relations to each other and patient's demographics. Method(s): This is a prospective study conducted on symptomatic adult patients proven to be infected with COVID-19. Detailed history was taken from each patient including onset of symptoms. Symptoms were followed up tightly. We focus on otorhinolaryngological (ORL) symptoms and their duration and onset in relation to other symptoms. Data were collected and analyzed in detail. Result(s): Six-hundred eighty-six patients were included in the study, their age ranged from 19-75 years old, and of them 55.1% were males. Cough was found in 53.1% of cases followed by sore throat in 45.8%, anosmia/ hyposmia in 42.3%, headache in 42%, rhinorrhea in 19.5%, dry mouth in 7.6%, globus in 6.1%, epistaxis in 4.4%, and hearing loss in 0.6%. In non-ORL symptoms, fever was found in 54.2%, malaise in 55.1%, dyspnea in 49.3%, and diarrhea in 27.2%. The first symptom was anosmia in 15.7% of cases, sore throat in 6.1 %, cough in 7.9%, and headache in 13.4% of cases. Fever was the first symptom in 22.7%, malaise in 25.1%, and diarrhea in 6.4%. Headache occurred for 5.5 +/- 2 days, anosmia/hyposmia 3 to > 30 days, sore throat 4.1 +/- 1.2 days, rhinorrhea 4.3 +/- 1.1, cough 7.4 +/- 2.5 days, fever 4.7 +/- 2 days, and malaise 6.5 +/- 2.4 days. The cluster of COVID-19-related symptoms showed nine principal components. Conclusion(s): Otorhinolaryngological symptoms are main symptoms in COVID-19 infection, and they should be frequently evaluated to detect suspected cases especially in pauci-symptomatic patients and to properly manage infected patients.Copyright © 2022, The Author(s).

Early Inflammatory Profiles Predict Maximal Disease Severity in Covid-19

Background: Better understanding of host inflammatory changes that precede development of severe COVID-19 could improve delivery of available antiviral and immunomodulatory therapies, and provide insights for the development of new therapies. Method(s): In plasma from individuals with COVID-19, sampled <=10 days from symptom onset from the All-Ireland Infectious Diseases Cohort study, we measured 61 biomarkers, including markers of innate immune and T cell activation, coagulation, tissue repair, lung injury, and immune regulation. We used principal component analysis (PCA) and k-means clustering to derive biomarker clusters, and univariate and multivariate ordinal logistic regression to explore association between cluster membership and maximal disease severity, adjusting for risk factors for severe COVID-19, including age, sex, ethnicity, BMI, hypertension and diabetes. Result(s): From March 2020-April 2021, we included 312 individuals, (median (IQR) age 62 (48-77) years, 7 (4-9) days from symptom onset, 54% male) in the analysis. PCA and clustering derived 4 clusters. Compared to cluster 1, clusters 2-4 were significantly older and of higher BMI but there were no significant differences in sex or ethnicity. Cluster 1 had low levels of inflammation, cluster 2 had higher levels of markers of tissue repair and endothelial activation (EGF, VEGF, PDGF, TGFalpha, serpin E1 and p-selectin). Cluster 3 and 4 were both characterised by higher overall inflammation, but compared to cluster 4, cluster 3 had downregulation of growth factors, markers of endothelial activation, and immune regulation (IL10, PDL1), but higher alveolar epithelial injury markers (RAGE, ST2). In univariate analysis, compared to cluster 1, cluster 3 had the highest odds of severe disease (OR (95% CI) 9.02 (4.62-18.31), followed by cluster 4: 5.59 (2.75-11.72) then cluster 2: 4.5 (2.38-8.81), all p < 0.05). Cluster 3 remained most strongly associated with severe disease in fully adjusted analyses;cluster 3: OR(95% CI) 5.99 (2.69-13.35), cluster 2: 3.14 (1.54-6.42), cluster 4: 3.13 (1.36-7.19), all p< 0.05). Conclusion(s): Distinct early inflammatory profiles predicted maximal disease severity independent of known risk factors for severe COVID-19. A cluster characterised by downregulation of growth factor and endothelial markers and early evidence of alveolar injury was associated with highest risk of developing severe COVID19. Whether this reflects a dysregulated inflammatory response that could improve targeted treatment requires further study. Heatmap of biomarker derived clusters and forest plot of association between clusters and disease severity. A: Heatmap demonstrating differences in biomarkers between clusters B: Forest plot demonstrating odds ratio of specific clusters for progressing to moderate or severe disease (reference Cluster 1), calculated using ordinal logistic regression. Odds ratio (95% CI) presented as unadjusted and fully adjusted (for age, sex, ethnicity, BMI, hypertension, diabetes, immunosuppression, smoking and baseline anticoagulant use). Maximal disease severity graded per the WHO severity scale.

VMCTE: Visualization-Based Malware Classification Using Transfer and Ensemble Learning

The Corona Virus Disease 2019 (COVID-19) effect has made telecommuting and remote learning the norm. The growing number of Internet-connected devices provides cyber attackers with more attack vectors. The development of malware by criminals also incorporates a number of sophisticated obfuscation techniques, making it difficult to classify and detect malware using conventional approaches. Therefore, this paper proposes a novel visualization-based malware classification system using transfer and ensemble learning (VMCTE). VMCTE has a strong anti-interference ability. Even if malware uses obfuscation, fuzzing, encryption, and other techniques to evade detection, it can be accurately classified into its corresponding malware family. Unlike traditional dynamic and static analysis techniques, VMCTE does not require either reverse engineering or the aid of domain expert knowledge. The proposed classification system combines three strong deep convolutional neural networks (ResNet50, MobilenetV1, and MobilenetV2) as feature extractors, lessens the dimension of the extracted features using principal component analysis, and employs a support vector machine to establish the classification model. The semantic representations of malware images can be extracted using various convolutional neural network (CNN) architectures, obtaining higher-quality features than traditional methods. Integrating fine-tuned and non-fine-tuned classification models based on transfer learning can greatly enhance the capacity to classify various families of malware. The experimental findings on the Malimg dataset demonstrate that VMCTE can attain 99.64%, 99.64%, 99.66%, and 99.64% accuracy, F1-score, precision, and recall, respectively. © 2023 Tech Science Press. All rights reserved.

Impact of COVID-19 lockdown on air quality analyzed through machine learning techniques.

After February 2020, the majority of the world's governments decided to implement a lockdown in order to limit the spread of the deadly COVID-19 virus. This restriction improved air quality by reducing emissions of particular atmospheric pollutants from industrial and vehicular traffic. In this study, we look at how the COVID-19 shutdown influenced the air quality in Lahore, Pakistan. HAC Agri Limited, Dawn Food Head Office, Phase 8-DHA, and Zeenat Block in Lahore were chosen to give historical data on the concentrations of many pollutants, including PM2.5, PM10 (particulate matter), NO2 (nitrogen dioxide), and O3 (ozone). We use a variety of models, including decision tree, SVR, random forest, ARIMA, CNN, N-BEATS, and LSTM, to compare and forecast air quality. Using machine learning methods, we looked at how each pollutant's levels changed during the lockdown. It has been shown that LSTM estimates the amounts of each pollutant during the lockout more precisely than other models. The results show that during the lockdown, the concentration of atmospheric pollutants decreased, and the air quality index improved by around 20%. The results also show a 42% drop in PM2.5 concentration, a 72% drop in PM10 concentration, a 29% drop in NO2 concentration, and an increase of 20% in O3 concentration. The machine learning models are assessed using the RMSE, MAE, and R-SQUARE values. The LSTM measures NO2 at 4.35%, O3 at 8.2%, PM2.5 at 4.46%, and PM10 at 8.58% in terms of MAE. It is observed that the LSTM model outperformed with the fewest errors when the projected values are compared with the actual values.

Effect of geographical factors on reference values of creatine kinase isoenzyme.

The aim of this study was to investigate the geographical spatial distribution of creatine kinase isoenzyme (CK-MB) in order to provide a scientific basis for clinical examination. The reference values of CK-MB of 8697 healthy adults in 137 cities in China were collected by reading a large number of literates. Moran index was used to determine the spatial relationship, and 24 factors were selected, which belonged to terrain, climate, and soil indexes. Correlation analysis was conducted between CK-MB and geographical factors to determine significance, and 9 significance factors were extracted. Based on R language to evaluate the degree of multicollinearity of the model, CK-MB Ridge model, Lasso model, and PCA model were established, through calculating the relative error to choose the best model PCA, testing the normality of the predicted values, and choosing the disjunctive kriging interpolation to make the geographical distribution. The results show that CK-MB reference values of healthy adults were generally correlated with latitude, annual sunshine duration, annual mean relative humidity, annual precipitation amount, and annual range of air temperature and significantly correlated with annual mean air temperature, topsoil gravel content, topsoil cation exchange capacity in clay, and topsoil cation exchange capacity in silt. The geospatial distribution map shows that on the whole, it is higher in the north and lower in the south, and gradually increases from the southeast coastal area to the northwest inland area. If the geographical factors are obtained in a location, the CK-MB model can be used to predict the CK-MB of healthy adults in the region, which provides a reference for us to consider regional differences in clinical diagnosis.

COVID-19 Vulnerability Mapping of Asian Countries.

OBJECTIVE: The objective of this study is to map vulnerability of Asian countries to the COVID-19 pandemic. METHOD: According to the Intergovernmental Panel on Climate Change (IPCC) 2007 framework for natural hazards, vulnerability is a function of exposure, sensitivity, and adaptive capacity. From an extensive literature review, we identified 16 socioeconomic, meteorological, environmental, and health factors that influence coronavirus disease 2019 (COVID-19) cases and deaths. The underlying factors of vulnerability were identified using principal component analysis. RESULTS: Our findings indicate that the percentage of the urban population, obesity rate, air connectivity, and the population aged 65 and over, diabetes prevalence, and PM2.5 levels all contributed significantly to COVID-19 sensitivity. Subsequently, governance effectiveness, human development index (HDI), vaccination rate, and life expectancy at birth, and gross domestic product (GDP) all had a positive effect on adaptive capacity. The estimated vulnerability was corroborated by a Pearson correlation of 0.615 between death per million population and vulnerability. CONCLUSION: This study demonstrates the application of universal indicators for assessing pandemic vulnerability for informed policy interventions such as the COVAX vaccine roll-out priority. Despite data limitations and a lack of spatiotemporal analysis, this study's methodological framework allows for ample data incorporation and replication.

Time evolution of cytokine profiles associated with mortality in COVID-19 hospitalized patients.

Background: High cytokine levels have been associated with severe COVID-19 disease. Although many cytokine studies have been performed, not many of them include combinatorial analysis of cytokine profiles through time. In this study we investigate the association of certain cytokine profiles and its evolution, and mortality in SARS-CoV2 infection in hospitalized patients. Methods: Serum concentration of 45 cytokines was determined in 28 controls at day of admission and in 108 patients with COVID-19 disease at first, third and sixth day of admission. A principal component analysis (PCA) was performed to characterize cytokine profiles through time associated with mortality and survival in hospitalized patients. Results: At day of admission non-survivors present significantly higher levels of IL-1α and VEGFA (PC3) but not through follow up. However, the combination of HGF, MCP-1, IL-18, eotaxine, and SCF (PC2) are significantly higher in non-survivors at all three time-points presenting an increased trend in this group through time. On the other hand, BDNF, IL-12 and IL-15 (PC1) are significantly reduced in non-survivors at all time points with a decreasing trend through time, though a protective factor. The combined mortality prediction accuracy of PC3 at day 1 and PC1 and PC2 at day 6 is 89.00% (p<0.001). Conclusions: Hypercytokinemia is a hallmark of COVID-19 but relevant differences between survivors and non-survivors can be early observed. Combinatorial analysis of serum cytokines and chemokines can contribute to mortality risk assessment and optimize therapeutic strategies. Three clusters of cytokines have been identified as independent markers or risk factors of COVID mortality.

Computational studies on the design of NCI natural products as inhibitors to SARS-CoV-2 main protease.

The pandemic coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in more than 5 million deaths globally. Currently there are no effective drugs available to treat COVID-19. The viral protease replication can be blocked by the inhibition of main protease that is encoded in polyprotein 1a and is therefore a potential protein target for drug discovery. We have carried out virtual screening of NCI natural compounds followed by molecular docking in order to identify hit molecules as probable SARS-CoV-2 main protease inhibitors. The molecular dynamics (MD) simulations of apo form in complex with N3, α-ketoamide and NCI natural products was used to validate the screened compounds. The MD simulations trajectories were analyzed using normal mode analysis and principal component analysis revealing dynamical nature of the protein. These findings aid in understanding the binding of natural products and molecular mechanisms of SARS-CoV-2 main protease inhibition.Communicated by Ramaswamy H. Sarma.

Outdoor Localization for a Mobile Robot under Different Weather Conditions Using a Deep Learning Algorithm

A fundamental issue in robotics is the precise localization of mobile robots in uncertain environments. Due to changing environmental patterns and lighting, localization under difficult perceptual conditions remains problematic. This paper presents an approach for locating an outdoor mobile robot using deep learning algorithms merge with 3D Light Detection and Ranging LiDAR data and RGB-D image. This approach is divided into three levels. The first is the training level, which involves scanning the localization area with a 3D LiDAR sensor and then converting the data into a 2.5D image based on the Principal Component Analysis. The testing is the second level in the Intensity Hue Saturation process. Then, the RGB and Depth images are combined to create a 2.5D fusion image. These datasets are trained and tested using Convolution Neural Networks. The K-Nearest Neighbor algorithm is used in the third level is the classification. The results show that the proposed approach is better in terms of accuracy of 97.46% and the Mean error distance is 0.6 meters.