Active environmental surveillance of SARS-CoV-2 in Midwestern United States meatpacking plants

This pilot project investigated environmental SARS-CoV-2 presence in seven Midwestern meatpacking plants from May 2020 to January 2021. This study investigated social distancing and infection control practices and incorporated environmental sampling of surfaces and air in employee common areas. All plants increased their social distancing efforts, increased the frequency of cleaning and disinfecting worker areas, and screened for symptomatic people to prevent entry into the workplace. 575 samples from common areas were collected and evaluated with RT-qPCR for the presence of SARS-CoV-2. 42/367 surface samples were positive, while no virus was detected in air samples. Case positive data from the counties surrounding each plant showed peak positive SARS-CoV-2 cases from 12–55 days before the virus was detected in the plant, indicating that environmental sampling is likely a lagging indicator of community and plant infection.

Time as the Rhythm of Theoretical Practices

Many scholars would agree today that no theory is timeless. But they would probably mean the historicity of theoretical thinking, including the concepts and preconceptions of time propagated by theoretical literary studies. In this paper, I will investigate a usually ignored but unavoidable problem: the rhythmic dimension of theorising. How do those practices, to which different theoretical attempts are linked, influence their orientation in time(s)? Gathering positive data of the past in positivism, revealing the work of formal devices of poetic language in formalism, following a rhetorical change in postmodernism or reducing every kind of change to historical construction are not only ideological patterns but practical rhythms considered as paradigmatic for other – and sometimes for all – happenings. Based on practice-oriented physics, I propose research of time projections by which literary reading and writing are transposed to the kinetic spaces of certain theoretical practices.

EFFECTS OF DISORGANIZED URBANIZATION ON ENVIRONMENT

Having overcome a stage of accentuated growth in urbanization (a 93% increase since 1950), today high levels are being maintained, but with a certain equilibrium. The countries whose urbanization levels have grown most are Colombia and Brazil, with an average annual growth of nearly 1.3% between 1950 and 2015.According to BBVA Research, urbanization in Latin America began earlier than in other regions and has managed to develop at a much faster pace. In addition, and keeping in mind the characteristics of Latin America, this increase in the levels of urbanization has greater merit, if one takes into account the low levels of income, capital, employment and productivity.In spite of the positive data on its development and growth, urbanization continues to be concentrated in a very limited number of cities. Only Mexico and Brazil have more than a dozen cities with over a million inhabitants, while countries such as Uruguay and Paraguay don´t have more than two cities with a population of more than one million residents.

EFFECTS OF DISORGANIZED URBANIZATION ON ENVIRONMENT

Having overcome a stage of accentuated growth in urbanization (a 93% increase since 1950), today high levels are being maintained, but with a certain equilibrium. The countries whose urbanization levels have grown most are Colombia and Brazil, with an average annual growth of nearly 1.3% between 1950 and 2015.According to BBVA Research, urbanization in Latin America began earlier than in other regions and has managed to develop at a much faster pace. In addition, and keeping in mind the characteristics of Latin America, this increase in the levels of urbanization has greater merit, if one takes into account the low levels of income, capital, employment and productivity.In spite of the positive data on its development and growth, urbanization continues to be concentrated in a very limited number of cities. Only Mexico and Brazil have more than a dozen cities with over a million inhabitants, while countries such as Uruguay and Paraguay don´t have more than two cities with a population of more than one million residents.

Lipid profile migration during the tilapia muscle steaming process revealed by a transactional analysis between MS data and lipidomics data

In this work, lipid profile migration from muscle to juice during the tilapia muscle steaming process was revealed by a transactional analysis of data from ultra-high-performance liquid chromatography coupled with Q Exactive (UHPLC-QE) Orbitrap mass spectrometry (MS) and lipidomics. Firstly, the lipids in tilapia muscles and juices at different steaming time points were extracted and examined by UHPLC-QE Orbitrap mass spectrometry. Secondly, a transactional analysis procedure was developed to analyze the data from UHPLC-QE Orbitrap MS and lipidomics. Finally, the corrected lipidomics data and the normalized MS data were used for lipid migration analysis. The results suggested that the transactional analysis procedure was efficient to significantly decrease UHPLC-QE Orbitrap MS workloads and delete the false-positive data (22.4–36.7%) in lipidomics data, which compensated the disadvantages of the current lipidomics method. The lipid changes could be disappearance, full migration into juice, appearance in juice, appearance in muscle, appearance in both muscle and juice, and retention in the muscle. Moreover, the results showed 9 (compared with 52), 5 (compared with 116), and 10 (compared with 178) of lipid class (compared with individual lipid) variables showed significant differences among the different steaming times (0, 10, 30, and 60 min) in all the muscles, juices, and muscle-juice systems, respectively. These results showed significant lipid profile migration from muscle to juice during the tilapia steaming process.

Machine learning for Drug-Virus Prediction

The 2019 Coronavirus (COVID-19) epidemic has recently hit most countries hard. Therefore, many researchers around the world are looking for a way to control this virus. Examining existing medications and using them to prevent this epidemic can be helpful. Drug repositioning solutions can be effective because designing and discovering a drug can be very time-consuming. In this study, we used a binary classifier learning method to predict the drug-virus relationship. The feature vector for each drug-virus pair is based on the similarity between drugs and the similarity between viruses. We calculated the similarities between the drugs using their structural properties (fingerprint) and their phenotype. We also calculated the similarities between viruses based on their genome sequence and the vector encoded by the Biobert model. Finally, using the HDVD dataset, we formed the similarity vectors of each drug-virus pair and considered it as input to neural network and random forest models. In these models, we randomly selected 20% of the positive data and the same amount of negative data. Finally, the performance of the proposed approach for this test data is considered, after five tests, as AUC=0.97 and AUPR = 0.96. We also used the Compressed Sensing (CS) matrix factorization model to predict the drug-virus association. After that, we investigated the importance of drug features in predicting drug-virus association by using Autoencoder and reducing the dimension of drug properties.

Machine learning for Drug-Virus Prediction

The 2019 Coronavirus (COVID-19) epidemic has recently hit most countries hard. Therefore, many researchers around the world are looking for a way to control this virus. Examining existing medications and using them to prevent this epidemic can be helpful. Drug repositioning solutions can be effective because designing and discovering a drug can be very time-consuming. In this study, we used a binary classifier learning method to predict the drug-virus relationship. The feature vector for each drug-virus pair is based on the similarity between drugs and the similarity between viruses. We calculated the similarities between the drugs using their structural properties (fingerprint) and their phenotype. We also calculated the similarities between viruses based on their genome sequence and the vector encoded by the Biobert model. Finally, using the HDVD dataset, we formed the similarity vectors of each drug-virus pair and considered it as input to neural network and random forest models. In these models, we randomly selected 20% of the positive data and the same amount of negative data. Finally, the performance of the proposed approach for this test data is considered, after five tests, as AUC=0.97 and AUPR = 0.96. We also used the Compressed Sensing (CS) matrix factorization model to predict the drug-virus association. We also investigated the importance of drug features in predicting drug-virus association by using Autoencoder and reducing the dimension of drug properties.

COVID-19 early detection for imbalanced or low number of data using a regularized cost-sensitive CapsNet

With the presence of novel coronavirus disease at the end of 2019, several approaches were proposed to help physicians detect the disease, such as using deep learning to recognize lung involvement based on the pattern of pneumonia. These approaches rely on analyzing the CT images and exploring the COVID-19 pathologies in the lung. Most of the successful methods are based on the deep learning technique, which is state-of-the-art. Nevertheless, the big drawback of the deep approaches is their need for many samples, which is not always possible. This work proposes a combined deep architecture that benefits both employed architectures of DenseNet and CapsNet. To more generalize the deep model, we propose a regularization term with much fewer parameters. The network convergence significantly improved, especially when the number of training data is small. We also propose a novel Cost-sensitive loss function for imbalanced data that makes our model feasible for the condition with a limited number of positive data. Our novelties make our approach more intelligent and potent in real-world situations with imbalanced data, popular in hospitals. We analyzed our approach on two publicly available datasets, HUST and COVID-CT, with different protocols. In the first protocol of HUST, we followed the original paper setup and outperformed it. With the second protocol of HUST, we show our approach superiority concerning imbalanced data. Finally, with three different validations of the COVID-CT, we provide evaluations in the presence of a low number of data along with a comparison with state-of-the-art.

Assessment for the Evaluation of E-Learning and Teaching by the Quality Matter Standards Post COVID-19

On 18 March, the WHO announced that COVID-19 was a global pandemic, and the Ministry of Health instituted a COVID-19 lockdown. After the period of restrictions ended, blended learning was initiated at the universities. The research aims to evaluate the responses of students, teachers, and quality units in the teaching and learning process by Quality Matter standards. The survey was constructed using a Google form. The research recorded positive data in most of the Quality Matter Standards (QM) in Architecture Engineering Program applied by 95%. Some weakness points were identified 5% and are discussed in this paper. The results by (QM) St1 (2.7/3), St2 (2.6/3), St3 (2.6/3), St4 (2.6/3), St5 (2.6/3), St6 (2.5/3), St7 (2.6/3), St8 (2.7/3). General conclusions are added for teachers to be applied to e-learning education.