Impact of meteorological factors on the COVID-19 transmission: A multicity study in China

The purpose of the present study is to explore the associations between novel coronavirus disease 2019 (COVID- 19) case counts and meteorological factors in 30 provincial capital cities of China. We compiled a daily dataset including confirmed case counts, ambient temperature (AT), diurnal temperature range (DTR), absolute humidity (AH) and migration scale index (MSI) for each city during the period of January 20th to March 2nd, 2020. First, we explored the associations between COVID-19 confirmed case counts, meteorological factors, and MSI using non-linear regression. Then, we conducted a two-stage analysis for 17 cities with more than 50 confirmed cases. In the first stage, generalized linear models with negative binomial distribution were fitted to estimate city-specific effects of meteorological factors on confirmed case counts. In the second stage, the meta-analysis was conducted to estimate the pooled effects. Our results showed that among 13 cities that have less than 50 confirmed cases, 9 cities locate in the Northern China with average AT below0 °C, 12 cities had average AHbelow4 g/m3, and one city (Haikou) had the highest AH (14.05 g/m3). Those 17 cities with 50 and more cases accounted for 90.6% of all cases in our study. Each 1 °C increase in AT and DTR was related to the decline of daily confirmed case counts, and the corresponding pooled RRs were 0.80 (95% CI: 0.75, 0.85) and 0.90 (95% CI: 0.86, 0.95), respectively. For AH, the association with COVID-19 case counts were statistically significant in lag 07 and lag 014. In addition,we found the all these associations increased with accumulated time duration up to 14 days. In conclusions, meteorological factors play an independent role in the COVID-19 transmission after controlling population migration. Local weather condition with low temperature, mild diurnal temperature range and low humidity likely favor the transmission

Modelling for understanding of COVID-19 spread and design of an isolation room in a hospital

We have modelled the transmission of coronavirus 2019 (COVID-19) in the isolation room of a patient suffering from severe acute respiratory syndrome coronavirus 2 (SARS CoV 2) at the Royal Brompton Hospital (RBH) in London. An adaptive mesh computational fluid dynamics (CFD) model was used for simulations of 3D spatial distribution of SARS CoV 2 in the room. The modelling set-up is based on data collected in the room during the patient stay. A number of numerical experiments have been carried out to provide an optimal design layout of the overall isolation room. Our focus has been on (1) the location of the air extractor and filtration rates; (2) the bed location of the patient; and (3) consideration of the health and safety of the staff working area

Prediction of Progression to Severe Stroke in Initially Diagnosed Anterior Circulation Ischemic Cerebral Infarction

Purpose: Accurate prediction of the progression to severe stroke in initially diagnosed nonsevere patients with acute–subacute anterior circulation nonlacuna ischemic infarction (ASACNLII) is important in making clinical decision. This study aimed to apply a machine learning method to predict if the initially diagnosed nonsevere patients with ASACNLII would progress to severe stroke by using diffusion-weighted images and clinical information on admission.Methods: This retrospective study enrolled 344 patients with ASACNLII from June 2017 to August 2020 on admission, and 108 cases progressed to severe stroke during hospitalization within 3–21 days. The entire data were randomized into a training set (n = 271) and an independent test set (n = 73). A U-Net neural network was employed for automatic segmentation and volume measurement of the ischemic lesions. Predictive models were developed and used for evaluating the progression to severe stroke using different feature sets (the volume data, the clinical data, and the combination) and machine learning methods (random forest, support vector machine, and logistic regression).Results: The U-Net showed high correlation with manual segmentation in terms of Dice coefficient of 0.806 and R2 value of the volume measurements of 0.960 in the test set. The random forest classifier of the volume + clinical combination achieved the best area under the receiver operating characteristic curve of 0.8358 (95% CI 0.7321–0.9269), and the accuracy, sensitivity, and specificity were 0.7780 (0.7397–0.7945), 0.7695 (0.6102–0.9074), and 0.8686 (0.6923–1.0), respectively. The Shapley additive explanation diagram showed the volume variable as the most important predictor.Conclusion: The U-Net was fully automatic and showed a high correlation with manual segmentation. An integrated approach combining clinical variables and stroke lesion volumes that were derived from the advanced machine learning algorithms had high accuracy in predicting the progression to severe stroke in ASACNLII patients

The Classification Method Study of Crops Remote Sensing with Deep Learning, Machine Learning, and Google Earth Engine

The extraction and classification of crops is the core issue of agricultural remote sensing. The precise classification of crop types is of great significance to the monitoring and evaluation of crops planting area, growth, and yield. Based on the Google Earth Engine and Google Colab cloud platform, this study takes the typical agricultural oasis area of Xiangride Town, Qinghai Province, as an example. It compares traditional machine learning (random forest, RF), object-oriented classification (object-oriented, OO), and deep neural networks (DNN), which proposes a random forest combined with deep neural network (RF+DNN) classification framework. In this study, the spatial characteristics of band information, vegetation index, and polarization of main crops in the study area were constructed using Sentinel-1 and Sentinel-2 data. The temporal characteristics of crops phenology and growth state were analyzed using the curve curvature method, and the data were screened in time and space. By comparing and analyzing the accuracy of the four classification methods, the advantages of RF+DNN model and its application value in crops classification were illustrated. The results showed that for the crops in the study area during the period of good growth and development, a better crop classification result could be obtained using RF+DNN classification method, whose model accuracy, training, and predict time spent were better than that of using DNN alone. The overall accuracy and Kappa coefficient of classification were 0.98 and 0.97, respectively. It is also higher than the classification accuracy of random forest (OA = 0.87, Kappa = 0.82), object oriented (OA = 0.78, Kappa = 0.70) and deep neural network (OA = 0.93, Kappa = 0.90). The scalable and simple classification method proposed in this paper gives full play to the advantages of cloud platform in data and operation, and the traditional machine learning combined with deep learning can effectively improve the classification accuracy. Timely and accurate extraction of crop types at different spatial and temporal scales is of great significance for crops pattern change, crops yield estimation, and crops safety warning

The Classification Method Study of Crops Remote Sensing with Deep Learning, Machine Learning, and Google Earth Engine

The extraction and classification of crops is the core issue of agricultural remote sensing. The precise classification of crop types is of great significance to the monitoring and evaluation of crops planting area, growth, and yield. Based on the Google Earth Engine and Google Colab cloud platform, this study takes the typical agricultural oasis area of Xiangride Town, Qinghai Province, as an example. It compares traditional machine learning (random forest, RF), object-oriented classification (object-oriented, OO), and deep neural networks (DNN), which proposes a random forest combined with deep neural network (RF+DNN) classification framework. In this study, the spatial characteristics of band information, vegetation index, and polarization of main crops in the study area were constructed using Sentinel-1 and Sentinel-2 data. The temporal characteristics of crops phenology and growth state were analyzed using the curve curvature method, and the data were screened in time and space. By comparing and analyzing the accuracy of the four classification methods, the advantages of RF+DNN model and its application value in crops classification were illustrated. The results showed that for the crops in the study area during the period of good growth and development, a better crop classification result could be obtained using RF+DNN classification method, whose model accuracy, training, and predict time spent were better than that of using DNN alone. The overall accuracy and Kappa coefficient of classification were 0.98 and 0.97, respectively. It is also higher than the classification accuracy of random forest (OA = 0.87, Kappa = 0.82), object oriented (OA = 0.78, Kappa = 0.70) and deep neural network (OA = 0.93, Kappa = 0.90). The scalable and simple classification method proposed in this paper gives full play to the advantages of cloud platform in data and operation, and the traditional machine learning combined with deep learning can effectively improve the classification accuracy. Timely and accurate extraction of crop types at different spatial and temporal scales is of great significance for crops pattern change, crops yield estimation, and crops safety warning

Interactive Influence of Soil Erosion and Cropland Revegetation on Soil Enzyme Activities and Microbial Nutrient Limitations in the Loess Hilly-Gully Region of China

Soil erosion is a major form of land degradation, especially in agroecosystems, which has been effectively controlled by vegetation restoration. However, the interactive role of erosion and cropland revegetation on soil enzyme activities and microbial nutrient limitations is less understood. To address this issue, we examined carbon (C), nitrogen (N), and phosphorus (P) in bulk soils and microbial biomass, enzyme activities, and microbial nutrient limitations in the 0–200 cm soils in eroded and deposited landscapes occupied by cropland, revegetated forest, and grassland. The results showed that the activities of C-, N-, and P-acquiring enzymes were larger in the deposited landscape than in the eroded landscape for 0–20 cm soils in forest and grassland but not in cropland. Microbial metabolism was co-limited by N and P, and the threshold element ratio (TERL) indicated that P was the most limiting factor. Microbial N limitation was lower in the deposited than the eroded zone, especially in surface soils in revegetated forest and grassland. The TERL value was larger at the deposited than at the eroded zone, and a greater difference was found in the surface soils of forest and grassland. Microbial nutrient limitations were mostly explained by C/P and N/P. Conclusively, the deposited areas were characterized by ameliorated enzyme activities, decreased microbial N limitation but relatively strengthened microbial P limitation compared to the eroded area, and such variations existed in the revegetated forest and grassland but not in the cropland, which thus contributes to a better understanding of C and nutrient cycling for agroecosystems and revegetation ecosystems in eroded environments

Evaluation of contact angle between asphalt binders and aggregates using Molecular Dynamics (MD) method

The objectives of this study are to explore the mechanism of the self-healing or flow of asphalt binder on the surface of the aggregates and evaluate the contact angle between the asphalt binder and aggregates. The mastic samples (small size) were prepared with asphalt binder and fine aggregates of a small size (below 0.3 mm). The microscale dynamic X-ray tomography was used to observe the flow of asphalt binder in the mastic sample at 353.15 K (80 °C) using the Advanced Photon Source (APS) beamline 2-BM at Argonne National Laboratory. The activation energy for flow of the asphalt binder and the contact angle between the asphalt binder and aggregates were analyzed to explain the self-healing characteristics of the asphalt binder materials. The Molecular Dynamics (MD) method was employed to simulate the flow process of the molecules of asphalt binder at different temperatures and mimic the contact angle difference between the asphalt binder and aggregate models. Simultaneously, in the laboratory, the contact angle goniometer was selected to measure the contact angle between the asphalt binder droplet and aggregates at different temperatures. The results of tests and MD simulations show that (1) asphalt binder diffused after heating from the X-ray images, and the stages and mechanism of the flow process of asphalt binder on aggregates were investigated; (2) low contact angle was observed in the interface model of asphalt binder and aggregates at high temperatures using the MD method. The wetting condition changed from partial non-wetting to wetting after heating in the interface model; (3) contact angle results between the asphalt binder and aggregates demonstrated flow steps of the asphalt binder material. The test data was also compared with the MD simulation results at different temperatures

Fast Classification of Thyroid Nodules with Ultrasound Guided-Fine Needle Biopsy Samples and Machine Learning

A rapid classification method was developed for the malignant and benign thyroid nodules with ultrasound guided-fine needle aspiration biopsy (FNAB) samples. With probe electrospray ionization mass spectrometry, the mass-scan data of FNAB samples were used as datasets for machine learning. The patients were marked as malignant (98 patients), benign (110 patients) or undetermined (42 patients) by experienced doctors in terms of ultrasound, the B-Raf (BRAF) gene, and cytopathology inspections. Pairwise coupling was performed on 163 ions to generate 3630 ion ratios as new features for classifier training. With the new features, the performance of the multilayer perception (MLP) classifier is much better than that with the 163 ions as features directly. After training, the accuracy of the MLP classifier is as high as 92.0%. The accuracy of the single-blind test is 82.4%, which proved the good generalization ability of the MLP classifier. The overall concordance is 73.0% between prediction and six-month follow-up for patients in the undetermined group. Especially, the classifier showed high accuracy for the undetermined patients with suspicious for papillary carcinoma diagnosis (90.9%). In summary, the machine learning method based on FNAB samples has potential for real clinical applications