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Background: Few studies have investigated the feasibility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a free-breathing golden-angle radial stack-of-stars volume-interpolated breath-hold examination (FB radial VIBE) sequence in the lung. Purpose: To investigate whether DCE-MRI using the FB radial VIBE sequence can assess morphological and kinetic parameters in patients with pulmonary lesions, with computed tomography (CT) as the reference. Material and Methods: In total, 43 patients (30 men; mean age = 64 years) with one lesion each were prospectively enrolled. Morphological and kinetic features on MRI were calculated. The diagnostic performance of morphological MR features was evaluated using a receiver operating characteristic (ROC) curve. Kinetic features were compared among subgroups based on histopathological subtype, lesion size, and lymph node metastasis. Results: The maximum diameter was not significantly different between CT and MRI (3.66 ± 1.62 cm vs. 3.64 ± 1.72 cm; P = 0.663). Spiculation, lobulation, cavitation or bubble-like areas of low attenuation, and lymph node enlargement had an area under the ROC curve (AUC) >0.9, while pleural indentation yielded an AUC of 0.788. The lung cancer group had significantly lower Ktrans, Ve, and initial AUC values than the other cause inflammation group (0.203, 0.158, and 0.589 vs. 0.597, 0.385, and 1.626; P < 0.05) but significantly higher values than the tuberculosis group (P < 0.05). Conclusion: Morphology features derived from FB radial VIBE have high correlations with CT, and kinetic analyses show significant differences between benign and malignant lesions. DCE-MRI with FB radial VIBE could serve as a complementary quantification tool to CT for radiation-free assessments of lung lesions. [ABSTRACT FROM AUTHOR]

Electrocardiography (ECG) is the gold standard for monitoring vital signs and for diagnosing, controlling, and preventing cardiovascular diseases (CVDs). However, ECG requires continuous user participation, and cannot be used for continuous cardiac monitoring. In contrast to ECG, photoplethysmography (PPG) devices do not require continued user involvement, and can offer ongoing and long-term detection capabilities. However, from a medical perspective, ECG can provide more information about the heart. Currently, most existing work contains different signals recorded from the same subject in training and test sets. This study proposes a neural network model based on a 1D convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network. This neural network model can directly reconstruct ECG signals from PPG signals. The learned features are captured from the CNN model and fed into the BiLSTM model. In order to verify the validity of the model, it is evaluated using the MIMIC II dataset in the completely subject-independent model (records are placed in a training set, and a test set appears once, but the test signal belongs to a record that is not in the training set). The Pearson's correlation coefficient between the reconstructed ECG and the reference ECG of the proposed model is 0.963 in the completely subject-independence model. The results of the proposed model are better than those of several cited state-of-the-art models. The results of our trained model indicate that we can obtain reconstructed ECGs that are highly similar to reference ECGs in the completely subject-independent model. [ABSTRACT FROM AUTHOR]

Soil total nitrogen (STN) plays an important role in soil fertility and N cycle. Detailed information about the spatial distribution of STN is vital to effective management of soil fertility and better understanding of the process of N cycle. To date, however, few studies have been conducted to digitally map the spatial variation of STN for rubber (Hevea brasiliensis) plantation at the regional scale in Hainan Island, China. In this study, a relatively new method, random forest (RF) was proposed to predict and map the spatial pattern of STN for the rubber plantation. A total of 2511 topsoil (0-20 cm) samples were collected, and their STN contents were measured. Then these soil samples were randomly divided into calibration dataset (1757 soil samples) and validation dataset (754 soil samples). Fourteen environmental variables were also collected. They are parent materials, mean precipitation, mean temperature, mean normalized difference vegetation index, elevation, slope, aspect, horizontal curvature, profile curvature, relief, convergence index, relative position index, stream power index, and topographic wetness index. In this study, stepwise linear regression (SLR), generalized additive mixed model (GAMM), classification and regression tree (CART), and random forest (RF) were used to predict and map the spatial distribution of STN for the rubber plantation. In addition, GAMM and CART were also employed to uncover relationships between STN and environmental variables and further to identify the main factors influencing STN variation. The RF model was developed to predict spatial variability of STN on the basis of parent materials, mean precipitation, mean temperature, and mean normalized difference vegetation index. Performance of RF was compared with SLR, GAMM, and CART. Mean error (ME), mean absolute error (MAE), root mean squared error (RMSE), and correlation coefficient between measured STN and predicted STN were selected as comparison criteria. Results showed that RF performed much better than SLR, GAMM, and CART in predicting and mapping the spatial distribution of STN for the rubber plantation at regional scale in this study. The RF model had much higher correlation coefficient value and lower prediction errors (ME, MAE, and RMSE) than SLR, GAMM, and CART. Values of correlation coefficient, ME, MAE, and RMSE were 0.82, -0.003 g/kg, 0.088 g/kg, and 0.131 g/kg, 0.69, 0.003 g/kg, 0.121 g/kg, and 0.162 g/kg, 0.70, -0.004 g/kg, 0.120 g/kg, and 0.160 g/kg, and 0.68, -0.008 g/kg, 0.121 g/kg, 0.163 g/kg for RF, CART, GAMM, and SLR equation, respectively. Moreover, RF model yielded a more realistic spatial distribution of STN than SLR, GAMM, and CART equations. Finally, results of CART and GAMM showed that the relationships between STN and selected environmental variables (parent materials, mean precipitation, mean temperature, and mean normalized difference vegetation index) were hierarchical and non-linear in this study area. Analysis of variable importance indicated that parent materials and mean precipitation were the most important factors influencing spatial distribution of STN for rubber plantation at regional scale in this study. Overall, the good performance of RF model could be ascribed to its good capabilities of dealing with non-linear and hierarchical relationships between STN and environmental variables. These results suggested that RF is a promising approach in predicting spatial distribution of STN for rubber plantation at regional scale, and can be applied to predict other soil properties in regions with complex soil-environmental relationships. [ABSTRACT FROM AUTHOR]

A rubber planation of the state farm in Hainan Island, China is traditionally managed with unified soil practices (e.g. fertilization, cultivation). This inevitably results in an inefficient use of resources since it ignores soil heterogeneity (e.g. variability in soil fertility and environmental conditions) of the rubber plantations. Soil management zones can be used to overcome the limitations above of the uniform soil management practices. However, studies on soil management zones are mainly carried out at the field scale. Additionally, data of some soil properties used to delineate soil management zones are difficult or expensive to acquire. This study selected easily available environmental variables and aimed to evaluate their validity in delineating soil management zones of rubber plantation at regional scale. Four types of environmental variables, including terrain attributes (elevation, slope, and aspect), parent materials, climate factors (precipitation and temperature) and vegetation index (normalized difference vegetation index), were selected as data source, and principal component analysis as well as fuzzy-C means clustering algorithm were applied to delineate soil management zones for a rubber plantation with an area of approximately 26000 ha. Two indices, fuzzy performance index (FPI), and normalized classification entropy (NCE) were used as criterion to determine the optimal number of soil management zones. Results showed that the optimal number of soil management zones for the rubber plantation was three. To test the validity of the soil management zones, 486 soil samples were collected and analyzed for 12 soil properties including pH, soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), available potassium (AK), exchangeable calcium (Ca), exchangeable magnesium (Mg), available sulfur (S), available copper (Cu), available ferrum (Fe), available manganese (Mn), and available zinc (Zn). One-way analysis of variance was employed to test the difference in the soil properties and environmental variables among the three soil management zones. Statistically significant differences in selected soil properties (except Zn) and environmental variables were found among the three management zones. In addition, the mean coefficients of variation (C.V.) of the soil properties and the environmental variables in the three management zones were much lower than that obtained before the management zones were applied to the rubber plantation. The results above verified that easily available environmental variables could be used to delineate soil management zones for rubber plantation at the regional scale. Further, the management practices corresponding to the characteristics of each zone should be adopted to improve the soil management efficiency of the rubber plantation. [ABSTRACT FROM AUTHOR]

Electrocardiography and photoplethysmography are non-invasive techniques that measure signals from the cardiovascular system. While the cycles of the two measurements are highly correlated, the correlation between the waveforms has rarely been studied. Measuring the photoplethysmogram (PPG) is much easier and more convenient than the electrocardiogram (ECG). Recent research has shown that PPG can be used to reconstruct the ECG, indicating that practitioners can gain a deep understanding of the patients' cardiovascular health using two physiological signals (PPG and ECG) while measuring only PPG. This study proposes a subject-based deep learning model that reconstructs an ECG using a PPG and is based on the bidirectional long short-term memory model. Because the ECG waveform may vary from subject to subject, this model is subject-specific. The model was tested using 100 records from the MIMIC III database. Of these records, 50 had a circulatory disease. The results show that a long ECG signal could be effectively reconstructed from PPG, which is, to our knowledge, the first attempt in this field. A length of 228 s of ECG was constructed by the model, which was trained and validated using 60 s of PPG and ECG signals. To segment the data, a different approach that segments the data into short time segments of equal length (and that do not rely on beats and beat detection) was investigated. Segmenting the PPG and ECG time series data into equal segments of 1-min width gave the optimal results. This resulted in a high Pearson's correlation coefficient between the reconstructed 228 s of ECG and referenced ECG of 0.818, while the root mean square error was only 0.083 mV, and the dynamic time warping distance was 2.12 mV per second on average., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Tang, Chen, Guo, Liang, Ward, Menon and Elgendi.)