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Abstract Background sphingosine-1-phosphate (S1P), a naturally occurring sphingolipid, has been involved in pulmonary interstitial remodeling signaling. However, no study has examined its clinical merits for interstitial lung disease (ILD). This study aimed to investigate the serum level of S1P in ILD patients and its clinical correlation with the severity of disease in the two main types of ILDs: the IPF and the CTD-ILD patients. Methods This retrospective observational pilot study included 67 ILD patients and 26 healthy controls. These patients were stratified into the IPF group (35) and the CTD-ILD group (32). The severity of ILD was evaluated through pulmonary function indicators and the length of hospital stay. Results Serum S1P level was statistically higher in ILD patients than in health control (p = 0.002), while the Serum S1P levels in CTD-ILD and IPF patients were comparable. Serum S1P level further showed statistically negative correlation with pulmonary function indexes (TLC% pred, FVC% pred and FEV1% pred) and positive correlation with length of hospital stay (r = -0.38, p = 0.04; r = -0.41, p = 0.02, r = -0.37, p = 0.04; r = 0.42, p = 0.02, respectively) in CTD-ILD patients, although serum S1P level was not significantly correlated with inflammatory indexes. The IPF patients failed to exhibit a significant correlation of serum S1P level with pulmonary function and length of hospital stay. Conclusions Serum S1P level might be a clinically useful biomarker in evaluating the severity of CTD-ILD patients rather than IPF patients.

Slope shape as a consequence of erosional landform development plays a prominent role in soil erosion. Clarifying the distribution of soil erosion and deposition patterns on different shaped slopes is crucial for soil erosion control. The aim of this study was to decipher the effects of slope shape on soil erosion and deposition patterns under natural rainfall conditions based on high-resolution unmanned aerial vehicle (UAV) data and geographic information system technology. Structure from motion (SfM)-UAV photogrammetry was carried out in four runoff plots with various slope shapes during the rainy season in 2021. Digital elevation models (DEMs) were developed for each slope shape before and after the rainy season. In addition to collecting runoff and sediment, the DEMs of difference were analyzed to quantify soil erosion and deposition patterns on various slope shapes in the rainy season. Results showed that the runoff volumes and sediment yields induced by rainfall were markedly different among various slope shapes. The mean runoff volume and sediment yield from the concave-convex slope were 1.09 ∼ 2.69 and 1.33 ∼ 27.16 times those of the other three slopes, respectively, with less sediment loss from the convex-concave slope and its combination slope. Slope shape exhibited a notable effect on the type of slope erosion and deposition. All four slopes showed considerable changes in surface elevation after the rainy season. The increase and decrease in surface elevation were concentrated in the range of –0.02 to –0.007 m and 0.007 to 0.02 m, respectively, with a low proportion of changes less than –0.03 m and greater than 0.03 m. The effectiveness of SfM-UAV in monitoring the microgeomorphic changes of slopes was verified by the consistency of soil erosion amounts based on sediment collection and SfM-UAV measurements. Reference values were provided to solve the threshold problem of slope length cutoff in soil erosion prediction models based on runoff plot experiments. Findings of this study could be useful for decision-making in soil erosion control and slope reconstruction.

IntroductionIndividual leaves in the image are partly veiled by other leaves, which create shadows on another leaf. To eliminate the interference of soil and leaf shadows on cotton spectra and create reliable monitoring of cotton nitrogen content, one classification method to unmanned aerial vehicle (UAV) image pixels is proposed.MethodsIn this work, green light (550 nm) is divided into 10 levels to limit soil and leaf shadows (LS) on cotton spectrum. How many shadow has an influence on cotton spectra may be determined by the strong correlation between the vegetation index (VI) and leaf nitrogen content (LNC). Several machine learning methods were utilized to predict LNC using less disturbed VI. R-Square (R2), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the performance of the model.Results(i) after the spectrum were preprocessed by gaussian filter (GF), SG smooth (SG), and combination of GF and SG (GF&SG), the significant relationship between VI and LNC was greatly improved, so the Standard deviation of datasets was also decreased greatly; (ii) the image pixels were classified twice sequentially. Following the first classification, the influence of soil on vegetation index (VI) decreased. Following secondary classification, the influence of soil and LS to VI can be minimized. The relationship between the VI and LNC had improved significantly; (iii) After classifying the image pixels, the VI of 2-3, 2-4, and 2-5 have a stronger relationship with LNC accordingly. Correlation coefficients (r) can reach to 0.5. That optimizes monitoring performance when combined with GF&SG to predict LNC, support vector machine regression (SVMR) has the better performance, R2, RMSE, and MAE up to 0.86, 1.01, and 0.71, respectively. The UAV image classification technique in this study can minimize the negative effects of soil and LS on cotton spectrum, allowing for efficient and timely predict LNC.

Timely and accurate estimation of Above-Ground-Biomass (AGB) in cotton is essential for precise production monitoring. The study was conducted in Shaya County, Aksu Region, Xinjiang, China. It employed an unmanned aerial vehicle (UAV) as a low-altitude monitoring platform to capture multispectral images of the cotton canopy. Subsequently, spectral features and textural features were extracted, and feature selection was conducted using Pearson’s correlation (P), Principal Component Analysis (PCA), Multivariate Stepwise Regression (MSR), and the ReliefF algorithm (RfF), combined with the machine learning algorithm to construct an estimation model of cotton AGB. The results indicate a high consistency between the mean (MEA) and the corresponding spectral bands in textural features with the AGB correlation. Moreover, spectral and textural feature fusion proved to be more stable than models utilizing single spectral features or textural features alone. Both the RfF algorithm and ANN model demonstrated optimization effects on features, and their combination effectively reduced the data redundancy while improving the model performance. The RfF-ANN-AGB model constructed based on the spectral and textural features fusion worked better, and using the features SIPI2, RESR, G_COR, and RE_DIS, exhibited the best performance, achieving a test sets R2 of 0.86, RMSE of 0.23 kg·m−2, MAE of 0.16 kg·m−2, and nRMSE of 0.39. The findings offer a comprehensive modeling strategy for the precise and rapid estimation of cotton AGB.

The asymmetric structure expands the voltage window of supercapacitors in aqueous electrolyte solutions, thereby significantly increasing energy density. However, exploring high-performance negative electrode materials is of utmost importance, on the premise that positive electrode materials have been extensively developed. This study explores the homogeneous composition of iron oxide and graphene via in situ laser induction on the precursor film of polyimide foam containing Fe3+ ions. 3D interconnected graphene was prepared under laser direct writing, while Fe3+ ions were thermally reduced to obtain iron oxide (Fe3+/Fe2+). By adjusting the concentration of Fe3+ ions in the precursor polyimide foam and laser parameter settings, the Fe3+/Fe2+ ratio was regulated to exhibit optimal electrochemical performance. When employed as a negative electrode material for assessing the electrochemical performance, it displayed a notable area capacitance of 654.3 mF cm2 at a current density of 1 mA cm2. In the assembled aqueous asymmetric supercapacitors, the specific energy density was 33.25 mW h cm2 at a power density of 750 mW cm2 and retained 86.8% of its capacitance even after 10 000 charge-discharge cycles. This method offers a new approach for synthesizing laserinduced multivalent compounds and serves as a reference for efficiently producing negative electrodes of an asymmetric supercapacitor. [ABSTRACT FROM AUTHOR]