The land use change is the primary factor in influencing the regional carbon emissions. Studying the effects of land use change on carbon emissions can provide supports for the development policies ...of carbon emission. Using land use and energy consumption data, this study measures carbon emissions from land use dynamics in the Beijing-Tianjin-Hebei region from 2000 to 2020. The standard deviation ellipse model is employed to investigate the distribution characteristics of the spatial patterns of carbon emissions, while the Geographically and Temporally Weighted Regression (GTWR) model is used to examine the contributing factors of carbon emissions and their spatial and temporal heterogeneity. Results indicate a consistently increasing trend in carbon emissions from land use in the Beijing-Tianjin-Hebei region from 2000 to 2020. Construction land is characterized with both the primary source and an increasing intensity of carbon emissions. Besides, the spatial distribution of carbon emissions from land use in the Beijing-Tianjin-Hebei region demonstrates an aggregation pattern from in the northeast-southwest direction towards the center, with a greater aggregation trend in the east–west direction compared to that in the south-north direction. During the study period, a positive correlation was documented between carbon emissions and factors including total population, economic development level, land use degree, and landscape patterns. This correlation showed a decreasing trend and reached a stable level at the end of the study period. Moreover, the analysis showed a negative correlation between industrial structure and carbon emissions, which showed an increasing trend and reached a relatively high level at the end of the study period.
To non-destructively and rapidly monitor the chlorophyll content of winter wheat leaves under CO2 microleakage stress, and to establish the quantitative relationship between chlorophyll content and ...sensitive bands in the winter wheat growing season from 2023 to 2024, the leakage rate was set to 1 L/min, 3 L/min, 5 L/min, and 0 L/min through field experiments. The dimensional reduction was realized, fractional differential processing of a wheat canopy spectrum was carried out, a multiple linear regression (MLR) and partial least squares regression (PLSR) estimation model was constructed using a SPA selection band, and the model’s accuracy was evaluated. The optimal model for hyperspectral estimation of wheat SPAD under CO2 microleakage stress was screened. The results show that the spectral curves of winter wheat leaves under CO2 microleakage stress showed a “red shift” of the green peak and a “blue shift” of the red edge. Compared with 1 L/min and 3 L/min, wheat leaves were more affected by CO2 at 5 L/min. Evaluation of the accuracy of the MLR and PLSR models shows that the MLR model is better, where the MLR estimation model based on 1.1, 1.8, 0.4, and 1.7 differential SPAD is the best for leakage rates of 1 L/min, 3 L/min, 5 L/min, and 0 L/min, with validation set R2 of 0.832, 0.760, 0.928, and 0.773, which are 11.528, 14.2, 17.048, and 37.3% higher than the raw spectra, respectively. This method can be used to estimate the chlorophyll content of winter wheat leaves under CO2 trace-leakage stress and to dynamically monitor CO2 trace-leakage stress in crops.
Chlorophyll content in wheat leaves reflects its growth and nutritional status, which can be used as a health index for field management. In order to evaluate the potential of hyperspectral data to ...estimate the chlorophyll content in wheat leaves, this study focused on the leaves of wheat at the flag-picking stage, flowering stage, grain-filling stage, and maturity stage. Based on the framework of five vegetation indexes, the spectral index was constructed by using the combination of 400–1000 nm bands. The correlation between the constructed spectral index and the measured chlorophyll value was analyzed, and the optimal spectral index was screened using the correlation coefficient. Based on the optimal spectral index, polynomial regression, random forest, decision tree, and artificial neural network were used to establish the estimation model for chlorophyll value, and the optimal model for estimating the chlorophyll value of wheat leaves was selected through model evaluation. The results showed that the five optimal spectral indices at the four growth stages were primarily composed of the red band, red edge band, and near-infrared band. The five optimal spectral indices during the grain-filling stage had the highest correlation with the chlorophyll value, and the absolute value of the correlation coefficient was greater than 0.73. The accuracy of the estimation model established in the four growth stages was different, with the estimation accuracy of the flag stage being the best, showing an R2 and RMSE of 0.79 and 2.63, respectively. These results indicate that hyperspectral data are suitable for estimating the chlorophyll value of wheat leaves, and the polynomial regression model of the flag-picking period can be used as the optimal model for estimating the chlorophyll value of wheat leaves.
Wheat is the main grain crop in our country, and the traditional wheat yield estimation method is time-consuming and laborious. By estimating wheat yield efficiently, quickly and non-destructively, ...agricultural producers can quickly obtain information about wheat yield, manage wheat fields more scientifically and accurately, and ensure national food security. Taking the Xinxiang Experimental Base of the Crop Science Research Institute, Chinese Academy of Agricultural Sciences as an example, hyperspectral data for the critical growth stages of wheat were pre-processed. A total of 27 vegetation indices were calculated from the experimental plots. These indices were then subjected to correlation analysis with measured wheat yield. Vegetation indices with Pearson correlation coefficients greater than 0.5 were selected. Five methods, including multiple linear regression, stepwise regression, principal component regression, neural networks and random forests, were used to construct wheat yield estimation models. Among the methods used, multiple linear regression, stepwise regression and the models developed using principal component analysis showed a lower modelling accuracy and validation precision. However, the neural network and random forest methods both achieved a modelling accuracy R2 greater than 0.6, with validation accuracy R2 values of 0.729 and 0.946, respectively. In addition, the random forest method had a lower cross-validation RMSE value, with values of 869.8 kg/hm−2, indicating a higher model accuracy. In summary, the random forest method provided the optimal estimation for wheat yield, enabling the timely and accurate pre-harvest wheat yield prediction, which has significant value for precision agriculture management and decision making.
Studying urban heat islands holds significance for the sustainable development of cities. This comprehensive study analyzed the temporal characteristics of a Surface Urban Heat Island and Canopy ...Layer Heat Island by employing Moderate-Resolution Imaging Spectroradiometer image data spanning from 2003 to 2020 over Beijing, China. Leveraging the Gaussian capacity model, the geometrical characteristics of the Surface Urban Heat Island and Canopy Layer Heat Island, such as intensity, center, direction, and range, were examined among three different timescales of day, month, and year. Results indicate that the intensities of the Surface Urban Heat Island and Canopy Layer Heat Island tend to have bigger seasonal variations during winter nights and summer daytime. In addition, at night the centers of Surface Urban Heat Island and Canopy Layer Heat Island are mainly concentrated in the range of 116.3°~116.4° E in longitude and 39.90°~39.95° N in latitude, while during the daytime they are more scattered, mainly in the range of 116.2°~116.5° E in longitude and 39.7°~40.0° N in latitude. In the hot season, the center of the heat island moves east to north, while in the cold season it moves west to south. Monthly average ellipse areas of Surface Urban Heat Island and Canopy Layer Heat Island vary more during the day than that at night, the maximum daytime differences were 2662 kmsup.2 and 2293 kmsup.2, while the maximum nighttime differences were 484 kmsup.2 and 265 kmsup.2. Overall, the average area is increasing, with the heat island center moving eastward and deflecting towards the northeast-southwest direction. The expansion of urban areas will continue to influence the movement and extent of heat islands. The study offers insights to inform strategies for mitigating urban heat islands.
Esophageal squamous cell carcinoma (ESCC) presents high morbidity and mortality. It was demonstrated that blood-derived vesicles can facilitate ESCC development and transmit regulating signals. ...However, the molecular mechanism of vesicle miRNA secreted by tumor cells affecting ESCC progression has not been explored.
The mRNA-related signaling pathways and differentially expressed genes were screened out in TCGA dataset. The levels of miRNA-105-5p and SPARCL1 were determined by qRT-PCR. Protein level determination was processed using Western blot. The interaction between the two genes was verified with the dual-luciferase method. A transmission electron microscope was utilized to further identify extracellular vesicles (EVs), and co-culture assay was performed to validate the intake of EVs.
experiments were conducted to evaluate cell function changes in ESCC. A mice tumor formation experiment was carried out to observe tumor growth
.
MiRNA-105-5p expression was increased in ESCC, while SPARCL1 was less expressed. MiRNA-105-5p facilitated cell behaviors in ESCC through targeting SPARCL1 and regulating the focal adhesion kinase (FAK)/Akt signaling pathway. Blood-derived external vesicles containing miRNA-105-5p and EVs could be internalized by ESCC cells. Then, miRNA-105-5p could be transferred to ESCC cells to foster tumorigenesis as well as cell behaviors.
EV-carried miRNA-105-5p entered ESCC cells and promoted tumor-relevant functions by mediating SPARCL1 and the FAK/Akt signaling pathway, which indicated that the treatment of ESCC
serum EVs might be a novel therapy and that miRNA-105-5p can be a molecular target for ESCC therapy.
To non-destructively and rapidly monitor the chlorophyll content of winter wheat leaves under COsub.2 microleakage stress, and to establish the quantitative relationship between chlorophyll content ...and sensitive bands in the winter wheat growing season from 2023 to 2024, the leakage rate was set to 1 L/min, 3 L/min, 5 L/min, and 0 L/min through field experiments. The dimensional reduction was realized, fractional differential processing of a wheat canopy spectrum was carried out, a multiple linear regression (MLR) and partial least squares regression (PLSR) estimation model was constructed using a SPA selection band, and the model’s accuracy was evaluated. The optimal model for hyperspectral estimation of wheat SPAD under COsub.2 microleakage stress was screened. The results show that the spectral curves of winter wheat leaves under COsub.2 microleakage stress showed a “red shift” of the green peak and a “blue shift” of the red edge. Compared with 1 L/min and 3 L/min, wheat leaves were more affected by COsub.2 at 5 L/min. Evaluation of the accuracy of the MLR and PLSR models shows that the MLR model is better, where the MLR estimation model based on 1.1, 1.8, 0.4, and 1.7 differential SPAD is the best for leakage rates of 1 L/min, 3 L/min, 5 L/min, and 0 L/min, with validation set Rsup.2 of 0.832, 0.760, 0.928, and 0.773, which are 11.528, 14.2, 17.048, and 37.3% higher than the raw spectra, respectively. This method can be used to estimate the chlorophyll content of winter wheat leaves under COsub.2 trace-leakage stress and to dynamically monitor COsub.2 trace-leakage stress in crops.
Recent studies have demonstrated that mesenchymal stem cells (MSCs) modulate the immune response and reduce lung injury in animal models. Currently, no clinical studies of the effects of MSCs in ...acute respiratory distress syndrome (ARDS) exist. The objectives of this study were first to examine the possible adverse events after systemic administration of allogeneic adipose-derived MSCs in ARDS patients and second to determine potential efficacy of MSCs on ARDS.
Twelve adult patients meeting the Berlin definition of acute respiratory distress syndrome with a PaO2/FiO2 ratio of < 200 were randomized to receive allogeneic adipose-derived MSCs or placebo in a 1:1 fashion. Patients received one intravenous dose of 1 × 106 cells/kg of body weight or saline. Possible side effects were monitored after treatment. Acute lung injury biomarkers, including IL-6, IL-8 and surfactant protein D (SP-D), were examined to determine the effects of MSCs on lung injury and inflammation.
There were no infusion toxicities or serious adverse events related to MSCs administration and there were no significant differences in the overall number of adverse events between the two groups. Length of hospital stay, ventilator-free days and ICU-free days at day 28 after treatment were similar. There were no changes in biomarkers examined in the placebo group. In the MSCs group, serum SP-D levels at day 5 were significantly lower than those at day 0 (p = 0.027) while the changes in IL-8 levels were not significant. The IL-6 levels at day 5 showed a trend towards lower levels as compared with day 0, but this trend was not statistically significant (p = 0.06).
Administration of allogeneic adipose-derived MSCs appears to be safe and feasible in the treatment of ARDS. However, the clinical effect with the doses of MSCs used is weak, and further optimization of this strategy will probably be required to reach the goal of reduced alveolar epithelial injury in ARDS.
Clinical trials.gov, NCT01902082.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
To explore the application of neural network algorithm model in lung cancer imaging, and provide reference for the application and development of artificial neural network (ANN) algorithm model in ...lung cancer medical mirroring, so as to promote the development of ANN in this field. Meanwhile, it is hoped that the application of neural network algorithms in medical imaging can improve the survival rate and cure rate of lung cancer. In this study, an ANN algorithm model was selected to establish a lung cancer recognition model. After determining the lung cancer lesion area, the image segmentation algorithm was used to separately display the lung cancer lesion area, and a comparison experiment was designed to verify the accuracy of the model. ANNs were used to identify lung cancer, which can be concluded that the accuracy is 94.6%, the sensitivity is 95.7%, and the specificity is 93.5%. By combining image retrieval methods with lung cancer image segmentation algorithms, the lesion area of lung cancer can be clearly displayed. Therefore, the lung cancer image segmentation algorithm based on the neural network model has good recognition performance. This research can provide reference for the application of neural network algorithm model in the field of cancer diagnosis and treatment.
The leaf chlorophyll content (LCC) of winter wheat, an important food crop widely grown worldwide, is a key indicator for assessing its growth and health status in response to CO2 stress. However, ...the remote sensing quantitative estimation of winter wheat LCC under CO2 stress conditions also faces challenges such as an unclear spectral sensitivity range, baseline drift, overlapping spectral peaks, and complex spectral response due to CO2 stress changes. To address these challenges, this study introduced the fractional order derivative (FOD) and continuous wavelet transform (CWT) techniques into the estimation of winter wheat LCC. Combined with the raw hyperspectral data, we deeply analyzed the spectral response characteristics of winter wheat LCC under CO2 stress. We proposed a stacking model including multiple linear regression (MLR), decision tree regression (DTR), random forest (RF), and adaptive boosting (AdaBoost) to filter the optimal combination from a large number of feature variables. We use a dual-band combination and vegetation index strategy to achieve the accurate estimation of LCC in winter wheat under CO2 stress. The results showed that (1) the FOD and CWT methods significantly improved the correlation between the raw spectral reflectance and LCC of winter wheat under CO2 stress. (2) The 1.2-order derivative dual-band index (RVI (R720, R522)) constructed by combining the sensitive spectral bands of the CO2 response of winter wheat leaves achieved a high-precision estimation of the LCC under CO2 stress conditions (R2 = 0.901). Meanwhile, the red-edged vegetation stress index (RVSI) constructed based on the CWT technique at specific scales also demonstrated good performance in LCC estimation (R2 = 0.880), verifying the effectiveness of the multi-scale analysis in revealing the mechanism of the CO2 impact on winter wheat. (3) By stacking the sensitive spectral features extracted by combining the FOD and CWT methods, we further improved the LCC estimation accuracy (R2 = 0.906). This study not only provides a scientific basis and technical support for the accurate estimation of LCC in winter wheat under CO2 stress but also provides new ideas and methods for coping with climate change, optimizing crop-growing conditions, and improving crop yield and quality in agricultural management. The proposed method is also of great reference value for estimating physiological parameters of other crops under similar environmental stresses.
PDF
