The Feasibility Study of Megavoltage Computed Tomographic (MVCT) Image for Texture Feature Analysis

Purpose: To determine whether radiomics texture features can be reproducibly obtained from megavoltage computed tomographic (MVCT) images acquired by Helical TomoTherapy (HT) with different imaging conditions.Methods: For each of the 195 textures enrolled, the mean intrapatient difference, which is considered to be the benchmark for reproducibility, was calculated from the MVCT images of 22 patients with early-stage non-small-cell lung cancer. Test–retest MVCT images of an in-house designed phantom were acquired to determine the concordance correlation coefficient (CCC) for these 195 texture features. Features with high reproducibility (CCC > 0.9) in the phantom test–retest set were investigated for sensitivities to different imaging protocols, scatter levels, and motion frequencies using a wood phantom and in-vitro animal tissues.Results: Of the 195 features, 165 (85%) features had CCC > 0.9. For the wood phantom, 124 features were reproducible in two kinds of scatter materials, and further investigations were performed on these features. For animal tissues, 108 features passed the criteria for reproducibility when one layer of scatter was covered, while 106 and 108 features of in-vitro liver and bone passed with two layers of scatter, respectively. Considering the effect of differing acquisition pitch (AcP), 97 features extracted from wood passed, while 103 and 59 features extracted from in-vitro liver and bone passed, respectively. Different reconstruction intervals (RI) had a small effect on the stability of the feature value. When AcP and RI were held consistent without motion, all 124 features calculated from wood passed, and a majority (122 of 124) of the features passed when imaging with a “fine” AcP with different RIs. However, only 55 and 40 features passed with motion frequencies of 20 and 25 beats per minute, respectively.Conclusion: Motion frequency has a significant impact on MVCT texture features, and features from MVCT were more reproducibility in different scatter conditions than those from CBCT. Considering the effects of AcP and RI, the scanning protocols should be kept consistent when MVCT images are used for feature analysis. Some radiomics features from HT MVCT images are reproducible and could be used for creating clinical prediction models in the future

Research on the Method of Urban Jobs-Housing Space Recognition Combining Trajectory and POI Data

With the gradual emergence of the separation and dislocation of urban jobs-housing space, rational planning of urban jobs-housing space has become the core issue of national land-spatial planning. To study the existing relationship between workspaces and living spaces, a new method to identify jobs-housing space is proposed, which not only considers the static spatial distribution of urban public facilities but also identifies the jobs-housing space by analyzing the real mobility characteristics of people from a humanistic perspective. This method provides a new framework for the identification of urban jobs-housing space by integrating point-of-interest (POI) and trajectory data. The method involves three steps: Firstly, based on the trajectory data, we analyze the characteristics of the dynamic flow of passengers in the grid and construct the living factors and working factors to identify the distribution of jobs-housing space. Secondly, we reclassify the POIs to calculate the category ratios of different types of POIs in the grid to identify the jobs-housing space. Finally, an OR operation is performed on the results obtained by the two methods to obtain the final recognition result. We selected Haikou City as the experimental area to verify the method proposed in this paper. The experimental results show that the recognition accuracy of the travel flow model is 72.43%, the POI quantitative recognition method’s accuracy is 74.94%, and the accuracy of the method proposed in this paper is 85.90%, which is significantly higher than the accuracy of the previous two methods. Therefore, the method proposed here can serve as a reference for subsequent research on urban jobs-housing space

Adaptability of shallow subsurface drip irrigation of alfalfa in an arid desert area of Northern Xinjiang.

A suitable irrigation method adopted to arid desert conditions, including a special soil structure and specialized plants, has been continuously studied and improved. A field study was conducted in the Awei irrigation area of Aletai in Xinjiang in 2015 and 2016 to investigate the applicability of shallow subsurface drip irrigation (SSDI) in an arid desert area. A completely randomized block design with three replications and three treatments for drip tape subsurface depths at 5, 10, 20 cm was established. The results indicated that the vertical distribution of the soil moisture of subsurface drip irrigation (SDI, buried depth at 20 cm) was mainly concentrated at 0-60 cm, while SSDI (buried depth at 5 and 10 cm) was concentrated at 0-30 cm. However, the roots distributions were concentrated at 0-30 cm for SDI and SSDI. The chlorophyll content and water consumption intensity for alfalfa first increased and then decreased in arid desert conditions. The dry yield and water use efficiency (WUE) of SSDI (buried depth at 10 cm) were higher than those of SDI. The SSDI was practical in arid desert conditions and the recommended buried depth was 10 cm

Quantitative Evaluation of Grassland SOS Estimation Accuracy Based on Different MODIS-Landsat Spatio-Temporal Fusion Datasets

Estimating the Start of Growing Season (SOS) of grassland on the global scale is an important scientific issue since it can reflect the response of the terrestrial ecosystem to environmental changes and determine the start time of grazing. However, most remote sensing data has coarse- temporal and spatial resolution, resulting in low accuracy of SOS retrieval based on remote sensing methods. In recent years, much research has focused on multi-source data fusion technology to improve the spatio-temporal resolution of remote sensing information, and to provide a feasible path for high-accuracy remote sensing inversion of SOS. Nevertheless, there is still a lack of quantitative evaluation for the accuracy of these data fusion methods in SOS estimation. Therefore, in this study, the SOS estimation accuracy is quantitatively evaluated based on the spatio-temporal fusion daily datasets through the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and other models in Xilinhot City, Inner Mongolia, China. The results show that: (1) the accuracy of SOS estimation based on spatio-temporal fusion daily datasets has been slightly improved, the average Root Mean Square Error (RMSE) of SOS based on 8d composite datasets is 11.1d, and the best is 9.7d (fstarfm8); (2) the estimation accuracy based on 8d composite datasets (RMSE¯ = 11.1d) is better than daily fusion datasets (RMSE¯ = 18.2d); (3) the lack of the Landsat data during the SOS would decrease the quality of the fusion datasets, which ultimately reduces the accuracy of the SOS estimation. The RMSE¯ of SOS based on all three models increases by 11.1d, and the STARFM is least affected, just increases 2.7d. The results highlight the potential of the spatio-temporal data fusion method in high-accuracy grassland SOS estimation. It also shows that the dataset fused by the STARFM algorithm and composed for 8 days is better for SOS estimation

Hypoxia Inhibits Cell Cycle Progression and Cell Proliferation in Brain Microvascular Endothelial Cells via the miR-212-3p/MCM2 Axis

Hypoxia impairs blood–brain barrier (BBB) structure and function, causing pathophysiological changes in the context of stroke and high-altitude brain edema. Brain microvascular endothelial cells (BMECs) are major structural and functional elements of the BBB, and their exact role in hypoxia remains unknown. Here, we first deciphered the molecular events that occur in BMECs under 24 h hypoxia by whole-transcriptome sequencing assay. We found that hypoxia inhibited BMEC cell cycle progression and proliferation and downregulated minichromosome maintenance complex component 2 (Mcm2) expression. Mcm2 overexpression attenuated the inhibition of cell cycle progression and proliferation caused by hypoxia. Then, we predicted the upstream miRNAs of MCM2 through TargetScan and miRanDa and selected miR-212-3p, whose expression was significantly increased under hypoxia. Moreover, the miR-212-3p inhibitor attenuated the inhibition of cell cycle progression and cell proliferation caused by hypoxia by regulating MCM2. Taken together, these results suggest that the miR-212-3p/MCM2 axis plays an important role in BMECs under hypoxia and provide a potential target for the treatment of BBB disorder-related cerebrovascular disease

Quantitative Evaluation of Grassland SOS Estimation Accuracy Based on Different MODIS-Landsat Spatio-Temporal Fusion Datasets

Estimating the Start of Growing Season (SOS) of grassland on the global scale is an important scientific issue since it can reflect the response of the terrestrial ecosystem to environmental changes and determine the start time of grazing. However, most remote sensing data has coarse- temporal and spatial resolution, resulting in low accuracy of SOS retrieval based on remote sensing methods. In recent years, much research has focused on multi-source data fusion technology to improve the spatio-temporal resolution of remote sensing information, and to provide a feasible path for high-accuracy remote sensing inversion of SOS. Nevertheless, there is still a lack of quantitative evaluation for the accuracy of these data fusion methods in SOS estimation. Therefore, in this study, the SOS estimation accuracy is quantitatively evaluated based on the spatio-temporal fusion daily datasets through the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and other models in Xilinhot City, Inner Mongolia, China. The results show that: (1) the accuracy of SOS estimation based on spatio-temporal fusion daily datasets has been slightly improved, the average Root Mean Square Error (RMSE) of SOS based on 8d composite datasets is 11.1d, and the best is 9.7d (fstarfm8); (2) the estimation accuracy based on 8d composite datasets (RMSE¯ = 11.1d) is better than daily fusion datasets (RMSE¯ = 18.2d); (3) the lack of the Landsat data during the SOS would decrease the quality of the fusion datasets, which ultimately reduces the accuracy of the SOS estimation. The RMSE¯ of SOS based on all three models increases by 11.1d, and the STARFM is least affected, just increases 2.7d. The results highlight the potential of the spatio-temporal data fusion method in high-accuracy grassland SOS estimation. It also shows that the dataset fused by the STARFM algorithm and composed for 8 days is better for SOS estimation

Study on the effect of semi-transparency on thermal insulation performance of silica aerogel composites

This paper centralizes its focus on elucidating the distinctive thermal insulation performance of silica aerogel composites, emphasizing their semitransparent attributes under diverse heating methodologies. Firstly, the theoretical prediction of conductive thermal conductivity is undertaken utilizing the spherical hollow cube model and Davies model. Subsequently, the radiative thermal conductivity is numerically derived employing the two-flux approximation model, integrating experimental measurements of optical radiation characteristic parameters. The prediction results under high-temperature are validated by the measured data via a Hot Disk instrument, utilizing the transient plane source method. Finally, the influence of semitransparent properties of the aerogel composites on thermal insulation performance is exhaustively investigated. This is conducted through a meticulous examination of temperature responses resulting from distinct heating modalities, namely contact heating, quartz lamp heating, and arc heated wind tunnel heating. The findings discernibly illustrate that these diverse heating mechanisms significantly impact the thermal insulation performance of aerogel composites endowed with semitransparent characteristics

Response Mechanism of Cotton Growth to Water and Nutrients under Drip Irrigation with Plastic Mulch in Southern Xinjiang

The effects of water and nutrient control measures on the cotton plant height, stem diameter, biomass, seed yield, and soil moisture under an irrigated plastic mulch production system were studied. Using field experiments in the 2018 cotton-growing season, 6 fertilization treatments (30-10.5-4.5 (N-P2O5-K2O), 24-8.4-3.6 (N-P2O5-K2O), 20-7-3 (N-P2O5-K2O), 16-5.6-2.4 (N-P2O5-K2O), 10-3.5-1.5 (N-P2O5-K2O), and 0-0-0 (N-P2O5-K2O) kg/mu) and 6 deficit irrigation treatments (40% PET, 60% PET, and 80% PET) were established at the cotton budding and flowering stages. Analysis of variance (ANOVA) (P<0.05) was used to evaluate the significant differences among the treatments. The results showed that the effects of the water and nutrient control measures were obvious. The irrigation water use efficiency (IWUE) was the highest under the 80% deficit irrigation (T7) treatment at the flowering stage (2.62 kg/m3). Increases in cotton plant height and stem diameter were promoted by mild or moderate deficit irrigation at the flowering stage, but normal growth and development were affected by severe deficit irrigation at any growth stage. The growth indexes of cotton increased with increasing fertilization, but significant differences between each fertilization gradient were not obvious. At the same time, excessive fertilization not only had a positive effect on the LAI (leaf area index) and yield but also caused fertilizer waste and unnecessary cotton growth. The cotton seed yield and single boll yield reached their highest values (566 kg/mu) under the 1.2 times fertilizer treatment (T9), but the 0.8 times fertilizer treatment had the highest IWUE among the nutrient control treatments (1.91 kg/m3). Therefore, it is suggested that deficit irrigation at 60~80% of the potential evapotranspiration (PET) at the flowering stage and 16-5.6-2.4 (N-P2O5-K2O) fertilizer be applied as an optimal water and nutrient management strategy to maximize the seed cotton yield, IWUE, and overall growth and development of cotton

Method of Entry Layout under Synergistic Effects of Abutment Stress and Dynamic Stress

In underground mining engineering, rocks around the entry are always subjected to large plastic deformation disasters, such as supporting body failure, roof rock collapse, and even rock burst under abutment stress and dynamic stress. To improve the stability of these rocks, the entry layout under abutment stress and dynamic stress (ELAD) method was put forward to protect the entry from high abutment stress and dynamic stress. Dynamic disturbance intensity (DDI) was determined as the key evaluation index in ELAD, which was divided into “Slightly Disturbed Type,” “Moderately Disturbed Type,” and “Violent Impact Type” by the dynamic disturbance threshold (DDT) and dynamic large-deformation threshold (DLT). The established servo calculation algorithm was applied into a dynamic and static numerical analysis model with FLAC3D500 software for the solving of DDT and DLT by the method of zero growth DDI of plastic failure zone and the engineering-permitted limitation deformation. This model was validated by comparing the displacement of entry with the measured results in the field. The model results validated that the entry should keep away from the dynamic stress of Violent Impact Type firstly and then be arranged in the zone where the dynamic stress belongs to Slightly Disturbed Type. DDT increases linearly and DLT decreases with a power function as the increasing of the abutment stress. ELAD method is reliable to protect this kind of underground entry and its applicability will be improved by the support resistance by comparing the results from ELAD with those from the widely used methods for field investigation discussion. The analysis procedure can be repeatable and necessary since the rock and coal materials may be different in geological and engineering conditions