Formulation and characterization of a novel, photoinitiated small intestinal sub-mucosal wound-healing hydrogel

Purpose: To design and characterize a novel 3-D photo-initiated small intestinal sub-mucosal (SIS) hydrogel for use as a scaffold.Methods: Two concentrations of hydrogel were used: 10 mg/mL SIS gel (designated as 1 % hydrogel) and 20 mg/mL SIS gel (designated as 2 % hydrogel). Cross-sections of the hydrogels were examined by scanning electron microscope. In vitro cell culture was carried out on the hydrogels, and cell count was obtained on each hydrogel at different time points. In addition, hematoxylin-eosin (H&E) staining was used to assess in vivo biodegradability of the gels, as well as tissue regeneration.Results: The 1 % hydrogel possessed a larger pore size (143 ± 22 μm) than the 2 % hydrogel (113 ± 17 μm) and showed significantly higher biodegradation rate (22.79 ± 2.47 % of gel left on day 5) than 2% hydrogel (35.37 ± 4.51 % of gel left on day 5) (p < 0.05). However, results from cell culture showed that the 2 % hydrogel had better biocompatibility than 1 % hydrogel. In vivo data revealed that the gels supported cell growth (cell count on days 3 and 5 were 48.33 ± 17.61 and 105.67 ± 21.36, respectively).Conclusion: These results suggest that SIS hydrogels have a high potential for application in tissue regeneration.Keywords: Extracellular matrix, Small intestinal sub-mucosa, Hydrogel, Wound healin

Recent Advancement of Synthetic Aperture Radar (SAR) Systems and Their Applications to Crop Growth Monitoring

Synthetic aperture radars (SARs) propagate and measure the scattering of energy at microwave frequencies. These wavelengths are sensitive to the dielectric properties and structural characteristics of targets, and less affected by weather conditions than sensors that operate in optical wavelengths. Given these advantages, SARs are appealing for use in operational crop growth monitoring. Engineering advancements in SAR technologies, new processing algorithms, and the availability of open-access SAR data, have led to the recent acceleration in the uptake of this technology to map and monitor Earth systems. The exploitation of SAR is now demonstrated in a wide range of operational land applications, including the mapping and monitoring of agricultural ecosystems. This chapter provides an overview of—(1) recent advancements in SAR systems; (2) a summary of SAR information sources, followed by the applications in crop monitoring including crop classification, crop parameter estimation, and change detection; and (3) summary and perspectives for future application development

A Spatio-Temporal Data Fusion Model for Generating NDVI Time Series in Heterogeneous Regions

Time series vegetation indices with high spatial resolution and high temporal frequency are important for crop growth monitoring and management. However, due to technical constraints and cloud contamination, it is difficult to obtain such datasets. In this study, a spatio-temporal vegetation index image fusion model (STVIFM) was developed to generate high spatial resolution Normalized Difference Vegetation Index (NDVI) time-series images with higher accuracy, since most of the existing methods have some limitations in accurately predicting NDVI in heterogeneous regions, or rely on very computationally intensive steps and land cover maps for heterogeneous regions. The STVIFM aims to predict the fine-resolution NDVI through understanding the contribution of each fine-resolution pixel to the total NDVI change, which was calculated from the coarse-resolution images acquired on two dates. On the one hand, it considers the difference in relationships between the fine- and coarse-resolution images on different dates and the difference in NDVI change rates at different growing stages. On the other hand, it neither needs to search similar pixels nor needs to use land cover maps. The Landsat-8 and MODIS data acquired over three test sites with different landscapes were used to test the spatial and temporal performance of the proposed model. Compared with the spatial and temporal adaptive reflectance fusion model (STARFM), enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion (FSDAF) method, the proposed STVIFM outperforms the STARFM and ESTARFM at three study sites and different stages when the land cover or NDVI changes were captured by the two pairs of fine- and coarse-resolution images, and it is more robust and less computationally intensive than the FSDAF

A New Regionalization Scheme for Effective Ecological Restoration on the Loess Plateau in China

To prevent potentially unsuitable activities during vegetation restoration, it is important to examine the impact of historical restoration activities on the target ecological system to inform future restoration policies. Taking the Loess Plateau of China as an example, a regionalization method and corresponding scheme were proposed to select suitable vegetation types (forested lands, woody grasslands/bushlands, grasslands, or xerophytic shrublands and semi-shrublands) for a given location using remote sensing technology in order to analyze the vegetation growth status before and after the largest ecological conservation project in the country: The Grain for Green Program (GTGP). To design the scheme, remote sensing data covering the periods before and after the implementation of the GTGP (the 1980s and 2001–2013) were collected, along with soil, meteorological, and topographic data. The net primary production (NPP) values for 2001–2013 were calculated using the Carnegie-Ames-Stanford Approach (CASA) model. Locations representing the native vegetation and the restored vegetation were first recognized using maps of vegetation cover. Then, for the restored vegetation area, the places suitable for planting the covered vegetation type were selected by comparing the NPP value of the corresponding vegetation type in the native vegetation area to the NPP value in the site under consideration. Third, half of these sites were uniformly selected based on their NPP value, and these areas and the native vegetation area were used as training regions. Based on weather, soil, and topographic data, a new regionalization scheme was designed using standardized Euclidean distances. Finally, data from the remainder of the Loess Plateau were used to validate the new regionalization scheme, which was also compared to an existing Chinese eco-geographical regionalization scheme. The results showed that the new regionalization scheme performed well, with an average potential classification accuracy of 81.81%. Compared with the eco-geographical regionalization scheme, the new scheme exhibited improved the consistency of vegetation dynamics, reflecting the potential to better guide vegetation restoration activities on the Loess Plateau

Bibliometric Analysis of Remote Sensing Research Trend in Crop Growth Monitoring: A Case Study in China

Remote sensing of crop growth monitoring is an important technique to guide agricultural production. To gain a comprehensive understanding of historical progression and current status, and future trend of remote sensing researches and applications in the field of crop growth monitoring in China, a study was carried out based on the publications from the past 20 years by Chinese scholars. Using the knowledge mapping software CiteSpace, a quantitative and qualitative analysis of research development, current hotspots, and future directions of crop growth monitoring using remote sensing technology in China was conducted. Furthermore, the relationship between high-frequency keywords and the emerging hot topics were visually analyzed. The results revealed that Chinese researchers paid more attention on keywords such as “vegetation index„, “crop growth„, “winter wheat„, “leaf area index (LAI)„, and “model„ in the field of crop growth monitoring, and “LAI„ and “unmanned aerial vehicle (UAV)„, appeared increasingly in frontier research of this discipline. Overall, bibliometric results from this CiteSpace-aided study provide a quantitative visualization to enrich our understanding on the historical development, current status, and future trend of crop growth monitoring in China