Hemostatic Dressing Immobilized with ε-poly-L-lysine and Alginate Coated Mesoporous Bioactive Glass Prevents Blood Permeation by Pseudo-Dewetting Behavior.

The integration of hemostats with cotton fabrics is recognized as an effective approach to improve the hemostatic performance of dressings. However, concerns regarding the uncontrollable absorption of blood by hydrophilic dressings and the risk of distal thrombosis from shed hemostatic agents are increasingly scrutinized. To address these issues, this work develops an advanced dressing (AQG) with immobilized nano-scale mesoporous bioactive glass (MBG) to safely and durably augment hemostasis. The doubly immobilized MBGs, pre-coated with ε-poly-L-lysine and alginate, demonstrate less than 1% detachment after ultrasonic washing. Notably, this MBG layer significantly promotes the adhesion, aggregation, and activation of red blood cells and platelets, adhered five times more red blood cells and 29 times more platelets than raw dressing, respectively. Specially, with the rapid formation of protein corona and amplification of thrombin, dense fibrin network is built on MBG layer and then blocked blood permeation transversely and longitudinally, showing an autophobic pseudo-dewetting behavior and allowing AQG to concentrate blood in situ and culminate in faster hemostasis with lower blood loss. Furthermore, the potent antibacterial properties of AQG extend its potential for broader application in daily care and clinical setting.

Evaluation of Several Satellite-Based Soil Moisture Products in the Continental US.

Satellite-based soil moisture products are suitable for large-scale regional monitoring due to the accessibility. Five soil moisture products including SMAP, ESA CCI, and AMSR2 (ascending, descending, and average) were selected in the continental United States (US) from 2016 to 2021. To evaluate the performance of the products and assess their applicability, ISMN (International Soil Moisture Network) data were used as the in situ measurement. PBIAS (Percentage of BIAS), R (Pearson correlation coefficient), RMSE (Root Mean Square Error), ubRMSE (unbiased RMSE), MAE (Mean Absolute Error), and MBE (Mean Bias Error) were selected for evaluation. The performance of five products over six observation networks and various land cover types was compared, and the differences were analyzed at monthly, seasonal, and annual scales. The results show that SMAP had the smallest deviation with the ISMN data because PBIAS was around -0.13, and MBE was around -0.02 m3/m3. ESA CCI performed the best in almost all aspects; its R reached around 0.7, and RMSE was only around 0.07 m3/m3 at the three time scales. The performance of the AMSR2 products varied greatly across the time scales, and increasing errors and deviations showed from 2016 to 2020. The PBO_H2O and USCRN networks could reflect soil moisture characteristics in the continental US, while iRON performed poorly. The evaluation of the networks was closely related to spatial distributions. All products performed better over grasslands and shrublands with R, which was greater than 0.52, and ubRMSE was around 0.1 m3/m3, while products performed worse over forests, where PBIAS was less than -0.62, and RMSE was greater than 0.2 m3/m3, except for ESA CCI. From the boxplot, SMAP was close to the ISMN data with differences less than 0.004 m3/m3 between the median and lower quartiles.

Nitrous oxide flux observed with tall-tower eddy covariance over a heterogeneous rice cultivation landscape.

Although croplands are known to be strong sources of anthropogenic N2O, large uncertainties still exist regarding their emission factors, that is, the proportion of N in fertilizer application that escapes to the atmosphere as N2O. In this study, we report the results of an experiment on the N2O flux in a landscape dominated by rice cultivation in the Yangtze River Delta, China. The observation was made with a closed-path eddy covariance system on a 70-m tall tower from October 2018 to December 2020 (27 months). Temperature and precipitation explained 78% of the seasonal and interannual variability in the observed N2O flux. The growing season (May to October) mean flux (1.14 nmol m-2 s-1) was much higher than the median flux found in the literature for rice paddies. The mean N2O flux during the observational period was 0.90 ± 0.71 nmol m-2 s-1, and the annual cumulative N2O emission was 7.6 and 9.1 kg N2O-N ha-1 during 2019 and 2020, respectively. The corresponding landscape emission factor was 3.8% and 4.6%, respectively, which were much higher than the IPCC default direct (0.3%) and indirect emission factors (0.75%) for rice paddies.

MiRNA-548c-5p downregulates inflammatory response in preeclampsia via targeting PTPRO.

Preeclampsia is a serious complication of pregnancy and leads to maternal hypertension and proteinuria. It remains a major health problem for mothers and babies across the world due to high maternal and fetal morbidity and mortality. Accumulated data have implicated the critical role of microRNA in preeclampsia. However, to date, the role of miR-548c-5p in preeclampsia remains vaguely understood. In this study, we first elucidate the role of miR-548c-5p and its underlying molecular mechanism in preeclampsia. Compared with healthy controls, miR-548c-5p was obviously downregulated in serum exosomes and placental mononuclear cells in patients with preeclampsia. Nonetheless, PTPRO was significantly upregulated and negatively associated with miR-548c-5p in placental mononuclear cells in patients with preeclampsia. PTPRO was a target of miR-548c-5p. PTPRO was downregulated in the miR-548c-5p-overexpressed macrophages. In addition, miR-548c-5p could inhibit the proliferation and activation of LPS-stimulated macrophages, as evidenced by decreased levels of inflammatory cytokines (IL-12 and TNF-α) and less nuclear translocation of pNF-κB in pTHP1 cells. MiR-548c-5p acts as an anti-inflammatory factor in preeclampsia. The axis of miR-548c-5p/PTPRO/NF-κB may provide novel targets for the diagnosis and treatment of preeclampsia.

[Analysis of Greenhouse Gas Emission Characteristics and Their Influencing Factors in the Algae Zone of Lake Taihu].

In order to identify CH4 and CO2 emission flux characteristics and their impact factors in the algal lake zone of Lake Taihu, CH4 and CO2 fluxes were observed by the improved closed chamber method in Meiliang Bay in Lake Taihu. The relationships between CH4 and CO2 flux and meteorological factors were analyzed. The results showed that CH4 and CO2 fluxes had obvious diurnal variations. The CH4 flux in the daytime was higher than that in the nighttime in spring; however, the CH4 flux in the nighttime was higher than that in the daytime in summer. The CO2 uptake flux in the daytime was higher than that in the nighttime in spring and summer. The algae zone of Lake Taihu was a CH4 source for the atmosphere. The average CH4 flux was 4.047 nmol ·(m2 ·s)-1 and 40.779 nmol ·(m2 ·s)-1 in spring and summer, respectively. The zone was the CO2 sink for the atmosphere in spring and summer. The average CO2 flux was -0.160 µmol ·(m2 ·s)-1 and -0.033 µmol ·(m2 ·s)-1 in spring and summer, respectively. On an hourly scale, the CH4 emission flux was positively correlated with air temperature and water temperature (r=0.20, P<0.01 and r=0.34, P<0.01, respectively). When wind speed was lower than 6 m ·s-1, the CH4 flux was positively correlated with wind speed (r=0.71, P<0.01). The CO2 uptake flux had a significant positive correlation with air temperature and wind speed (r=0.14, P<0.01 and r=0.33, P<0.05, respectively). However, the CO2 uptake flux was negatively correlated with air pressure and solar radiation (r=-0.41, P<0.01 and r=-0.35, P<0.01, respectively). The CO2 efflux had a significant positive correlation with wind speed (r=0.40, P<0.05). The CO2 efflux was negatively correlated with solar radiation (r=-0.35, P<0.01). On a daily scale, the CH4 emission flux had a significant positive correlation with air temperature and water temperature (r=0.83, P<0.01 and r=0.78, P<0.01, respectively).

Spatial-Temporal Variation of Drought in China from 1982 to 2010 Based on a modified Temperature Vegetation Drought Index (mTVDI).

Droughts cause huge losses of society and environment, therefore it is important to study the spatial-temporal pattern of drought. The traditional remote sensing drought indices (AVI, VCI and TCI) only consider the single factor representing the soil moisture (surface temperature or NDVI). The comprehensive remote sensing drought indices (VSWI and TVDI) can estimate the soil moisture more accurately, but they are not suitable for large scale region especially with great elevation variation. In this study, a modified Temperature Vegetation Drought Index (mTVDI) was constructed based on the correction of elevation and dry edge. Compared with the traditional drought indices, mTVDI had a better relationship with soil moisture in all selected months (R = -0.376, -0.406, -0.459, and -0.265, p < 0.05). mTVDI was used to analyze the spatial-temporal patterns of drought in China from 1982 to 2010. The results showed that droughts appeared more frequently in Northwest China and the southwest of Tibet while drought centers of North and Southwest China appeared in Huanghuaihai Plain and Yunnan-Guizhou Plateau respectively. The frequency of drought was increasing as a whole while the frequency of severe drought increased significantly by 4.86% and slight drought increased slowly during 1982 to 2010. The results are useful for the understanding of drought and policy making of climate change.

Quantitative assessments of water-use efficiency in Temperate Eurasian Steppe along an aridity gradient.

Water-use efficiency (WUE), defined as the ratio of net primary productivity (NPP) to evapotranspiration (ET), is an important indicator to represent the trade-off pattern between vegetation productivity and water consumption. Its dynamics under climate change are important to ecohydrology and ecosystem management, especially in the drylands. In this study, we modified and used a late version of Boreal Ecosystem Productivity Simulator (BEPS), to quantify the WUE in the typical dryland ecosystems, Temperate Eurasian Steppe (TES). The Aridity Index (AI) was used to specify the terrestrial water availability condition. The regional results showed that during the period of 1999-2008, the WUE has a clear decreasing trend in the spatial distribution from arid to humid areas. The highest annual average WUE was in dry and semi-humid sub-region (DSH) with 0.88 gC mm-1 and the lowest was in arid sub-region (AR) with 0.22 gC mm-1. A two-stage pattern of WUE was found in TES. That is, WUE would enhance with lower aridity stress, but decline under the humid environment. Over 65% of the region exhibited increasing WUE. This enhancement, however, could not indicate that the grasslands were getting better because the NPP even slightly decreased. It was mainly attributed to the reduction of ET over 70% of the region, which is closely related to the rainfall decrease. The results also suggested a similar negative spatial correlation between the WUE and the mean annual precipitation (MAP) at the driest and the most humid ends. This regional pattern reflected the different roles of water in regulating the terrestrial ecosystems under different aridity levels. This study could facilitate the understanding of the interactions between terrestrial carbon and water cycles, and thus contribute to a sustainable management of nature resources in the dryland ecosystems.

Historical grassland desertification changes in the Horqin Sandy Land, Northern China (1985-2013).

Since rural reforms in the 1980s, both the state and local governments of China have devoted great efforts to combating desertification through a number of eco-environmental restoration campaigns, resulting in burgeoning contention at all levels of government and sparking public concern. Monitoring and accurately assessing the statuses and trends of grassland desertification are important for developing effective restoration strategies. The Horqin Sandy Land (HSL), a very typical desertified grassland (DG) with better hydrothermal conditions among sandy lands in north China, was recently selected (1985-2013) to assess the spatiotemporal dynamic performances of grassland desertification before and after implementing restoration projects. Landsat images (TM/ETM+/OLI), field investigations and expert review were integrated to form a classification scheme for the HSL. Then, spectral mixture analysis and the decision-tree method were used to extract bare-sand ratios and vegetation cover fraction dynamics. A favourable phenomenon of DG was seen to be reversed in an accelerated pace during 2001-2013, despite challenge from both climatic and anthropogenic factors. However, overexploitation of grassland (especially for farming) and ground water for irrigation has led to remarkable decreases in the ground water level in recent decades, which should be highly concerning regarding the formulation of restoration campaigns in the sandy land.

Drought trends based on the VCI and its correlation with climate factors in the agricultural areas of China from 1982 to 2010.

Drought is a type of natural disaster that has the most significant impacts on agriculture. Regional drought monitoring based on remote sensing has become popular due to the development of remote sensing technology. In this study, vegetation condition index (VCI) data recorded from 1982 to 2010 in agricultural areas of China were obtained from advanced very high resolution radiometer (AVHRR) data, and the temporal and spatial variations in each drought were analyzed. The relationships between drought and climate factors were also analyzed. The results showed that from 1982 to 2010, the agricultural areas that experienced frequent and severe droughts were mainly concentrated in the northwestern areas and Huang-Huai Plain. Moreover, the VCI increased in the majority of agricultural areas, indicating that the drought frequency decreased over time, and the decreasing trend in the southern region was more notable than that in the northern region. A correlation analysis showed that temperature and wind velocity were the main factors that influenced drought in the agricultural areas of China. From a regional perspective, excluding precipitation, the climate factors had various effects on drought in different regions. However, the correlation between the VCI and precipitation was low, possibly due to the widespread use of artificial irrigation technology, which reduces the reliance of agricultural areas on precipitation.

An algorithm for retrieving land surface temperatures using VIIRS data in combination with multi-sensors.

A practical algorithm was proposed to retrieve land surface temperature (LST) from Visible Infrared Imager Radiometer Suite (VIIRS) data in mid-latitude regions. The key parameter transmittance is generally computed from water vapor content, while water vapor channel is absent in VIIRS data. In order to overcome this shortcoming, the water vapor content was obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) data in this study. The analyses on the estimation errors of vapor content and emissivity indicate that when the water vapor errors are within the range of ±0.5 , the mean retrieval error of the present algorithm is 0.634 K; while the land surface emissivity errors range from -0.005 to +0.005, the mean retrieval error is less than 1.0 K. Validation with the standard atmospheric simulation shows the average LST retrieval error for the twenty-three land types is 0.734 K, with a standard deviation value of 0.575 K. The comparison between the ground station LST data indicates the retrieval mean accuracy is -0.395 K, and the standard deviation value is 1.490 K in the regions with vegetation and water cover. Besides, the retrieval results of the test data have also been compared with the results measured by the National Oceanic and Atmospheric Administration (NOAA) VIIRS LST products, and the results indicate that 82.63% of the difference values are within the range of -1 to 1 K, and 17.37% of the difference values are within the range of ±2 to ±1 K. In a conclusion, with the advantages of multi-sensors taken fully exploited, more accurate results can be achieved in the retrieval of land surface temperature.

[Progress in retrieving land surface temperature for the cloud-covered pixels from thermal infrared remote sensing data].

Land surface temperature (LST), which reflects surface properties, is one of the key parameters in the physics of land surface processes from local through global scales. LST is very required in time and space for a wide variety of scientific studies and thermal infrared (TIR) remote sensing applications. Satellite TIR channels are very available for LST retrieval but only in clear skies. However, when the surface is obscured by clouds, the actual retrieved LST for the corresponding pixel is, or is contaminated by, the cloud top temperature. Lacking understanding of the complex relationships between clouds and LST, the estimation of LST for cloud-covered pixels poses a big problem and challenge for thermal remote sensing scientists. In the present paper, a review of algorithms and approaches related to LST retrieval for cloud-covered pixels from TIR data is presented, and the characteristics of each method are also discussed. Directions for future research to improve the accuracy of satellite-derived LST for cloud-covered pixels are then suggested.

Derivation of land surface temperature for Landsat-8 TIRS using a split window algorithm.

Land surface temperature (LST) is one of the most important variables measured by satellite remote sensing. Public domain data are available from the newly operational Landsat-8 Thermal Infrared Sensor (TIRS). This paper presents an adjustment of the split window algorithm (SWA) for TIRS that uses atmospheric transmittance and land surface emissivity (LSE) as inputs. Various alternatives for estimating these SWA inputs are reviewed, and a sensitivity analysis of the SWA to misestimating the input parameters is performed. The accuracy of the current development was assessed using simulated Modtran data. The root mean square error (RMSE) of the simulated LST was calculated as 0.93 °C. This SWA development is leading to progress in the determination of LST by Landsat-8 TIRS.

An efficient approach for pixel decomposition to increase the spatial resolution of land surface temperature images from MODIS thermal infrared band data.

Land surface temperature (LST) images retrieved from the thermal infrared (TIR) band data of Moderate Resolution Imaging Spectroradiometer (MODIS) have much lower spatial resolution than the MODIS visible and near-infrared (VNIR) band data. The coarse pixel scale of MODIS LST images (1000 m under nadir) have limited their capability in applying to many studies required high spatial resolution in comparison of the MODIS VNIR band data with pixel scale of 250-500 m. In this paper we intend to develop an efficient approach for pixel decomposition to increase the spatial resolution of MODIS LST image using the VNIR band data as assistance. The unique feature of this approach is to maintain the thermal radiance of parent pixels in the MODIS LST image unchanged after they are decomposed into the sub-pixels in the resulted image. There are two important steps in the decomposition: initial temperature estimation and final temperature determination. Therefore the approach can be termed double-step pixel decomposition (DSPD). Both steps involve a series of procedures to achieve the final result of decomposed LST image, including classification of the surface patterns, establishment of LST change with normalized difference of vegetation index (NDVI) and building index (NDBI), reversion of LST into thermal radiance through Planck equation, and computation of weights for the sub-pixels of the resulted image. Since the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with much higher spatial resolution than MODIS data was on-board the same platform (Terra) as MODIS for Earth observation, an experiment had been done in the study to validate the accuracy and efficiency of our approach for pixel decomposition. The ASTER LST image was used as the reference to compare with the decomposed LST image. The result showed that the spatial distribution of the decomposed LST image was very similar to that of the ASTER LST image with a root mean square error (RMSE) of 2.7 K for entire image. Comparison with the evaluation DisTrad (E-DisTrad) and re-sampling methods for pixel decomposition also indicate that our DSPD has the lowest RMSE in all cases, including urban region, water bodies, and natural terrain. The obvious increase in spatial resolution remarkably uplifts the capability of the coarse MODIS LST images in highlighting the details of LST variation. Therefore it can be concluded that, in spite of complicated procedures, the proposed DSPD approach provides an alternative to improve the spatial resolution of MODIS LST image hence expand its applicability to the real world.

[Effects of water stress on red-edge parameters and yield in wheat cropping].

The objective of the present paper is to study the influence of water stress on wheat spectrum red edge parameters by using field wheat spectrum data obtained from water stress experiment. Firstly, the authors analyzed the influence of water stress on wheat spectrum reflectance. Then the authors got the wheat red edge position and red edge peak through calculating wheat spectrum first-order differential and analyzed the influence of water stress on wheat red edge parameters. Finally the authors discussed the relationship between red peak and wheat yield. The results showed that the wheat red edge position shows "red shift" at the beginning of the wheat growth period and "blue shift" at the later period of the wheat growth period under the water stress experiment. Also, the red edge peak of the wheat showed that red edge peak increased with the water stress sharpening at the beginning of the wheat growth period, and then the red edge peak reduced with the water stress sharpening. The wheat red edge peak presented positive correlation with the wheat yield before the elongation period, and exhibited negative correlation after that period.

An index method to evaluate growers' pesticide use for identifying on-farm innovations and effective alternative pest management strategies: a case study of winegrape in Madera County, California.

Winegrape is an important perennial crop in California, USA. Each year California winegrape farming consumes about 20 million kilograms of pesticides that have been a pollutant source to the fresh water systems of the state. The variation of pesticide use among winegrape growers has been significant. It has been observed that some growers have developed effective ways to reduce pesticide use, yet control pests efficiently to ensure harvest. Identification of the growers with low and high pesticide use is very helpful to extension programs that aim on reducing pesticide environmental risk. In this study, an index approach is proposed to quantitatively measure pesticide use intensity at grower level. An integrated pesticide use index is developed by taking pesticide quantity and toxicity into account. An additive formula and a multiplying formula were used to calculate the pesticide use index, i.e., PUI and PUIM. It was found that both PUI and PUIM were capable of identifying the low and high pesticide users while PUI was slightly more conservative than PUIM. All pesticides used in California winegrape farming were taken into account for calculating the indices. Madera County, one of the largest winegrape producers in California, was taken as an example to test the proposed approach. In year 2000, among the total 208 winegrape growers, 28 with PUI≤10 and 34 with 1060, identified as high pesticide users, had large-sized vineyards, i.e., more fields and large planted areas. They used all types of pesticides and many compounds, which indicated that their pest controls heavily depended on pesticides rather than on-farm management. Through the case study, the proposed approach proved to be useful for analyzing the growers' pesticide use intensities and interpreting their pesticide use behaviors, which led to a new start point for further investigation of searching ways to reduce pesticide environmental risk.

Remote Sensing of Agro-droughts in Guangdong Province of China Using MODIS Satellite Data.

A practical approach was developed in the study for drought monitoring in Guangdong province of China on the basis of vegetation supply water index (VSWI) and precipitation distance index (PDI). A comprehensive index for assessment of agro-drought severity (SADI) was then established from the normalized VSWI and PDI. Using MODIS satellite images and precipitation data from ground-observed meteorological stations, we applied the approach to Guangdong for drought monitoring in 2006. The monitoring results showed that the drought severity on average was very low in the province during the main growing season from May to September in 2006. However, seasonal variation of the severity was also obvious in difference counties of the province. Higher severity of drought could be seen in the periods of late-June (In China each month is traditionally divided into 3 periods. Each is with 10 days and has different names. This division system is mainly with consideration of farming seasons hence has been widely used as the basis of drought monitoring periods in China. In order to keep this tradition, we define, for example, for June, the early-June as the period from 1st to 10th of June, the mid-June as the period from 11th to 20th, and the late-June as the period from 21st to 30th. So mid-August denotes the period from 11th to 20th of August, and early-July the period from 1st to 10th of July, and so on.), early-July, mid-August and late-September. Regionally, Leizhou Peninsula in the west had the most serious drought before mid-May. Validation indicated that our monitoring results were generally consistent with the drought statistics data and the results from Chinese National Satellite Meteorological Center (CNSMC), which used only remote sensing data. This consistence confirmed the applicability of our approach for drought monitoring. Our better identification of drought severity in Leizhou Peninsula of western Guangdong than that of CNSMC might suggest that the approach developed in the study was able to provide a better alternative to increase the accuracy of drought monitoring for agricultural administration and farming.