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Abstract Background Recombinant human Interleukin receptor antagonist (rhIL-Ra) can bind to the IL-1 receptor on the cell membrane and reversibly blocks the proinflammatory signaling pathway. However, its effect on allergic rhinitis (AR) and the underlying mechanism remains unknown. This study aims to investigate the efficacy of recombinant human interleukin-1 receptor antagonist (rhIL-1Ra) on AR guinea pigs. Methods Guinea pigs were systemically sensitized by intraperitoneal injection and topical intranasal instillation with ovalbumin within 21 days. Animals administrated with saline served as the normal control. The AR animals were randomly divided into the model group and distinct concentrations of rhIL-1Ra and budesonide treatment groups. IL-1β and ovalbumin specific IgE levels were detected by ELISA kits. Nasal mucosa tissues were stained with hematoxylin & eosin (HE) for histological examination. Results It was found that the numbers of sneezing and nose rubbing were remarkably reduced in rhIL-1Ra and budesonide-treated guinea pigs. Besides, rhIL-1Ra distinctly alleviated IgE levels in serum and IL-1β levels in nasal mucus, together with decreased exfoliation of epithelial cells, eosinophilic infiltration, tissue edema and vascular dilatation. Conclusions rhIL-1Ra is effective in AR guinea pigs and may provide a novel potential choice for AR treatments.

Deep learning (DL) has made tremendous strides in object detection in remote sensing (RS). Still, due to the limitation of small objects' nature (e.g., size, shape, and texture), their detecting performance is less than half that of medium/large objects in closed-set conditions (all test classes are known in the training phase). In the real world, small-object detection (SOD) over wide areas is far more challenging: models trained with limited positives cannot robustly handle unknown classes (i.e., negatives or backgrounds with similar features to positives), namely, the open-set recognition (OSR) problem. To better perform deep-learning-based SOD, we built a global large-scale training set—flying aircraft (FlyingAC), including ∼3.0 × 105 positives and 2.5 × 105 negatives. We then proposed a series of optimal strategies involving set construction, model training, set volume, model structure, and inference mode. Evaluated based on the FlyingAC, the proposed optimal strategies can mitigate the OSR problem and enhance the inference efficiency. Specifically, we found that (i) training the DL models together with positives and negatives is the most effective way to alleviate the OSR problem (improving the F1 score from 11 % to >90 %). (ii) Optimum training sets volumes exist for DL-based SOD. For the FlyingAC, the optimum volume is 2.0 × 105 positives and 1.5 × 105 negatives. (iii) Optimizing the DL model, such as FPN, attention mechanism, and loss function, can improve the SOD precision. Still, the improvement from the model level is much smaller than the optimization from the dataset level. And (iv) For wide-area detection, the candidate-based inference mode we designed can reduce the inference time to one-fifth and the number of false positives by ∼50 % compared to the commonly used sliding-window inference mode.

Abstract Background Neural stem cells (NSCs)-derived extracellular vesicles (EVs) possess great potential in treating severe neurological and cerebrovascular diseases, as they carry the modulatory and regenerative ingredients of NSCs. Induced pluripotent stem cells (iPSCs)-derived NSCs culture represents a sustainable source of therapeutic EVs. However, there exist two major challenges in obtaining a scalable culture of NSCs for high-efficiency EVs production: (1) the heterogeneity of iPSC-derived NSCs culture impairs the production of high-quality EVs and (2) the intrinsic propensity of neuronal or astroglial differentiation of NSCs during prolonged culturing reduces the number of NSCs for preparing EVs. A NSCs strain that is amenable to stable self-renewal and proliferation is thus greatly needed for scalable and long-term culture. Methods Various constructs of the genes encoding the orphan nuclear receptor NR2E1 (TLX) were stably transfected in iPSCs, which were subsequently cultured in a variety of differentiation media for generation of iNSCsTLX. Transcriptomic and biomarker profile of iNSCsTLX were investigated. In particular, the positivity ratios of Sox2/Nestin and Musashi/Vimentin were used to gauge the homogeneity of the iNSCsTLX culture. The iNSCs expressing a truncated version of TLX (TLX-TP) was expanded for up to 45 passages, after which its neuronal differentiation potential and EV activity were evaluated. Results Stable expression of TLX-TP could confer the iPSCs with rapid and self-driven differentiation into NSCs through stable passaging up to 225 days. The long-term culture of NSCs maintained the highly homogenous expression of NSC-specific biomarkers and potential of neuronal differentiation. EVs harvested from the TLX-expressing NSCs cultures exhibited anti-inflammatory and neuroprotective activities. Conclusions iPSC-derived NSCs stably expressing TLX-TP is a promising cell line for scalable production of EVs, which should be further exploited for therapeutic development in neurological treatment.

Alkaline extraction is an important process in the integrated biorefining of leafy biomass to obtain protein, but the resulting alkaline protein extract (APE11 alkaline protein extract: APE.) may have poor emulsification properties for food applications. In this study, the components in the APE fractionations obtained by size exclusion chromatography were determined. The emulsification properties of APE were determined using oil/water with a ratio of 7:3. Whey protein and soybean protein isolate were used as controls while enzymes were used to improve APE's emulsification properties. The results showed that the APE could be divided into three fractions with protein content of 83, 56, and 34%. Carbohydrates mainly derived from homogalacturonan pectin were mostly in Fraction 2, while Fraction 3 consisted of peptides, oligosaccharides, and free polyphenols. The APE had similar emulsification capacity and emulsification stability as those of whey protein and soybean isolate. The emulsion made by the APE had a creaming index of 92% with emulsification activity index value of 44 m2 g−1, and these numbers could retain after storing at 25 °C for 15 days. The emulsification properties of the APE can be further improved by carbohydrate degradation. With the use of Viscozyme® L, the emulsification activity index value of treated APE was increased by 60%, and then still retained at 67 m2 g−1 after storing for 15 days. Treated by either pepsin or alkaline protease, the emulsification properties of APE were decreased, suggesting the key role of protein in APE for emulsification.

Since the shale Oil/Gas revolution, gas flaring and venting in the United States has garnered increasing attention. There is a pressing need to understand the spatial–temporal characteristics of gas flaring and track the associated greenhouse gas emissions. In this context, we use a thermal anomaly index (TAI) incorporating the Google Earth Engine (GEE) cloud computation and local batch processing for monitoring gas flaring and characterizing its spatial–temporal dynamics. We then apply a quantitative analysis of satellite-based carbon dioxide (CO2) and methane (CH4) in the gas flaring region. Here, we generate a gas flaring sites inventory in Texas from 2013 to 2022 based on > 83,500 multi-source moderate-resolution images (including 74,627 Sentinel-2 Multispectral Instrument [MSI] images and 8,969 Landsat-8 Operational Land Imager [OLI] images). Validations and comparisons demonstrate that our method is reliable for MSI and OLI images, with an overall accuracy of > 95 % and a low commission rate and omission rate. We detected 217,034 gas flares from 9,296 flaring sites in Texas, and the majority (>92 %) were found in the central and western regions of the Permian Basin and the Eagle Ford Shale. The number of detected gas flaring sites vastly outnumbered the existing Visible Infrared Imaging Radiometer Suite (VIIRS) fire products, with an upward trend from 2013 to 2019 and a downward trend from 2020 to 2022. Notably, the gas flaring sites dropped significantly at the beginning of the COVID-19 pandemic (from December 2019 to May 2020), with the lowest average monthly growth rate of −14.38 %, and fell to the level of mid-2017. Application of gas flaring data identifies the localized greenhouse gas (GHG) emission hotspots in Texas and demonstrates that the increased effect of CH4 released from gas flaring regions was significantly stronger than that of CO2. These findings can provide references for monitoring similar small industrial sources in the future, can be used as an essential supplement to low-resolution fire products, and improve our understanding of CO2 and CH4 emissions from gas flaring at fine spatial scales.

Monitoring the coastline dynamic can provide the basis for the balance of sediment erosion and deposition. The evaluation of coastal stability is beneficial to decision makers for the rational development and ecological conservation of coastal resources. The present study first collected 61 scenes of remote sensing images and extracted the multi-temporal coastlines from the years 1990–2020 in Jiangsu Province, China using an improved waterline method. Given the characteristics of gentle slopes of our study area, we modified the coastlines using actual tidal level data to avoid the influence from different tidal regimes. Finally, the coastal stability analysis was conducted on the central coast of Jiangsu, which experiences frequent changes in erosion and siltation. The results showed that the coastline has changed significantly; the natural coastline decreased by 116 km, while the artificial coastline increased by 108 km. the area of tidal flats decreased by 1152 km2, and the average width of the tidal flats decreased from 8.83 km to 3.55 km. In general, the coastline advanced seawards for many years, mainly due to sediment siltation and tidal flat reclamation, with annual average rates of siltation and reclamation of 9.67 km/a and 40.75 km/a, respectively. The node of siltation and erosion migrated 1.8 km southwards, moving from the Sheyang Estuary to the Doulong Port. The coastal stability gradually decreased from north to south, by values of 88.5 km (40%) for stable coast and 63.97 km (28.9%) for extremely unstable coast. The most unstable coast came from frequent reclamation areas. The method in this study is expected to provide a reference for evaluating the stability of typical muddy coasts, and our results can provide a basis for the sustainable development, utilization, and protection of coastal areas.

The long-term venting and combustion of offshore associated gas have substantial adverse effects on the ecological environment, so characterizing the global proliferation of offshore gas flaring areas is very important for marine environmental protection and climate change research. However, the use of a single fire/light remote sensing product makes it difficult to conduct long-term observations. In this study, we detected global offshore gas flaring areas during the 27-year interval from 1992 to 2018, using temporal and spatial complementarity of six different remote sensing data products, which are as follows: DMSP-OLS Nighttime Lights; (A)ATSRs; MODIS and VIIRS active fire products; and VIIRS Night Fire and Nighttime Light. Our aim was to achieve more comprehensive extraction results and to analyze a longer time-interval than has been attempted previously. In addition, the resulting map of the global proliferation of offshore gas flaring areas enables their locational and temporal characteristics to be visualized.

Optical remote sensing (ORS) of reflected sun light has been used to assess oil spills in the ocean for several decades. While most applications are toward simple presence/absence detections based on the spatial contrast between oiled water and oil-free water, recent advances indicate the possibility of classifying oil types and quantifying oil volumes based on their spectral contrasts with oil-free water. However, a review of the current literature suggests that there is still confusion on whether this is possible and, if so, how. Here, based on the recent findings from numerical models, laboratory measurements, and applications to satellite or airborne imagery, we attempt to clarify this situation by summarizing (1) the optics behind oil spill remote sensing, and in turn, (2) how to interpret optical remote sensing imagery based on optical principles. In the end, we discuss the existing limitations and challenges as well as pathways forward to advance ORS of oil spills.

Overwintering fires are a historically rare phenomenon but may become more prevalent in the warming boreal region. Overwintering fires have been studied to a limited extent in boreal North America; however, their role and contribution to fire regimes in Siberia are still largely unknown. Here, for the first time, we quantified the proportion of overwintering fires and their burned areas in Yakutia, eastern Siberia, using fire, lightning, and infrastructure data. Our results demonstrate that overwintering fires contributed to 3.2 ± 0.6% of the total burned area during 2012–2020 over Yakutia, compared to 31.4 ± 6.8% from lightning ignitions and 51.0 ± 6.9% from anthropogenic ignitions (14.4% of the burned area had unknown cause), but they accounted for 7.5 ± 0.7% of the burned area in the extreme fire season of 2020. In addition, overwintering fires have different spatiotemporal characteristics than lightning and anthropogenic fires, suggesting that overwintering fires need to be incorporated into fire models as a separate fire category when modelling future boreal fire regimes.

The critical angle is the angle at which the contrast of oil slicks reverse their contrasts against the surrounding oil-free seawater under sunglint. Accurate determination of the critical angle can help estimate surface roughness and refractive index of the oil slicks. Although it’s difficult to determine a certain critical angle, the potential critical angle range help to improve the estimation accuracy. In this study, the angle between the viewing direction and the direction of mirror reflection is used as an indicator for quantifying the critical angle and could be calculated from the solar/viewing geometry from observations of the Moderate Resolution Imaging Spectroradiometer (MODIS). The natural seep oil slicks in the Gulf of Mexico were first delineated using a customized segmentation approach to remove noise and apply a morphological filter. On the basis of the histograms of the brightness values of the delineated oil slicks, the potential range of the critical angle was determined, and then an optimal critical angle between oil slicks and seawater was then determined from statistical and regression analyses in this range. This critical angle corresponds to the best fitting between the modeled and observed surface roughness of seep oil slicks and seawater.

The South China Sea (SCS) is one of the most important fishery resource bases in the world. Marine fisheries, as a crucial component of regional food security and national revenue, raise wide concern about marine ecology, social-economic and political consequences at regional, national and local scales. The large-scale dynamic detection and analysis of fishing activity in the SCS is still unclear because of the accessibility of in-site data, finite automatic identification system (AIS) usage, complex geopolitics and poor additional data coverage. Nighttime light imagery (NTL) derived from low light imaging sensors and the popularity of light fishing in the SCS offers a unique way to unveil fishing activities and its dynamics. In this study, we proposed a set of algorithms for automatic detection of nighttime fishing activity and provided the first large-scale dynamic analysis of nighttime fishing activity in the SCS using monthly Visible Infrared Imaging Radiometer Suite (VIIRS) images between 2012 and 2019. The proposed method effectively minimized the spatio-temporal fluctuations in radiance values of background and their implications to ship detection by integrating high radiance gradient detection and local adaptive thresholding. Further, nighttime fishing activity trajectories were decomposed into trend and seasonal components by using Hilbert-Huang transformation (HHT) to accurately access general trends and the seasonality of nighttime fishing activity in the SCS. The typical subregions analysis, environmental driver analysis, correlation coefficient analysis and hot spot analysis were integrated to characterize the nighttime fishing activity. It appears that the nighttime fishing activity in the SCS exhibited spatio-temporal variability and heterogeneity and was shaped by policy and natural factors such as holidays, annual Chinese fishery moratoria in the Chinese Exclusive Economic Zone (EEZ) and seasonal tropical storm activity.

A band-to-band mis-registration (BBMR) error often occurs in remote sensing (RS) images acquired by multi-spectral push broom spectrometers such as the Sentinel-2 Multi-spectral Instrument (MSI), leading to adverse impacts on the reliability of further RS applications. Although the systematic band-to-band registration conducted during the image production process corrects most BBMR errors, there are still quite a few images being observed with discernible BBMR. Thus, a quick BBMR detection method is needed to assess the quality of online RS products. We here propose a hybrid framework for detecting BBMR between the visible bands in MSI images. This framework comprises three main steps: first, candidate chips are captured based on Google Earth Engine (GEE) spatial analysis functions to shrink the valid areas inside image scenes as potential target chips. The redundant data pertaining to the local operation process are thus narrowed down. Second, spectral abnormal areas are precisely extracted from inside every single chip, excluding the influences of clouds and water surfaces. Finally, the abnormal areas are matched pixel by pixel between bands, and the best-fit coordinates are then determined to compare with tolerance. Here, the proposed method was applied to 71,493 scenes of MSI Level-1C images covering China and its surrounding areas on the GEE platform. From these images, 4356 chips from 442 scenes were detected with inter-band offsets among the visible bands. Further manual visual inspection revealed that the proposed method had an accuracy of 98.07% at the chip scale and 88.46% at the scene scale.

Detecting rivers from remotely sensed imagery is an initial yet important step in space-based river studies. This paper proposes an automatic approach to enhance and detect complete river networks. The main contribution of this work is the characterization of rivers according to their Gaussian-like cross-sections and longitudinal continuity. A Gabor filter was first employed to enhance river cross-sections. Rivers are better discerned from the image background after filtering but they can be easily corrupted owing to significant gray variations along river courses. Path opening, a flexible morphological operator, was then used to lengthen the river channel continuity and suppress noise. Rivers were consistently discerned from the image background after these two-step processes. Finally, a global threshold was automatically determined and applied to create binary river masks. River networks of the Yukon Basin and the Greenland Ice Sheet were successfully detected in two Landsat 8 OLI panchromatic images using the proposed method, yielding a high accuracy (~97.79%), a high true positive rate (~94.33%), and a low false positive rate (~1.76%). Furthermore, experimental tests validated the high capability of the proposed method to preserve river network continuity.

Tidal creeks are an important component of the intertidal zone and are essential for maintaining the balance between sedimentary processes and the hydrodynamic environment. A quantitative analysis of the morphological characteristics of tidal creeks is essential for understanding their processes of evolution and to evaluate the stability of tidal flats. This study describes the morphological characteristics of tidal creeks using a high-resolution airborne LiDAR DEM. The parameters include the number, order, length, width, depth, and width/depth ratio. The results show that the number and degree of development tidal creeks along the central coast of Jiangsu are higher than those in the radial sandbanks, and the mean width and length increase with the increasing tidal creek order. The number, length, and depth of tidal creeks in the salt marsh zone with well-developed vegetation are higher than those areas with little vegetation cover. The number of tidal creeks in the mid-upper intertidal zone is the largest, while the length and width of tidal creeks in the lower intertidal zone are the greatest. The differences in these characteristics are mainly related to the vegetation distribution, tidal flat width, and hydrodynamic conditions. Our findings potentially provide guidelines for coastal management and the evaluation of tidal flat stability.

Human activities such as the rapid development of marine aquaculture in the central Jiangsu coast have had a marked impact on the tidal flat morphology. This research focuses on characterizing the spatial expansion of laver cultivation and its influence on the sedimentary evolution of tidal flats in the central Jiangsu coast. First, seasonal digital elevation models (DEMs) were established using 160 satellite images with medium resolution. Then, laver aquaculture regions were extracted from 50 time-series satellite images to calculate the area and analyze the spatial distribution and expansion of these areas. Finally, seasonal and intra-annual sedimentary evolution patterns of both aquaculture and non-aquaculture regions were determined using the constructed DEMs. Our results show that aquaculture regions have gradually expanded to the north and peripheral domains of the entire sand ridge since 1999 and by 2013, the seaward margins of each sandbank developed into dense cultivation regions. Additionally, the aquaculture regions increased from 11.99 km2 to 295.28 km2. The seasonal sedimentary evolution patterns indicate that deposition occurs during the winter and erosion during the summer. Thus, the aquaculture regions experience deposition in certain elevation intervals during the laver growing period and in the non-growing period, alluvial elevation intervals in the aquaculture regions are eroded and erosive ones are deposited in order to maintain the balance between scouring and silting. The sedimentary evolution of each sandbank is heterogeneous due to their different locations and the difference in sediment transport. The intra-annual evolution pattern is characterized by deposition in the high tidal flats and erosion in low ones. Hydrodynamic conditions and laver cultivation dominate partial sedimentary evolution, which gradually shapes the beach surface.

Although the topography of tidal flats is important for understanding their evolution, the spatial and temporal sampling frequency of such data remains limited. The waterline method has the potential to retrieve past tidal flat topography by utilizing large archives of satellite images. This study performs a quantitative analysis of the relationship between the accuracy of tidal flat digital elevation models (DEMs) that are based on the waterline method and the factors that influence the DEMs. The three major conclusions of the study are as follows: (1) the coverage rate of the waterline points and the number of satellite images used to create the DEM are highly linearly correlated with the error of the resultant DEMs, and the former is more significant in indicating the accuracy of the resultant DEMs than the latter; (2) both the area and the slope of the tidal flats are linearly correlated with the error of the resultant DEMs; and (3) the availability analysis of the archived satellite images indicates that the waterline method can retrieve tidal flat terrains from the past forty years. The upper limit of the temporal resolution of the tidal flat DEM can be refined to within one year since 1993, to half a year since 2004 and to three months since 2009.

Data registration is a prerequisite for the integration of multi-platform laser scanning in various applications. A new approach is proposed for the semi-automatic registration of airborne and terrestrial laser scanning data with buildings without eaves. Firstly, an automatic calculation procedure for thresholds in density of projected points (DoPP) method is introduced to extract boundary segments from terrestrial laser scanning data. A new algorithm, using a self-extending procedure, is developed to recover the extracted boundary segments, which then intersect to form the corners of buildings. The building corners extracted from airborne and terrestrial laser scanning are reliably matched through an automatic iterative process in which boundaries from two datasets are compared for the reliability check. The experimental results illustrate that the proposed approach provides both high reliability and high geometric accuracy (average error of 0.44 m/0.15 m in horizontal/vertical direction for corresponding building corners) for the final registration of airborne laser scanning (ALS) and tripod mounted terrestrial laser scanning (TLS) data.

Wadis, ephemeral dry rivers in arid desert regions that contain water in the rainy season, are often manifested as braided linear channels and are of vital importance for local hydrological environments and regional hydrological management. Conventional methods for effectively delineating wadis from heterogeneous backgrounds are limited for the following reasons: (1) the occurrence of numerous morphological irregularities which disqualify methods based on physical shape; (2) inconspicuous spectral contrast with backgrounds, resulting in frequent false alarms; and (3) the extreme complexity of wadi systems, with numerous tiny tributaries characterized by spectral anisotropy, resulting in a conflict between global and local accuracy. To overcome these difficulties, an automated method for extracting wadis (AMEW) from Landsat-8 Operational Land Imagery (OLI) was developed in order to take advantage of the complementarity between Water Indices (WIs), which is a technique of mathematically combining different bands to enhance water bodies and suppress backgrounds, and image processing technologies in the morphological field involving multi-scale Gaussian matched filtering and a local adaptive threshold segmentation. Evaluation of the AMEW was carried out in representative areas deliberately selected from Jordan, SW Arabian Peninsula in order to ensure a rigorous assessment. Experimental results indicate that the AMEW achieved considerably higher accuracy than other effective extraction methods in terms of visual inspection and statistical comparison, with an overall accuracy of up to 95.05% for the entire area. In addition, the AMEW (based on the New Water Index (NWI)) achieved higher accuracy than other methods (the maximum likelihood classifier and the support vector machine classifier) used for bulk wadi extraction.