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Abstract Background To identify key genes associated with cisplatin resistance in ovarian cancer, a comprehensive analysis was conducted on three datasets from the GEO database and through experimental validation. Methods Gene expression profiles were retrieved from the GEO database. DEGs were identified by comparing gene expression profiles between cisplatin-sensitive and resistant ovarian cancer cell lines. The identified genes were further subjected to GO, KEGG, and PPI network analysis. Potential inhibitors of key genes were identified through methods such as LibDock nuclear molecular docking. In vitro assays and RT-qPCR were performed to assess the expression levels of key genes in ovarian cancer cell lines. The sensitivity of cells to chemotherapy and proliferation of key gene knockout cells were evaluated through CCK8 and Clonogenic assays. Results Results showed that 12 genes influenced the chemosensitivity of the ovarian cancer cell line SKOV3, and 9 genes were associated with the prognosis and survival outcomes of ovarian cancer patients. RT-qPCR results revealed NDRG1, CYBRD1, MT2A, CNIH3, DPYSL3, and CARMIL1 were upregulated, whereas ERBB4, ANK3, B2M, LRRTM4, EYA4, and SLIT2 were downregulated in cisplatin-resistant cell lines. NDRG1, CYBRD1, and DPYSL3 knock-down significantly inhibited the proliferation of cisplatin-resistant cell line SKOV3. Finally, photofrin, a small-molecule compound targeting CYBRD1, was identified. Conclusion This study reveals changes in the expression level of some genes associated with cisplatin-resistant ovarian cancer. In addition, a new small molecule compound was identified for the treatment of cisplatin-resistant ovarian cancer.

Abstract Considerable attention has been directed towards exploring the potential efficacy of miR-155 in the realm of cancer immunotherapy. Elevated levels of miR-155 in dendritic cells (DCs) have been shown to enhance their maturation, migration, cytokine secretion, and their ability to promote T cell activation. In addition, overexpression of mir155 in M2 macrophages boost the polarization towards the M1 phenotype. Conversely, miR-155 has the propensity to induce the accumulation of immunosuppressive cells like regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs) in the tumor tissue. To account for this discrepancy, it is imperative to get help from a drug that could deal with immunosuppressive effect. Curcumin (CUR) exhibits the capacity to prompt Tregs converse into T helper 1 cells, fostering the polarization of M2 tumor-associated macrophage towards the M1 phenotype, and impeding the recruitment and aggregation of MDSCs within the tumor microenvironment. Nonetheless, CUR is known to exert an immunosuppressive impact on DCs by hindering the expression of maturation markers, cytokines, and chemokines, thereby prevent DCs response to immunostimulatory agents. Hence, a reactive oxygen species/glutathione dual responsive drug conveyance platform (CUR/miR155@DssD-Hb NPs) was devised to co-deliver CUR and miR155, with the aim of exploring their synergistic potential in bolstering a sustained and robust anti-tumor immune response. In vitro and in vivo results have suggested that CUR/miR155@DssD-Hb NPs can effectively inhibit the viability of 4T1 and B16F10 tumor cells, trigger the release of damage associated molecular patterns, stimulate DCs maturation, subsequent activation of CD8+ T cells, diminish immunosuppressive cell populations (MDSCs, Tregs, M2 TAMs and exhausted T cells), promote the formation of long-term immunity and lessen the formation of metastatic nodules in the lungs. In summary, the co-delivery system integrating CUR and miR155 (CUR/miR155@DssD-Hb NPs) demonstrates promise as a promising strategy for the immunotherapy of melanoma and triple negative breast cancer. Graphical abstract

In order to explore the effect of hot air circulation heating on the functional value of radish sprouts, red, green, and white radish sprouts were used as the research subjects in this study. Different temperature and time of hot air circulation heating were used to analyze the changes in active substances such as total phenolic substances, anthocyanins, total phenolic acids, flavonoids, VC, and glucosinolates, as well as the antioxidant activity changes such as DPPH free radical scavenging ability and reducing ability. A comprehensive evaluation was conducted based on the distance method of superior and inferior solutions. The results showed that low-temperature short-term (60 ℃ for 30 s) hot air circulation heating had the greatest positive effect on the retention rate of anthocyanins and VC content in white radish sprouts, with retention rates reaching 77.8% and 85.3%, respectively. At the same time, the retention rates of total phenolic acids in green and white radish sprouts were 99.3% and 82.5% by short-term (60 ℃ for 30 s) hot air circulation heating at low temperature, which had a positive effect on the retention of active substance content in the two types of radish sprouts. In addition, the experimental group (100 ℃ for 60 s) had a certain positive effect on the increase of total phenolic substances, flavonoids, and glucosinolates content in radish sprouts. Moreover, comprehensive evaluation found that short-term hot air circulation heating (60 ℃ for 30 s) was the best heating condition for maintaining the functional value and antioxidant activity of the three types of radish sprouts. This experiment provided theoretical basis and data support for the in-depth study of hot air circulation heating of sprouts.

Long-term mapping of winter wheat is vital for assessing food security and formulating agricultural policies. Landsat data are the only available source for long-term winter wheat mapping in the North China Plain due to the fragmented landscape in this area. Although various methods, such as index-based methods, curve similarity-based methods and machine learning-based methods, have been developed for winter wheat mapping based on remote sensing, the former two often require satellite data with high temporal resolution, which are unsuitable for Landsat data with sparse time-series. Machine learning is an effective method for crop classification using Landsat data. Yet, applying machine learning for winter wheat mapping in the North China Plain encounters two main issues: 1) the lack of adequate and accurate samples for classifier training; and 2) the difficulty of training a single classifier to accomplish the large-scale crop mapping due to the high spatial heterogeneity in this area. To address these two issues, we first designed a sample selection rule to build a large sample set based on several existing crop maps derived from recent Sentinel data, with specific consideration of the confusion error between winter wheat and winter rapeseed in the available crop maps. Then, we developed an optimal zoning method based on the quadtree region splitting algorithm with classification feature consistency criterion, which divided the study area into six subzones with uniform classification features. For each subzone, a specific random forest classifier was trained and used to generate annual winter wheat maps from 2013 to 2022 using Landsat 8 OLI data. Field sample validation confirmed the high accuracy of the produced maps, with an average overall accuracy of 91.1% and an average kappa coefficient of 0.810 across different years. The derived winter wheat area also has a good correlation (R2 = 0.949) with census area at the provincial level. The results underscore the reliability of the produced annual winter wheat maps. Additional experiments demonstrate that our proposed optimal zoning method outperforms other zoning methods, including Köppen climate zoning, wheat planting zoning and non-zoning methods, in enhancing wheat mapping accuracy. It indicates that the proposed zoning is capable of generating more reasonable subzones for large-scale crop mapping.

Agricultural applications of remote sensing data typically require high spatial resolution and frequent observations. The increasing availability of high spatial resolution imagery meets the spatial resolution requirement well. However, the long revisit period and frequent cloud contamination severely compromise their ability to monitor crop growth, which is characterized by high temporal heterogeneity. Many spatiotemporal fusion methods have been developed to produce synthetic images with high spatial and temporal resolutions. However, these existing methods focus on fusing low and medium spatial resolution satellite data in terms of model development and validation. When it comes to fusing medium and high spatial resolution images, the applicability remains unknown and may face various challenges. To address this issue, we propose a novel spatiotemporal fusion method, the dual-stream spatiotemporal decoupling fusion architecture model, to fully realize the prediction of high spatial resolution images. Compared with other fusion methods, the model has distinct advantages: (a) It maintains high fusion accuracy and good spatial detail by combining deep-learning-based super-resolution method and partial least squares regression model through edge and color-based weighting loss function; and (b) it demonstrates improved transferability over time by introducing image gradient maps and partial least squares regression model. We tested the StarFusion model at 3 experimental sites and compared it with 4 traditional methods: STARFM (spatial and temporal adaptive reflectance fusion), FSDAF (flexible spatiotemporal data fusion), Fit-FC (regression model fitting, spatial filtering, and residual compensation), FIRST (fusion incorporating spectral autocorrelation), and a deep learning base method—super-resolution generative adversarial network. In addition, we also investigated the possibility of our method to use multiple pairs of coarse and fine images in the training process. The results show that multiple pairs of images provide better overall performance but both of them are better than other comparison methods. Considering the difficulty in obtaining multiple cloud-free image pairs in practice, our method is recommended to provide high-quality Gaofen-1 data with improved temporal resolution in most cases since the performance degradation of single pair is not significant.

Reconstructing high-quality Normalized Difference Vegetation Index time series data is essential for ecological and agricultural applications but remains challenging in heavily cloudy areas. Fusing Sentinel SAR and optical data with deep learning could be helpful but is also challenging for stable models due to unstable SAR-NDVI relationships caused by imaging mechanism differences and environmental complexities. In this study, we developed a new Bidirectional Recurrent Imputation for Optical-SAR fusion (BRIOS) model to reconstruct high-quality Sentinel-2 NDVI time series data. BRIOS designs a two-layer recurrent architecture that integrates the autocorrelation of discrete, cloud-free NDVI observations into the model for a more stable SAR-NDVI relationship. Evaluating BRIOS against three baseline methods (GF-SG spatiotemporal fusion, Harmonic regression interpolation, and MCNN-Seq deep learning) across three full Sentinel-2 tiles in reconstructing 8-day NDVI time series, BRIOS consistently outperformed in scenarios of either random or continuously missing data, as evidenced by lower RMSE values (e.g. 0.075 for BRIOS vs. 0.108 for GF-SG vs. 0.143 for Harmonic regression vs. 0.303 for MCNN-Seq), better Edge index, and high linear correlation coefficients (R values up to 0.97). Further ablation experiments revealed that deep integration of NDVI autocorrelation features and SAR temporal change patterns has improved the stability and generalization of BRIOS. Discussions on the model's scalability across various cloud sizes and training dataset sizes affirm its practicality for broad-scale application in vegetation monitoring under challenging cloudy conditions.

Multispectral reflectance—signals reflected from the Earth's surface across different wavelengths—is a primary data source for most remote sensing applications. However, obtaining complete cloud-free multispectral reflectance time series during the vegetation growing season remains challenging due to cloud contamination and limitations of existing reconstruction methods. To address the challenge, this study proposed a novel normalized difference vegetation index (NDVI)-guided bi-directional recurrent reconstruction model for multispectral reflectance time series (referred to as “NB-Re3”), which aimed to reconstruct dense time series of reflectance images by exploiting the dependence of NDVI on multispectral reflectance. NB-Re3 utilizes a temporal convolutional network to capture the temporal trends in the NDVI data and a bidirectional long short-term memory to integrate the temporal features of the NDVI with the cloud-free reflectance data. The architecture establishes a robust dynamic NDVI-reflectance relationship while capturing temporal dependencies and multispectral autocorrelations of multiple spectral bands. We compared the performance of NB-Re3 with four representative methods (MNSPI, HANTS, STAIR, and U-TILSE) in reconstructing multispectral reflectance time series, ranging from the visible bands to near-infrared and short-wave infrared bands, at two challenging sites: the irrigated area of Colleambally, Australia, and the cultivated area of Rikaze on the Southern Tibetan Plateau, China. The result showed that NB-Re3 kept superiority with the lowest root-mean-square error values and highest correlation coefficients values. The effectiveness of integrating high-quality NDVI time series and using multispectral autocorrelation to improve reflectance time-series reconstruction was further confirmed by the ablation experiments. It is concluded that NB-Re3 shows promise for generating long-term cloud-free reflectance time-series products tailored for ecological and agricultural applications.

Ischemic stroke is one of the leading causes of death and disability globally, but its treatment options are limited due to therapeutic window and reperfusion injury constraints. Microglia, astrocytes, and oligodendrocytes are the major components of the neurovascular unit, and there is substantial evidence suggesting their contributions to maintaining homeostasis in the central nervous system. Neuroglial cells participate in neuronal physiological functions and the repair of damaged neurons through various communication methods, including gap junctions, chemical signaling, and extracellular vesicles, in conjunction with other components of the neurovascular unit. Ischemia-induced microglia and astrocytes polarize into ''M1/M2'' and ''A1/A2'' phenotypes and exert neurotoxic or neuroprotective effects by releasing soluble factors, secreting extracellular vesicles, and forming syncytia networks in the acute (72 h), and chronic phases (>6 weeks). Apoptosis of oligodendrocytes due to ischemic hypoxia leads to white matter injury, causing long-term cognitive dysfunction, and promoting oligodendrogenesis is a crucial direction for achieving functional recovery in ischemic stroke. In this article, we summarize the cellular interactions following cerebral ischemia, analyze the roles of neuroglial cells through gap junctions, chemical signaling, and extracellular vesicles in different stages of ischemic stroke, and further explore strategies for intervening in ischemic stroke.

Strawberries rapidly deteriorate postharvest, necessitating effective measures to extend their shelf life. This study focused on developing an eco-friendly chitosan-based protective film for strawberry preservation. Strawberries were treated with a coating solution containing varying concentrations of hawthorn leaf extract (HLE) (0.4%, 0.7%, and 1.0%), 1.5% chitosan (CH), and 1% acetic acid. The results demonstrated that coating strawberry fruit with 1% CH-HLE notably delayed fruit spoilage. In-depth analysis revealed that, compared with the uncoated strawberry fruits, the 1% CH-HLE coating effectively reduced weight loss, the respiration intensity, malondialdehyde (MDA) levels, and superoxide anion (O2·−) production. Additionally, the coated strawberries exhibited improved firmness, total soluble solids (TSS), vitamin C (Vc) content, titratable acidity (TA), and total phenolic compound (TPC) content. The enzyme activities of superoxide dismutase (SOD) and catalase (CAT) in the CH-HLE-coated strawberries were greater than those in their uncoated counterparts. The application of a 1% CH-HLE coating successfully delayed spoilage and extend the shelf life of the strawberries by approximately 4–5 days. These findings suggest that CH-HLE has significant potential as a resource for protecting fruits and vegetables, offering an environmentally sustainable solution for postharvest preservation.

The wind energy industry has expanded in recent years. Promotion of the Sustainable Development Goals (SDGs) is expected to further increase the scale of the wind energy industry. Determining the location and quantity of wind turbines is crucial for monitoring the development status of the wind energy industry and evaluating wind energy production. In this study, we propose a method for simultaneously detecting and positioning wind turbines in remote sensing images, namely, Wind Turbine YOLO (WT-YOLO), based on the You Only Look Once version 5 model (YOLOv5). The wind turbine hub, base, and shadow hub are treated as key points in the proposed method. Regression terms are incorporated into the head of the YOLOv5 basic framework to predict the location of these three key points. The base point is utilized to determine the exact position of the wind turbine. A multibackground and multiresolution wind-turbine image dataset is constructed by sampling high-resolution images from Google Earth. The WT-YOLO method outperforms existing methods in both wind turbine detection and positioning on different types of land cover backgrounds and multiresolution images of the constructed dataset. In the spatial resolution range of 0.6 m to 5.4 m, WT-YOLO exhibits enhanced wind-turbine detection, where the average precision (AP) is 5.92 % to 15.43 % higher than that of existing wind-turbine detection methods. Wind-turbine positioning by WT-YOLO has a mean distance error (MDE) that is 16.06 m to 21.59 m lower than that of existing wind-turbine positioning methods. A comparative analysis showed that the shadow and the three key points are effective features for wind-turbine detection. The proposed WT-YOLO model can support detection, positioning and counting for wind turbines worldwide.

Rapeseed mapping is important for national food security and government regulation of land use. Various methods, including empirical index-based and machine learning-based methods, have been developed to identify rapeseed using remote sensing. Empirical index-based methods commonly employed empirically designed indices to enhance rapeseed’s bright yellow spectral feature during the flowering period, which is straightforward to implement. Unfortunately, the heavy cloud cover in the flowering period of China would lead to serious omission errors; and the required flowering period varies spatially and yearly, which often cannot be acquired accurately. Machine learning-based methods mitigate the reliance on clear observations during the flowering period by inputting all-season imagery to adaptively learn features. However, it is difficult to collect sufficient samples across all of China considering the large intraclass variation in both land cover types of rapeseed and non-rapeseed. This study proposed an automated rapeseed mapping approach integrating rule-based sample generation and a one-class classifier (RSG-OC) to overcome the shortcomings of the two types of methods. First, a set of sample selection rules based on empirical indices of rapeseed were developed to automatically generate samples in cloud-free pixels during the predicted flowering period throughout China. Second, all available features composited based on the rapeseed phenological calendar were used for classification to eliminate the phenology differences in different regions. Third, a specific sample augmentation that removes the observation in the flowering period was employed to improve the generalization to the pixels without cloud-free observation in the flowering period. Finally, to avoid the need for diverse samples of nonrapeseed classes, a typical one-class classifier, positive unlabeled learning implemented by random forest (PUL-RF) trained by the generated samples, was applied to map rapeseed. With the proposed method, China rapeseed was mapped at 20 m resolution during 2017–2021 based on the Google Earth Engine (GEE). Validation on six typical rapeseed planting areas demonstrates that RSG-OC achieves an average accuracy of 94.90%. In comparison, the average accuracy of the other methods ranged from 83.33% to 88.25%, all of which were poorer than the proposed method. Additional experiments show that the performance of RSG-OC was not sensitive to cloud contamination, inaccurate predicted flowering time and the threshold of sample selection rule. These results indicate that the rapeseed maps produced in China are overall reliable and that the proposed method is an effective and robust method for annual rapeseed mapping across China.

Developing denoising algorithms for hyperspectral remote sensing images (HSIs) can alleviate noise problem, improve data utilization as well as the accuracy of subsequent applications. However, existing denoising techniques are usually unstable due to the variations of landscapes, resulting in local distortion of HSIs, especially in heterogeneous areas. To tackle this issue, we propose a spatial–spectral interactive restoration (SSIR) framework by exploiting the complementarity of model-based and data-driven methods. Specifically, a deep learning-based denoising module that incorporates both convolutional neural networks (CNN) and Swin Transformer (TF) blocks is designed. This denoiser can achieve local–global dependencies modeling and content-based interactions to better capture global heterogeneity differences in HSIs. Moreover, we introduce an unsupervised unmixing module that utilizes spectral autocorrelation as prior information to effectively capture the differences in reflectance characteristics among different land cover components. This parameter-free module further improves the generalization ability of SSIR and enables stable denoising performance across different scenarios. Both modules are iteratively updated and fuel each other in SSIR. The proposed SSIR is shown to outperform others in preserving spatial details, maintaining spectral fidelity, and adapting to different landscapes based on simulated and real experiments conducted on various HSIs under diverse noise conditions.

Lung cancer is the leading cause of cancer death world-wide and its treatment remains a challenge in clinic, especially for non-small cell lung cancer (NSCLC). Thus, more effective therapeutic strategies are required for NSCLC treatment. Quercetin (Que) as a natural flavonoid compound has gained increasing interests due to its anticancer activity. However, poor water solubility, low bioavailability, short half-life, and weak tumor accumulation hinder in vivo applications and antitumor effects of Que. In this study, we developed Que-loaded mixed micelles (Que-MMICs) assembled from 1,2-distearoyl-sn-glycero-3-phosphoethanolamine–poly(ethylene glycol)–biotin (DSPE–PEG–biotin) and poly(ethylene glycol) methyl ether methacrylate–poly[2-(dimethylamino) ethyl acrylate]–polycaprolactone (PEGMA–PDMAEA–PCL) for NSCLC treatment. The results showed that Que was efficiently encapsulated into the mixed micelles and the encapsulation efficiency (EE) was up to 85.7%. Cellular uptake results showed that biotin conjugation significantly improved 1.2-fold internalization of the carrier compared to that of non-targeted mixed micelles. In vitro results demonstrated that Que-MMICs could improve cytotoxicity (IC50 = 7.83 μg/mL) than Que-MICs (16.15 μg/mL) and free Que (44.22 μg/mL) to A549 cells, which efficiently induced apoptosis and arrested cell cycle. Furthermore, Que-MMICs showed satisfactory tumor targeting capability and antitumor efficacy possibly due to the combination of enhanced permeability and retention (EPR) and active targeting effect. Collectively, Que-MMICs demonstrated high accumulation at tumor site and exhibited superior anticancer activity in NSCLC bearing mice model.

Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or selecting such features valid for specific crop types requires prior knowledge and thus remains an open challenge. Convolutional neural networks (CNNs) can effectively overcome this issue with their advanced ability to generate high-level features automatically but are still inadequate in mining spectral features compared to mining spatial features. This study proposed an enhanced spectral feature called Stacked Spectral Feature Space Patch (SSFSP) for CNN-based crop classification. SSFSP is a stack of two-dimensional (2D) gridded spectral feature images that record various crop types’ spatial and intensity distribution characteristics in a 2D feature space consisting of two spectral bands. SSFSP can be input into 2D-CNNs to support the simultaneous mining of spectral and spatial features, as the spectral features are successfully converted to 2D images that can be processed by CNN. We tested the performance of SSFSP by using it as the input to seven CNN models and one multilayer perceptron model for crop type classification compared to using conventional spectral features as input. Using high spatial resolution hyperspectral datasets at three sites, the comparative study demonstrated that SSFSP outperforms conventional spectral features regarding classification accuracy, robustness, and training efficiency. The theoretical analysis summarizes three reasons for its excellent performance. First, SSFSP mines the spectral interrelationship with feature generality, which reduces the required number of training samples. Second, the intra-class variance can be largely reduced by grid partitioning. Third, SSFSP is a highly sparse feature, which reduces the dependence on the CNN model structure and enables early and fast convergence in model training. In conclusion, SSFSP has great potential for practical crop classification in precision agriculture.

Measurement(s) nighttime light intensity Technology Type(s) satellite radiometer

With the increasingly widespread use of sub-pixel mapping techniques in land cover/use mapping, more accurate area information is often required for a specific land cover type in a particular study region. However, the bias of area counted from sub-pixel maps (called area bias below), and the inadequate understanding of the area bias's origin and influential factors pose a challenge to using this information accurately. Traditional model-assisted estimators combining the map and the reference sample showed unreliable performances in the case of small sample sizes collected in target regions. This work presented a theoretical analysis of the origin of area bias. It then proposed a novel bias-adjusted estimator which can effectively deal with the small sample sizes. The theoretical analysis illustrated that area bias mainly originates from two terms, i.e., the abundance-dependent error and the probability distribution of abundances. We next developed a stratified bias-adjusted area estimator named the two-term method (TTM) by incorporating the sub-pixel map and a reference sample obtained from both target and external regions. We validated the effects of different sub-pixel mapping methods, different spatial resolutions, the varying spatial structures of statistical units on area bias, and the performance of TTM in correcting the biased areas in multiple cases. The results showed that area bias varied from zero to approximately 20% with the variation of three influential factors. TTM effectively corrected the biased area values to nearly the true values, showing approximate equivalence with the traditional stratified regression estimator (STRE) when adequate reference samples are collected sorely inside target regions. However, in cases of small samples from target regions, TTM showed significant superiority over STRE in reducing the variance and MSE due to the incorporation of external reference samples. We conclude that the theoretical analysis resulted in a better understanding of area bias counted from sub-pixel maps and an improved area estimator for dealing with the cases of small sample sizes inside target regions.

The spatial extent and values of nighttime light (NTL) data are widely used to reflect the scope and intensity of human activities, such as extracting urban boundaries, spatializing population density, analyzing economic development levels, etc. DMSP-OLS and NPP-VIIRS are widely used global NTL datasets, but their severe inconsistencies hinder long-time series studies. At present, global coverage, long time series, and public NTL products are still rare and have room for improvement in terms of pixel-scale correction, temporal and spatial consistency, etc. We proposed a set of inter-correction methods for DMSP-OLS and NPP-VIIRS based on two corrected DMSP-OLS and NPP-VIIRS products, i.e., CCNL-DMSP and VNL-VIIRS, with the goal of temporal and spatial consistency at the pixel-scale. A pixel-scale corrected nighttime light dataset (PCNL, 1992–2021) that met the needs of pixel-scale studies was developed through outlier removal, resampling, masking, regression, and calibration processes, optimizing spatial and temporal consistency. To examine the quality of PCNL, we compared it with two existing global long time series NTL products, i.e., LiNTL and ChenNTL, in terms of overall accuracy, spatial consistency, temporal consistency, and applicability in the socio-economic field. PCNL demonstrates great overall accuracy at both the pixel-scale (R2: 0.93) and the city scale (R2: 0.98). In developing, developed, and war regions, PCNL shows excellent spatial consistency. At global, national, urban, and pixel-scales, PCNL has excellent temporal consistency and can portray stable trends in stable developing regions and abrupt changes in areas experiencing sudden development or disaster. Globally, PCNL has a high correlation coefficient with GDP (r: 0.945) and population (r: 0.971). For more than half of the countries, the correlation coefficients of PCNL with GDP and population are higher than the results of ChenNTL and LiNTL. PCNL can analyze the dynamic changes in socio-economic characteristics over the past 30 years at global, regional, and pixel-scales.

Phenology camera (PhenoCam) data and the derived green chromatic coordinate (GCC) time series are commonly used to track seasonal changes in canopy greenness. However, the GCC time series is noisy because color distortion commonly exists in the captured photographs owing to the varying illumination conditions and nonlinear response of the consumer-grade camera to the incoming light. Hence, we proposed an optimal color composition (OCC) method to generate high-quality daily photographic time series by compositing multiple photographs captured in a single day. First, the optimal acquisition time with good illumination conditions and correct exposure settings is determined for each pixel throughout the day based on a comprehensive color index, combining the brightness and saturation. A virtual photograph consisting of the selected digital numbers acquired at the optimal time is then composited for each day. Finally, the daily GCC time series is calculated based on the virtual photographs. By testing the photographs of six forest sites, the proposed method was compared with the commonly used 90th percentile (Per90) filter. The results show that the daily photographs composited using the OCC method were more homogeneous with less shaded areas compared to those selected by the Per90 filter, and the corresponding GCC time series derived from the OCC method is more stable and less influenced by varying atmospheric conditions and solar angles than the Per90 filter. These results indicate that the OCC method can generate high-quality daily photographic time series with the potential to better indicate seasonal color changes in the forest canopy.

Background: Collagen peptides (CP) have been shown to protect against osteoporosis. Yet, its mechanism on anti-osteoporosis remains unclear. Method: Cell viability, levels of alkaline phosphatase (ALP), reactive oxygen species (ROS), malondialdehyde (MDA) and superoxide dismutase (SOD) of hydrogen peroxide (H2O2)-induced oxidative stress in Saos-2 cells pretreated with CP were examined. The effect and regulatory of CP on FBXW7-p100-NF-κB axis and the relationship between the molecules were explored by overexpressing FBXW7, silencing p100 or NF-κB in Saos-2 cells. Western blot and RT-PCR were performed to determine the expressions of FBXW7, p100 and NF-κB. Hematoxylin & Eosin (H&E) staining was performed to determine morphological indexes of bone tissues in ovariectomized-induced osteoporosis rats treated with CP. Result: CP increased cell viability, ALP and antioxidant activities in H2O2-induced oxidative stress in Saos-2 cells. Results on cell viability, ALP, antioxidant levels, protein and gene expression levels in FBXW7-overexpressed, p100-silenced and NF-κB-silenced Saos-2 cells indicate that FBXW7, p100 and NF-κB are the key regulators and CP treatment modulates FBXW7-p100-NF-κB axis in Saos-2 cells. In addition, pretreatment and treatment with CP in ovariectomized rats improved osteoporosis. Conclusion: CP could effectively prevent and alleviate osteoporosis. The protective effect of CP on osteoporosis was achieved in part by inhibiting oxidative damage of osteoblasts and promoting bone formation.

Cancer-derived small extracellular vesicles (sEVs) serve as critical mediators of cell-to-cell communication. Manzamine A (MA), a unique marine-derived alkaloid with various bioactivities, exerts anticancer effects against several kinds of tumors, but it remains unclear whether it has the same activity against breast cancer. Here, we proved that MA inhibits MDA-MB-231 and MCF-7 cell proliferation, migration, and invasion in a time- and dose-dependent manner. In addition, MA promotes autophagosome formation but suppresses autophagosome degradation in breast cancer cells. Importantly, we also found that MA stimulates sEVs secretion and increases autophagy-related protein accumulation in secreted sEVs, further potentiated by autophagy inhibitor chloroquine (CQ). Mechanistically, MA decreases the expression level of RIP1, the key upstream regulator of the autophagic pathway, and reduces the acidity of lysosome. Overexpression of RIP1 activated AKT/mTOR signaling, thus attenuating MA-induced autophagy and the corresponding secretion of autophagy-associated sEVs. Collectively, these data suggested that MA is a potential inhibitor of autophagy by preventing autophagosome turnover, and RIP1 mediates MA-induced secretory autophagy, which may be efficacious for breast cancer treatment.

Many studies have investigated the impacts of typhoons on natural vegetation, but the influencing factor of urban vegetation damage from super typhoon is not clear. Therefore, this study investigated the vegetation damage patterns in eight cities affected by Typhoon Mangkhut (the 2nd strongest tropical storm worldwide in 2018) using the normalized difference vegetation index (NDVI) derived from Sentinel-2 images. The vegetation damage maps have an overall accuracy of 97% using the very high-resolution WorldView-3 images as reference data. The results show that (1) The typhoon-induced vegetation damage show high spatial heterogeneity in urban areas and varies with land cover types. Residential greenspace and street trees are more susceptible to typhoon disturbance than natural vegetation. (2) Wind intensity is still an important factor in urban vegetation damage (r2 = 0.43, P value

High spatial resolution images have been widely applied to the monitoring of land surfaces during the past decades. However, satellite and unmanned aerial vehicle (UAV) sensors (e.g., IKONOS, and QuickBird) usually only provide visible and near-infrared spectral bands with a high spatial resolution. Such limited spectral bands at a high spatial resolution are not adequate for crucial environmental and agricultural studies that require essential bands (e.g., the red-edge (RE) and the short-wave infrared (SWIR)), such as land cover classification and the estimation of plant physiological parameters. On the contrary, abundant spectral information is commonly available on sensors with a medium spatial resolution (e.g., Landsat, and Sentinel-2). Existing spatial-spectral fusion methods can leverage the unique advantages of these two types of sensors, and produce synthesized images with both spatial and spectral detail. However, most spatial-spectral methods ignore the inherent geometric registration errors when using multi-source data, inevitably introducing the errors into the fusion result. To address the limitation, this study presents a new spatial-spectral fusion method, the Misregistration-Resistant Data Fusion approach (MRDF), to add new spectral bands to the high spatial resolution images from medium resolution images. The proposed method is composed of a local-model-based similar pixel searching and a global partial least square (PLS) regression, and has the following advantages: (1) the local model utilizes neighborhood similar pixels to generate predictions robust against geometric misregistration; (2) the combined model integrates a global PLS regression to improve fusion accuracy for the SWIR bands; (3) the extended-box strategy and spatial filtering can relieve geometric errors when minimizing residuals for the SWIR bands. Satellite and airborne data from three sites were used to compare the performance of MRDF with seven other typical methods. Results showed that MRDF not only produced accurate fusion results with above-average efficiency but also improved the classification accuracy, which substantiated its great potential for various applications.

Central business districts (CBDs) play a crucial role in urban economic activities. Thus, the location and boundaries of CBDs identified by the unified standards are essential for comparative analyses in urban geography. However, past research mainly applied specific data or sensitive methods to delimitate CBDs within local knowledge in the case study, there remains no automated standardization technique for identifying and delimitating CBDs across the globe. This paper proposed a new method for identifying CBDs based on nighttime lights (NTL) to overcome the above limitations. The main advantages of this method include (1) the use of available high-quality global Black Marble products, which are the basis of a standardized delineation of CBDs and (2) the use of more characteristics of CBD (i.e., the brightness) and NTL negative angular effects that can reflect high-rise building. The proposed method was employed in 14 cities in China and the U.S., and the results showed that China cities needed five NTL indexes and U.S. cities needed two NTL indexes to distinguish CBD and non-CBD successfully. Therefore, our approach is recommended for CBD detection and delineation over large areas.

High spatiotemporal resolution normalized difference vegetation index (NDVI) time-series imagery is required for monitoring vegetation dynamics with dense observations and spatial details, and in recent years, many spatiotemporal data fusion methods have been proposed to fulfill this need. However, strict data requirements and inappropriate modeling strategies often limit their performance, particularly under poor conditions of available input data. In this study, we proposed a Spatiotemporal fusion method to Simultaneously generate Full-length normalized difference vegetation Index Time series (SSFIT) with a high spatial resolution and frequent coverage. The utilization of temporal information across sensors contributes to its two distinct features: (1) no cloud-free high-spatial-resolution image is required and (2) high-spatial-resolution images on multiple prediction dates are generated at the same time. Comparison experiments were conducted by simulating ideal and challenging conditions of input time-series data in two characteristic areas, and the proposed methods were also compared with four typical methods: The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), Fit-FC (regression model Fitting, spatial Filtering and residual Compensation), and Improved FSDAF. The results demonstrate that SSFIT yields a better overall prediction accuracy and efficiency under ideal input conditions (average root mean square error of 0.1037 and 0.0713, average correlation coefficient of 0.9180 and 0.7875, and computation time of 109 and 120 s, computed in the two test areas) and is more robust against the decrease in available input data under challenging conditions. SSFIT is thus expected to be extended to various remote sensing products and support applications for monitoring land surface dynamics.

Study Region: This study was conducted in the typical agroforestry ecosystem named Linpan settlements (or simply Linpan) in the Chengdu Plain, located in southwestern China. Study Focus: The aims of this study were: (1) monitoring the seasonal variability in rainfall partitioning for tree plots in Linpan comprised of seven evergreen and four deciduous tree species from April 2018 to April 2019; (2) evaluating the effect of rainfall factors and Linpan tree structure on throughfall (TF), stemflow (SF), and canopy interception (I). New Hydrological Insights for the Region: Only a few studies have focused on the rainfall partitioning of trees in agroforestry ecosystems. Our results confirm that the mean TF, SF, and I values of Linpan trees, accounted for 57.4 %, 14.3 %, and 28.3 % of the total rainfall volume, respectively. Evergreen trees showed a lower TF% (56.51 %) than that of deciduous trees (59.16 %), but a slightly higher SF% (14.28 %) and I% (29.21 %) than those of deciduous trees (14.21 % and 26.64 %, respectively). The values of TF, SF, and I were very significantly associated with both gross precipitation (GP) and rainfall intensity. However, evergreen trees TF% was only significantly associated with GP, while evergreen trees SF% was only correlated with rainfall intensity. I% values of evergreen trees and deciduous trees exhibited negative non-linear correlations (p ≤ 0.05) with rainfall intensity and GP, respectively. Only SF and SF% were significantly negatively correlated with tree structure. DBH and tree height had opposite effects on SF. Both LAI and tree height showed a negative relationship with SF% of Linpan trees. These results have the potential to inform better regulation of runoff and maintaining a sustainable hydrological cycle of agroforestry ecosystem in the Chengdu Plain.

Among cancers, colorectal cancer (CRC) has one of the highest annual incidence and death rates. Considering severe adverse reactions associated with classical chemotherapy medications, traditional Chinese medicines have become potential drug candidates. In the current study, the effects of cryptotanshinone (CPT), a major component of Salvia miltiorrhiza Bunge (Danshen) on CRC and underlying mechanism were explored. First of all, data from in vitro experiments and in vivo zebrafish models indicated that CPT selectively inhibited the growth and proliferation of HCT116 and SW620 cells while had little effect on SW480 cells. Secondly, both ER stress and autophagy were associated with CRC viability regulation. Interestingly, ER stress inhibitor and autophagy inhibitor merely alleviated cytotoxic effects on HCT116 cells in response to CPT stimulation, while have little effect on SW620 cells. The significance of apoptosis, autophagy and ER stress were verified by clinical data from CRC patients. In summary, the current study has revealed the anti-cancer effects of CPT in CRC by activating autophagy signaling mediated by ER stress. CPT is a promising drug candidate for CRC treatment.

Shrub encroachment into arid and semi-arid grasslands has elicited extensive research attention worldwide under the background of climate change and increasing anthropogenic activities. Shrub encroachment may considerably impact local ecosystems and economies, including the conversion of the structure and function of ecosystems, the shift in ambient conditions, and the weakness of local stock farming capacity. This article reviews recent research progresses on the shrub encroachment process and mechanism, shrub identification and dynamic monitoring using remote sensing, and modeling and simulation of the shrub encroachment process and dynamics. These studies can help to evaluate the ecological effect of shrub encroachment, and thus, practically manage and recover the ecological environment of degraded areas. However, the lack of effective measures and data for monitoring shrub encroachment at a large spatial scale severely limits research on the mechanism, modeling, and simulation of shrub encroachment, and the shrub encroachment stages can hardly be quantitatively defined, resulting in insufficient analysis and simulation of shrub encroachment for different spatiotemporal scales and stages shift. Improvement in remote sensing-based shrub encroachment dynamic monitoring might be crucial for analyzing and understanding the process and mechanism of shrub encroachment, and multi-disciplinary and multi-partnerships are required in the shrub encroachment studies.

Soil water content (SWC) is a crucial parameter in ecology, agriculture, hydrology, and engineering studies. Research on non-invasive monitoring of SWC has been a long-lasting topic in these fields. Ground penetrating radar (GPR), a non-destructive geophysical technique, has the advantages of high resolution, deep detection depth, and high efficiency in SWC measurements at medium scale. It has been successfully applied in field investigations. This paper summarizes the recent progress in developing GPR-based SWC measurement methods, including reflected wave, ground wave, surface reflection, borehole GPR, full waveform inversion, average envelope amplitude, and frequency shift methods. The principles, advantages, limitations, and applications of these methods are described in detail. A comprehensive technical framework, which comprises the seven methods, is proposed to understand their principles and applicability. Two key procedures, namely, data acquisition and data processing, are emphasized as crucial to method applications. The suitable methods that will satisfy diverse application demands and field conditions are recommended. Future development, potential applications, and advances in hardware and data processing techniques are also highlighted.

In recent decades, shrubs dominated by the genus Caragana have expanded in a large area in Xilin Gol grassland, Inner Mongolia, China. This study comprehensively evaluated the performances of multiple factors for mapping shrub coverage across the Xilin Gol grassland based on the spectral and temporal signatures of Sentinel-2 imagery, and for the first time produced a large-scale shrub coverage mapping result in this region. Considering the regional differences and gradients in the types and sizes of shrub in the study area, the study area was divided into three subregions based on precipitation data, i.e., west, middle and east regions. The shrub coverage estimation accuracy from dry- and wet-year data, different types of vegetation indices (VIs) and multiple regression methods were compared in each subregion, and the key phenological periods were selected. We also compared the accuracy of four mapping strategies, which were pairwise combinations of zoning (i.e., subregions divided by precipitation) and non-zoning, and full time series of VIs and key phenological period. Results show that the mapping accuracy in a dry year (2017) is higher than that in a wet year (2018). The optimal VIs and key phenological periods show high spatial variability. In terms of mapping strategies, the accuracy of zoning is higher than that of non-zoning. The root mean square error (RMSE), overall accuracy (OA) and recall for ‘zoning + full time series (or + key phenological period)’ strategy were 0.052 (0.055), 76.4% (79.7%) and 91.7% (94.6%), respectively, while for ‘non-zoning + full time series (or + key phenological period)’ strategy were 0.057 (0.060), 75.5% (74.6%) and 91.7% (88.6%), respectively. The mapping using VIs in key phenological periods is better than that of using full time series in the low-value prediction of shrub cover. Based on the strategy of ‘zoning + key phenological period’, the shrub coverage map of the whole region was generated with a RMSE of 0.055, OA of 80% and recall of 95%. This study not only provides the first large-scale mapping data of shrub coverage, but also provides reference for shrub dynamic monitoring in this area.

Recently, the requirements regarding the environment of nursing homes are high, because the elderly are a vulnerable group with limited adaptive capacity to respond to transient environmental change. This paper presents a field investigation on the influence of transient thermal comfort changes between the indoor and outdoor spaces (i.e., air temperature (Ta), solar radiation (SR), relative humidity (RH), wind speed (WS), and the thermal comfort indices of Universal Thermal Index (UTCI)) on the willingness of the elderly to use outdoor spaces of the Wanxia nursing home of Chengdu City. Results indicated that, in summer, the mean UTCI values of indoor and corridor spaces corresponded to the level of moderate heat stress, while those of road and garden corresponded to the strong heat stress level. Road and garden spaces even showed moderate heat stress in spring. Approximately 28.93% (139) of the elderly living here used outdoor spaces every day. The morning period (from 9:00 a.m. to 10:00 a.m.) was the elderly’s favorited period for using outdoor spaces in seasons. The microclimatic transient differences between indoor and outdoor spaces ranged from 0.47 °C to 2.93 °C (|ΔTa|), from 86.09 W/m2 to 206.76 W/m2 (|ΔSR|), from 5.29% to 14.76% (ΔRH), from 0.01 m/s to 0.07 m/s (|ΔWS|), and from 0.25 °C to 2.25 °C (ΔUTCI). These big microclimate differences could cause enormous health risks for the elderly in the process of indoor and outdoor space conversion. The minimal transient change occurred between corridors and indoors. Pearson correlation analysis indicated ΔTa and ΔRH between indoor and outdoor spaces were the primary meteorological factors that influenced the elderly’s willing to use outdoor spaces. The elderly preferred to live in a constant Ta and RH environment. Only when the ΔTa and ΔRH are small enough to resemble a steady-state (ΔUTCI ≤ 0.5 °C), ΔWS and ΔSI could affect the elderly’s choice of using outdoor space. Optimal design strategies were put forward for reducing the transient differences between indoor and outdoor microclimates to inspire the elderly to use outdoor spaces safely, including improving outdoor canopy coverage and indoor mechanical ventilation.

Chitosanase is a significant chitosan-degrading enzyme involved in industrial applications, which forms chitooligosaccharides (COS) as reaction products that are known to have various biological activities. In this study, the gene csnS was cloned from a deep-sea bacterium Serratia sp. QD07, as well as over-expressed in Escherichia coli, which is a new chitosanase encoding gene. The recombinant strain was cultured in a 5 L fermenter, which yielded 324 U/mL chitosanases. After purification, CsnS is a cold-adapted enzyme with the highest activity at 60°C, showing 37.5% of the maximal activity at 0°C and 42.6% of the maximal activity at 10°C. It exhibited optimum activity at pH 5.8 and was stable at a pH range of 3.4–8.8. Additionally, CsnS exhibited an endo-type cleavage pattern and hydrolyzed chitosan polymers to yield disaccharides and trisaccharides as the primary reaction products. These results make CsnS a potential candidate for the industrial manufacture of COS.

The water loss caused by evaporation heat dissipation is non-negligible for a natural draft counter-flow wet cooling tower (NDWCT), which is evidently influenced by many change factors. Based on Merkel method, the Merkel number is revised in consideration of the effect of water loss. Taking the NDWCTs equipped for 300MW and 600MW power plants as experimental objects, a mathematical model is established for predicting the evaporation loss of the NDWCT. The accuracy of the model is verified by experimental data, and the mathematical model for predicting evaporation loss is feasible. Then, the effect of change factors, including mass flow rate of circulating water and air parameters, on the tower evaporation loss is studied in the case of fixed the thermal load dissipated by the condenser. Results show that the evaporation loss varies little with the change of mass flow rate of circulating water. Therefore, Water conservation cannot be achieved by changing mass flow rate. With the increase of dry bulb temperature, the evaporation loss, as well as the rate of evaporation loss caused by unit temperature drop increases. As the relative air humidity increases, the NDWCT outlet water temperature increases, whereas the evaporation loss and the rate of evaporation loss caused by unit temperature difference decrease.

Three-dimensional (3-D) Monte Carlo-based radiative transfer (MCRT) models are usually used for benchmarking in intercomparisons of the canopy radiative transfer (RT) simulations. However, the 3-D MCRT models are rarely applied to develop remote sensing algorithms to estimate essential climate variables of forests, due mainly to the difficulties in obtaining realistic stand structures for different forest biomes over regional to global scales. Fortunately, some of important tree structure parameters such as canopy height and tree density distribution have been available globally. This enables to run the intermediate complexities of the 3-D MCRT models. We consequently developed a statistical approach to generate forest structures with intermediate complexities depending on the inputs of canopy height and tree density. It aims at facilitating applications of the 3-D MCRT models to develop remote sensing retrieval algorithms. The proposed approach was evaluated using field measurements of two boreal forest stands at Estonia and USA, respectively. Results demonstrated that the simulations of bidirectional reflectance factor (BRF) based on the measured forest structures agreed well with the BRF based on the generated structures from the proposed approach with the root mean square error (RMSE) and relative RMSE (rRMSE) ranging from 0.002 to 0.006 and from 0.7% to 19.8%, respectively. Comparison of the computed BRF with corresponding MODIS reflectance data yielded RMSE and rRMSE lower than 0.03 and 20%, respectively. Although the results from the current study are limited in two boreal forest stands, our approach has the potential to generate stand structures for different forest biomes.

Forests play a vital role in combating gradual developmental deficiencies and balancing regional ecosystems, yet they are constantly disturbed by man-made or natural events. Therefore, developing a timely and accurate forest disturbance detection strategy is urgently needed. The accuracy of traditional detection algorithms depends on the selection of thresholds or the formulation of complete rules, which inevitably reduces the accuracy and automation level of detection. In this paper, we propose a new multitemporal convolutional network framework (MT-CNN). It is an integrated method that can realize long-term, large-scale forest interference detection and distinguish the types (forest fire and harvest/deforestation) of disturbances without human intervention. Firstly, it uses the sliding window technique to calculate an adaptive threshold to identify potential interference points, and then a multitemporal CNN network is designed to render the disturbance types with various disturbance duration periods. To illustrate the detection accuracy of MT-CNN, we conducted experiments in a large-scale forest area (about 990 km2) on the west coast of the United States (including northwest California and west Oregon) with long time-series Landsat data from 1986 to 2020. Based on the manually annotated labels, the evaluation results show that the overall accuracies of disturbance point detection and disturbance type recognition reach 90%. Also, this method is able to detect multiple disturbances that continuously occurred in the same pixel. Moreover, we found that forest disturbances that caused forest fire repeatedly appear without a significant coupling effect with annual temporal and precipitation variations. Potentially, our method is able to provide large-scale forest disturbance mapping with detailed disturbance information to support forest inventory management and sustainable development.

The belowground root zone of influence (ZOI) is fundamental to the study of the root–root and root–soil interaction mechanisms of plants and is vital for understanding changes in plant community compositions and ecosystem processes. However, traditional root research methods have a limited capacity to measure the actual ZOIs within plant communities without destroying them in the process. This study has developed a new approach to determining the ZOIs within natural plant communities. First, ground-penetrating radar (GPR), a non-invasive near-surface geophysical tool, was used to obtain a dataset of the actual spatial distribution of the coarse root system in a shrub quadrat. Second, the root dataset was automatically clustered and analyzed using the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm to determine the ZOIs of different plants. Finally, the shape, size, and other characteristics of each ZOI were extracted based on the clustering results. The proposed method was validated using GPR-obtained root data collected in two field shrub plots and one simulation on a dataset from existing literature. The results show that the shrubs within the studied community exhibited either segregated and aggregated ZOIs, and the two types of ZOIs were distinctly in terms of shape and size, demonstrating the complexity of root growth in response to changes in the surrounding environment. The ZOIs extracted based on GPR survey data were highly consistent with the actual growth pattern of shrub roots and can thus be used to reveal the spatial competition strategies of plant roots responding to changes in the soil environment and the influence of neighboring plants.

To analyze the root-soil water relationship at the stand level, we integrated ground-penetrating radar (GPR), which characterized the distribution of lateral coarse roots (>2 mm in diameter) of shrubs (Caragana microphylla Lam.), with soil core sampling, which mapped soil water content (SWC) distribution. GPR surveys and soil sampling were carried out in two plots (Plot 1 in 2017 and Plot 2 in 2018) with the same size (30 × 30 m2) in the sandy soil of the semi-arid shrubland in northern China. First, the survey area was divided into five depth intervals, i.e., 0–20, 20–40, 40–60, 60–80, and 80–100 cm. Each depth interval was then divided into three zones in the horizontal direction, including root-rich canopy-covered area, root-rich canopy-free area, and root-poor area, to indicate different surface distances to the canopy. The generalized additive models (GAMs) were used to analyze the correlation between root distribution density and SWC after the spatial autocorrelation of each variable was eliminated. Results showed that the root-soil water relationship varies between the vertical and horizontal directions. Vertically, more roots are distributed in soil with high SWC and fewer roots in soil with low SWC. Namely, root distribution density is positively correlated with SWC in the vertical direction. Horizontally, the root-soil water relationship is, however, more complex. In the canopy-free area of Plot 1, the root-soil water relationship was significant (p < 0.05) and negatively correlated in the middle two depth intervals (20–40 cm and 40–60 cm). In the same two depth intervals in the canopy-free area of Plot 2, the root-soil water relationship was also significant (p < 0.01) but non-monotonic correlated, that is, with the root distribution density increasing, the mean SWC decreased first and then increased. Moreover, we discussed possible mechanisms, e.g., root water uptake, 3D root distribution, preferential flow along roots, and different growing stages, which might lead to the spatially anisotropic relationship between root distribution and SWC at the stand level. This study demonstrates the advantages of GPR in ecohydrology studies at the field scale that is challenging for traditional methods. Results reported here complement existing knowledge about the root-soil water relationship in semi-arid environments and shed new insights on modeling the complex ecohydrological processes in the root zone.

Mapping and tracing the changes in canola planting areas and yields in China are of great significance for macro-policy regulation and national food security. The bright yellow flower is a distinctive feature of canola, compared to other crops, and is also an important factor in predicting canola yield. Thus, yellowness indices were previously used to detect the canola flower using aerial imagery or median-resolution satellite data like Sentinel-2. However, it remains challenging to map the canola planting area and to trace long-term canola yields in China due to the wide areal extent of cultivation, different flowering periods in different locations and years, and the lack of high spatial resolution data within a long-term period. In this study, a novel canola index, called the enhanced area yellowness index (EAYI), for mapping canola flowers and based on Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data, was developed. There are two improvements in the EAYI compared with previous studies. First, a method for estimating flowering period, based on geolocation and normalized difference vegetation index (NDVI) time-series, was established, to estimate the flowering period at each place in each year. Second, the EAYI enhances the weak flower signal in coarse pixels by combining the peak of yellowness index time-series and the valley of NDVI time-series during the estimated flowering period. With the proposed EAYI, canola flowering was mapped in five typical canola planting areas in China, during 2003-2017. Three different canola indices proposed previously, the normalized difference yellowness index (NDYI), ratio yellowness index (RYI) and Ashourloo canola index (Ashourloo CI), were also calculated for a comparison. Validation using the samples interpreted through higher resolution images demonstrated that the EAYI is better correlated with the reference canola coverage with R2 ranged from 0.31 to 0.70, compared to the previous indices with R2 ranged from 0.02 to 0.43. Compared with census canola yield data, the total EAYI was well correlated with actual yield in Jingmen, Yili and Hulun Buir, and well correlated with meteorological yields in all five study areas. In contrast, previous canola indices show a very low or even a negative correlation with both actual and meteorological yields. These results indicate that the EAYI is a potential index for mapping and tracing the change in canola areas, or yields, with MODIS data.

The measures to reduce the impact of evaporation loss in a natural draft counter-flow wet cooling tower (NDWCT) have important implications for water conservation and emissions reduction. A mathematical model of evaporation loss in the NDWCT was established by using a modified Merkel method. The NDWCTs in the 300 MW and 600 MW power plant were taken as the research objects. Comparing experimental values with calculated values, the relative error was less than 3%. Then, the effect of air parameters on evaporation loss of NDWCT was analyzed. The results showed that, with the increase of dry bulb temperature, the evaporation heat dissipation and the evaporation loss decreased, while the rate of evaporation loss caused by unit temperature difference increased. The ambient temperature increased by 1 °C and the evaporation loss was reduced by nearly 26.65 t/h. When the relative air humidity increased, the evaporation heat dissipation and evaporation loss decreased, and the rate of evaporation loss caused by unit temperature difference decreased. When relative air humidity increased by 1%, the outlet water temperature rose by about 0.08 °C, and the evaporation loss decreased by about 5.63 t/h.

The central heating technology with thermal storage technology is an important means to realize thermoelectric decoupling, meet heating demand, reduce primary energy consumption, and protect the ecological environment. For this paper, the numerical simulation method was used to study the temperature variation of large-capacity hot water storage tank (HWST) in an actual combined heat and power system. The influence of various factors, including the length diameter ratio, water supply temperature, and water supply flow, as well as the orifice diameter and number of the water distributor, on the flow uniformity and performance of the HWST was investigated. The results show that the heat storage efficiency and flow uniformity of the HWST can be improved by properly increasing the water supply flow, the orifice diameter, and number of the water distributor. Increasing the length diameter ratio can improve the flow uniformity, but it will reduce the heat storage efficiency of the HWST. Increasing the water supply temperature can increase heat storage efficiency of the HWST and accelerate the stratification of cold and hot water in the tank. Besides, the comprehensive analysis of the non-dimensional exergy loss calculation results, velocity field, and temperature field show that there is a certain coupling relationship between the non-dimensional exergy loss and flow uniformity at the initial stage of heat storage. In practical application, the influence of these factors on flow uniformity, heat storage efficiency, and non-dimensional exergy loss should be comprehensively considered in order to achieve the best heat storage and release performance of the HWST. This paper provides some engineering guidance for the application of large-capacity heat storage tanks in the combined heat and power (CHP) system.

The frozen phenomenon is unfavorable for the direct air-cooled condensers (DACCs) in a very cold area. The effect of crosswind on frozen phenomena in DACCs at the representative 2 × 350 MW thermal power units was investigated numerically. Results showed that when the crosswind velocity was 4 m·s−1, the number of frozen air-cooled units reached a maximum of six. The increase of vortex range in the air-cooled unit was one of the important reasons to restrain the formation of frozen phenomena at a crosswind velocity from 4 m·s−1 to 12 m·s−1. The frozen phenomena in the DACC disappeared when the crosswind velocity was 12 m·s−1. As the crosswind velocity continued to increase to 28 m·s−1, the frozen region mainly appeared at the position of column 1 row 4, where the airflow rate was the maximum and the inlet air temperature was the minimum among all air-cooled units. This phenomenon occurred because there existed a relatively high-pressure zone near the inlet of each frozen air-cooled unit. In addition, although the frozen area increased from one-third of the air-cooled unit surface to half with the crosswind velocity from 20 m·s−1 to 28 m·s−1, the flow characteristics and the size of vortices in the air-cooled unit were similar in the above two crosswind conditions. Therefore, the key influencing factor became the airflow rate and the inlet air temperature of the air-cooled units under strong crosswind conditions. This study has important guiding significance for the antifreezing design and operation of DACCs.

As prebiotics, galacto-oligosaccharides (GOSs) can improve the intestinal flora and have important applications in medicine. β-galactosidases could promote the synthesis of GOSs in lactose and catalyze the hydrolysis of lactose. In this study, a new β-galactosidase gene (gal2A), which belongs to the glycoside hydrolase family 2, was cloned from marine bacterium Alteromonas sp. QD01 and expressed in Escherichia coli. The molecular weight of Gal2A was 117.07 kDa. The optimal pH and temperature of Gal2A were 8.0 and 40 °C, respectively. At the same time, Gal2A showed wide pH stability in the pH range of 6.0–9.5, which is suitable for lactose hydrolysis in milk. Most metal ions promoted the activity of Gal2A, especially Mn2+ and Mg2+. Importantly, Gal2A exhibited high transglycosylation activity, which can catalyze the formation of GOS from milk and lactose. These characteristics indicated that Gal2A may be ideal for producing GOSs and lactose-reducing dairy products.

Accurate mapping of winter wheat over a large area is of great significance for guiding policy formulation related to food security, farmland management, and the international food trade. Due to the complex phenological features of winter wheat, the cloud contamination in time-series imagery, and the influence of the soil/snow background on vegetation indices, there remains no effective method for mapping winter wheat at a medium spatial resolution (10–30 m). In this study, we proposed a novel method called phenology-time weighted dynamic time warping (PT-DTW) for identifying winter wheat based on Sentinel 2A/B time-series data. The main advantages of PT-DTW include (1) the use of phenological features in two periods, i.e., the greenness increase before winter and greenness decrease after heading, which are common to all winter wheat and are distinct from the features of other land cover types, and (2) the use of the normalized differential phenology index (NDPI) instead of traditional vegetation indices to provide more robust vegetation information and to suppress the adverse impacts of soil and snow cover, especially during the before-winter growth period. The proposed PT-DTW method was employed for winter wheat mapping based on Sentinel 2A/B data on the Huang-Huai Plain, China. Validation with visually interpreted samples showed that the produced winter wheat map achieved an overall classification accuracy of 89.98% and a kappa coefficient of 0.7978, outperforming previous winter wheat classification methods. Moreover, the planting area derived from PT-DTW agreed well with census data at the municipal level, with a coefficient of determination of 0.8638, indicating that the winter wheat map produced at 20 m resolution was reliable overall. Therefore, the PT-DTW method is recommended for winter wheat mapping over large areas.

There have been many studies and much attention paid to spatial sharpening for thermal imagery. Among them, TsHARP, based on the good correlation between vegetation index and land surface temperature (LST), is regarded as a standard technique because of its operational simplicity and effectiveness. However, as LST is affected by other factors (e.g., soil moisture) in the areas with low vegetation cover, these areas cannot be well sharpened by TsHARP. Thin plate spline (TPS) is another popular downscaling technique for surface data. It has been shown to be accurate and robust for different datasets; however, it has not yet been attempted in thermal sharpening. This paper proposes to combine the TsHARP and TPS methods to enhance the advantages of each. The spatially explicit errors of these two methods were firstly estimated in theory, and then the results of TPS and TsHARP were combined with the estimation of their errors. The experiments performed across various landscapes and data showed that the proposed combined method performs more robustly and accurately than TsHARP.

Alginate oligosaccharides (AOS) show versatile bioactivities. Although various alginate lyases have been characterized, enzymes with special characteristics are still rare. In this study, a polysaccharide lyase family 7 (PL7) alginate lyase-encoding gene, aly08, was cloned from the marine bacterium Vibrio sp. SY01 and expressed in Escherichia coli. The purified alginate lyase Aly08, with a molecular weight of 35 kDa, showed a specific activity of 841 U/mg at its optimal pH (pH 8.35) and temperature (45 °C). Aly08 showed good pH-stability, as it remained more than 80% of its initial activity in a wide pH range (4.0−10.0). Aly08 was also a thermo-tolerant enzyme that recovered 70.8% of its initial activity following heat shock treatment for 5 min. This study also demonstrated that Aly08 is a polyG-preferred enzyme. Furthermore, Aly08 degraded alginates into disaccharides and trisaccharides in an endo-manner. Its thermo-tolerance and pH-stable properties make Aly08 a good candidate for further applications.

Most vegetation indices (VIs) of remote sensing were designed based on the concept of soil-line, which represents a linear correlation between bare soil reflectance at the red and near-infrared (NIR) bands. Unfortunately, the soil-line can only suppress brightness variation, not color differences of bare soil. Consequently, soil variation has a considerable impact on vegetation indices, although significant efforts have been devoted to this issue. In this study, a new soil-line is established in a new feature space of the NIR band and a virtual band that combines the red and shortwave-infrared (SWIR) bands (0.74ρred+0.26ρswir). Then, plus versions of vegetation indices (VI+), i.e., normalized difference vegetation index plus (NDVI+), enhanced vegetation index plus (EVI+), soil-adjusted vegetation index plus (SAVI+), and modified soil-adjusted vegetation index plus (MSAVI+), are proposed based on the new soil-line, which replaces the red band with the red-SWIR band in the vegetation indices. Soil spectral data from several spectral libraries confirm that bare soil has much less variation for VI+ than the original VI. Simulation experiments show that VI+ correlates better with fractional vegetation coverage (FVC) and leaf area index (LAI) than original VI. Ground measured LAI data collected from BigFoot, VALERI, and other previous references also confirm that VI+ derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data correlates better with ground measured LAI than original VI. These data analyses suggest that replacing the red band with the red-SWIR band can reduce the sensitivity of VIs to soil background. We recommend employing the proposed NDVI+, EVI+, SAVI+, and MSAVI+ in applications of large area, sparse vegetation, or when soil color variation cannot be neglected, although sensitivity to soil moisture and clay content might cause slight side effects for the proposed VI+s.

Chitosanases play an important role in chitosan degradation, forming enzymatic degradation products with several biological activities. Although many chitosanases have been discovered and studied, the enzymes with special characteristics are still rather rare. In this study, a new chitosanase, CsnM, with an apparent molecular weight of 28 kDa was purified from the marine bacterium Pseudoalteromonas sp. SY39. CsnM is a cold-adapted enzyme, which shows highest activity at 40 °C and exhibits 30.6% and 49.4% of its maximal activity at 10 and 15 °C, respectively. CsnM is also a thermo-tolerant enzyme that recovers 95.2%, 89.1% and 88.1% of its initial activity after boiling for 5, 10 and 20 min, respectively. Additionally, CsnM is an endo-type chitosanase that yields chitodisaccharide as the main product (69.9% of the total product). It’s cold-adaptation, thermo-tolerance and high chitodisaccharide yield make CsnM a superior candidate for biotechnological application to produce chitooligosaccharides.

Chitooligosaccharides (CHOS) have gained increasing attention because of their important biological activities. Enhancing the efficiency of CHOS production essentially requires screening of novel chitosanase with unique characteristics. Therefore, a rapid and efficient one-step affinity purification procedure plays important roles in screening native chitosanases. In this study, we report the design and synthesis of affinity resin for efficient purification of native chitosanases without any tags, using chitodisaccharides (CHDS) as an affinity ligand, to couple with Sepharose 6B via a spacer, cyanuric chloride. Based on the CHDS-modified affinity resin, a one-step affinity purification method was developed and optimized, and then applied to purify three typical glycoside hydrolase (GH) families: 46, 75, and 80 chitosanase. The three purified chitosanases were homogeneous with purities of greater than 95% and bioactivity recovery of more than 40%. Moreover, we also developed a rapid and efficient affinity purification procedure, in which tag-free chitosanase could be directly purified from supernatant of bacterial culture. The purified chitosanases samples using such a procedure had apparent homogeneity, with more than 90% purity and 10⁻50% yield. The novel purification methods established in this work can be applied to purify native chitosanases in various scales, such as laboratory and industrial scales.

Deep learning techniques have boosted the performance of hyperspectral image (HSI) classification. In particular, convolutional neural networks (CNNs) have shown superior performance to that of the conventional machine learning algorithms. Recently, a novel type of neural networks called capsule networks (CapsNets) was presented to improve the most advanced CNNs. In this paper, we present a modified two-layer CapsNet with limited training samples for HSI classification, which is inspired by the comparability and simplicity of the shallower deep learning models. The presented CapsNet is trained using two real HSI datasets, i.e., the PaviaU (PU) and SalinasA datasets, representing complex and simple datasets, respectively, and which are used to investigate the robustness or representation of every model or classifier. In addition, a comparable paradigm of network architecture design has been proposed for the comparison of CNN and CapsNet. Experiments demonstrate that CapsNet shows better accuracy and convergence behavior for the complex data than the state-of-the-art CNN. For CapsNet using the PU dataset, the Kappa coefficient, overall accuracy, and average accuracy are 0.9456, 95.90%, and 96.27%, respectively, compared to the corresponding values yielded by CNN of 0.9345, 95.11%, and 95.63%. Moreover, we observed that CapsNet has much higher confidence for the predicted probabilities. Subsequently, this finding was analyzed and discussed with probability maps and uncertainty analysis. In terms of the existing literature, CapsNet provides promising results and explicit merits in comparison with CNN and two baseline classifiers, i.e., random forests (RFs) and support vector machines (SVMs).

Cloud removal is a prerequisite for the application of Landsat datasets, as such satellite images are invariably contaminated by clouds. Clouds affect the transmission of radiation signal to different degrees because of their different thicknesses, shapes, heights and distributions. Existing methods utilize pixel replacement to remove thick clouds and pixel correction techniques to rectify thin clouds in order to retain the land surface information in contaminated pixels. However, a major limitation of these methods refers to their deficiency in retrieving land surface reflectance when both thick clouds and thin clouds exist in the images, as the two types of clouds differ in the transmission of radiation signal. As most remotely sensed images show rather complex cloud contamination patterns, an efficient method to alleviate both thin and thick cloud effects is in need of development. To this end, the paper proposes a new method to rectify cloud contamination based on the cloud detection of iterative haze-optimized transformation (IHOT) and the cloud removal of cloud trajectory (IHOT-Trajectory). The cloud trajectory is able to take consideration of signal transmission for different levels of cloud contamination, which characterizes the spectral response of a certain type of land cover under increasing cloud thickness. Specifically, this method consists in four steps. First, the cloud thicknesses of contaminated pixels are estimated by the IHOT. Second, areas affected by cloud shadows are marked. Third, cloud trajectories are fitted with the aid of neighboring similar pixels under different cloud thickness. Last, contaminated areas are rectified according to the relationship between the land surface reflectance and the IHOT. The experimental results indicate that the proposed approach is able to effectively remove both the thin and thick clouds and erase the cloud shadows of Landsat images under different scenarios. In addition, the proposed method was compared with the dark object subtraction (DOS), the modified neighborhood similar pixel interpolator (MNSPI) and the multitemporal dictionary learning (MDL) methods. Quantitative assessments show that the IHOT-Trajectory method is superior to the other cloud removal methods overall. For specific spectral bands, the proposed method performs better than other methods in visible bands, whereas it does not necessarily perform better in infrared bands.