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Abstract Fluorogenic RNA aptamers have various applications, including use as fluorescent tags for imaging RNA trafficking and as indicators of RNA-based sensors that exhibit fluorescence upon binding small-molecule fluorophores in living cells. Current fluorogenic RNA:fluorophore complexes typically emit visible fluorescence. However, it is challenging to develop fluorogenic RNA with near-infrared (NIR) fluorescence for in vivo imaging and sensing studies. To address this issue, we identify and modulate red fluorescent protein-like fluorophores to bind Squash, a highly folded fluorogenic RNA. One of these fluorophores, DFQL-1T, exhibits photostable NIR fluorescence when bound to Squash, enabling RNA visualization in living mammalian cells and mice. With Squash:DFQL-1T complexes, we generate RNA-based sensors for detecting non-coding RNAs and small molecule targets in living mammalian cells and in mice. These studies reveal a fluorogenic RNA:fluorophore complex that can be readily developed into NIR fluorescent RNA tags for in vivo imaging and sensing.

Cotton is one of the most significant cash crops in the world, and it is also the main source of natural fiber for textiles. It is crucial for cotton management to identify the spatiotemporal distribution of cotton planting areas timely and accurately on a fine scale. However, previous research studies have predominantly concentrated on specific years using remote sensing data. Challenges still exist in the extraction of cotton areas for long time series with high accuracy. To address this issue, a novel cotton sample selection method was proposed and the machine learning method is employed to effectively identify the long time series cotton planting areas at a 30-m resolution scale. Bortala and Shuanghe in Xinjiang, China, were selected as the study cases to demonstrate the approach. Specifically, the cropland in this study was extracted by using an object-oriented classification method with Landsat images and the results were optimized as the vectorized boundary of croplands. Then, the cotton samples were selected using the Normalized Difference Vegetation Index (NDVI) series of Moderate Resolution Imaging Spectroradiometer (MODIS) based on its phenological characteristics. Next, cotton was identified based on the croplands from 2000 to 2020 by using the machine learning model. Finally, the performance was evaluated, and the spatiotemporal distribution characteristics of cotton planting areas were analyzed. The results showed that the proposed approach can achieve high accuracy at a fine spatial resolution. The performance evaluation indicated the applicability and suitability of the method, there is a good correlation between the extracted cotton areas and statistical data, and the cotton area of the study area showed an increasing trend. The cotton spatial distribution pattern developed from dispersion to agglomeration. The proposed approach and the derived 30-m cotton maps can provide a scientific reference for the optimization of agricultural management.

Abstract This open-label, single-arm, phase 2 trial evaluated the efficacy and safety of neoadjuvant sintilimab combined with anlotinib and chemotherapy, followed by adjuvant sintilimab, for resectable NSCLC. Forty-five patients received anlotinib (10 mg, QD, PO, days 1–14), sintilimab (200 mg, day 1), and platinum-based chemotherapy of each three-week cycle for 3 cycles, followed by surgery within 4–6 weeks. Adjuvant sintilimab (200 mg) was administered every 3 weeks. The primary endpoint was achieving a pathological complete response (pCR). From June 10, 2021 through October 10, 2023, 45 patients were enrolled and composed the intention-to-treat population. Twenty-six patients (57.8%) achieved pCR, and 30 (66.7%) achieved major pathological response (MPR). Forty-one patients underwent surgery. In the per-protocol set (PP set), 63.4% (26/41) achieved pCR, and 73.2% achieved MPR. The median event-free survival was not attained (95% CI, 25.1-NE). During the neoadjuvant treatment phase, grade 3 or 4 treatment-related adverse events were observed in 25 patients (55.6%), while immune-related adverse events were reported in 7 patients (15.6%). We assessed vascular normalization and infiltration of immune-related cells by detecting the expression of relevant cell markers in NSCLC tissues with mIHC. Significant tumor microenvironment changes were observed in pCR patients, including reduced VEGF+ cells and CD4+Foxp3+ Treg cells, and increased perivascular CD4+ T cells, CD39+CD8+ T cells, and M1 macrophages. In conclusion, perioperative sintilimab and neoadjuvant anlotinib plus chemotherapy achieved pCR in a notable proportion of patients with resectable NSCLC and were associated with profound changes in the tumour microenvironment (ClinicalTrials.gov NCT05400070).

Abstract Background We aimed to evaluate microaneurysms (MAs) after treatment with anti-vascular endothelial growth factor (anti-VEGF) therapy to understand causes of chronic edema and anti-VEGF resistance. Methods Patients with non-proliferative diabetic retinopathy, with or without macular edema were recruited. Optical coherence tomography angiography (OCTA) MAs-related parameters were observed, including the maximum diameter of overall dimensions, material presence, and flow signal within the lumen. OCTA parameters also included central macular thickness (CMT), foveal avascular zone, superficial and deep capillary plexuses, and non-flow area measurements on the superficial retinal slab. Results Overall, 48 eyes from 43 patients were evaluated. CMT differed significantly between the diabetic macular edema (DME ) and non-DME (NDME) groups at 1st, 2nd, 3rd, and 6th months of follow-up (P

Abstract Previous studies typically assumed a constant total organic carbon (OC) storage in the lake water column, neglecting its significant variability within a changing world. Based on extensive field data and satellite monitoring techniques, we demonstrate considerable spatiotemporal variability in OC concentration and storage for 24,366 Chinese lakes during 1984–2023. Here we show that dissolved OC concentration is high in northwest saline lakes and particulate OC concentration is high in southeast eutrophic lakes. Along with increasing OC concentration and water volume, dissolved and particulate OC storage increase by 44.6% and 33.5%, respectively. Intensified human activities, water input, and wind disturbance are the key drivers for increasing OC storage. Moreover, higher OC storage further leads to an 11.0% increase in nationwide OC burial and a decrease in carbon emissions from 71.1% of northwest lakes. Similar changes are occurring globally, which suggests that lakes are playing an increasingly important role in carbon sequestration.

Study region: the Yellow River (YR) in China. Study focus: Due to climate change and human activities, YR channel morphology has undergone significant spatiotemporal variations. Yet, a comprehensive understanding of channel migration in YR and its driving factors remains unclear. Here, we developed a multi-index water extraction method to track the changes in surface water and river channel migration of YR based on Landsat imagery since the 1980s. New hydrological insights for the region: We find that the average surface water area of YR over the past four decades is 4013 km2, with 73.5 % of permanent surface water. Notably, the surface water extent has experienced a 9 % increase since the 1980s, while the river channel has undergone a 12.2 % decrease. The YR channel’s centerline exhibits diverse change patterns across the entire basin, which can be broadly categorized into six types ranging from unchanged to reverse migration. We identify that climate, particularly temperature and precipitation, contributed 71 % of channel changes in the upper reaches, while 65 % of changes in the lower reaches are from human activities, including reservoir operations and water management policies. Our results unveil the variations in water extent and channel migration of the YR, offering new insights into the interactions between channel migration and climate change and human activities in the YR over the past four decades.

ABSTRACTRivers act as the principal channels for transporting terrigenous dissolved organic carbon (DOC) to lakes and reservoirs. Satellite remote sensing-based river monitoring is difficult due to the narrow river form and high spatiotemporal heterogeneity of DOC components. The unique advantages of unmanned aerial vehicles (UAVs) facilitate river DOC concentration monitoring. The DOC concentration in 8 major tributaries (average width: 109.62 m) and shoreside of the Lake Chaohu Basin were retrieved via a hyperspectral UAV. The results showed that (1) the DOC concentration was significantly correlated with the water remote sensing reflectance ([Formula: see text]) of 402, 429-438, 440–451 and 458–462 nm in the blue band (r2: 0.11 to 0.13; p

Abstract Background Antiangiogenic treatment and immunochemotherapy effectively treat patients with advanced esophageal cancer. However, there remains a dearth of studies concerning neoadjuvant therapy for resectable esophageal cancer. Methods The study focused on patients with T2–4NxM0 resectable esophageal carcinoma. Neoadjuvant treatment involved administering anlotinib (10 mg orally, once a day, 2 weeks on and 1 week off) for antiangiogenesis and sintilimab (200 mg) and chemotherapy for three cycles. Surgical treatment was performed 4–6 weeks after the last chemotherapy cycle was completed. The primary endpoints assessed were pathological complete response (pCR) and safety. Results Out of the 34 screened patients, 17 were successfully enrolled in the study, and 14 completed the entire treatment process. The pCR was 35.3% (6/17). However, two patients experienced mortality. The occurring rate of grade 3 or higher complications after the surgery was 78.6% (11/14) according to Clavien–Dindo classification. Specifically, anastomotic leakage was observed in 57.1% (8/14) of the patients. Conclusion Compared to neoadjuvant chemotherapy, the current regimen demonstrated improved pCR. However, it did not show significant improvement compared to immunochemotherapy. It is essential to exercise caution when using this treatment approach in patients with esophageal cancer as it might increase postoperative complications, especially anastomotic leakage.

Yellow River is famous for its exceptionally higher suspended sediment concentrations (SSC), displaying significant spatiotemporal heterogeneity across diverse sections. Although SSC monitoring of the Yellow River and some of its tributaries has been achieved using Landsat data, it remains unclear whether the inclusion of higher spatial resolution satellites can expand the spatiotemporal monitoring capabilities for the Yellow River and most of its tributaries. In this study, we employed Sentinel-2 imagery, offering superior spatiotemporal resolution, to develop a higher-accurate SSC model and quantitatively evaluated its potential to improve the spatiotemporal coverage of SSC monitoring compared to Landsat satellites. For the Yellow River in the Loess Plateau, the optimized Sentinel-2 model exhibited superior accuracy, achieving R2 = 0.91, root mean square error of 728.76 mg/L, and unbiased percentage difference of 16.75%. Notably, distinct SSC distribution differences were observed across different rivers, indicating significant spatial heterogeneity (SSC: 0.58 – 3.01 × 105 mg/L). Moreover, Sentinel-2 showed a significant increase in observation frequency and spatial coverage (204.08% and 107.15%, respectively) compared to Landsat. An additional 35.29% increase in observation frequency was achieved through the combined satellite observation method. Furthermore, based on river width statistics, we found that upgrading the spatial resolution from 10 m to 1 m enhanced the coverage of observable river segments in the Loess Plateau by approximately 47.96%, and by about 50.56% globally. This study established a crucial scientific foundation for integrating Sentinel-2 and Landsat, enabling finer-scale monitoring and management of river sediment.

Abstract Acute pancreatitis (AP) is a severe gastrointestinal inflammatory disease with increasing mortality and morbidity. Glycyrrhiza glabra, commonly known as Liquorice, is a widely used plant containing bioactive compounds like Glycyrrhizin, which possesses diverse medicinal properties such as anti-inflammatory, antioxidant, antiviral, and anticancer activities. The objective of this study is to investigate the active components, relevant targets, and underlying mechanisms of the traditional Chinese medicine Glycyrrhiza glabra in the treatment of AP. Utilizing various computational biology methods, we explored the potential targets and molecular mechanisms through Glycyrrhizin supplementation. Computational results indicated that Glycyrrhizin shows promising pharmacological potential, particularly with mitogen-activated protein kinase 3 (MAPK3) protein (degree: 70), forming stable complexes with Glycyrrhizin through ionic and hydrogen bonding interactions, with a binding free energy (ΔGbind) of -33.01 ± 0.08 kcal/mol. Through in vitro experiments, we validated that Glycyrrhizin improves primary pancreatic acinar cell injury by inhibiting the MAPK/STAT3/AKT signaling pathway. Overall, MAPK3 emerges as a reliable target for Glycyrrhizin’s therapeutic effects in AP treatment. This study provides novel insights into the active components and potential targets and molecular mechanisms of natural products.

Dryland lakes are indispensable to regional water resource systems. Ebinur Lake, the largest saline lake in Xinjiang Uygur Autonomous Region, is vital for regional biodiversity and environmental stability but has been facing the predicament of gradual shrinkage in recent decades. In this study, we proposed a new dual-index method for Landsat (-5, -7, -8, and -9) data to extract water with the combinations of the normalized difference water index (NDWI) and the modified NDWI for turbid waters (NDWIturbid). The dual-index method showed a high overall accuracy of 96.36% for Ebinur Lake. Landsat series images from 1992 to 2023 were employed to acquire the water areas of Ebinur Lake. The results showed that, over the past three decades, the area of Ebinur Lake exhibited a fluctuating decreasing trend, with an average lake area of 568.74 ± 152.43 km². The northwest intermittent water areas showed significant changes, and there was a close connection between the northwest and core water areas. Seasonally, the lake area decreased from spring to autumn. River inflow, driven by rainfall and human activities, was the primary factor affecting the inter/inner annual changes in Ebinur Lake. Furthermore, due to the valley effects, wind was found to be a critical factor in the diurnal changes in the water areas. This study should deepen the understanding of the variations of Ebinur Lake and benefit local water resource management.

The Sentinel-2 Multi-Spectral Instrument (MSI) is characterized by short revisit times (5 days), red-edge spectral bands (665 nm and 705 nm), and a high spatial resolution (10 m), making it highly suitable for monitoring water quality in both inland and coastal waters. Unlike SeaWiFS, which can adjust its viewing angles to minimize sunglint, the Sentinel-2 MSI operates with fixed near-nadir angles, which makes it more susceptible to sunglint. Additionally, the complex optical properties of water pose challenges in accurately determining its water-leaving reflectance. Therefore, we compared the effectiveness of six atmospheric correction (AC) algorithms (POLYMER, MUMM, DSF, C2RCC, BP, and GRS) in correcting sunglint using two typical lakes in Xinjiang, China, as examples. The results indicated that POLYMER achieved the highest overall evaluation score (1.61), followed by MUMM (1.21), while BP exhibited the lowest performance (0.62). Specifically, POLYMER showed robust performance at the 665 nm band with RMSE = 0.0012 sr−1, R2 = 0.74, and MAPE = 30.68%, as well as at the 705 nm band with RMSE = 0.0014 sr−1, R2 = 0.42, and MAPE = 38.44%. At the 443, 490, and 560 nm bands, MUMM showed better performance (RMSE ≤ 0.0026 sr−1, R2 ≥ 0.86, MAPE ≤ 28.20%). In terms of band ratios, POLYMER exhibited the highest accuracy (RMSE ≤ 0.093 and MAPE ≤ 22.2%), particularly for the ratio Rrs(490)/Rrs(560) (R2 = 0.71). In general, POLYMER is the best choice for the sunglint correction of Xinjiang’s clean lakes. This study assessed the capability of different AC algorithms for sunglint correction and enhanced the monitoring capability of MSI data in clean waters.

The tradeoffs between the spatial and temporal resolutions for the remote sensing instruments limit their capacity to monitor the eutrophic status of inland lakes. Spatiotemporal fusion (STF) provides a cost-effective way to obtain remote sensing data with both high spatial and temporal resolutions by blending multisensor observations. However, remote sensing reflectance (Rrs) over water surface with a relatively low signal-to-noise ratio is prone to be contaminated by large uncertainties in the fusion process. To present a comprehensive analysis on the influence of processing and modeling errors, we conducted an evaluation study to understand the potential, uncertainties, and limitations of using STF for monitoring chlorophyll a (Chla) concentration in an inland eutrophic water (Chaohu Lake, China). Specifically, comparative tests were conducted on the Sentinel-2 and Sentinel-3 image pairs. Three typical STF methods were selected for comparison, i.e., Fit-FC, spatial and temporal nonlocal filter-based fusion model, and the flexible spatiotemporal data fusion. The results show as follows: (a) among the influencing factors, atmospheric correction uncertainties and geometric misregistration have larger impacts on the fusion results, compared with radiometric bias between the imaging sensors and STF modeling errors; and (b) the machine-learning-based Chla inversion accuracy of the fusion data [R2 = 0.846 and root mean square error (RMSE) = 17.835 μg/l] is comparable with that of real Sentinel-2 data (R2 = 0.856 and RMSE = 16.601 μg/l), and temporally dense Chla results can be produced with the integrated Sentinel-2 and fusion image datasets. These findings will help to provide guidelines to design STF framework for monitoring aquatic environment of inland waters with remote sensing data.

Harmful algal blooms (HABs) in lakes have been a salient problem of global water environment due to their harmful impacts. Present situation and future tendency of HABs in nearshore areas are more important than those in other areas of lakes, but traditional researches generally ignored this point. This study developed a novel framework aiming to timely and accurately grasp both present conditions and accumulation risks of HABs in nearshore areas of lakes. By designing a quantitative monitoring method, land-based video monitoring devices around lakes were used to obtain present situation of HABs in nearshore areas rapidly, periodically and automatically. On this basis, a coupled simulation model was established to predict accumulation risks of HABs in nearshore areas using five indexes, i.e., shoreline index, chlorophyll-a index, algal bloom index, wind direction index and dynamic index. Experimental results indicated that the accuracy of HAB monitoring in nearshore areas is quite satisfying (85.20%) and the prediction results of HABs accumulation in nearshore areas is reasonable and credible. This study demonstrated a high value of the developed collaborative framework in emergency prevention and control of HABs in lakes.

Abstract Background Few highly accurate tests can diagnose central lymph node metastasis (CLNM) of papillary thyroid cancer (PTC). Genetic sequencing of tumor tissue has allowed the targeting of certain genetic variants for personalized cancer therapy development. Methods This study included 488 patients diagnosed with PTC by ultrasound-guided fine-needle aspiration biopsy, collected clinicopathological data, analyzed the correlation between CLNM and clinicopathological features using univariate analysis and binary logistic regression, and constructed prediction models. Results Binary logistic regression analysis showed that age, maximum diameter of thyroid nodules, capsular invasion, and BRAF V600E gene mutation were independent risk factors for CLNM, and statistically significant indicators were included to construct a nomogram prediction model, which had an area under the curve (AUC) of 0.778. A convolutional neural network (CNN) prediction model built with an artificial intelligence (AI) deep learning algorithm achieved AUCs of 0.89 in the training set and 0.78 in the test set, which indicated a high prediction efficacy for CLNM. In addition, the prediction models were validated in the subclinical metastasis and clinical metastasis groups with high sensitivity and specificity, suggesting the broad applicability of the models. Furthermore, CNN prediction models were constructed for patients with nodule diameters less than 1 cm. The AUCs in the training set and test set were 0.87 and 0.76, respectively, indicating high prediction efficacy. Conclusions The deep learning-based multifeature integration prediction model provides a reference for the clinical diagnosis and treatment of PTC.

Algal blooms are a major environmental challenge for lakes and reservoirs and pose severe threats to water on both aquatic and human health. Conventional algorithms used for algal bloom detection based on remote sensing reflectance proved to be effective in some lakes. However, it is still difficult to obtain high accuracy for multiple lakes using single-threshold-based detection. Currently, machine learning (ML) algorithms have been applied to pinpoint algal bloom locations with excellent results, but the ability of different ML models to be applied in different lakes is still unknown. This article presents the performance of algal bloom detection with commonly used ML algorithms in Chinese eutrophic inland lakes based on Sentinel-2 images. A series of comprehensive tests for accuracy, stability, and robustness was designed for four ML models, including random forest (RF), extreme gradient boosting, artificial neural network, and support vector machine, which were tested in Lake Taihu, Lake Chaohu, and Lake Dianchi. In addition, the index-based methods, including floating algae index and adjusted floating algae index, were also calculated for comparison with ML methods. The results showed that the RF model outperformed other ML models. The comparison results between the RF model and algal indexes revealed that the overall accuracy of RF remained above 0.90. Even with a single lake dataset used as training samples, the RF still maintained a fairly high accuracy of 0.88 for other lakes. To summarize, the four ML models demonstrate promising potential for algal bloom detection across different lakes and provide a practical reference for further applications.

To address the issue of limited application scenarios associated with connectivity assurance based on two-dimensional (2D) trajectory planning, this paper proposes an improved deep reinforcement learning (DRL) -based three-dimensional (3D) trajectory planning method for cellular unmanned aerial vehicles (UAVs) communication. By considering the 3D space environment and integrating factors such as UAV mission completion time and connectivity, we develop an objective function for path optimization and utilize the advanced dueling double deep Q network (D3QN) to optimize it. Additionally, we introduce the prioritized experience replay (PER) mechanism to enhance learning efficiency and expedite convergence. In order to further aid in trajectory planning, our method incorporates a simultaneous navigation and radio mapping (SNARM) framework that generates simulated 3D radio maps and simulates flight processes by utilizing measurement signals from the UAV during flight, thereby reducing actual flight costs. The simulation results demonstrate that the proposed approach effectively enable UAVs to avoid weak coverage regions in space, thereby reducing the weighted sum of flight time and expected interruption time.

Stationary surveillance cameras deployed around lakes can provide continuous real-time observations of key water areas for harmful algal bloom (HAB). They can be used to supplement remote sensing-based monitoring in situations that satellites cannot handle. While some cameras were initially installed for other purposes, and the poses are not fixed during operation, hence, detecting HABs remains a challenging task due to the diverse surface features present in image frames. A novel three-step machine learning approach was proposed in this paper to address this problem. The acquired images are initially classified using the first model, and images with certain HABs undergo further examination. A second model is employed to generate a water mask, thereby eliminating interferences from non-water features. Finally, the third model is applied to detect and identify HABs specifically within water areas. The experiments showed that the three steps implemented in sequence can effectively extract distinct HABs from RGB images captured under various shooting poses. The overall pixel-level accuracy, intersection over union, and F1 score reached 0.83, 0.76, and 0.76, respectively, on 1969 images from August to September 2020. The novelty of our approach is attributed to that the combination of the three steps can significantly abate the adverse influence of an external environment; thus, the final detection can be performed with satisfactory accuracy. In practice, the approach was applied in Lake Chaohu and consistently reports the real-time status of HABs along the bank. It exhibits substantial potential for the application in eutrophic lakes to avoid HAB-induced secondary disasters.

BackgroundThe rate of breast-conserving surgery is very low in China, compared with that in developed countries; most breast cancer patients receive mastectomy. It is great important to explore the possibility of omitting axillary lymph node dissection (ALND) in early-stage breast cancer patients with 1 or 2 positive sentinel lymph nodes (SLNs) in China. The aim of this study was to develop a nomogram based on elastography for the prediction of the risk of non-SLN (NSLN) metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs.MethodsA total of 601 breast cancer patients were initially recruited. According to the inclusion and exclusion criteria, 118 early-stage breast cancer patients with 1 or 2 positive SLNs were finally enrolled and were assigned to the training cohort (n=82) and the validation cohort (n=36), respectively. In the training cohort, the independent predictors were screened by logistic regression analysis and then were used to conducted the nomogram for the prediction of NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. The calibration curves, concordance index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and Decision curve analysis (DCA) were used to verified the performance of the nomogram.ResultsThe multivariable analysis showed that the enrolled patients with positive HER2 expression (OR=6.179, P=0.013), Ki67≥14% (OR=8.976, P=0.015), larger lesion size (OR=1.038, P=0.045), and higher Emean (OR=2.237, P=0.006) were observed to be the independent factors of NSLN metastasis. Based on the above four independent predictors, a nomogram was conducted to predict the risk of the NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. The nomogram showed good discrimination in the prediction of NSLN metastasis, with bias-corrected C-index of 0.855 (95% CI, 0.754-0.956) and 0.853 (95% CI, 0.724-0.983) in the training and validation cohorts, respectively. Furthermore, the AUC was 0.877 (95%CI: 0.776- 0.978) and 0.861 (95%CI: 0.732-0.991), respectively, indicating a good performance of the nomogram. The calibration curve suggested a satisfactory agreement between the predictive and actual risk in both the training (χ2 = 11.484, P=0.176, HL test) and validation (χ2 = 6.247, p = 0.620, HL test) cohorts, and the obvious clinical nets were revealed by DCA.ConclusionsWe conducted a satisfactory nomogram model to evaluate the risk of NSLN metastasis in early-stage breast cancer patients with 1 or 2 SLN metastases. This model could be considered as an ancillary tool to help such patients to be selectively exempted from ALND.

Abstract Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide, with high incidence and mortality rates and low survival rates. However, the detailed molecular mechanism of ESCC progression remains unclear. Here, we first showed significantly higher WNT5A and SNAIL expression in ESCC samples than in corresponding paracancerous samples. High WNT5A and SNAIL expression levels correlated positively with lymphatic metastasis and poor prognosis for patients with ESCC based on immunohistochemical (IHC) staining of 145 paired ESCC samples. Spearman’s correlation analyses confirmed the strong positive correlation between WNT5A and SNAIL expression, and patients with ESCC presenting coexpression of WNT5A and SNAIL had the worst prognosis. Then, we verified that the upregulation of WNT5A promoted ESCC cell metastasis in vivo and in vitro, suggesting that WNT5A might be a promising therapeutic target for the prevention of ESCC. Furthermore, WNT5A overexpression induced the epithelial-mesenchymal transition via histone deacetylase 7 (HDAC7) upregulation, and HDAC7 silencing significantly reversed WNT5A-induced SNAIL upregulation and ESCC cell metastasis. In addition, we used HDAC7 inhibitors (SAHA and TMP269) to further confirm that HDAC7 participates in WNT5A-mediated carcinogenesis. Based on these results, HDAC7 is involved in WNT5A-mediated ESCC progression, and approaches targeting WNT5A and HDAC7 might be potential therapeutic strategies for ESCC.

Abstract Lakes are sentinels of global changes and integrators of processes in watersheds. Changes of lake water quality can be observed in shifts in lake color, however, the prevalence of shifts and trends in color and relevant factors remain elusive. Here, a comprehensive examination of color in 2,550 Chinese lakes from 1984 to 2021 revealed that color in 68% of the lakes shifted toward shorter visual wavelengths. Lakes in the Tibetan Plateau had larger declines in wavelength than lakes in other areas. The factors associated with reduced visual wavelengths varied in different ecoregions. A warmer and wetter climate in deep lakes in western China is associated with shifts toward blue. Increased vegetation in watershed and decreased wind are associated with green‐yellow shifting to green‐cyan color in shallow lakes of eastern China. Our study highlights the heterogeneous controls of climate and humans on changing patterns of lake colors.

Following consecutive years of governance efforts, there has been a substantial reduction in sediment transport in the Yellow River, resulting in significant changes in its water–sediment dynamics. This necessitates precise monitoring of sediment-bearing tributary inflows, a crucial requirement for effective governance strategies on the Loess Plateau’s current developmental stage. While satellite remote sensing technology has been widely used to estimate suspended particulate matter concentration (CSPM) in open water bodies like oceans and lakes, its application in narrow rivers presents challenges related to hybrid pixel and proximity effects. As a result, the effectiveness and competence of satellite remote sensing in monitoring CSPM in such confined river environments are reduced. This study attempted to use unmanned aerial vehicle (UAV) remote sensing with multispectral technology to invert CSPM in the Wuding River, a sediment-bearing Yellow River tributary. A novel CSPM concentration inversion model was introduced for highly turbid river settings. The results showed that the accuracy of the new band ratio model in this study is significantly improved compared with the existing models. The validation dataset had a coefficient of determination (R2) of 0.83, a root mean square error (RMSE) of 3.73 g/L, and a mean absolute percentage error (MAPE) of 44.95% (MAPE is 40.68% at 1–20 g/L, and 12.37% at >20 g/L). On this basis, the UAV also monitored the impacts of heavy rainfall on the CSPM, resulting in a rapid rise and fall in CSPM over a period of ten hours. This study demonstrated the potential of UAV remote sensing for CSPM monitoring in extremely turbid narrow rivers (tens to tens of meters), especially before and after rainfall sediment production events, which can provide technical support for accurate sediment management and source identification in the main tributaries of the Yellow River and help realize the goal of high-quality development of the Yellow River Basin.

Water clarity, indexed by Secchi disk depth (Zsd), comprehensively reflects water quality and can be monitored by satellite. Zsd may vary across lakes globally, driven by natural and human activities (climate, land use/land cover, lake morphological characteristics, etc.). Based on Sentinel-3 Ocean and Land Color Instrument (OLCI) imagery, we explored the driving factors of Zsd spatiotemporal patterns in lakes across Eastern China (including the Northeast Plain and Mountain Lake (NPML) Zone and the Eastern Plain Lake (EPL) Zone). Spatially, the responses of driving factors were assessed using feature importance and partial dependence in the random forest model. The results showed that the impacts of natural factors on the spatial variations of Zsd (accounting for 52.00 % of contributions) are comparable to that of human activities (accounting for 48.00 % of contributions). The dynamic sediment ratio (DSR = the square root of the surface area divided by the average depth) showed the highest impact (accounting for 19.64 % of contributions). Thus, small lakes have relatively small DSR indices, making them less susceptible to wind- and wave-induced sediment resuspension, leading to higher Zsd than large lakes. Seasonally, the variations could be linked with the East Asian monsoon climate, characterized by hot and rainy summers. Moreover, the month with the highest Zsd was postponed from June-July in EPL lakes (south) to August-September in NPML lakes (north), consistent with the movement of the rainfall belt of the East Asian monsoon. This study could facilitate the knowledge of water quality in Eastern China as well as other developed areas and provide support for the sustainable development and management of lakes.

Phragmites australis (common reed) is a widely distributed emergent aquatic vegetation species in many wetland ecosystems, and its aboveground biomass (AGB) is an important parameter for evaluating the carbon–nitrogen cycle in wetlands. Satellite remote sensing (RS) is a powerful tool used to monitor the spatio-temporal distribution of AGB within reedbeds over a large area. However, when building AGB models based on satellite data, especially medium resolution satellites, it is difficult to obtain ample and properly measured AGB samples which can be matched with image pixels due to the inaccessibility of the wetlands. In this study, we proposed a solution based on the unmanned aerial vehicle (UAV) and Sentinel-2 data, which allowed us to estimate and successfully map the AGB of Phragmites australis in the Nan Da Gang Wetland Reserve (NDG) in China’s Hebei Province close to the Bohai sea. First, in an experimental area (EA) of NDG, an AGB model (R2 = 0.74, RMSE = 174 g/m2) was built based on NDVI(534, 734) and canopy height derived from UAV data, and an AGB map was obtained of the EA. Second, the AGB map was resampled to the pixel of the Sentinel-2 image, and an AGB sample set was matched with the acquired spatial resolution of the Sentinel-2 image. Finally, based on the sample set, an AGB model (R2 = 0.59, RMSE = 194 g/m2) was built using RVI derived from the Sentinel-2 image, which allowed us to map the Phragmites australis AGB in the NDG wetland reedbed. The study illustrated well that a UAV can be proficient in obtaining enough AGB samples matched with satellite pixels to build satellite-based AGB estimation models.

Ferroptosis is a more relatively recently identified type of programmed cell death, which is associated with tumor progression. However, the mechanism underlying the effect of ferroptosis-related long non-coding RNAs (lncRNAs) in lung adenocarcinoma (LUAD) remains elusive. Therefore, the current study aimed to investigate the role of ferroptosis-related lncRNAs in LUAD and to develop a prognostic model. The clinicopathological characteristics of patients and the gene sequencing data were obtained from The Cancer Genome Atlas, while the ferroptosis-associated mRNAs were downloaded from the FerrDb database. A ferroptosis-related lncRNA signature was established with Least Absolute Shrinkage and Selection Operator Cox regression analysis. Furthermore, the risk scores of ferroptosis-related lncRNAs were calculated and LUAD patients were then assigned to high- and low-risk groups based on the median risk score. The prognostic model was established by K-M plotters and nomograms. Gene set enrichment analysis (GSEA) was performed to evaluate the association between immune responses and ferroptosis-related lncRNAs. A total of 10 ferroptosis-related lncRNAs were identified as independent predictors of LUAD outcome, namely RP11-386M24.3, LINC00592, FENDRR, AC104699.1, AC091132.1, LANCL1-AS1, LINC-PINT, IFNG-AS1, LINC00968 and AC006129.2. The area under the curve verified that the established signatures could determine LUAD prognosis. The nomogram model was used to assess the predictive accuracy of the established signatures. Additionally, GSEA revealed that the 10 ferroptosis-related lncRNAs could be involved in immune responses in LUAD. Overall, the results of the current study may provide novel insights into the development of novel therapies or diagnostic strategies for LUAD.

BackgroundIn this single-arm study, the efficacy and safety of neoadjuvant pembrolizumab plus chemotherapy were evaluated in patients with resectable esophageal squamous cell carcinoma (ESCC).MethodsThis study included patients with ESCC of clinical stages II–IVA who underwent surgery within 4 to 6 weeks after completing treatment with pembrolizumab (200 mg) combined with a conventional chemotherapy regimen (3 cycles). The safety and efficacy of this combination treatment were evaluated as primary endpoints of the study.ResultsFrom April 2019 to August 2020, a total of 18 patients (including 14 men) were enrolled, of whom 13 patients progressed to surgery. Postoperative pathology revealed a major pathological response (MPR) in 9 cases (9/13, 69.2%) and a pathological complete response (pCR) in 6 cases (6/13, 46.2%). Five patients (5/18, 27.8%) experienced serious treatment-related adverse events (AEs) of grades 3–4. At the time of data cutoff (Mar 25, 2022), the shortest duration of follow-up was 17.8 months. Programmed death-ligand 1 (PD-L1) expression in pretreatment specimens was not significantly associated with the percentage of residual viable tumor (RVT) (r=−0.55, P=0.08). Changes in counts of CD68+ macrophage between pre- and post-treatment specimens were weakly correlated with RVT (r=0.71; P=0.07), while a positive correlation was observed between postoperative forkhead box P3-positive (Foxp3)+T cells/CD4+Tcells ratios and RVT (r=0.84, P=0.03).ConclusionsThe combination of neoadjuvant immunotherapy and chemotherapy for ESCC is associated with a high pathological response and immunologic effects in the tumor microenvironment (TME). It has acceptable toxicity and great efficacy, suggesting a strong rationale for its further evaluation in randomized clinical trials (RCTs).Trial RegistrationChiCTR2100048917.

Spatial covariance matrix (SCM) plays an important role in super-resolution direction-of-arrival (DOA) estimation, which can be derived using traditional sample average approach. Hybrid analog-digital structure has been applied in massive multiple-input multiple-output (MIMO) systems for reducing the cost in millimeter-wave communications. However, the SCM cannot be obtained because the received signals at the antennas are unavailable to the digital receiver in hybrid massive MIMO systems. Based on our previous work, we will present an adaptive reconstruction approach in this paper where the SCM can be derived iteratively using the stochastic gradient descent algorithm. In this way, direct matrix inverse can be avoided so that the computational complexity for SCM reconstruction can be reduced significantly. Converge analyzes and numerical simulations are carried out to demonstrate the advantageous performance of the adaptive reconstruction approach.

Water color is an important parameter in water quality assessment. However, the existing water color investigations have mostly focused on the lakes with areas greater 1 km2. In order to improve the understanding of the color of water bodies in China, a cloud-free composite image of China for the summer of 2015 was generated using time-series of Landsat-8 imagery and the best-available-pixel (BAP) compositing algorithm. Then, the first Forel–Ule index (FUI) water color product with a resolution of 30 m was produced for China using the generated BAP composite and the Google Earth Engine computing platform. Finally, the first national-scale assessment of the FUI of natural lakes with an area >0.01 km2 (N = 60026) was conducted based on the generated FUI product. The generated FUI product was shown to have a high degree of consistency with in situ water surface reflectance-derived FUI (R2 = 0.90, P in situ Secchi depth (R2 = 0.90, P 2 = 0.62, P 2) found to be yellow was much larger than for large lakes (area ≥ 1 km2) (50% against 28%). Our results will provide important information that can be used for preserving and restoring inland water resources.

Being able to accurately estimate inherent optical properties (IOPs) at long time scales is key to comprehending the aquatic biological and biogeochemical responses to long-term global climate change. We employed the near-infrared band and combined it with four “common bands” at visible wavelengths (around 443, 490, 551, and 670 nm) to adjust the IOPs data processing system, IDASv2. We applied the IDASv2 algorithm further to correct for the residual error in images of turbid waters. We evaluated the performance of the IDASv2 algorithm using datasets covering a wide range of natural water types from clear open ocean to turbid coastal and inland waters. Due to the water-leaving signals’ sensitivity to the optically significant constituents of highly turbid waters, the near-infrared band was very important for retrieving IOPs from those waters. In our analysis, we found that the IDASv2 algorithm provided IOPs data with Rrs) data because of the strong absorption of pure water. We tested the IDASv2 algorithm with numerically simulated and satellite observed data of turbid water. After applying IDASv2, the IOPs data were accurately determined from Rrs data contaminated by the residual error. Furthermore, the mean intermission difference between Medium Resolution Spectral Imager 2 and Visible Infrared Imaging Radiometer Rrs data at 443 and 551 nm decreased from 8%–25% to 1%–9%. These results suggest that we can accurately estimate IOPs data for natural waters including naturally clear and turbid waters.

Oblique photography technology based on UAV (unmanned aerial vehicle) provides an effective means for the rapid, real-scene 3D reconstruction of geographical objects on a watershed scale. However, existing research cannot achieve the automatic and high-precision reconstruction of water regions due to the sensitivity of water surface patterns to wind and waves, reflections of objects on the shore, etc. To solve this problem, a novel rapid reconstruction scheme for water regions in 3D models of oblique photography is proposed in this paper. It extracts the boundaries of water regions firstly using a designed eight-neighborhood traversal algorithm, and then reconstructs the triangulated irregular network (TIN) of water regions. Afterwards, the corresponding texture images of water regions are intelligently selected and processed using a designed method based on coordinate matching, image stitching and clipping. Finally, the processed texture images are mapped to the obtained TIN, and the real information about water regions can be reconstructed, visualized and integrated into the original real-scene 3D environment. Experimental results have shown that the proposed scheme can rapidly and accurately reconstruct water regions in 3D models of oblique photography. The outcome of this work can refine the current technical system of 3D modeling by UAV oblique photography and expand its application in the construction of twin watershed, twin city, etc.

Lakes are important natural sources of atmospheric methane (CH4), which should be considered in global CH4 budget estimations. However, the CH4 emissions from lakes can be highly variable, and the emissions from the river inlet region were less studied, which seriously hamper our understanding of CH4 budget estimates of lakes. Here, field measurements from over six years (2012 to 2017) at Lake Taihu, a large eutrophic shallow lake with a complicated river network, were conducted to address the issue. Results show that the river inlet region of the lake was a hot spot of CH4 emission. The CH4 emission at the river inlet region with an annual mean value of 0.183 mmol m−2 d−1 was seven times higher than that at pelagic region (0.028 mmol m−2 d−1). Peak CH4 emission occurred in warm seasons, and the lowest in cold seasons at the pelagic region, but peak emission occurred in cold seasons at the river inlet region. The seasonal CH4 emission at the pelagic region can be explained by water temperature; however, less temperature dependency of CH4 emission at the river inlet region was found. The variability of CH4 emission was linked to pollution loadings, and CH4-rich water in the inflowing river likely sustained the large CH4 emission of river inlet region.

Algal blooms happen widely in eutrophic lakes. The satellite-derived surface algal bloom area and chlorophyll-a (Chl-a) concentration are commonly used as indicators to judge the water quality. However, vertical migration of phytoplankton may seriously affect the assessment accuracy of water quality with the surface status as an indicator. In fact, the water column-integrated algal biomass is better for describing the water quality. To remotely estimate the algal biomass considering the vertical Chl-a profile (uniform or otherwise), this paper proposed a novel process-oriented algorithm applied successfully in Lake Chaohu, China. First, we built a decision tree to identify the pixel-based Chl-a profile types using the floating algae index, surface Chl-a concentration, and wind speed. Then, the nonuniform profile was expressed as a power decay function (Chl-a(z)=n1×zn2) and parameterized using the surface Chl-a concentration. Finally, the algal biomass was derived from MODIS/Aqua satellite data. The estimation results were acceptable and the bias was −19.95%. The monthly mean algal biomass varied significantly and exhibited two peaks in spring and summer. The annual mean algal biomass increased from 2003 to 2010 (Pearson r = 0.82; p

Summary: The recent mass mortality event of more than 330 African elephants in Botswana has been attributed to biotoxins produced by cyanobacteria; however, scientific evidence for this is lacking. Here, by synthesizing multiple sources of data, we show that, during the past decades, the widespread hypertrophic waters in Southern Africa have entailed an extremely high risk and frequent exposure of cyanotoxins to the wildlife within this area, which functions as a hotspot of mammal species richness. The hot and dry climatic extremes have most likely acted as the primary trigger of the recent and perhaps also of prehistoric mass mortality events. As such climate extremes are projected to become more frequent in Southern Africa in the near future, there is a risk that similar tragedies may take place, rendering African megafauna species, especially those that are already endangered, in risk of extinction. Moreover, cyanotoxin poisoning amplified by climate change may have unexpected cascading effects on human societies. Seen within this perspective, the tragic mass death of the world's largest terrestrial mammal species serves as an alarming early warning signal of future environmental catastrophes in Southern Africa. We suggest that systematic, quantitative cyanotoxin risk assessments are made and precautionary actions to mitigate the risks are taken without hesitation to ensure the health and sustainability of the megafauna and human societies within the region. Public Summary: • Cyanotoxin is the most likely cause for the massive death of elephants in Botswana. • Mammal species hotspot in Africa is under increasing risk of cyanotoxin poisoning. • This tragic event serves as an alarming early warning signal of future catastrophes. • Systematic assessments on cyanotoxin risk are strongly suggested in Southern Africa.

Lake Dianchi is one of the most eutrophic lakes in China. The decline in water quality and the occurrence of massive algal blooms pose a significant threat to the health and environmental safety of the water ecosystem, making Lake Dianchi a key concern for algal bloom management in China. Obtaining the spatiotemporal dynamics of algal blooms for the longest time possible is crucial to algal bloom management and future prediction. However, it is difficult to acquire a long-term record of algal blooms from a single sensor in order to cover a more extended period of eutrophication in the lake due to the limitation of the spatial and temporal resolution of the sensors. In this study, Landsat and Moderate-Resolution Imaging Spectroradiometer (MODIS) images were combined with the Floating Algae Index (FAI) to reconstruct a unified time series of bloom areas to analyze the algal bloom dynamics in Lake Dianchi in recent decades. Regarding the interannual variation, the bloom area showed an increasing trend from 1987 to 2021, with larger bloom areas in 1991–1992, 2000–2003, 2012–2013, and 2020–2021. In terms of seasonal characteristics, the bloom area was significantly more prominent in the rainy season compared with the dry season during the year. The spatial distribution of the bloom frequency showed a pattern of higher frequencies in the north and lower frequencies in the south. From 2000 to 2021, the initial bloom time and bloom duration showed a trend of delaying and then advancing and decreasing and then increasing, respectively. We analyzed the importance of long-term records of algal blooms and found that the percentage of rainy season images is an essential factor in reconstructing time series based on different sensors. In addition, the relationship between environmental factors and algal blooms was analyzed. The results show that wind speed and air temperature were the main meteorological factors controlling the interannual variation in algal blooms in Lake Dianchi. Water quality factors such as nutrients have less of an influence on the variation in algal blooms because the algal growth demand has been met. Environmental management measures taken by local governments have led to improvements in the lake’s trophic state, and continued strengthening of environmental pollution control is expected to curb the algal blooms in Lake Dianchi. This study provides a long-term record of algal blooms in Lake Dianchi, which provides essential reference information for a comprehensive understanding of the development process of algal blooms in Lake Dianchi and its sustainable development.

Climate change and human activities have been heavily affecting oceanic and inland waters, and it is critical to have a comprehensive understanding of the aquatic optical properties of lakes. Since many key watercolor parameters of Qinghai Lake are not yet available, this paper aims to study the spatial and temporal variations of the water clarity (i.e., Secchi-disk depth, ZSD) and suspended particulate matter concentration (CSPM) in Qinghai Lake from 2001 to 2020 using MODIS images. First, the four atmospheric correction models, including the NIR–SWIR, MUMM, POLYMER, and C2RCC were tested. The NIR–SWIR with decent accuracy in all bands was chosen for the experiment. Then, four existing models for ZSD and six models for CSPM were evaluated. Two semi-analytical models proposed by Lee (2015) and Jiang (2021) were selected for ZSD (R2 = 0.74) and CSPM (R2 = 0.73), respectively. Finally, the distribution and variation of the ZSD and CSPM were derived over the past 20 years. Overall, the water of Qinghai Lake is quite clear: the monthly mean ZSD is 5.34 ± 1.33 m, and CSPM is 2.05 ± 1.22 mg/L. Further analytical results reveal that the ZSD and CSPM are highly correlated, and the relationship can be formulated with ZSD=8.072e−0.212CSPM (R2 = 0.65). Moreover, turbid water mainly exists along the edge of Qinghai Lake, especially on the northwestern and northeastern shores. The variation in the lakeshore exhibits some irregularity, while the main area of the lake experiences mild water quality deterioration. Statistically, 81.67% of the total area is dominated by constantly increased CSPM, and the area with decreased CSPM occupies 4.56%. There has been distinct seasonal water quality deterioration in the non-frozen period (from May to October). The water quality broadly deteriorated from 2001 to 2008. The year 2008 witnessed a sudden distinct improvement, and after that, the water quality experienced an extremely inconspicuous degradation. This study can fill the gap regarding the long-time monitoring of water clarity and total suspended matter in Qinghai Lake and is expected to provide a scientific reference for the protection and management of the lake.

Water transparency, usually denoted by Secchi disk depth (SSD), represents the first-order description of water quality and has important implications for the diversity and productivity of aquatic life. In China, lakes supply freshwater and ecosystem services to nearly a billion people. Therefore, real time monitoring of lake transparency is of great significance. Moreover, understanding how and why transparency varies in space and time in response to different driving forces is needed to understand, manage, and predict lake water quality. Based on the time-saving and low-cost Google Earth Engine cloud platform, this study developed a new algorithm for quickly mapping SDDs in Chinese lakes. SDDs were retrieved for 412 Chinese lakes (> 20 km2) for the period 2000–2018. Results demonstrated that lake water depth spatially differentiated transparency. Deep lakes usually had high transparency and water depth explained 88.81 % of the spatial variations. With increasing catchment vegetation coverage and lake water depth, 70.15 % of lakes witnessed increasing transparency during 2000–2018. Of these 42.72 % were significant (p < 0.05). Transparency of deep lakes was generally determined by phytoplankton density not sediment resuspension. Minimum transparency occurred in summer. Future increases in lake water levels in response to factors such as climate change may contribute to further improvements in transparency. Management should focus on controlling eutrophication and increasing vegetation cover in catchments.

Algal blooms and the resulting deterioration of water quality have threatened the environmental health of inland lakes. This study investigated the spatiotemporal dynamics of gypsum blooms in the Salton Sea, the largest inland water body in California. An innovative gypsum bloom index (GI) was proposed to detect gypsum blooms based on Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra data for three bands (469, 555, and 859 nm) during 2000–2018. Discoloration (from green to reddish brown) was observed in the gypsum bloom-infested waters mapped using the hue, saturation, and value (HSV) color model. We found that: (1) the GI is able to effectively map gypsum blooms in the Salton Sea with a relatively high accuracy (85 %); (2) the largest gypsum bloom area was observed in summer (August and September), with notable gypsum blooms lasting more than 40 days in 2003, 2005, 2006, 2016, and 2018; (3) the gypsum bloom-infested waters shifted from the center to the southeastern shore of the lake during 2003–2018, and the frequency of gypsum blooms significantly increased since 2008. Meanwhile, the temporal and spatial differences between the northwestern and southeastern lake increased over the observation period; and (4) discoloration occurred in the gypsum bloom-infested waters and lasted for 14 days on average, longer periods of discoloration (more than 20 days) occurred d in 2002, 2016, and 2018. The reconstruction of gypsum bloom dynamics in the Salton Sea provides valuable information for the practical management and restoration of the Salton Sea.

A rapid degradation of aquatic vegetations in Taihu Lake has roused a wide attention in recent years. Giving large-scale harvesting activity on aquatic vegetation since 2012, whether water eutrophication or the human harvest activity induced the degradation remains controversial and unclear. In this study, based on Landsat and HJ-CCD data acquired from 1984 to 2016 and a 12-year field observation (2005–2016) of water quality, a method was proposed to quantitatively assess impacts of harvesting activity and water quality change on degradations of both floating-leaved aquatic vegetation (FAV) and submerged aquatic vegetation (SAV) in Taihu Lake. First, areas of FAV and SAV covers from 1984 to 2016 in Taihu Lake were mapped using the satellite data, and then the mapped areas were modified to those on a reference date by using phenological curves of FAV and SAV covers. Next, correlations between water quality data and FAV and SAV covers were analyzed by using Pearson correlation analysis based on the data before implementing the human harvesting activity (i.e., before 2012), and multiple general linear models were established based on the selected water quality variables with p-value

Direction-of-arrival (DOA) estimation in the presence of mutual coupling among the antennas is an important problem in direction finding systems. To address the mutual coupling issue, a polynomial manifold interpolation approach is proposed in this paper. In this approach, a set of sample manifold vectors corresponding to pre-determined DOA grids are acquired and stored in advance in the direction finding receiver. The manifold vector corresponding to the DOA of interest can be obtained via polynomial interpolation over the sample manifold vectors. Then, MUtiple SIgnal Classification (MUSIC) algorithm can be used over the interpolated manifold vector to obtain the desired DOA estimation. Compared to the linear manifold interpolation, the density of the pre-determined DOA grids can be greatly reduced for polynomial manifold interpolation, and thus the burden for acquisition and storage of the sample manifold vectors can be also reduced substantially. Simulation results are also presented in this paper to demonstrate the effectiveness of the proposed approach.

River water quality (RWQ) data has obvious characteristics of spatial and temporal distribution, and tables are conventionally exploited for storage of multi-period monitoring data of RWQ; however, neither effective visualization nor accurate analysis of the obtained data can be realized due to its dispersion character. In this paper, a novel spatiotemporal data model is proposed for RWQ data to realize conveniently data representation and spatiotemporal analysis. In this model, a spatial point, containing both location and dynamic water quality information, is considered as the basic element of river spaces, and methods of expanding a point to a line segment, a flat surface and a cube are designed respectively so as to make this model be applicable to different generalizations of river spaces. Moreover, a temporal data storage structure is designed so that efficient inquiry and advanced analysis of RWQ data can be guaranteed and the occupied memory space can be reduced. Finally, case studies are conducted by performing 3D visualization, trend analysis and anomaly identification on RWQ data, the result of which showing that tridimensional representation of RWQ data can be realized efficiently, the computational complexity is reduced significantly and the occupied memory space of monitoring data is effectively economized. Accordingly, the proposed spatiotemporal data model can contribute to the efficient visualization and advanced analysis of RWQ data.

Abstract The aim of this study was to evaluate the effects of intravitreal injection of neural stem cells (NSCs) originating from human umbilical cord-derived mesenchymal stem cells (UC-MSCs) on neurodegeneration of diabetic retinopathy (DR) in rats. UC-MSCs were isolated and passaged, followed by induction to NSCs in neural differentiation medium. Four weeks following NSC transplantation, treatment attenuated retinal vascular dysfunction compared with non-treated rats, and BDNF and Thy-1 expression was significantly higher in the treated group than in the control group. Treatment of diabetic rats with NSCs prevented the decrease in BDNF levels caused by diabetes. The average leakage of Evans Blue (EB) dye in the treated group was significantly less than that in the control group. These morphological improvements were accompanied by a restoration of vision, as documented by F-ERG. NSCs originating from MSCs demonstrated a neuroprotective effect by increasing the number of surviving RGCs and significantly reducing the progression of DR. Thus, transplantation of NSCs could be a novel strategy for the treatment of neurodegeneration in DR.

Lake phenology is essential for understanding the lake freeze-thaw cycle effects on terrestrial hydrological processes. The Qinghai-Tibetan Plateau (QTP) has the most extensive ice reserve outside of the Arctic and Antarctic poles and is a sensitive indicator of global climate changes. Qinghai Lake, the largest lake in the QTP, plays a critical role in climate change. The freeze-thaw cycles of lakes were studied using daily Moderate Resolution Imaging Spectroradiometer (MODIS) data ranging from 2000–2018 in the Google Earth Engine (GEE) platform. Surface water/ice area, coverage, critical dates, surface water, and ice cover duration were extracted. Random forest (RF) was applied with a classifier accuracy of 0.9965 and a validation accuracy of 0.8072. Compared with six common water indexes (tasseled cap wetness (TCW), normalized difference water index (NDWI), modified normalized difference water index (MNDWI), automated water extraction index (AWEI), water index 2015 (WI2015) and multiband water index (MBWI)) and ice threshold value methods, the critical freeze-up start (FUS), freeze-up end (FUE), break-up start (BUS), and break-up end (BUE) dates were extracted by RF and validated by visual interpretation. The results showed an R2 of 0.99, RMSE of 3.81 days, FUS and BUS overestimations of 2.50 days, and FUE and BUE underestimations of 0.85 days. RF performed well for lake freeze-thaw cycles. From 2000 to 2018, the FUS and FUE dates were delayed by 11.21 and 8.21 days, respectively, and the BUS and BUE dates were 8.59 and 1.26 days early, respectively. Two novel key indicators, namely date of the first negative land surface temperature (DFNLST) and date of the first positive land surface temperature (DFPLST), were proposed to comprehensively delineate lake phenology: DFNLST was approximately 37 days before FUS, and DFPLST was approximately 20 days before BUS, revealing that the first negative and first positive land surface temperatures occur increasingly earlier.

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash–Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application.

The extension and frequency of algal blooms in surface waters can be monitored using remote sensing techniques, yet knowledge of their vertical distribution is fundamental to determine total phytoplankton biomass and understanding temporal variability of surface conditions and the underwater light field. However, different vertical distribution classes of phytoplankton may occur in complex inland lakes. Identification of the vertical profile classes of phytoplankton becomes the key and first step to estimate its vertical profile. The vertical distribution profile of phytoplankton is based on a weighted integral of reflected light from all depths and is difficult to determine by reflectance data alone. In this study, four Chla vertical profile classes (vertically uniform, Gaussian, exponential and hyperbolic) were found to occur in three in situ vertical surveys (28 May, 19–24 July and 10–12 October) in a shallow eutrophic lake, Lake Chaohu. We developed and validated a classification and regression tree (CART) to determine vertical phytoplankton biomass profile classes. This was based on an algal bloom index (Normalized Difference algal Bloom Index, NDBI) applied to both in situ remote sensing reflectance (Rrs) and MODIS Rayleigh-corrected reflectance (Rrc) data in combination with data of local wind speed. The results show the potential of retrieving Chla vertical profiles information from integrated information sources following a decision tree approach.

As the fifth largest freshwater lake in China, Lake Chaohu has drawn increasing attention due to the decline in water quality and the occurrence of massive algal blooms. We applied an algae pixel-growing algorithm to MODIS Terra or Aqua data (2100 images) to characterize surface floating algae bloom dynamics from 2000 to 2013 with respect to meteorological and lake nutrient conditions. The results show an increase in surface algal bloom coverage, frequency, and duration with a trend toward earlier bloom formation. Importantly, spatial and temporal patterns in the historically less compromised eastern and middle lake areas show that water quality conditions are deteriorating. This has occurred at the same time as lake management has made a catchment scale effort to reduce impact. Our results show that nutrient concentrations were not the main driver of inter-annual bloom variations. Local meteorological conditions, in particular wind speed and temperature, played an important role in the dynamics of floating algal bloom. This highlights the important challenges for lake management.

For near real-time water applications, the Moderate Resolution Imaging Spectroradiometers (MODIS) on Terra and Aqua are currently the only satellite instruments that can provide well-calibrated top-of-atmosphere (TOA) radiance data over the global aquatic environments. However, TOA radiance data in the MODIS ocean bands over turbid atmosphere in east China often saturate, leaving only four land bands to use. In this study, an approach based on Empirical Orthogonal Function (EOF) analysis has been developed and validated to estimate chlorophyll a concentrations (Chla, μg/L) in surface waters of Taihu Lake, the third largest freshwater lake in China. The EOF approach analyzed the spectral variance of normalized Rayleigh-corrected reflectance (Rrc) data at 469, 555, 645, and 859 nm, and subsequently related that variance to Chla using 28 concurrent MODIS and field measurements. This empirical algorithm was then validated using another 30 independent concurrent MODIS and field measurements. Image analysis and radiative transfer simulations indicated that the algorithm appeared to be tolerant to aerosol perturbations, with unbiased RMS uncertainties of

Aquatic vegetation plays an important role in maintaining the balance of lake ecosystems. Thus, classifying and mapping aquatic vegetation is a priority for lake management. Classification tree (CT) approaches have been used successfully to map aquatic vegetation from spectral indices obtained from remotely sensed images. However, due to the effects of extrinsic and intrinsic factors, applying a CT model developed for imagery from one date to imagery from another date or a different dataset likely would reduce the classification accuracy. In this study, three spectral features (SFs) were selected to develop a CT model for identifying aquatic vegetation in Taihu Lake. Three traditional CT models with three SFs were developed using CT analysis based on satellite images acquired on 11 July, 16 August and 26 September 2013, and corresponding ground-truth samples, from the Huangjing-1A/B Charge-Coupled Device (HJ-CCD) images, environment and disaster reduction small satellites that were launched by China Center for Resources Satellite Data and Application (CRESDA). The overall accuracies of traditional CT models were 82%, 80% and 84%. We then tested two methods to modify CT model thresholds to adjust the traditional CT models based on image date to determine if the results would enable us to map and classify aquatic vegetation for periods when no ground-based data were available. We assessed the results with ground-truth samples and area agreement with traditional CT models. Results showed that CT models modified from a linear adjustment based on the relationship between ranked values of SFs between two image dates produced map accuracies comparable with those obtained from the traditional CT models and suggest that the method we proposed is feasible for mapping aquatic vegetation types in lakes when ground data are not available.