Eutrophication evolution of lakes in China: Four decades of observations from space.

The lake eutrophication is highly variable in both time and location, and greatly restricts the sustainable development of water resources. The lack of national eutrophication evaluation for multi-scale lakes limits the pertinent governance and sustainable management of water quality. In this study, a remote sensing approach was developed to capture 40-year dynamics of trophic state index (TSI) for nationwide lakes in China. 32% of lakes (N = 1925) in China were eutrophic and 26% were oligotrophic, and a longitudinal pattern was discovered, with the 40-year average TSI of 62.26 in the eastern plain compared to 23.72 in the Tibetan Plateau. A decreasing trend was further observed in the past four decades with a correlation of -0.16, which was mainly discovered in the Tibetan Plateau lakes (r > -0.90, p < 0.01). The contribution of climate change and human activities was quantified and varied between lake zones, with anthropogenic factors playing a dominant role in the east plain lakes (88%, N = 473) and large lakes are subject to a more complex driving mechanism (≥ 3 driving factors). The study expands the spatiotemporal scale for eutrophication monitoring and provides an important base for strengthening lake management and ecological services.

Tracing sediment sources in a plain river network area by using optimized experimental design and reflectance spectroscopy.

Soil erosion in a plain river network area with dense rivers, fertile land, and agricultural development is easily causes river siltation, agricultural non-point source pollution, and water eutrophication. Therefore, the negative impact of the sediment on the environment cannot be underestimated. Most traditional sediment fingerprint tracing studies have focused on mountain basins and lack a scheme suitable for plain river network sediment tracing. Here, a typical plain river network in the Taihu Basin was selected as the study area. The flow structure and characteristics were analysed, and a sampling scheme for the stream segment and a two-step model of sediment tracing in a plain river network were proposed to quantitatively distinguish the types of sediment sources. The results indicated that the traditional discriminant function analysis adequately distinguishes the contribution rate of basin soil and has a good validation accuracy (R2 = 0.96, root mean square error of calibration = 5.91 %), whereas Random Forest obtains better discrimination results by mining non-linear information in the soil spectra of different land types, with R2 values of 0.89, 0.83, and 0.80 for farmland, forest, and grassland, respectively. The average proportion of soil in the sediment in the watershed was 23 %, and the proportion of soil in the watershed increased from upstream to downstream. The sediment sources of the Caoqiao, Yincun, and Shaoxiang Rivers mainly came from grassland (44 %), forest (39 %), and farmland (42 %), respectively. Land-use distribution, water conservation facilities, and soil particle size were the main factors affecting these sources. Each river adopts measures to remove the corresponding pollutants, optimise water and soil conservation measures for riverbank green belts and forest, and regularly clean up silt in water conservancy ditches and rivers, which can reduce the pollution impact caused by sediment.

Potential of Mie-Fluorescence-Raman Lidar to Profile Chlorophyll a Concentration in Inland Waters.

Vertical distribution of phytoplankton is crucial for assessing the trophic status and primary production in inland waters. However, there is sparse information about phytoplankton vertical distribution due to the lack of sufficient measurements. Here, we report, to the best of our knowledge, the first Mie-fluorescence-Raman lidar (MFRL) measurements of continuous chlorophyll a (Chl-a) profiles as well as their parametrization in inland water. The lidar-measured Chl-a during several experiments showed good agreement with the in situ data. A case study verified that MFRL had the potential to profile the Chl-a concentration. The results revealed that the maintenance of subsurface chlorophyll maxima (SCM) was influenced by light and nutrient inputs. Furthermore, inspired by the observations from MFRL, an SCM model built upon surface Chl-a concentration and euphotic layer depth was proposed with root mean square relative difference of 16.5% compared to MFRL observations, providing the possibility to map 3D Chl-a distribution in aquatic ecosystems by integrated active-passive remote sensing technology. Profiling and modeling Chl-a concentration with MFRL are expected to be of paramount importance for monitoring inland water ecosystems and environments.

Vertical distribution analysis and total mass estimation of nitrogen and phosphorus in large shallow lakes.

Analysing the vertical distribution of nutrient salts and estimating the total mass of lake nutrients is helpful for the management of lake nutrient status and the formulation of drainage standards in basins. However, studies on nitrogen (N) and phosphorus (P) in lakes have focused on obtaining measures of N and P concentrations, but no understanding exists on the vertical distribution of N and P in the entire water column. The present study proposes algorithms for estimating the total masses of N/P per unit water column (ALGO-TNmass/ALGO-TPmass) for shallow eutrophic lakes. Using Lake Taihu as an example, the total masses of nutrients in Lake Taihu in the historical period were obtained, and the algorithm performance was discussed. The results showed that the vertical distribution of nutrients decreased with increasing depth and exhibited a quadratic distribution. Surface nutrients and chlorophyll-a concentrations play important roles in the vertical distribution of nutrients. Based on conventional surface water quality indicators, algorithms for the vertical nutrient concentration in Lake Taihu were proposed. Both algorithms had good accuracy (ALGO-TNmass R2 > 0.75, RMSE <0.57; ALGO-TPmass R2 > 0.80, RMSE ≤0.50), the ALGO-TPmass had better applicability than the ALGO-TNmass, and had good accuracy in other shallow lakes. Therefore, deducing the TPmass using conventional water quality indicators in surface water, which not only simplifies the sampling process but also provides an opportunity for remote sensing technology to monitor the total masses of nutrients, is feasible. The long-term average total mass of N was 11,727 t, showing a gradual downward trend before 2010, after which it stabilised. The maximum and minimum intra-annual total N masses were observed in May and November, respectively. The long-term average total mass of P was 512 t, showing a gradual downward trend before 2010, and a slow upward trend thereafter. The maximum and minimum intra-annual total masses of P occurred in August and February or May, respectively. The correlation between the total mass of N and meteorological conditions was not obvious, whereas some influence on the total mass of P was evident, particularly water level and wind speed.

Bayesian Uncertainty Modeling for P300-Based Brain-Computer Interface.

P300 potential is important to cognitive neuroscience research, and has also been widely applied in brain-computer interfaces (BCIs). To detect P300, many neural network models, including convolutional neural networks (CNNs), have achieved outstanding results. However, EEG signals are usually high-dimensional. Moreover, since collecting EEG signals is time-consuming and expensive, EEG datasets are typically small. Therefore, data-sparse regions usually exist within EEG dataset. However, most existing models compute predictions based on point-estimate. They cannot evaluate prediction uncertainty and tend to make overconfident decisions on samples located in data-sparse regions. Hence, their predictions are unreliable. To solve this problem, we propose a Bayesian convolutional neural network (BCNN) for P300 detection. The network places probability distributions over weights to capture model uncertainty. In prediction phase, a set of neural networks can be obtained by Monte Carlo sampling. Integrating the predictions of these networks implies ensembling. Therefore, the reliability of prediction can be improved. Experimental results demonstrate that BCNN can achieve better P300 detection performance than point-estimate networks. In addition, placing a prior distribution over the weight acts as a regularization technique. Experimental results show that it improves the robustness of BCNN to overfitting on small dataset. More importantly, with BCNN, both weight uncertainty and prediction uncertainty can be obtained. The weight uncertainty is then used to optimize the network through pruning, and the prediction uncertainty is applied to reject unreliable decisions so as to reduce detection error. Therefore, uncertainty modeling provides important information to further improve BCI systems.

Horizontal and vertical migration of cyanobacterial blooms in two eutrophic lakes observed from the GOCI satellite.

Under the variations of natural conditions (temperature, wind speed, light, et al.) and self-regulation of buoyancy, cyanobacterial blooms can change rapidly in a short time. The Geostationary Ocean Color Imager (GOCI) can provide hourly monitoring of the dynamics of algal blooms (eight times per day), and has potential in observing the horizontal and vertical movement of cyanobacterial blooms. Based on the fractional floating algae cover (FAC), the diurnal dynamics and migration of floating algal blooms were evaluated, and the horizontal and vertical migration speed of phytoplankton was estimated from the proposed algorithm in two eutrophic lakes, Lake Taihu and Lake Chaohu in China. The locations, number, and area of algal bloom patches showed the hotspots and horizontal movement of bloom patches. The spatial and seasonal variations of the vertical velocities indicated that both the rising and sinking speed were higher in summer and autumn than those in spring and winter. The factors affecting diurnal horizontal and vertical migrations of phytoplankton were analyzed. Diffuse horizontal irradiance (DHI), direct normal irradiance (DNI), and temperature had significant positive relationships with FAC in the morning. Wind speed contributed 18.3 and 15.1% to the horizontal movement speed in Lake Taihu and Lake Chaohu, respectively. The rising speed was more related to DNI and DHI in Lake Taihu and Lake Chaohu with contribution of 18.1 and 16.6%. The horizontal and vertical movement of algae provide important information for understanding phytoplankton dynamics and the prediction and warning of algal blooms in lake management.

GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality.

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.

Detection of aberrant DNA methylation patterns in sperm of male recurrent spontaneous abortion patients.

Aberrant DNA methylation patterns in sperm are a cause of embryonic failure and infertility, and could be a critical factor contributing to male recurrent spontaneous abortion (RSA). The purpose of this study was to reveal the potential effects of sperm DNA methylation levels in patients with male RSA. We compared sperm samples collected from fertile men and oligoasthenospermia patients. Differentially methylated sequences were identified by reduced representation bisulfite sequencing (RRBS) methods. The DNA methylation levels of the two groups were compared and qRT-PCR was used to validate the expression of genes showing differential methylation. The results indicated that no difference in base distribution was observed between the normal group and the patient group. However, the chromosome methylation in these two groups was markedly different. One site was located on chromosome 8 and measured 150 bp, while the other sites were on chromosomes 9, 10, and X and measured 135 bp, 68 bp, and 136 bp, respectively. In particular, two genes were found to be hypermethylated in these patients, one gene was DYDC2 (placed in the differential methylation region of chromosome 10), and the other gene was NXF3 (located on chromosome X). Expression levels of DYDC2 and NXF3 in the RSA group were significantly lower than those in the normal group (P < 0.05). Collectively, these results demonstrated that changes in DNA methylation might be related to male RSA. Our findings provide important information regarding the potential role of sperm DNA methylation in human development.

5d → 4f transition of a lanthanide-activated MGa2S4 (M = Ca, Sr) semiconductor for mechanical-to-light energy conversion mediated by structural distortion.

Materials exhibiting mechanoluminescence (ML) are a class of smart materials capable of mechanical-to-light energy conversion. Thus, ML materials have been widely used in various electronic applications such as smart sensors, security systems, human-machine interfaces, and energy harvesting systems. Herein, we report a centrosymmetric ML semiconductor host material family MGa2S4 (M = Ca, Sr), which features in-layered structures constructed with unique distorted bi-tetrahedral [Ga2S2S4/2] lattice units. It exhibited similar structural characteristics to the well-known ML semiconductor host ZnS. Remarkably, the lanthanide ions of 5d → 4f transition-activated hosts showed sensitive and high ML luminance under natural lighting upon mechanical stimulation; thus, an efficient mechanical-to-light energy conversion of a self-powered display was achieved. Moreover, because of structural distortion and strain-gradient-induced electrical polarization in the ML host material upon mechanical stimulation, a ML mechanism based on the synergy effect between local electronic polarization and flexoelectricity was proposed. This study facilitates a deeper understanding of the relationship between the structure and underlying ML, and promotes further development of ML-material-based products and technologies.

Automatic Walking Method of Construction Machinery Based on Binocular Camera Environment Perception.

In this paper, we propose an end-to-end automatic walking system for construction machinery, which uses binocular cameras to capture images of construction machinery for environmental perception, detects target information in binocular images, estimates the relative distance between the current target and cameras, and predicts the real-time control signal of construction machinery. This system consists of two parts: the binocular recognition ranging model and the control model. Objects within 5 m can be quickly detected by the recognition ranging model, and at the same time, the distance of the object can be accurately ranged to ensure the full perception of the surrounding environment of the construction machinery. The distance information of the object, the feature information of the binocular image, and the control signal of the previous stage are sent to the control model; then, the prediction of the control signal of the construction machinery can be output in the next stage. In this way, the automatic walking experiment of the construction machinery in a specific scenario is completed, which proves that the model can control the machinery to complete the walking task smoothly and safely.

Development of remote sensing algorithm for total phosphorus concentration in eutrophic lakes: Conventional or machine learning?

Phosphorus is a limiting nutrient in freshwater ecosystems. Therefore, the estimation of total phosphorus (TP) concentration in eutrophic water using remote sensing technology is of great significance for lake environmental management. However, there is no TP remote sensing model for lake groups, and thus far, specific models have been used for specific lakes. To address this issue, this study proposes a framework for TP estimation. First, three algorithm development frameworks were compared and applied to the development of an algorithm for Lake Taihu, which has complex water environment characteristics and is a representative of eutrophic lakes. An Extremely Gradient Boosting (BST) machine learning framework was proposed for developing the Taihu TP algorithm. The machine learning algorithm could mine the relationship between FAI and TP in Lake Taihu, where the optical properties of the water body are dominated by phytoplankton. The algorithm exhibited robust performance with an R2 value of 0.6 (RMSE = 0.07 mg/L, MRE = 43.33%). Then, a general TP algorithm (R2 = 0.64, RMSE = 0.06 mg/L, MRE = 34.13%) was developed using the proposed framework and tested in seven other lakes using synchronous image data. The algorithm accuracy was found to be affected by aquatic vegetation and enclosure aquaculture. Third, compared with field investigations in other studies on Lake Taihu, the Taihu TP algorithm showed good performance for long-term TP estimation. Therefore, the machine learning framework developed in this study has application potential in large-scale spatio-temporal TP estimation in eutrophic lakes.

Noctiluca blooms in the East China Sea bounded by ocean fronts.

A recent study demonstrated the possibility of satellite-based detection of surface blooms of the heterotrophic dinoflagellate, red Noctiluca scintillans (RNS). The study further documented RNS bloom patterns in the East China Sea (ECS) between 2000 and 2017. Here, complemented by more recent satellite observations between 2018 and 2020, the 21-year bloom record shows that while bloom distributions vary in different years and annual cumulative bloom footprint shows an increasing trend, the 21-year cumulative bloom footprint is bounded by major ocean fronts such as the Kuroshio Front. Of all observations, 2020 is a critical year to "complete" the footprint as extra discharge from the 2020 Yangtze River flood event, combined with ocean currents, transports the bloom to the most northeast location of the footprint although the riverine influence reaches at least 128°E, well beyond the RNS footprint.

Molten Salt Shielded Synthesis of Monodisperse Layered CaZnOS-Based Semiconductors for Piezophotonic and X-Ray Detection Applications.

CaZnOS-based semiconductors are the only series of material system discovered that can simultaneously realize a large number of dopant elements to directly fulfill the highly efficient full-spectrum functionality from ultraviolet to near-infrared under the same force/pressure. Nevertheless, owing to the high agglomeration of the high temperature solid phase manufacturing process, which is unable to control the crystal morphology, the application progress is limited. Here, the authors report first that CaZnOS-based fine monodisperse semiconductor crystals with various doping ions are successfully synthesized by a molten salt shielded method in an air environment. This method does not require inert gas ventilation, and therefore can greatly reduce the synthesis cost and more importantly improve the fine control of the crystal morphology, along with the crystals' dispersibility and stability. These doped semiconductors can not only realize different colors of mechanical-to-optical energy conversion, but also can achieve multicolor luminescence under low-dose X-ray irradiation, moreover their intensities are comparable to the commercial NaI:Tl. They can pave the way to the new fields of advanced optoelectronic applications, such as piezophotonic systems, mechanical energy conversion and harvesting devices, intelligent sensors, and artificial skin as well as X-ray applications.

Effect of BMP-Wnt-Nodal signal on stem cell differentiation.

The generation of germ cells from embryonic stem cells in vitro has current historical significance. Western blot, qPCR, immunofluorescence and flow cytometry assays were used to investigate the differences in expression levels of totipotency and specific markers for Wnt regulation and the related signalling pathways during primordial germ cell-like cell (PGCLC) induction and differentiation. During PGCLC induction, activation of WNT3a increased the expression of NANOG, SOX2 and OCT4, but Mvh, DAZL, Blimp1, TFAP2C, Gata4, SOX17, EOMES, Brachyury and PRDM1 expression levels were significantly reduced. Inhibition of the WNT signal demonstrated the opposite effect. Similarly, inhibitors of BMP and the Nodal/Activin signal were used to determine the effect of signal pathways on differentiation. CER1 affected the Wnt signal and differentiation, but the inhibitor SB only regulated differentiation. BMP-WNT-NODAL were mainly responsible for regulating differentiation. Our results provide a reliable theoretical basis and feasibility for further clinical medical research.

Capsule Network for ERP Detection in Brain-Computer Interface.

Event-related potential (ERP) is bioelectrical activity that occurs in the brain in response to specific events or stimuli, reflecting the electrophysiological changes in the brain during cognitive processes. ERP is important in cognitive neuroscience and has been applied to brain-computer interfaces (BCIs). However, because ERP signals collected on the scalp are weak, mixed with spontaneous electroencephalogram (EEG) signals, and their temporal and spatial features are complex, accurate ERP detection is challenging. Compared to traditional neural networks, the capsule network (CapsNet) replaces scalar-output neurons with vector-output capsules, allowing the various input information to be well preserved in the capsules. In this study, we expect to utilize CapsNet to extract the discriminative spatial-temporal features of ERP and encode them in capsules to reduce the loss of valuable information, thereby improving the ERP detection performance for BCI. Therefore, we propose ERP-CapsNet to perform ERP detection in a BCI speller application. The experimental results on BCI Competition datasets and the Akimpech dataset show that ERP-CapsNet achieves better classification performances than do the state-of-the-art techniques. We also use a decoder to investigate the attributes of ERPs encoded in capsules. The results show that ERP-CapsNet relies on the P300 and P100 components to detect ERP. Therefore, ERP-CapsNet not only acts as an outstanding method for ERP detection, but also provides useful insights into the ERP detection mechanism.

Optimized remote sensing estimation of the lake algal biomass by considering the vertically heterogeneous chlorophyll distribution: Study case in Lake Chaohu of China.

Due to the difference of vertical distribution of algae in lakes, it is necessary to carry out remote sensing estimation of algal biomass based on the vertically heterogeneous distribution of chlorophyll in order to improve the accuracy of biomass inversion. A new algorithm is proposed and validated to measure algal biomass in Lake Chaohu based on the Moderate Resolution Imaging Spectrometer (MODIS) images. The algal biomass index (ABI) is defined as the difference in remote-sensing reflectance (Rrs, sr-1) at 555 nm normalized against two baselines with one formed linearly between Rrs(859) and Rrs(469) and another formed linearly between Rrs(645) and Rrs(469). Both theory and model simulations show that ABI has a good relation with the algal biomass in the euphotic zone (R2 = 0.88, p < 0.01, N = 50). Field data were further used to estimate the biomass outside the euphotic layer through an empirical algorithm. The ABI algorithm was applied to MODIS Rayleigh-corrected reflectance (Rrc) data after testing the sensitivity to sun glint and thickness of aerosols, which showed an acceptable precision (root mean square error < 21.31 mg and mean relative error < 16.08%). Spectral analyses showed that ABI algorithm was immune to concentration of colored dissolved organic matter (CDOM) but relatively sensitive to suspended particulate inorganic matter (SPIM), which can be solved by using Turbid Water Index (TWI) though in such a challenging environment. A long-term (2012-2017) estimation of algal biomass was further calculated based on the robust algorithm, which shows both seasonal and spatial variations in Lake Chaohu. Tests of ABI algorithm on Sentinel-3 OLCI demonstrates the potential for application in other remote sensors, which meets the need of observation using multi-sensor remote sensing in the future.

Effect of Health Care and Rehabilitation Nursing and Analysis of Neurovascular Preservation of Patients Undergoing Reconstruction of Severed Finger under X-ray Image Examination.

OBJECTIVE: The study aimed to provide a guideline for continuous rehabilitation nursing for patients with severe finger replantation, and establish a satellite contact point for patients with severe finger replantation after discharge, so as to ensure scientific and effective rehabilitation training after discharge and explore the role of continuous rehabilitation nursing measurement in severe finger rehabilitation and neurovascular preservation. METHODS: A total of 380 patients accepting neurovascular preservation finger replantation in the hand surgery department were divided into an observation group and a control group according to the number of hospitalizations. All patients underwent reconstructive surgery of severed finger. X-ray filming was used to monitor the postoperative nursing effect of neurovascular preservation of severed finger. The discharged patient information questionnaire was filled 3 days before the discharge. Then, a patient information database was established, and rehabilitation training was performed. Finally, sexual rehabilitation nursing follow-up intervention, telephone follow-up, and cross-referring intervention were carried out. Postoperative x-ray images were taken regularly to observe the recovery of reconstructed finger. RESULTS: There was no difference in daily life ability scores and hand function scores between the 2 groups at discharge (P > 0.05). The daily life ability scores and hand function scores of the observation group were better than those of the control group at 1 and 6 months after discharge (P < 0.05), the difference is statistically significant. As the discharge time prolonged, the daily life ability score and hand function score of the 2 groups of patients gradually increased. X-ray images showed that the replanted finger body was well developed, phalanx was in good antithesis, and there was no epiphysis. CONCLUSIONS: Continuous rehabilitation nursing measures should be taken after the replantation of the severed finger after neurovascular preservation, which provides standardized rehabilitation training standards for patients with replantation of severed finger after discharge, improves patient training compliance, promotes hand function recovery as soon as possible, and enables patients to return to society as soon as possible, which is worthy of clinical promotion and application.