Restore polder and aquaculture enclosure to the lake: Balancing environmental protection and economic growth for sustainable development.

The restoration of lakes and their buffer zones is crucial for understanding the intricate interplay between human activities and natural ecosystems resulting from the implementation of environmental policies. In this study, we investigated the ecological restoration of shallow lakes and buffer zones in the Yangtze-Huaihe River Basin, specifically focusing on the removal of polder and aquaculture enclosure areas within the lakes. By examining data from eight shallow lakes and their corresponding buffer zones, encompassing lake morphology, water quality parameters, and land use/land cover (LULC) data spanning from 2008 to 2022, which shed light on the complex relationships involved. During the process of restoring polder and aquaculture enclosure areas, we observed a general decrease in the extent of polders and aquaculture enclosures within the lakes. Notably, the removal of aquaculture enclosures had a more pronounced effect (reduction rate of 83.37 %) compared to the withdrawal of polders (reduction rate of 48.76 %). Linear regression analysis revealed a significant decrease in the concentrations of seven water quality parameters, including COD, CODMn, TN, TP, NH3-N, Chl-a, and F, while pH and DO factors exhibit a distinct increasing trend. The results of redundancy analysis and Pearson correlation analysis demonstrated significant correlations between the area of polders and aquaculture enclosures and the changes in lake water quality. Encouragingly, the withdrawal of polders and the removal of aquaculture enclosures had a positive impact on the lake water quality improvement. In contrast, the LULC in the buffer zones of the lakes experienced a gradual decline owing to land degradation, resulting in a reduction in ecosystem service value (ESV). These results offer valuable support for policymakers in their endeavors to restore lake water quality, mitigate the degradation of buffer zones land, and promote the sustainable development of land and water resources.

Attachment and sleep quality in adults: A multilevel meta-analysis of actor and partner effect.

Within the context of sleep, attachment is hypothesized to play a central role in regulating bedtime affect and arousal. While previous studies have suggested a link between attachment and sleep quality, a meta-analysis specifically examining this association in adults has been lacking. To address this gap, we conducted a series of multilevel meta-analyses of 28 studies on this topic. Our results indicated a correlation between attachment anxiety and an individual's own sleep quality (r = -0.16, p < 0.001), as well as their partner's sleep quality (r = -0.10, p < 0.05). There was also a negative correlation between attachment avoidance and an individual's sleep quality (r = -0.15, p < 0.001) as well as their partner's sleep quality (r = -0.16, p < 0.01). Additionally, the relationships were moderated by several variables, including age, sleep measurement, and gender. Further analysis indicated that attachment anxiety was associated with poorer subjective sleep quality (PSQI) (r = -0.23, p < 0.001), longer sleep latency (r = -0.10, p < 0.05), increased wakefulness after sleep onset (r = -0.09, p < 0.05), and greater daytime sleepiness (r = -0.20, p < 0.01). Attachment avoidance was associated with poorer self-reported sleep quality (PSQI) (r = -0.16, p < 0.001), longer time to fall asleep (r = -0.15, p < 0.05), and increased daytime sleepiness (r = -0.15, p < 0.05). In summary, the findings of the current study supported the association between attachment insecurity and poorer sleep quality in both individuals and their partners. These findings hold important implications for future interventions aimed at improving sleep quality by addressing attachment-related concerns.

Corpus callosum and cerebellum participate in semantic dysfunction of Parkinson's disease: a diffusion tensor imaging-based cross-sectional study.

Language dysfunction is common in Parkinson's disease (PD) patients, among which, the decline of semantic fluency is usually observed. This study aims to explore the relationship between white matter (WM) alterations and semantic fluency changes in PD patients. 127 PD patients from the Parkinson's Progression Markers Initiative cohort who received diffusion tensor imaging scanning, clinical assessment and semantic fluency test (SFT) were included. Tract-based special statistics, automated fiber quantification, graph-theoretical and network-based analyses were performed to analyze the correlation between WM structural changes, brain network features and semantic fluency in PD patients. Fractional anisotropy of corpus callosum, anterior thalamic radiation, inferior front-occipital fasciculus, and uncinate fasciculus, were positively correlated with SFT scores, while a negative correlation was identified between radial diffusion of the corpus callosum, inferior longitudinal fasciculus, and SFT scores. Automatic fiber quantification identified similar alterations with more details in these WM tracts. Brain network analysis positively correlated SFT scores with nodal efficiency of cerebellar lobule VIII, and nodal local efficiency of cerebellar lobule X. WM integrity and myelin integrity in the corpus callosum and several other language-related WM tracts may influence the semantic function in PD patients. Damage to the cerebellum lobule VIII and lobule X may also be involved in semantic dysfunction in PD patients.

Predicting drug-protein interactions by preserving the graph information of multi source data.

Examining potential drug-target interactions (DTIs) is a pivotal component of drug discovery and repurposing. Recently, there has been a significant rise in the use of computational techniques to predict DTIs. Nevertheless, previous investigations have predominantly concentrated on assessing either the connections between nodes or the consistency of the network's topological structure in isolation. Such one-sided approaches could severely hinder the accuracy of DTI predictions. In this study, we propose a novel method called TTGCN, which combines heterogeneous graph convolutional neural networks (GCN) and graph attention networks (GAT) to address the task of DTI prediction. TTGCN employs a two-tiered feature learning strategy, utilizing GAT and residual GCN (R-GCN) to extract drug and target embeddings from the diverse network, respectively. These drug and target embeddings are then fused through a mean-pooling layer. Finally, we employ an inductive matrix completion technique to forecast DTIs while preserving the network's node connectivity and topological structure. Our approach demonstrates superior performance in terms of area under the curve and area under the precision-recall curve in experimental comparisons, highlighting its significant advantages in predicting DTIs. Furthermore, case studies provide additional evidence of its ability to identify potential DTIs.

Optimizing the hydraulic performance of a baffled horizontal subsurface flow constructed wetland through computational fluid dynamics modelling.

Baffled constructed wetlands (CWs) offer a promising solution to address low hydraulic efficiency in traditional CWs. However, there is a research gap in the field regarding the optimal length and quantity of baffles, and their comprehensive effects on hydraulic efficiency. This study is the first CFD-based assessment to comprehensively investigate the combined influence of baffle length and the number of baffles on the hydraulic efficiency of CWs. Using OpenFOAM simulations at a laboratory scale, various baffle configurations were examined with lengths ranging from 0.4 m to 0.58 m and baffle numbers varying from 0 to 11. Experimental tracer tests were conducted to validate the simulations. The high correlation coefficient (R2) between the tracer test results and simulations (ranging between 0.84 and 0.93) further underscores the reliability of the findings. Residence time distributions (RTDs) were derived from the temporal evolution of the outlet concentration of a tracer. The results indicate that augmenting the number of baffles under a fixed baffle length has a greater impact on the RTD curves, causing a backward displacement of the peak time. However, when the number of baffles is three or fewer, extending the baffle length does not significantly affect the RTD. When the baffle length is held constant at 0.58 m, there is a 58% enhancement in hydraulic efficiency as the number of baffles increases from 0 to 5. However, when maintaining a constant number of 11 baffles, increasing the baffle length from 0.4 to 0.5 m results in only a 5.5% improvement in hydraulic efficiency. Moreover, a generalized predictive equation for hydraulic efficiency was derived based on the CFD results and dimensional analysis. The study enhances the optimization of constructed wetland design by providing greater understanding of hydrodynamic behavior, leading to improved performance and applicability in practical environmental engineering.

Convolutional symmetric compressed look-up-table method for 360° dynamic color 3D holographic display.

In this paper, we propose a convolutional symmetric compressed look-up-table (CSC-LUT) method to accelerate computer-generated hologram (CGH) computation based on the Fresnel diffraction theory and LUT. The proposed method can achieve one-time high-quality fast generation of color holograms by utilizing dynamic convolution operation, which is divided three processes. Firstly, the pre-calculated data of maximum horizontal modulation factor is compressed in 1D array by coordinate symmetry. Then, the test object is resampled to satisfy convolutional translation invariance. Finally, the dynamic convolution operation is used to simplify CGH computation process rather than the point-by-point computation. Numerical simulation and optical experimental results show that our proposed method can achieve faster computation speed, higher reconstruction quality and wider application compared to conventional SC-LUT method. The further optimization method for parallel acceleration on the GPU framework can achieve real-time (>24fps) color holographic display corresponding to three perspectives of a 3D scene.

Synthesis of fused 3-trifluoromethyl-1,2,4-triazoles via base-promoted [3 + 2] cycloaddition of nitrile imines and 1H-benzo[d]imidazole-2-thiols.

Here we report a strategy for the facile assembly of fused 3-trifluoromethyl-1,2,4-triazoles, which are difficult to synthesize using traditional strategies, in 50-96% yields through a triethylamine-promoted intermolecular [3 + 2] cycloaddition pathway. This protocol features high efficiency, good functional group tolerance, mild conditions, and easy operation. Furthermore, a gram-scale reaction and product derivatizations were carried out smoothly to illustrate the practicability of this method.

Research on High Precision Stiffness Modeling Method of Redundant Over-Constrained Parallel Mechanism.

Traditional stiffness modeling methods do not consider all factors comprehensively, and the modeling methods are not unified, lacking a global stiffness model. Based on screw theory, strain energy and the virtual work principle, a static stiffness modeling method for redundant over-constrained parallel mechanisms (PMs) with clearance was proposed that considers the driving stiffness, branch deformation, redundant driving, joint clearance and joint contact deformation. First, the driving stiffness and branch deformation were considered. According to the strain energy and Castiliano's second theorem, the global stiffness matrix of the ideal joint mechanism was obtained. The offset of the branch was analyzed according to the restraint force of each branch. The mathematical relationship between the joint clearance and joint contact deformation and the end deformation was established. Based on the probability statistical model, the uncertainty of the offset value of the clearance joint and the contact area of the joint caused by the coupling of the branch constraint force was solved. Finally, taking a 2UPR-RR-2RPU redundant PM as an example, a stiffness simulation of the mechanism was carried out using the finite element method. The research results show that the high-precision stiffness modeling method proposed in this paper is correct, and provides an effective method for evaluating the stiffness performance of the PM.

Holographic display using layered computer-generated volume hologram.

The spatial frequency of the reconstructed image of planar computer-generated hologram(CGH) is limited by the sampling interval and the lack of thickness. To break through this limitation of planar CGH, we propose a new computer-generated volume hologram(CGVH) for full-color dynamic holographic three-dimensional(3D) display, and an iteration-free layered CGVH generation method. The proposed CGVH is equivalent to a volume hologram sampled discretely in three directions. The generation method employs the layered angular spectral diffraction to calculate the light field in the layered CGVH, and then encodes it into a CGVH. Numerical simulation results show that the CGVH can accurately reconstruct full-color 3D objects, where better imaging quality, more concentrated diffraction energy, denser reconstructed spatial frequency information, and larger viewing angle are achieved. The proposed CGVH is expected to be applied to realize dynamic modulation, wavelength multiplexing, and angle multiplexing in various optical fields in the future.

Effects of organic pollutants on struvite crystallization kinetics and the molecular mechanism of inhibition on crystal growth.

The abilities of improving phosphorus (P) resources sustainability and reducing water eutrophication make struvite crystallization technology attract increasing interest in wastewater treatment, but struvite crystallization process may be affected by various impurities in wastewater. In this study, the effects of nine representative ionic surfactants including three types (anionic, cationic and zwitterionic) on crystallization kinetics and product quality of struvite were investigated, and the influencing mechanism was further probed. The results demonstrated that anionic surfactants significantly inhibit crystal growth so as to reduce crystal size especially in a-axis direction, change crystal morphology and decrease P recovery efficiency, and also lead to a slight decline in product purity. In contrast, cationic and zwitterionic surfactants have no obvious influence on the formation of struvite. A series of experimental characterizations and molecular simulations collectively revealed that the inhibition of crystal growth by anionic surfactants is attributed to the adsorption of anionic surfactant molecules on struvite crystal surface and subsequent blockage of active growth sites. The binding ability of surfactant molecules with the Mg2+ exposed on struvite crystal surface was highlighted to be the most essential factor determining the adsorption behavior and adsorption capacity. Anionic surfactants with stronger binding ability with Mg2+ have more intense inhibitory effect, but a large molecular volume of anionic surfactants will weaken the adsorption capacity on crystal surface so as to reduce the inhibitory effect. Contrastively, cationic and zwitterion surfactants without binding ability with Mg2+ have no inhibitory effect. These findings enable us to have a clearer understanding of the impact of organic pollutants on struvite crystallization and make a preliminary judgment on the organic pollutants that may have the ability to inhibit the crystal growth of struvite.

Uncovering the Molecular Mechanisms of Cosolvency and Predicting the Cosolvency Phenomenon by Molecular Simulations: A Case Study of Amino Acids.

Cosolvency is an intriguing thermodynamic phenomenon. However, the lack of theoretical research restricts its development and its further applications. In this work, l-alanine, l-phenylalanine, and L-tryptophan were selected as model substances to investigate the mechanism of cosolvency at the molecular level. First, the dissolution behaviors of three amino acids were characterized to determine the solvent ratios at the occurrence of the cosolvency phenomenon. Furthermore, amino acid molecules undergo a shift in molecular conformation, which leads to changes in inter/intramolecular interactions. A molecular dynamics simulation method was proposed to calculate the trends of inter/intramolecular interactions, demonstrating that the maximum point of the inter/intramolecular interaction ratio exactly corresponds to the occurrence of the cosolvency. The cosolvency phenomenon of l-proline and l-threonine was predicted successfully based on this simulation method. These results are likely to provide in-depth comprehension and guidance for predicting the cosolvency phenomenon of amino-acid-like substances.

Au Nanoparticles/HfO2/Fully Depleted Silicon-on-Insulator MOSFET Enabled Rapid Detection of Zeptomole COVID-19 Gene With Electrostatic Enrichment Process.

In this work, a novel sensing structure based on Au nanoparticles/HfO2/fully depleted silicon-on-insulator (AuNPs/HfO2/FDSOI) MOSFET is fabricated. Using such a planar double gate MOSFET, the electrostatic enrichment (ESE) process is proposed for the ultrasensitive and rapid detection of the coronavirus disease 2019 (COVID-19) ORF1ab gene. The back-gate (BG) bias can induce the required electric field that enables the ESE process in the testing liquid analyte with indirect contact with the top-Si layer. It is revealed that the ESE process can rapidly and effectively accumulate ORF1ab genes close to the HfO2 surface, which can significantly change the MOSFET threshold voltage ([Formula: see text]). The proposed MOSFET successfully demonstrates the detection of zeptomole (zM) COVID-19 ORF1ab gene with an ultralow detection limit down to 67 zM (~0.04 copy/[Formula: see text]) for a test time of less than 15 min even in a high ionic-strength solution. Besides, the quantitative dependence of [Formula: see text] variation on COVID-19 ORF1ab gene concentration from 200 zM to 100 femtomole is also revealed, which is further confirmed by TCAD simulation.

Effects of the molecular weight of hyaluronan on the conformation and release kinetics of self-assembled 5-fluorouracil-loaded lysozyme-hyaluronan colloidal nanoparticles.

Lysozyme (LYS) and hyaluronan with low (HA1: 3 kDa), medium (HA2: 120 kDa), and high (HA3: 1200 kDa) molecular weights were used to fabricate lysozyme-hyaluronan colloidal nanoparticles using a green self-assembly method. Fourier transform infrared spectroscopy indicated that hydrogen bonding, hydrophobic and electrostatic interactions promoted the formation of the colloidal nanoparticles. The hydrophobic area of prepared colloidal nanoparticles was quantified using a pyrene fluorescent probe, and the results showed that the LYS-HA3 nanoparticles had the strongest hydrophobic capacity. Furthermore, 5-fluorouracil (5-Fu) was used to evaluate encapsulation performance, demonstrating that the LYS-HA3 nanoparticles had the highest encapsulation ability (>90 %). All prepared 5-Fu-loaded lysozyme-hyaluronan (5-Fu@LYS-HA) colloidal nanoparticles exhibited excellent long-term storage stability at 4 °C for 60 days. Cellular uptake and in vitro release results indicated that the LYS-HA2 nanoparticles exhibited the highest cellular uptake efficiency, and the LYS-HA3 nanoparticles had the best slow-release effect, while the release process was mainly controlled by the combination of Fickian diffusion and structural relaxation, respectively. This study demonstrates the influence of molecular weight on the conformational and structural properties of colloidal nanoparticles, which has implications for the design of insoluble drug self-assembly systems.

Superpixel-based sub-hologram method for real-time color three-dimensional holographic display with large size.

One of the biggest challenges for large size three-dimensional (3D) holographic display based on the computer-generated hologram (CGH) is the trade-off between computation time and reconstruction quality, which has limited real-time synthesis of high-quality holographic image. In this paper, we propose a superpixel-based sub-hologram (SBS) method to reduce the computation time without sacrificing the quality of the reconstructed image. The superpixel-based sub-hologram method divides the target scene into a collection of superpixels. The superpixels are composed of adjacent object points. The region of the superpixel-based sub-hologram corresponding to each superpixel is determined by an approximation method. Since the size and the complexity of the diffraction regions are reduced, the hologram generation time is decreased significantly. The computation time has found to be reduced by 94.89% compared with the conventional sub-hologram method. It is shown that the proposed method implemented on the graphics processing unit (GPU) framework can achieve real-time (> 24 fps) color three-dimensional holographic display with a display size of 155.52 mm × 276.48 mm.

Experimental demonstration of a local active phase compensation method for a phase encoding quantum key distribution system.

An efficient phase stabilization method is required in quantum key distribution (QKD) systems for stability in practical applications. The existing active phase compensation method has limitations in multi-node network applications, especially in network-scale applications based on measurement-device-independent QKD systems. In this study, we propose a local active phase compensation scheme that can realize phase compensation independently for each interferometer node. We performed experimental demonstrations in the BB84 phase encoding system based on a Faraday-Michelson interferometer. The average QBER rates of the system under two different forms of the reference light were found to be 1.9% and 1.6%. This scheme can also be applied to other QKD systems and has potential for application in future quantum communication networks.

Recognition of terminal buds of densely-planted Chinese fir seedlings using improved YOLOv5 by integrating attention mechanism.

Accurate and timely information on the number of densely-planted Chinese fir seedlings is essential for their scientific cultivation and intelligent management. However, in the later stage of cultivation, the overlapping of lateral branches among individuals is too severe to identify the entire individual in the UAV image. At the same time, in the high-density planting nursery, the terminal bud of each seedling has a distinctive characteristic of growing upward, which can be used as an identification feature. Still, due to the small size and dense distribution of the terminal buds, the existing recognition algorithm will have a significant error. Therefore, in this study, we proposed a model based on the improved network structure of the latest YOLOv5 algorithm for identifying the terminal bud of Chinese fir seedlings. Firstly, the micro-scale prediction head was added to the original prediction head to enhance the model's ability to perceive small-sized terminal buds. Secondly, a multi-attention mechanism module composed of Convolutional Block Attention Module (CBAM) and Efficient Channel Attention (ECA) was integrated into the neck of the network to enhance further the model's ability to focus on key target objects in complex backgrounds. Finally, the methods including data augmentation, Test Time Augmentation (TTA) and Weighted Boxes Fusion (WBF) were used to improve the robustness and generalization of the model for the identification of terminal buds in different growth states. The results showed that, compared with the standard version of YOLOv5, the recognition accuracy of the improved YOLOv5 was significantly increased, with a precision of 95.55%, a recall of 95.84%, an F1-Score of 96.54%, and an mAP of 94.63%. Under the same experimental conditions, compared with other current mainstream algorithms (YOLOv3, Faster R-CNN, and PP-YOLO), the average precision and F1-Score of the improved YOLOv5 also increased by 9.51-28.19 percentage points and 15.92-32.94 percentage points, respectively. Overall, The improved YOLOv5 algorithm integrated with the attention network can accurately identify the terminal buds of densely-planted Chinese fir seedlings in UAV images and provide technical support for large-scale and automated counting and precision cultivation of Chinese fir seedlings.

Enabling the drug combination of celecoxib through a spherical co-agglomeration strategy with controllable and stable drug content and good powder properties.

Combining celecoxib with other chemopreventive drugs is a promising method of chemoprevention for cancer, especially for colorectal cancer. However, the traditional drug combination approaches are restricted with high-cost apparatus, complex and numerous unit operations. This work aims to develop an efficient spherical co-agglomeration strategy for celecoxib in combination with lovastatin, which can achieve drug combination in a single crystallization unit. The ternary solvent system was determined based on molecular simulation, and then a stable spherical agglomeration process was developed through the design of molar fraction of anti-solvent (MFA) and stirring rate to produce spherical agglomerates with high sphericity (84.2-89.9 %) and narrow size distribution. On this basis, celecoxib-benzoic acid spherical co-agglomerates were designed to form a complete spherical co-agglomeration strategy, which includes solvent system selection, spherical agglomeration and spherical co-agglomeration. Finally, celecoxib-lovastatin spherical co-agglomerates with synergistic efficacy were successfully produced by this strategy, with controllable and stable drug content (fluctuation < 2.7 %), good powder properties, and improved tabletability.

Determining heavy metal pollution in sediments from the largest impounded lake in the eastern route of China's South-to-North Water Diversion Project: Ecological risks, sources, and implications for lake management.

The eastern route of the South-to-North Water Diversion Project (ER-SNWDP) is a major human health project designed to alleviate the water scarcity in the Beijing-Tianjin-Hebei region in China. Impounded lake water security is directly related to the water diversion project effectiveness. At present, there is not a thorough understanding of the sediment heavy metals in Lake Hongze, the largest impounded lake of the ER-SNWDP. Consequently, this study reports a distribution analysis of Cu, Zn, Pb, Cr, Cd, As, Hg, and Ni in 101 sediment samples from Lake Hongze; we, utilized the enrichment factor, geoaccumulation index, and potential ecological risk index for the are to determine the ecological risk of heavy metals. The heavy metal source was examined with correlation analysis and principal component analysis-multiple linear regressions. The results showed that the average heavy metal content (Cu, Zn, Pb, Cr, Cd, As, Hg, Ni) were 0.03-1.57 times greater than the Jiangsu Province background values. Cd, As, and Hg were the main contributors to the Lake Hongze ecological risk. Spatially, the open water area was the most polluted among the four lake parts, and most of the flushing area had a low ecological risk. Chengzi bay and the western lake area have similar risk profiles, but are lower than the open water area risk. Source analysis showed that nonpoint-source agricultural pollution and industrial production were important pollution sources, while a considerable portion of the heavy metal content came from atmospheric deposition and natural sources. This study identified the main contamination areas and revealed the possible sources of each heavy metal; as such, this study can serve as a reference for the remediation and management of Lake Hongze to ensure the water safety of the ER-SNWDP.

Understanding the Iron-Cobalt Synergies in ZSM-5: Enhanced Peroxymonosulfate Activation and Organic Pollutant Degradation.

Iron- and cobalt-based heterogeneous catalysts are widely applied for activating peroxymonosulfate (PMS) to degrade organic pollutants. However, few studies have unveiled the clear synergistic mechanism of iron and cobalt in ZSM-5. In this paper, the synergistic mechanism of enhanced PMS activation was revealed by constructing iron and cobalt bimetal modified ZSM-5 zeolite catalysts (FeCo-ZSM-5). The tetracycline hydrochloride (TCH) degradation experiments showed that the catalytic activity of FeCo-ZSM-5-2:3 was much higher than those of Fe-ZSM-5 and Co-ZSM-5. In addition, the influences of catalyst dosage, PMS concentration, reaction temperature, initial pH, and coexisting ions on TCH removal were systematically investigated in this paper. Density functional theory calculations indicated that Co was the main active site for PMS adsorption, and Fe increased the area of Co's positive potential mapped to the electron cloud. The Fe-Co bimetallic doping increased the area of positive potential mapped to the electron cloud and benefited the adsorption of PMS on the catalyst surface, which revealed the synergistic mechanism of bimetals. Electron paramagnetic resonance spectra and quenching experiments showed that sulfate radicals, singlet oxygen, and hydroxyl radicals were involved in the degradation of TCH. Furthermore, liquid chromatography-mass spectrometry was conducted to propose possible degradation pathways. This work provides certain guiding significance in understanding the synergistic effect of heterogeneous catalysts for tetracycline wastewater treatment.

Single-shot lensfree on-chip quantitative phase microscopy with partially coherent LED illumination.

We propose a single-shot lens-free phase retrieval (SSLFPR) method in a lens-free on-chip microscopy (LFOCM) system based on a partially coherent light emitting diode (LED) illumination. The finite bandwidth (∼23.95 nm) of LED illumination is divided into a series of quasi-monochromatic components according to the LED spectrum measured by a spectrometer. When the "virtual wavelength scanning" phase retrieval method is combined with the dynamic phase support constraint, the resolution loss caused by the spatiotemporal partial coherence of the light source can be effectively compensated. At the same time, the nonlinearity characteristics of the support constraint help to further improve the imaging resolution, accelerate the convergence of the iteration process, and greatly eliminate the artifacts. Based on the proposed SSLFPR method, we demonstrate that the phase information of samples (including phase resolution target and polystyrene microspheres) illuminated by a LED can be accurately retrieved based on one single diffraction pattern. The SSLFPR method has a half-width resolution of 977 nm across a large field-of-view (FOV) of 19.53 mm2, which is 1.41 × the resolution of the conventional approach. We also imaged living Henrietta Lacks (HeLa) cells cultured in vitro, further demonstrating the real-time single-shot quantitative phase imaging (QPI) capability of SSLFPR for dynamic samples. Given its simple hardware, high throughput, and single-frame high-resolution QPI capability, SSLFPR is expected to be adopted in a wide range of biological and medical applications.