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.

Synthesis, pharmacological evaluation, and modeling of novel quaternary ammonium salts derived from ß-carboline containing an imidazole moiety as angiogenesis inhibitors.

In this study, a series of novel ß-carboline condensed imidazolium derivatives (7a-7y) were designed and synthesized by incorporating imidazolium salt structures into ß-carboline. The cytotoxicity of compounds 7a-7y was evaluated in various cancer cell lines, including lung cancer (A549), gastric cancer (BGC-823), mouse colon cancer (CT-26), liver cancer (Bel-7402), and breast cancer (MCF-7), using the MTT assay. Most compounds exhibited significant activity against one or more of the cancer cell lines. Notably, compounds 7 g, 7o, 7r, 7 s, 7u, 7v, 7x, and 7w showed the highest cytotoxic activity (IC50 < 2 µM) in the tested tumor cell lines. Compound 7x demonstrated cytotoxic activities of 1.3 ± 0.3 µM (for BGC-823), 2.4 ± 0.4 µM (against A549), 7.8 ± 0.9 µM (for Bel-7402), and 9.8 ± 1.4 µM (against CT-26). The chick chorioallantoic membrane assay revealed significant anti-angiogenic potential of compound 7x. Molecular imprinting studies suggested the anti-angiogenic effect of compound 7x might be attributed to inhibition of VEGFR2 kinase. Molecular docking and molecular dynamics further indicate that its activity may be primarily associated with the potential inhibition of VEGFR2. Our research outcomes have provided valuable lead compounds for the development of novel antitumor drugs and have offered beneficial insights for subsequent drug design and optimization.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Functional group induced transformations in stacking and electron structure in Mo2CTx/NiS heterostructures.

The two-dimensional transition metal carbide/nitride family (MXenes) has garnered significant attention due to their highly customizable surface functional groups. Leveraging modern material science techniques, the customizability of MXenes can be enhanced further through the construction of associated heterostructures. As indicated by recent research, the Mo2CTx/NiS heterostructure has emerged as a promising candidate exhibiting superior physical and chemical application potential. The geometrical structure of Mo2CTx/NiS heterostructure is modeled and six possible configurations are validated by Density Functional Theory simulations. The variation in functional groups leads to structural changes in Mo2CTx/NiS interfaces, primarily attributed to the competition between van der Waals and covalent interactions. The presence of different functional groups results in significant band fluctuations near the Fermi level for Ni and Mo atoms, influencing the role of atoms and electron's ability to escape near the interface. This, in turn, modulates the strength of covalent interactions at the MXenes/NiS interface and alters the ease of dissociation of the MXenes/NiS complex. Notably, the Mo2CO2/NiS(P63/mmc) heterostructure exhibits polymorphism, signifying that two atomic arrangements can stabilize the structure. The transition process between these polymorphs is also simulated, further indicating the modulation of the electronic level of properties by a sliding operation.

Limited Impacts of Water Diversion on Micro-eukaryotic Community along the Eastern Route of China's South-to-North Water Diversion Project.

The Eastern Route of the South-to-North Water Diversion Project (ER-SNWDP) represents a crucial initiative aimed at alleviating water scarcity in China's northern region. Understanding the dynamics governing the composition and assembly processes of micro-eukaryotic communities within the canal during different water diversion periods holds paramount significance for the effective management of the ER-SNWDP. Our study systematically tracks the dynamics of the micro-eukaryotic community and its assembly processes along the 1045.4 km of canals and four impounded lakes, totaling 3455 km2, constituting the ER-SNWDP during a complete water diversion cycle, utilizing high-throughput sequencing, bioinformatics tools, and null modeling algorithms. The primary objectives of this study are to elucidate the spatial-temporal succession of micro-eukaryotic communities as the water diversion progresses, to delineate the relative importance of deterministic and stochastic processes in community assembly, and to identify the pivotal factors driving changes in micro-eukaryotic communities. Our findings indicate notable variations in the composition and diversity of micro-eukaryotic communities within the ER-SNWDP across different water diversion periods and geographic locations (P < 0.05). This variation is influenced by a confluence of temporal and environmental factors, with limited impacts from water diversion. In essence, the assembly of micro-eukaryotic communities within the ER-SNWDP primarily stemmed from heterogeneous selection driven by deterministic processes. Water diversion exhibited a tendency to decrease community beta diversity while augmenting the influence of stochastic processes in community assembly, albeit this effect attenuated over time. Furthermore, our analysis identified several pivotal environmental parameters, notably including nitrite-nitrogen, nitrate-nitrogen, orthophosphate, and water temperature, as exerting significant effects on micro-eukaryotic communities across different water diversion periods. Collectively, our study furnishes the inaugural comprehensive exploration of the dynamics, assembly processes, and influencing factors governing micro-eukaryotic communities within the ER-SNWDP, thus furnishing indispensable insights to inform the water quality management of this important project.

Declined nutrients stability shaped by water residence times in lakes and reservoirs under climate change.

Water quality stability in lakes and reservoirs is essential for drinking water safety and ecosystem health, especially given the frequent occurrence of extreme climate events. However, the relationship between water quality stability and water residence time (WRT) has not been well elucidated. In this study, we explored the relationship based on nitrogen (N) and phosphorus (P) concentrations data in 11 lakes and 49 reservoirs in the Yangtze-Huaihe River basin from 2010 to 2022. Additionally, we examined the effects of hydrometeorological characteristics, the geomorphology of water bodies and catchments, and land use on the WRT, establishing a link between climate change and the stability of N and P in these water bodies. The results showed that a significant correlation between the stability of N and P in lakes and reservoirs and their WRT. The longer WRT tends to coincide with decreased stability and higher nutrient concentrations. Hydrometeorological factors are the primary factors on the WRT, with precipitation exerting the greatest effect, particularly under extreme drought. In recent years, extreme climatic events have intensified the fluctuations of WRT, resulting in a renewed increase in N and P concentrations and deterioration in stability. These findings highlight the importance of incorporating meteorological and hydrological factors alongside reinforcing ecological restoration into lake and reservoir management strategies, and providing a scientific basis for future efforts aimed at enhancing lake and reservoir water quality stability and safeguarding aquatic ecosystems.

Light-Operated Diverse Logic Gates Enabled by Modulating Time-Dependent Fluorescence of Dissipative Self-Assemblies.

Light-fueled dissipative self-assembly possesses enormous potential in the field of optical information due to controllable time-dependent optical signals, but remains a great challenge for constructing intelligent light-operated logic circuits due to the limited availability of optical signal inputs and outputs. Herein, a series of light-fueled dissipative self-assembly systems with variable optical signals are reported to realize diverse logic gates by modulating time-dependent fluorescence variations of the loaded fluorophores. Three kinds of alkyl trimethylammonium homologs are employed to co-assemble with a merocyanine-based photoinduced amphiphile separately to construct a series of dissipative self-assemblies, showing unexpectedly different fluorescence control behaviors of loaded fluorophores during light irradiation and thermal relaxation processes. The opposite monotonicity of time-dependent emission intensity is achieved just by changing the excitation wavelength. Furthermore, by varying the types of trimethylammoniums and excitation wavelengths, a robust logic system is accomplished, integrating AND, XNOR, and XOR functions, which provides an effective pathway for advancing information transmission applications.

Pursuing Extreme Descriptive Power and Generality in Chemical Bond Theories: A Method to Decipher "Interatomic Genomes" from Interatomic Electron Structures.

The description and analysis of chemical bonds have been difficult following the popularization of electronic structure calculations. Although many attempts have been made from the perspective of electronic structure, the sheer volume of information in the electronic structure has left contemporary chemical bond analysis methods grappling with an inescapable "Trilemma" where the model briefness, generality, and descriptiveness (descriptive power) cannot be obtained simultaneously. To push the generality and descriptiveness to their extremes, herein a general machine learning-based framework is introduced to compact chemical bonds into a detailed residue-by-residue "genome" with matched encoding/decoding tools. The framework fuses the quantum mechanical aspects, auto feature extraction, nanostructures and/or simulations, and generative models. The encoded genomes are information-dense and decodable, where 100% generality is guaranteed. The descriptiveness of genomes appears to be broader than most known models. As a proof of concept, the realization presented in this work compacts the complete information regarding two critical chemical bonds in thiolate-protected gold nanoclusters, the S-Au and Au-Au bonds, from a Bosonic-Fermionic character perspective into 8-valued genomes. The machine learning component is trained based on 26,528 density functional theory simulated electron localization function images. With an exploration of the space span for the genome, bond polarization, hybridization, intrusion of other atoms, alignments, crystal orientation, atomic motions, and more details are observed. Furthermore, it has emerged from extensive generation tests that molecules and solids can be integrated in such a concise manner than is typically achieved with purely geometric representations. To showcase the intraclass complexity of S-Au and Au-Au bonds visually, a roadmap is plotted by summarizing and correlating the similarities of 8-value-genomes. Furthermore, genomes can be associated with realistic indices easily with a simple multilayer perception architecture as a simple calculating tool. Besides, there are 3 sets of applications, including a set of chemisorption, a set of molecular dynamical analysis, and a set of ultrafast processes, showcasing the interpretability potentials of interatomic genomes in the geometric structures, kinetic properties, and vibration characteristics of molecular systems. As the framework rose to the challenge of nanoclusters from a complicated mesoscopic family of material, the displayed generality and comprehensiveness indicate that the model may "understand" chemical bonds in a machine's way.

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.

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.