An algorithm to identify patients aged 0-3 with rare genetic disorders.

BACKGROUND: With over 7000 Mendelian disorders, identifying children with a specific rare genetic disorder diagnosis through structured electronic medical record data is challenging given incompleteness of records, inaccurate medical diagnosis coding, as well as heterogeneity in clinical symptoms and procedures for specific disorders. We sought to develop a digital phenotyping algorithm (PheIndex) using electronic medical records to identify children aged 0-3 diagnosed with genetic disorders or who present with illness with an increased risk for genetic disorders. RESULTS: Through expert opinion, we established 13 criteria for the algorithm and derived a score and a classification. The performance of each criterion and the classification were validated by chart review. PheIndex identified 1,088 children out of 93,154 live births who may be at an increased risk for genetic disorders. Chart review demonstrated that the algorithm achieved 90% sensitivity, 97% specificity, and 94% accuracy. CONCLUSIONS: The PheIndex algorithm can help identify when a rare genetic disorder may be present, alerting providers to consider ordering a diagnostic genetic test and/or referring a patient to a medical geneticist.

Dispelling the Fake: Social Bot Detection Based on Edge Confidence Evaluation.

Social bot detection is essential for maintaining the safety and integrity of online social networks (OSNs). Graph neural networks (GNNs) have emerged as a promising solution. Mainstream GNN-based social bot detection methods learn rich user representations by recursively performing message passing along user-user interaction edges, where users are treated as nodes and their relationships as edges. However, these methods face challenges when detecting advanced bots interacting with genuine accounts. Interaction with real accounts results in the graph structure containing camouflaged and unreliable edges. These unreliable edges interfere with the differentiation between bot and human representations, and the iterative graph encoding process amplifies this unreliability. In this article, we propose a social Bot detection method based on Edge Confidence Evaluation (BECE). Our model incorporates an edge confidence evaluation module that assesses the reliability of the edges and identifies the unreliable edges. Specifically, we design features for edges based on the representation of user nodes and introduce parameterized Gaussian distributions to map the edge embeddings into a latent semantic space. We optimize these embeddings by minimizing Kullback-Leibler (KL) divergence from the standard distribution and evaluate their confidence based on edge representation. Experimental results on three real-world datasets demonstrate that BECE is effective and superior in social bot detection. Additionally, experimental results on six widely used GNN architectures demonstrate that our proposed edge confidence evaluation module can be used as a plug-in to improve detection performance.

Research on the Application and Mechanisms of Electroactive Microorganisms in Toxicants Monitoring: A Review.

A biological treatment is the core process for removing organic pollutants from industrial wastewater. However, industrial wastewater often contains large amounts of toxic and harmful pollutants, which can inhibit the activity of microorganisms in a treatment system, precipitate the deterioration of effluent quality, and threaten water ecological security from time to time. In most of the existing anaerobic biological treatment processes, toxic effects on microorganisms are determined according to the amounts of end-products of the biochemical reactions, and the evaluation results are relatively lacking. When microorganisms contact toxic substances, changes in biological metabolic activity precede the accumulation of reaction products. As sensitive units, electroactive microorganisms can generate electrical signals, a change in which can directly reflect the toxicity level. The applications of electroactive microorganisms for the toxicity monitoring of wastewater are very promising. Further attention needs to be paid to considering the appropriate evaluation index, the influence of the environment on test results, mechanisms, and other aspects. Therefore, we reviewed the literature regarding the above aspects in order to provide a research foundation for the practical application of electroactive microorganisms in toxicant monitoring.

Surfactant Micelle-Driven High-Efficiency and High-Resolution Length Separation of Carbon Nanotubes for Electronic Applications.

High-efficiency extraction of long single-wall carbon nanotubes (SWCNTs) with excellent optoelectronic properties from SWCNT solution is critical for enabling their application in high-performance optoelectronic devices. Here, a straightforward and high-efficiency method is reported for length separation of SWCNTs by modulating the concentrations of binary surfactants. The results demonstrate that long SWCNTs can spontaneously precipitate for binary-surfactant but not for single-surfactant systems. This effect is attributed to the formation of compound micelles by binary surfactants that squeeze the free space of long SWCNTs due to their large excluded volumes. With this technique, it can readily separate near-pure long (≥500 nm in length, 99% in content) and short (≤500 nm in length, 98% in content) SWCNTs with separation efficiencies of 26% and 64%, respectively, exhibiting markedly greater length resolution and separation efficiency than those of previously reported methods. Thin-film transistors fabricated from extracted semiconducting SWCNTs with lengths >500 nm exhibit significantly improved electrical properties, including a 10.5-fold on-state current and 14.7-fold mobility, compared with those with lengths <500 nm. The present length separation technique is perfectly compatible with various surfactant-based methods for structure separations of SWCNTs and is significant for fabrication of high-performance electronic and optoelectronic devices.

Differences in greenhouse gas emissions and microbial communities between underground and conventionally constructed wastewater treatment plants.

Large quantities of greenhouse gases (GHGs) are emitted into the atmosphere during wastewater treatment. In this study, GHG and microbial samples were collected from four wastewater treatment plants (WWTPs), and their differences and relationships were assessed. The study showed that, compared with conventionally constructed WWTPs, well-established gas collection systems in underground WWTPs facilitate comprehensive collection and accurate accounting of GHGs. In aboveground WWTPs, capped anoxic ponds promote methane production releasing it at 2-8 times the rate of uncapped emissions, in contrast to nitrous oxide emissions. Moreover, a stable subsurface environment allows for smaller fluctuations in daily GHG emissions and higher microbial diversity and abundance. This study highlights differences in GHG emission fluxes and microbial communities in differently constructed WWTPs, which are useful for control and accurate accounting of GHG emissions.

Validation of biomechanical assessment of coronary plaque vulnerability based on intravascular optical coherence tomography and digital subtraction angiography.

Background: It has been suggested that biomechanical factors may influence plaque development. However, key determinants for assessing plaque vulnerability remain speculative. Methods: In this study, a two-dimensional (2D) structural mechanical analysis and a three-dimensional (3D) fluid-structure interaction (FSI) analysis were conducted based on intravascular optical coherence tomography (IV-OCT) and digital subtraction angiography (DSA) data sets. In the 2D study, 103 IV-OCT slices were analyzed. An in-depth morpho-mechanic analysis and a weighted least absolute shrinkage and selection operator (LASSO) regression analysis were conducted to identify the crucial features related to plaque vulnerability via the tuning parameter (λ). In the 3D study, the coronary model was reconstructed by fusing the IV-OCT and DSA data, and a FSI analysis was subsequently performed. The relationship between vulnerable plaque and wall shear stress (WSS) was investigated. Results: The influential factors were selected using the minimum criteria (λ-min) and one-standard error criteria (λ-1se). In addition to the common vulnerable factor of the minimum fibrous cap thickness (FCTmin), four biomechanical factors were selected by λ-min, including the average/maximal displacements and average/maximal stress, and two biomechanical factors were selected by λ-1se, including the average/maximal displacements. Additionally, the positions of the vulnerable plaques were consistent with the sites of high WSS. Conclusions: Functional indices are crucial for plaque status assessment. An evaluation based on biomechanical simulations might provide insights into risk identification and guide therapeutic decisions.

A Light Multi-View Stereo Method with Patch-Uncertainty Awareness.

Multi-view stereo methods utilize image sequences from different views to generate a 3D point cloud model of the scene. However, existing approaches often overlook coarse-stage features, impacting the final reconstruction accuracy. Moreover, using a fixed range for all the pixels during inverse depth sampling can adversely affect depth estimation. To address these challenges, we present a novel learning-based multi-view stereo method incorporating attention mechanisms and an adaptive depth sampling strategy. Firstly, we propose a lightweight, coarse-feature-enhanced feature pyramid network in the feature extraction stage, augmented by a coarse-feature-enhanced module. This module integrates features with channel and spatial attention, enriching the contextual features that are crucial for the initial depth estimation. Secondly, we introduce a novel patch-uncertainty-based depth sampling strategy for depth refinement, dynamically configuring depth sampling ranges within the GRU-based optimization process. Furthermore, we incorporate an edge detection operator to extract edge features from the reference image's feature map. These edge features are additionally integrated into the iterative cost volume construction, enhancing the reconstruction accuracy. Lastly, our method is rigorously evaluated on the DTU and Tanks and Temples benchmark datasets, revealing its low GPU memory consumption and competitive reconstruction quality compared to other learning-based MVS methods.

Simultaneous Wastewater Treatment and Resources Recovery by Forward Osmosis Coupled with Microbial Fuel Cell: A Review.

Osmotic microbial fuel cells (OsMFCs) with the abilities to simultaneously treat wastewater, produce clean water, and electricity provided a novel approach for the application of microbial fuel cell (MFC) and forward osmosis (FO). This synergistic merging of functions significantly improved the performances of OsMFCs. Nonetheless, despite their promising potential, OsMFCs currently receive inadequate attention in wastewater treatment, water reclamation, and energy recovery. In this review, we delved into the cooperation mechanisms between the MFC and the FO. MFC facilitates the FO process by promoting water flux, reducing reverse solute flux (RSF), and degrading contaminants in the feed solution (FS). Moreover, the water flux based on the FO principle contributed to MFC's electricity generation capability. Furthermore, we summarized the potential roles of OsMFCs in resource recovery, including nutrient, energy, and water recovery, and identified the key factors, such as configurations, FO membranes, and draw solutions (DS). We prospected the practical applications of OsMFCs in the future, including their capabilities to remove emerging pollutants. Finally, we also highlighted the existing challenges in membrane fouling, system expansion, and RSF. We hope this review serves as a useful guide for the practical implementation of OsMFCs.

Low SAA4 gene expression is associated with advanced HCC stage and a poor prognosis.

At present, although there are tumor markers for hepatocellular carcinoma (HCC), markers with better predictive efficiency are needed. SAA4 gene expression in liver tumor and paracancerous tissues was analyzed using The Cancer Genome Atlas database. The differentially expressed genes (DEGs) were analyzed and visualized by heatmap and volcano plot. Survival analysis was performed based on SAA4 expression. SAA4 expression was compared in patients grouped based on clinicopathological features, and gene set enrichment analysis (GSEA) was conducted. Immunohistochemical staining was used to verify the SAA4 protein staining intensity from The Human Protein Atlas database and our center's samples. The diagnostic value of SAA4 for HCC was evaluated by receiver operating characteristic curves. SAA4 was expressed at low levels in HCC tissues, and low SAA4 expression was associated with a poor prognosis in HCC. In addition, SAA4 expression decreased with HCC progression. There were 188 upregulated DEGs and 1551 downregulated DEGs between the high and low SAA4 expression groups. Complement and coagulation cascades, fatty acid metabolism, and ECM receptor interaction were significantly enriched in the GSEA. SAA4 had good predictive efficacy for HCC and even early HCC and was superior to AFP. In general, low SAA4 expression was associated with advanced HCC stage and a poor prognosis. In addition, SAA4 may be helpful for the diagnosis of early HCC and may become a novel tumor marker with good predictive power for HCC.

Effect of NO2 on N2 O production and NOx emission reduction in NH3 Selective Catalytic Reduction.

With the introduction of increasingly strict emission regulations, reducing nitrogen oxide (NOx ) emissions and nitrous oxide (N2 O) production from diesel engines have become the focus of research. At high temperature, the reaction of NO2 in the catalyst generates the intermediate product NH4 NO3 , which first crystallizes below 300 °C. These crystals tend to block the pores and inhibit the reaction. Subsequently, N2 O is produced through the decomposition of NH4 NO3 , leading to additional pollution. Therefore, the concentration of NO2 has a direct impact on both the NOx conversion efficiency and the generation of N2 O, requiring consideration of the optimal proportion of NO2 in SCR. Considering these two factors, it is concluded that the optimal amount of NO2 varies with temperature. To improve the NOx conversion rate of the Cu-SSZ-13 catalyst at low temperatures and reduce N2 O generation, the optimal NO2 ratio of the Cu-SSZ-13 catalyst under various operating conditions is studied using numerical simulations. As the temperature rises, the optimal NO2 /NOx ratio first increases and then decreases. Under the optimal NO2 /NOx ratio, the NOx conversion rate significantly increases, while N2 O generation decreases considerably. The optimal NO2 /NOx ratio also provides suggestions for the optimization of the DOC-DPF-DCR system.

Geometric phase-encoded stimuli-responsive cholesteric liquid crystals for visualizing real-time remote monitoring: humidity sensing as a proof of concept.

Liquid crystals are a vital component of modern photonics, and recent studies have demonstrated the exceptional sensing properties of stimuli-responsive cholesteric liquid crystals. However, existing cholesteric liquid crystal-based sensors often rely on the naked eye perceptibility of structural color or the measurement of wavelength changes by spectrometric tools, which limits their practical applications. Therefore, developing a platform that produces recognizable sensing signals is critical. In this study, we present a visual sensing platform based on geometric phase encoding of stimuli-responsive cholesteric liquid crystal polymers that generates real-time visual patterns, rather than frequency changes. To demonstrate this platform's effectiveness, we used a humidity-responsive cholesteric liquid crystal polymer film encoded with a q-plate pattern, which revealed that humidity causes a shape change in the vortex beam reflected from the encoded cholesteric liquid crystal polymers. Moreover, we developed a prototype platform towards remote humidity monitoring benefiting from the high directionality and long-range transmission properties of laser beams carrying orbital angular momentum. Our approach provides a novel sensing platform for cholesteric liquid crystals-based sensors that offers promising practical applications. The ability to generate recognizable sensing signals through visual patterns offers a new level of practicality in the sensing field with stimuli-responsive cholesteric liquid crystals. This platform might have significant implications for a broad readership and will be of interest to researchers working in the field of photonics and sensing technology.

Cross-linked α-Ni(OH)2 nanosheets with a Ni3+-rich structure for accelerating electrochemical oxidation of 5-hydroxymethylfurfural.

Electrochemical oxidation of biomass-based 5-hydroxymethylfurfural (HMF) is an effective approach for achieving the high-value products of 2,5-furandicarboxylic acid (FDCA). However, the restricted formation of high-valence metal active species for electrocatalysts results in a sluggish kinetic process of HMF oxidation reaction (HMFOR). Herein, we fabricated the Ni3+-rich cross-linked α-Ni(OH)2 nanosheets for accelerating the HMFOR through an anion-mediated strategy. It is identified that the Cl- ions with strong penetrability replace a portion of lattice oxygen atoms in α-Ni(OH)2 to form Ni-Cl bonds, contributing to breaking the inherent lattice order and generating a special Ni3+-rich structure. Owing to the promoted adsorption and accelerated oxidation of hydroxyl and aldehyde groups by the affluent Ni3+ active species, the large oxidation current density of 116.5 mA cm-2 and HMFOR kinetic constant of 0.067 min-1 has been achieved at 1.45 V (vs. RHE). By analyzing the oxidation products, the FDCA yield and Faradic efficiency are both higher than 99.25 % and 99.36 % for five successive determinations. Therefore, this work provides an insightful anion-mediated strategy for designing high-performance electrocatalysts for biomass conversion application.

Two Antimicrobial Peptides Derived from Bacillus and Their Properties.

Growth promotion and disease prevention are important strategies in the modern husbandry industry, and for this reason, antibiotics are widely used as animal feed additives. However, the overuse of antibiotics has led to the serious problem of increasing resistance of pathogenic microorganisms, posing a major threat to the environment and human health. "Limiting antibiotics" and "Banning antibiotics" have become the inevitable trends in the development of the livestock feed industry, so the search for alternative antimicrobial agents has become a top priority. Antimicrobial peptides (AMPs) produced by Bacillus spp. have emerged as a promising alternative to antibiotics, due to their broad-spectrum antimicrobial activity against resistant pathogens. In this study, two strains of Bacillus velezensis 9-1 and B. inaquosorum 76-1 with good antibacterial activity were isolated from commercial feed additives, and the antimicrobial peptides produced by them were purified by ammonium sulfate precipitation, anion exchange chromatography, gel chromatography, and RP-HPLC. Finally, two small molecule peptides, named peptide-I and peptide-II, were obtained from strain 9-1 and 76-1, respectively. The molecular weight and sequences of the peptides were analyzed and identified by LC-MS/MS, which were 988.5706 Da and VFLENVLR, and 1286.6255 Da and FSGSGSGTAFTLR, respectively. The results of an antibacterial activity and stability study showed that the two peptides had good antibacterial activity against Staphylococcus aureus, B. cereus, and Salmonella enterica, and the minimum inhibitory concentrations were 64 µg/mL and 16 µg/mL, 32 µg/mL and 64 µg/mL, and 8 µg/mL and 8 µg/mL, respectively. All of them have good heat, acid, and alkali resistance and protease stability, and can be further developed as feed antibiotic substitutes.

Serum and urinary essential trace elements in association with major depressive disorders: a case-control study.

Introduction: The etiology and pathophysiology of major depressive disorders (MDDs) remain unclear. Increasing evidence has demonstrated that essential trace elements (ETEs), such as iodine (I), zinc (Zn), copper (Cu), selenium (Se), cobalt (Co), and molybdenum (Mo), play vital roles in MDDs. Methods: In total, 72 patients with MDD and 75 healthy controls (HCs) in the Zhumadian Second People's Hospital, Henan Province, China were recruited in our study. The levels of different ETEs were examined in both serum and urine, using an inductively coupled plasma mass spectrometer (ICP-MS), for both the MDD patients and HCs. Results: The serum levels of I, Se, Cu, and Mo were significantly lower in the MDD patients compared to the HCs (p < 0.05), and the urinary levels of I and Zn were significantly higher in the MDD patients compared to the HCs (p < 0.05). The serum concentration of I (Q3: OR = 0.210, Q4: OR = 0.272) was negatively associated with MDD after adjusting for potential confounders, including age, gender, and BMI, and the urinary concentration of I (Q4: OR = 2.952) was positively associated. Conclusions: The higher levels of I, Se, Cu, and Mo in serum might be protective against the development of MDD, and the excess I and Zn in urine may be associated with MDD pathogenesis. Future research needs to gain a deeper understanding of the metabolic pathways of ETEs, especially I, Se, Zn, Cu, and Mo, in MDD, and their role in the pathogenesis of depression.

Complete genome sequence of Proteiniborus sp. MB09-C3, isolated from the feces of the black soldier fly larvae.

Here, we report the complete genome sequence of Proteiniborus sp. MB09-C3 (= BCRC 81405), isolated from the feces of black soldier fly (Hermetia illucens) larvae. The genome of strain MB09-C3 was selected for further species delineation and comparative genomic analysis.

Impact analysis of operating conditions on carbon dioxide reduction in microbial electrosynthesis: Insight into the substance utilization and microbial response.

Microbial electrosynthesis (MES) is facing a series of problems including low energy utilization and production efficiency of high value-added products, which seriously hinder its practical application. In this study, a more practical direct current power source was used and the anaerobic activated sludge from wastewater treatment plants was inoculated to construct the acetic acid-producing MES. The operating conditions of acetic acid production were further optimized and the specific mechanisms involving the substance utilization and microbial response were revealed. The optimum conditions were the potential of 3.0 V and pH 6.0. Under these conditions, highly electroactive biofilms formed and all kinds of substances were effectively utilized. In addition, dominant bacteria (Acetobacterium, Desulfovibrio, Sulfuricurvum, Sulfurospirillum, and Fusibacter) had high abundances. Under optimal conditions, acetic acid-forming characteristic genera (Acetobacterium) had the highest relative abundance (Biocathode-25.82 % and Suspension-17.24 %). This study provided references for the optimal operating conditions of MES and revealed the corresponding mechanisms.

Naringenin improves muscle endurance via activation of the Sp1-ERRγ transcriptional axis.

Skeletal muscle function declines in the aging process or disease; however, until now, skeletal muscle has remained one of the organs most undertreated with medication. In this study, naringenin (NAR) was found to build muscle endurance in wild-type mice of different ages by increasing oxidative myofiber numbers and aerobic metabolism, and it ameliorates muscle dysfunction in mdx mice. The transcription factor Sp1 was identified as a direct target of NAR and was shown to mediate the function of NAR on muscle. Moreover, the binding site of NAR on Sp1 was further validated as GLN-110. NAR enhances the binding of Sp1 to the CCCTGCCCTC sequence of the Esrrg promoter by promoting Sp1 phosphorylation, thus upregulating Esrrg expression. The identification of the Sp1-ERRγ transcriptional axis is of great significance in basic muscle research, and this function of NAR has potential implications for the improvement of muscle function and the prevention of muscle atrophy.

Deep learning-based multi-stage postoperative type-b aortic dissection segmentation using global-local fusion learning.

Objective.Type-b aortic dissection (AD) is a life-threatening cardiovascular disease and the primary treatment is thoracic endovascular aortic repair (TEVAR). Due to the lack of a rapid and accurate segmentation technique, the patient-specific postoperative AD model is unavailable in clinical practice, resulting in impracticable 3D morphological and hemodynamic analyses during TEVAR assessment. This work aims to construct a deep learning-based segmentation framework for postoperative type-b AD.Approach.The segmentation is performed in a two-stage manner. A multi-class segmentation of the contrast-enhanced aorta, thrombus (TH), and branch vessels (BV) is achieved in the first stage based on the cropped image patches. True lumen (TL) and false lumen (FL) are extracted from a straightened image containing the entire aorta in the second stage. A global-local fusion learning mechanism is designed to improve the segmentation of TH and BR by compensating for the missing contextual features of the cropped images in the first stage.Results.The experiments are conducted on a multi-center dataset comprising 133 patients with 306 follow-up images. Our framework achieves the state-of-the-art dice similarity coefficient (DSC) of 0.962, 0.921, 0.811, and 0.884 for TL, FL, TH, and BV, respectively. The global-local fusion learning mechanism increases the DSC of TH and BV by 2.3% (p< 0.05) and 1.4% (p< 0.05), respectively, based on the baseline. Segmenting TH in stage 1 can achieve significantly better DSC for FL (0.921 ± 0.055 versus 0.857 ± 0.220,p< 0.01) and TH (0.811 ± 0.137 versus 0.797 ± 0.146,p< 0.05) than in stage 2. Our framework supports more accurate vascular volume quantifications compared with previous segmentation model, especially for the patients with enlarged TH+FL after TEVAR, and shows good generalizability to different hospital settings.Significance.Our framework can quickly provide accurate patient-specific AD models, supporting the clinical practice of 3D morphological and hemodynamic analyses for quantitative and more comprehensive patient-specific TEVAR assessments.

Environmental regulations, green development of agriculture, and residents' health-empirical analysis of Yangtze River Economic Belt in China.

Environmental pollution and food safety have become key public health issues to be addressed in China. Since they are closely related to the green development of agriculture, it is of great practical significance to elucidate the intrinsic relationships between green development of agriculture, environmental regulation and residents' health. Based on the panel data of the Yangtze River Economic Belt from 2011 to 2020, this study investigates the impacts of environmental regulation and green development of agriculture on residents' health and the influencing mechanism by applying fixed effects method, mediating effectsmethod and the spatial Dubin method. Results show that the use of chemical fertilizers, pesticides and agricultural films is harmful to residents' health; environmental regulation has a negative correlation with the green development of agriculture and affect residents' health through mediating effects; the green development of agriculture has negative spillover effects on residents' health, indicating that purchasing finished products instead of producing locally reduces the input of production factors such as chemical fertilizers and pesticides and transfers health risks associated with agricultural production activities to neighboring areas. Intensifying command-and-control environmental regulation will induce the expansion of hidden economic activities and harm local residents' health, while intensifying market-incentive environmental regulation will lead to the 'Pollution Haven' phenomenon because of the 'race to the bottom', in government and is harmful to the health of residents in neighboring areas. Therefore, it is necessary to formulate reasonable and feasible policies and strengthen the control and prevention of agricultural pollution to enhance green development of agriculture and improve residents' health.

Can financial development improve environmental quality? New findings from spatial measures of Chinese urban panel data.

China's economy has achieved remarkable success, while it has also paid a high environmental cost. Environmental pollution not only causes great economic losses, but also severely restricts the development of society. Both theory and practice demonstrate that financial development plays a significant role in environmental governance, but the internal mechanism of its impacts has remained to be explored. It is vital to investigate the influence mechanism of financial development on the environmental quality in order to accomplish sustainable economic development through finance and enhance environmental quality concurrently. This paper, using mediating model, spatial Durbin and spatial error model, constructs a theoretical framework financial development on environmental quality from two dimensions, i.e., Financial Interrelations Ratio (FIR) and Financial Efficiency (FE), based on panel data of 234 cities in China from 2010 to 2019. And the results are as follows: (1) the improvement of Financial Interrelations Ratio (FIR) and Financial Efficiency (FE) had not yet reached the level of environmental pollution improvement; (2) in terms of mechanism, the rise of the level of financial development promoted economic growth, but inhibited the optimization of industrial structure, which increased industrial pollution emissions and deteriorates environmental quality; (3) urban environment had significant spatial dependence; (4) the impact of financial development on environmental quality in eastern region has been transformed into an improvement effect, while it still shows a deteriorating effect in central and western regions. Some policy recommendations related to the use of financial development to improve environmental quality are proposed at last, which will help to promote ecological protection and high quality synergistic economic development in China.