RNAI-FRID: novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction.

Different ribonucleic acids (RNAs) can interact to form regulatory networks that play important role in many life activities. Molecular biology experiments can confirm RNA-RNA interactions to facilitate the exploration of their biological functions, but they are expensive and time-consuming. Machine learning models can predict potential RNA-RNA interactions, which provide candidates for molecular biology experiments to save a lot of time and cost. Using a set of suitable features to represent the sample is crucial for training powerful models, but there is a lack of effective feature representation for RNA-RNA interaction. This study proposes a novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction (named RNAI-FRID). Diverse base features are first extracted from RNA data to contain more sample information. Then, the extracted base features are used to construct the complex features through an arithmetic-level method. It greatly reduces the feature dimension while keeping the relationship between molecule features. Since the dimension reduction may cause information loss, in the process of complex feature construction, the arithmetic mean strategy is adopted to enhance the sample information further. Finally, three feature ranking methods are integrated for feature selection on constructed complex features. It can adaptively retain important features and remove redundant ones. Extensive experiment results show that RNAI-FRID can provide reliable feature representation for RNA-RNA interaction with higher efficiency and the model trained with generated features obtain better performance than other deep neural network predictors.

Mining plant endogenous target mimics from miRNA-lncRNA interactions based on dual-path parallel ensemble pruning method.

The interactions between microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) play important roles in biological activities. Specially, lncRNAs as endogenous target mimics (eTMs) can bind miRNAs to regulate the expressions of target messenger RNAs (mRNAs). A growing number of studies focus on animals, but the studies on plants are scarce and many functions of plant eTMs are unknown. This study proposes a novel ensemble pruning protocol for predicting plant miRNA-lncRNA interactions at first. It adaptively prunes the base models based on dual-path parallel ensemble method to meet the challenge of cross-species prediction. Then potential eTMs are mined from predicted results. The expression levels of RNAs are identified through biological experiment to construct the lncRNA-miRNA-mRNA regulatory network, and the functions of potential eTMs are inferred through enrichment analysis. Experiment results show that the proposed protocol outperforms existing methods and state-of-the-art predictors on various plant species. A total of 17 potential eTMs are verified by biological experiment to involve in 22 regulations, and 14 potential eTMs are inferred by Gene Ontology enrichment analysis to involve in 63 functions, which is significant for further research.

Lysosome-Targeted and pH-Activatable Phototheranostics for NIR-II Fluorescence Imaging-Guided Nasopharyngeal Carcinoma Phototherapy.

Currently, clinical therapeutic strategies for nasopharyngeal carcinoma (NPC) confront insurmountable dilemmas in which surgical resection is incomplete and chemotherapy/radiotherapy has significant side effects. Phototherapy offers a maneuverable, effective, and noninvasive pattern for NPC therapy. Herein, we developed a lysosome-targeted and pH-responsive nanophototheranostic for near-infrared II (NIR-II) fluorescence imaging-guided photodynamic therapy (PDT) and photothermal therapy (PTT) of NPC. A lysosome-targeted S-D-A-D-S-type NIR-II phototheranostic molecule (IRFEM) is encapsulated within the acid-sensitive amphiphilic DSPE-Hyd-PEG2k to form IRFEM@DHP nanoparticles (NPs). The prepared IRFEM@DHP exhibits a good accumulation in the acidic lysosomes for facilitating the release of IRFEM, which could disrupt lysosomal function by generating an amount of heat and ROS under laser irradiation. Moreover, the guidelines of NIR-II fluorescence enhance the accuracy of PTT/PDT for NPC and avoid damage to normal tissues. Remarkably, IRFEM@DHP enable efficient antitumor effects both in vitro and in vivo, opening up a new avenue for precise NPC theranostics.

Photocatalytic Conversion of Diluted CO2 into Tunable Syngas via Modulating Transition Metal Hydroxides.

The conversion of diluted CO2 into tunable syngas via photocatalysis is critical for implementing CO2 reduction practically, although the efficiency remains low. Herein, we report the use of graphene-modified transition metal hydroxides, namely, NiXCo1-X-GR, for the conversion of diluted CO2 into syngas with adjustable CO/H2 ratios, utilizing Ru dyes as photosensitizers. The Ni(OH)2-GR cocatalyst can generate 12526 µmol g-1 h-1 of CO and 844 µmol g-1 h-1 of H2, while the Co(OH)2-GR sample presents a generation rate of 2953 µmol g-1 h-1 for CO and 10027 µmol g-1 h-1 for H2. Notably, by simply altering the addition amounts of nickel and cobalt in the transition metal composite, the CO/H2 ratios in syngas can be easily regulated from 18:1 to 1:4. Experimental characterization of composites and DFT calculations suggest that the differing adsorption affinities of CO2 and H2O over Ni(OH)2-GR and Co(OH)2-GR play a significant role in determining the selectivity of CO and H2 products, ultimately affecting the CO/H2 ratios in syngas. Overall, these findings demonstrate the potential of graphene-modified transition metal hydroxides as efficient photocatalysts for CO2 reduction and syngas production.

Nano-cutting mechanism of ion implantation-modified SiC: reducing subsurface damage expansion and abrasive wear.

This study utilized ion implantation to modify the material properties of silicon carbide (SiC) to mitigate subsurface damage during SiC machining. The paper analyzed the mechanism of hydrogen ion implantation on the machining performance of SiC at the atomic scale. A molecular dynamics model of nanoscale cutting of an ion-implanted SiC workpiece using a non-rigid regular tetrakaidecahedral diamond abrasive grain was established. The study investigated the effects of ion implantation on crystal structure phase transformation, dislocation nucleation, and defect structure evolution. Results showed ion implantation modification decreased the extension depth of amorphous structures in the subsurface layer, thereby enhancing the surface and subsurface integrity of the SiC workpiece. Additionally, dislocation extension length and volume within the lattice structure were lower in the ion-implanted workpiece compared to non-implanted ones. Phase transformation, compressive pressure, and cutting stress of the lattice in the shear region per unit volume were lower in the ion-implanted workpiece than the non-implanted one. Taking the diamond abrasive grain as the research subject, the mechanism of grain wear under ion implantation was explored. Grain expansion, compression, and atomic volumetric strain wear rate were higher in the non-implanted workpiece versus implanted ones. Under shear extrusion of the SiC workpiece, dangling bonds of atoms in the diamond grain were unstable, resulting in graphitization of the diamond structure at elevated temperatures. This study established a solid theoretical and practical foundation for realizing non-destructive machining at the atomic scale, encompassing both theoretical principles and practical applications.

Tumor Microenvironment-Responsive One-for-All Molecular-Engineered Nanoplatform Enables NIR-II Fluorescence Imaging-Guided Combinational Cancer Therapy.

The activable NIR-based phototheranostic nanoplatform (NP) is considered an efficient and reliable tumor treatment due to its strong targeting ability, flexible controllability, minimal side effects, and ideal therapeutic effect. This work describes the rational design of a second near-infrared (NIR-II) fluorescence imaging-guided organic phototheranostic NP (FTEP-TBFc NP). The molecular-engineered phototheranostic NP has a sensitive response to glutathione (GSH), generating hydrogen sulfide (H2S) gas, and delivering ferrocene molecules in the tumor microenvironment (TME). Under 808 nm irradiation, FTEP-TBFc could not only simultaneously generate fluorescence, heat, and singlet oxygen but also greatly enhance the generation of reactive oxygen species to improve chemodynamic therapy (CDT) and photodynamic therapy (PDT) at a biosafe laser power of 0.33 W/cm2. H2S inhibits the activity of catalase and cytochrome c oxidase (COX IV) to cause the enhancement of CDT and hypothermal photothermal therapy (HPTT). Moreover, the decreased intracellular GSH concentration further increases CDT's efficacy and downregulates glutathione peroxidase 4 (GPX4) for the accumulation of lipid hydroperoxides, thus causing the ferroptosis process. Collectively, FTEP-TBFc NPs show great potential as a versatile and efficient NP for specific tumor imaging-guided multimodal cancer therapy. This unique strategy provides new perspectives and methods for designing and applying activable biomedical phototheranostics.

PlncRNA-HDeep: plant long noncoding RNA prediction using hybrid deep learning based on two encoding styles.

BACKGROUND: Long noncoding RNAs (lncRNAs) play an important role in regulating biological activities and their prediction is significant for exploring biological processes. Long short-term memory (LSTM) and convolutional neural network (CNN) can automatically extract and learn the abstract information from the encoded RNA sequences to avoid complex feature engineering. An ensemble model learns the information from multiple perspectives and shows better performance than a single model. It is feasible and interesting that the RNA sequence is considered as sentence and image to train LSTM and CNN respectively, and then the trained models are hybridized to predict lncRNAs. Up to present, there are various predictors for lncRNAs, but few of them are proposed for plant. A reliable and powerful predictor for plant lncRNAs is necessary. RESULTS: To boost the performance of predicting lncRNAs, this paper proposes a hybrid deep learning model based on two encoding styles (PlncRNA-HDeep), which does not require prior knowledge and only uses RNA sequences to train the models for predicting plant lncRNAs. It not only learns the diversified information from RNA sequences encoded by p-nucleotide and one-hot encodings, but also takes advantages of lncRNA-LSTM proposed in our previous study and CNN. The parameters are adjusted and three hybrid strategies are tested to maximize its performance. Experiment results show that PlncRNA-HDeep is more effective than lncRNA-LSTM and CNN and obtains 97.9% sensitivity, 95.1% precision, 96.5% accuracy and 96.5% F1 score on Zea mays dataset which are better than those of several shallow machine learning methods (support vector machine, random forest, k-nearest neighbor, decision tree, naive Bayes and logistic regression) and some existing tools (CNCI, PLEK, CPC2, LncADeep and lncRNAnet). CONCLUSIONS: PlncRNA-HDeep is feasible and obtains the credible predictive results. It may also provide valuable references for other related research.

PmliPred: a method based on hybrid model and fuzzy decision for plant miRNA-lncRNA interaction prediction.

MOTIVATION: The studies have indicated that not only microRNAs (miRNAs) or long non-coding RNAs (lncRNAs) play important roles in biological activities, but also their interactions affect the biological process. A growing number of studies focus on the miRNA-lncRNA interactions, while few of them are proposed for plant. The prediction of interactions is significant for understanding the mechanism of interaction between miRNA and lncRNA in plant. RESULTS: This article proposes a new method for fulfilling plant miRNA-lncRNA interaction prediction (PmliPred). The deep learning model and shallow machine learning model are trained using raw sequence and manually extracted features, respectively. Then they are hybridized based on fuzzy decision for prediction. PmliPred shows better performance and generalization ability compared with the existing methods. Several new miRNA-lncRNA interactions in Solanum lycopersicum are successfully identified using quantitative real time-polymerase chain reaction from the candidates predicted by PmliPred, which further verifies its effectiveness. AVAILABILITY AND IMPLEMENTATION: The source code of PmliPred is freely available at http://bis.zju.edu.cn/PmliPred/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Earth-Abundant MoS2 and Cobalt Phosphate Dual Cocatalysts on 1D CdS Nanowires for Boosting Photocatalytic Hydrogen Production.

Cocatalysts play a significant role in accelerating the catalytic reactions of semiconductor photocatalyst. In particular, a semiconductor assembled with dual cocatalysts, i.e., reduction and oxidation cocatalysts, can obviously enhance the photocatalytic performance because of the synergistic effect of fast consumption of photogenerated electrons and holes simultaneously. However, in most cases, noble metal cocatalysts are employed, which tremendously increases the cost of the photocatalysts and restricts their large-scale applications. Herein, on the platform of one-dimensional (1D) CdS nanowires, we have utilized the earth-abundant dual cocatalysts, MoS2 and cobalt phosphate (Co-Pi), to construct the CdS@MoS2@Co-Pi (CMC) core-shell hybrid photocatalysts. In this dual-cocatalyst system, Co-Pi is in a position to expedite the migration of holes from CdS, while MoS2 acts as an electron transporter as well as active sites to accelerate the surface water reduction reaction. Taking the advantages of the dual-cocatalyst system, the prepared CMC hybrid shows an obvious enhancement of both the photoactivity and photostability toward hydrogen production compared with bare 1D CdS nanowires and binary hybrids (CdS@MoS2 and CdS@Co-Pi). This work highlights the promising prospects for rational utilization of earth-abundant dual cocatalysts to design low-cost and efficient hybrids toward boosting photoredox catalysis.

Chiral-at-Metal Rh(III) Complex Catalyzed Cascade Reduction-Michael Addition Reaction.

An enantioselective three-component cascade reduction-Michael addition reaction catalyzed by chiral-at-metal Rh(III) complexes has been developed. With a Hantzsch ester as the hydride source, a number of malononitrile derivatives were prepared in good yields and excellent enantioselectivities. A model that accounts for the origin and influence factors of the stereoselectivity has been proposed based on experiments.

Insight into the Role of Size Modulation on Tuning the Band Gap and Photocatalytic Performance of Semiconducting Nitrogen-Doped Graphene.

Considerable attention has been focused on transforming graphene (GR) into semiconducting GR by diverse strategies, which can perform as one type of promising photocatalyst toward various photoredox reactions. Herein, we report a facile alkali-assisted hydrothermal method for simultaneous tailoring of the lateral size of GR and nitrogen (N) doping into the GR matrix, by which small-sized N-doped GR (S-NGR) can be obtained. For comparison, large-sized N-doped GR (L-NGR) has also been achieved through the same hydrothermal treatment except for the addition of alkali. The photocatalytic activity test shows that S-NGR exhibits much higher activity than L-NGR toward the degradation of organic pollutants under visible-light irradiation. Structure-photoactivity correlation analysis and characterization suggest that the underlying origin for the significantly enhanced visible-light photoactivity of S-NGR in comparison with L-NGR can be assigned to the lateral size decrease in the NGR sheet, which is able to tune the band gap of semiconducting NGR, to facilitate the separation and transfer of photogenerated charge carriers, and to improve the adsorption capacity of NGR toward the reactant. It is expected that this work will cast new light on the judicious utilization of semiconducting GR with controlled size modulation and heteroatom doping to tune its physicochemical properties, thereby advancing further developments in the rational design of more efficient semiconducting GR materials for diverse applications in photocatalysis.

Chiral Rhodium(III) Complex-Catalyzed Cascade Michael-Alkylation Reactions: Enantioselective Synthesis of Cyclopropanes.

Chiral Rh(III) complex catalyzed highly efficient enantioselective cyclopropanation of α,ß-unsaturated 2-acyl imidazoles or pyridine with 2-bromomalonate is developed to generate corresponding multisubstituted cyclopropanes in 70-99% yields with 93→99% enantioselectivity. The proficiency of the process is also demonstrated in gram scale reaction maintaining same reactivity and selectivity level in lower catalyst loading. Moreover, the developed methodology is applicable in challenging synthesis of biscyclopropane scaffold with 97% ee in a single operation.

Scutellarin Prevents Nonalcoholic Fatty Liver Disease (NAFLD) and Hyperlipidemia via PI3K/AKT-Dependent Activation of Nuclear Factor (Erythroid-Derived 2)-Like 2 (Nrf2) in Rats.

BACKGROUND Nonalcoholic fatty liver disease (NAFLD) is a condition characterized by excessive fat accumulation in the form of triglycerides. The incidence of NAFLD and hyperlipidemia, with their associated risks of end-stage liver and cardiovascular diseases, is increasing rapidly. This study aimed to investigate the effects of scutellarin on the experimental NAFLD in high-fat diet fed and chronic stress rats, and its possible mechanism. MATERIAL AND METHODS Sprague-Dawley rats were fed with high-fat diet and subjected to chronic stress for 12 weeks, and administered orally with scutellarin for 4 weeks (n=8), and then blood and livers were harvested for analyzing. Enzyme activity assay, immunofluorescence, Western blot, and quantitative RT-PCR were performed to analyze the factors of the oxidant/antioxidant system and pathway. RESULTS After the high-fat diet and chronic stress administration for 12 weeks, serum and liver lipid metabolism of treatment groups with the different doses of SCU effectively improved and the degree of oxidative damage reduced. Using Western blot assay and immunofluorescence (IF) staining assay, Nrf2, HO-1, and PI3K, and AKT proteins significantly increased after SCU treatment for 4 weeks (P<0.01). The hepatic mRNA expression of HO-1, NQO1, and Nrf2 in SCU treatment groups was upregulated significantly through quantitative RT-PCR assay (P<0.05). However, compared to the positive control group, no difference was detected in the SCU (100 or 300 mg/kg) groups (P>0.05). These results indicate that SCU protects against NAFLD in rats via attenuation of oxidative stress. CONCLUSIONS The antioxidant effects of SCU on NAFLD are possibly dependent on PI3K/AKT activation with subsequent Nrf2 nuclear translocation, which increases expression of HO-1 and NQO1. We therefore suggest that breviscapine may be a potentially useful therapeutic strategy for NAFLD and hyperlipidemia.

[Study on metabolites from a sponge-derived fungus].

We have carried out the investigation on a sponge-derived fungus,which was identified as Emericella variecolor from the south sea of China. Two new chemical constituents,(+)-2-acetyl-dihydroterrein (1) and (+)-3-acetyl-dihydroterrein (2),with four known compounds,anditomin (3),andilesin A (4),andilesin C (5) and andilesin B (6),were isolated from this fungus by column chromatography over silica gel, Sephadex LH-20, and ODS. The structures of 1 and 2 were elucidated by spectroscopic methods including NMR,HR-ESI-MS,and CD.

Indole Alkaloids from the Leaves of Nauclea officinalis.

Three new indole alkaloids, named naucleamide G (1), and nauclealomide B and C (5 and 6), were isolated from the n-BuOH-soluble fraction of an EtOH extract of the leaves of Nauclea officinalis, together with three known alkaloids, paratunamide C (2), paratunamide D (3) and paratunamide A (4). The structures with absolute configurations of the new compounds were identified on the basis of 1D and 2D NMR, HRESIMS, acid hydrolysis and quantum chemical circular dichroism (CD) calculation. According to the structures of isolated indole alkaloids, their plausible biosynthetic pathway was deduced.

Machinability and Surface Properties of Cryogenic Poly(methyl methacrylate) Machined via Single-Point Diamond Turning.

Poly(methyl methacrylate) (PMMA), with a glass transition temperature (Tg) over 100 °C, shows good mechanical and optical properties and has broad applications after being machined with single-point diamond turning (SPDT) at room temperature. Because of the high Tg, current efforts mostly focus on optimizing machining parameters to improve workpiece precision without considering the modification of material properties. Cryogenic cooling has been proven to be an effective method in assisting ultra-precision machining for certain types of metals, alloys, and polymers, but has never been used for PMMA before. In this work, cryogenic cooling was attempted during the SPDT of PMMA workpieces to improve surface quality. The machinability and surface properties of cryogenically cooled PMMA were investigated based on the mechanical properties at corresponding temperatures. Nanoindentation tests show that, when temperature is changed from 25 °C to 0 °C, the hardness and Young's modulus are increased by 37% and 22%, respectively. At these two temperature points, optimal parameters including spindle speed, feed rate and cut depth were obtained using Taguchi methods to obtain workpieces with high surface quality. The surface quality was evaluated based on the total height of the profile (Pt) and the arithmetic mean deviation (Ra). The measurement results show that the values of Pt and Ra of the workpiece machined at 0 °C are 124 nm and 6 nm, respectively, while the corresponding values of that machined at 25 °C are 291 nm and 11 nm. The test data show that cryogenic machining is useful for improving the form accuracy and reducing the surface roughness of PMMA. Moreover, the relationship between temperature, material properties and machinability weas established with dynamic mechanical analysis (DMA) data and a theoretical model. This can explain the origin of the better surface quality of the cryogenic material. The basis of this is that temperature affects the viscoelasticity of the polymer and the corresponding mechanical properties due to relaxation. Then, the material property changes will affect surface profile formation during machining. The experimental results and theoretical analysis show that cryogenically cooled PMMA has good machinability and improved surface quality when using SPDT compared to that at ambient temperature.

BatchEval Pipeline: batch effect evaluation workflow for multiple datasets joint analysis.

As genomic sequencing technology continues to advance, it becomes increasingly important to perform joint analyses of multiple datasets of transcriptomics. However, batch effect presents challenges for dataset integration, such as sequencing data measured on different platforms, and datasets collected at different times. Here, we report the development of BatchEval Pipeline, a batch effect workflow used to evaluate batch effect on dataset integration. The BatchEval Pipeline generates a comprehensive report, which consists of a series of HTML pages for assessment findings, including a main page, a raw dataset evaluation page, and several built-in methods evaluation pages. The main page exhibits basic information of the integrated datasets, a comprehensive score of batch effect, and the most recommended method for removing batch effect from the current datasets. The remaining pages exhibit evaluation details for the raw dataset, and evaluation results from the built-in batch effect removal methods after removing batch effect. This comprehensive report enables researchers to accurately identify and remove batch effects, resulting in more reliable and meaningful biological insights from integrated datasets. In summary, the BatchEval Pipeline represents a significant advancement in batch effect evaluation, and is a valuable tool to improve the accuracy and reliability of the experimental results. Availability & Implementation: The source code of the BatchEval Pipeline is available at https://github.com/STOmics/BatchEval.

Classification of anatomical morphology of cystic duct and its association with gallstone.

BACKGROUND: Gallstones are common lesions that often require surgical intervention. Laparoscopic cholecystectomy is the treatment of choice for symptomatic gallstones. Preoperatively, the anatomical morphology of the cystic duct (CD), needs to be accurately recognized, especially when anatomical variations occur in the CD, which is otherwise prone to bile duct injury. However, at present, there is no optimal classification system for CD morphology applicable in clinical practice, and the relationship between anatomical variations in CDs and gallstones remains to be explored. AIM: To create a more comprehensive clinically applicable classification of the morphology of CD and to explore the correlations between anatomic variants of CD and gallstones. METHODS: A total of 300 patients were retrospectively enrolled from October 2021 to January 2022. The patients were divided into two groups: The gallstone group and the nongallstone group. Relevant clinical data and anatomical data of the CD based on magnetic resonance cholangiopancreatography (MRCP) were collected and analyzed to propose a morphological classification system of the CD and to explore its relationship with gallstones. Multivariate analysis was performed using logistic regression analyses to identify the independent risk factors using variables that were significant in the univariate analysis. RESULTS: Of the 300 patients enrolled in this study, 200 (66.7%) had gallstones. The mean age was 48.10 ± 13.30 years, 142 (47.3%) were male, and 158 (52.7%) were female. A total of 55.7% of the patients had a body mass index (BMI) ≥ 24 kg/m2. Based on the MRCP, the CD anatomical typology is divided into four types: Type I: Linear, type II: n-shaped, type III: S-shaped, and type IV: W-shaped. Univariate analysis revealed differences between the gallstone and nongallstone groups in relation to sex, BMI, cholesterol, triglycerides, morphology of CD, site of CD insertion into the extrahepatic bile duct, length of CD, and angle between the common hepatic duct and CD. According to the multivariate analysis, female, BMI (≥ 24 kg/m2), and CD morphology [n-shaped: Odds ratio (OR) = 10.97, 95% confidence interval (95%CI): 5.22-23.07, P < 0.001; S-shaped: OR = 4.43, 95%CI: 1.64-11.95, P = 0.003; W-shaped: OR = 7.74, 95%CI: 1.88-31.78, P = 0.005] were significantly associated with gallstones. CONCLUSION: The present study details the morphological variation in the CD and confirms that CD tortuosity is an independent risk factor for gallstones.

EAGS: efficient and adaptive Gaussian smoothing applied to high-resolved spatial transcriptomics.

BACKGROUND: The emergence of high-resolved spatial transcriptomics (ST) has facilitated the research of novel methods to investigate biological development, organism growth, and other complex biological processes. However, high-resolved and whole transcriptomics ST datasets require customized imputation methods to improve the signal-to-noise ratio and the data quality. FINDINGS: We propose an efficient and adaptive Gaussian smoothing (EAGS) imputation method for high-resolved ST. The adaptive 2-factor smoothing of EAGS creates patterns based on the spatial and expression information of the cells, creates adaptive weights for the smoothing of cells in the same pattern, and then utilizes the weights to restore the gene expression profiles. We assessed the performance and efficiency of EAGS using simulated and high-resolved ST datasets of mouse brain and olfactory bulb. CONCLUSIONS: Compared with other competitive methods, EAGS shows higher clustering accuracy, better biological interpretations, and significantly reduced computational consumption.

Rationally Designed S-Scheme CeO2/g-C3N4 Heterojunction for Promoting Visible Light Driven CO2 Photoreduction into Syngas.

Exploring low-cost visible light photocatalysts for CO2 reduction to produce proportionally adjustable syngas is of great significance for meeting the needs of green chemical industry. A S-Scheme CeO2/g-C3N4 (CeO2/CN) heterojunction was constructed by using a simple two-step calcination method. During the photocatalytic CO2 reduction process, the CeO2/CN heterojunction can present a superior photocatalytic performance, and the obtained CO/H2 ratios in syngas can be regulated from 1:0.16 to 1:3.02. In addition, the CO and H2 production rate of the optimal CeO2/CN composite can reach 1169.56 and 429.12 µmol g-1 h-1, respectively. This superior photocatalytic performance is attributed to the unique S-Scheme photogenerated charge transfer mechanism between CeO2 and CN, which facilitates rapid charge separation and migration, while retaining the excellent redox capacity of both semiconductors. Particularly, the variable valence Ce3+/Ce4+ can act as electron mediator between CeO2 and CN, which can promote electron transfer and improve the catalytic performance. This work is expected to provide a new useful reference for the rational construction of high efficiency S-Scheme heterojunction photocatalyst, and improve the efficiency of photocatalytic reduction of CO2, promoting the photocatalytic reduction of CO2 into useful fuels.