Realized Record Capacity and Rate Performance of Dual-Carbon Batteries Constructed by Introducing Superhard Nanodiamonds.

Dual-ion batteries (DIBs) are becoming an important technology for energy storage. To overcome the disadvantages of traditional electrodes and electrolytes, here we assemble a dual-carbon DIB with nanodiamond (ND)-modified crimped graphene (DCG) and electrolyte. The DCG anode and cathode realize high capacities of 1121 mA h g-1 and 149 mA h g-1, respectively, at 0.1 A g-1. The corresponding DCG//DCG full cells present a high capacity of 143 mA h g-1 at 1 A g-1 after 3300 cycles, which is superior to most reported results. Achieving these record performances is strongly dependent on the formed DCG electrodes with expanded interlayer spacing and abundant active sites, and NDs dispersed in DCG and electrolytes are very helpful for enhancing the storage of both cations and anions, effectively suppressing the irreversible decomposition of electrolytes. This work breaks through the bottleneck of graphitic-based DIBs, paving the way for realizing high-performance DIBs applied in industry.

Spatial Steerability of GANs via Self-Supervision from Discriminator.

Generative models make huge progress to the photorealistic image synthesis in recent years. To enable human to steer the image generation process and customize the output, many works explore the interpretable dimensions of the latent space in GANs. Existing methods edit the attributes of the output image such as orientation or color scheme by varying the latent code along certain directions. However, these methods usually require additional human annotations for each pretrained model, and they mostly focus on editing global attributes. In this work, we propose a self-supervised approach to improve the spatial steerability of GANs without searching for steerable directions in the latent space or requiring extra annotations. Specifically, we design randomly sampled Gaussian heatmaps to be encoded into the intermediate layers of generative models as spatial inductive bias. Along with training the GAN model from scratch, these heatmaps are being aligned with the emerging attention of the GAN's discriminator in a self-supervised learning manner. During inference, users can interact with the spatial heatmaps in an intuitive manner, enabling them to edit the output image by adjusting the scene layout, moving, or removing objects. Moreover, we incorporate DragGAN into our framework, which facilitates fine-grained manipulation within a reasonable time and supports a coarse-to-fine editing process. Extensive experiments show that the proposed method not only enables spatial editing over human faces, animal faces, outdoor scenes, and complicated multi-object indoor scenes but also brings improvement in synthesis quality. Code, models, and demo video are available at https://genforce.github.io/SpatialGAN/.

Validating the performance of organ dysfunction scores in children with infection: A cohort study.

PURPOSE: We aimed to validate the performance of six available scoring models for predicting hospital mortality in children with suspected or confirmed infections. METHODS: This single-center retrospective cohort study included pediatric patients admitted to the PICU for infection. The primary outcome was hospital mortality. The six scores included the age-adapted pSOFA score, SIRS score, PELOD2 score, Sepsis-2 score, qSOFA score, and PMODS. RESULTS: Of the 5,356 children admitted to the PICU, 9.1% (488) died, and 25.1% (1,342) had basic disease with a mortality rate of 12.7% (171); 65.3% (3,499) of the patients were younger than 2 years, and 59.4% (3,183) were male. The discrimination abilities of the pSOFA and PELOD2 scores were superior to those of the other models. The calibration curves of the pSOFA and PELOD2 scores were consistent between the predictions and observations. Elevated lactate levels were a risk factor for mortality. CONCLUSION: The pSOFA and PELOD2 scores had superior predictive performance for mortality. Given the relative unavailability of items and clinical operability, the pSOFA score should be recommended as an optimal tool for acute organ dysfunction in pediatric sepsis patients. Elevated lactate levels are related to a greater risk of death from infection in children in the PICU.

Defective Diamane: A Superior Sensor for Toxic Gases Capture and Detection with Excellent Selectivity, Sensitivity, and Reversibility at Room Temperature.

The toxic gases emitted from industrial production have caused significant damage to the environment and human health, necessitating efficient gas sensors for their detection and removal. In this work, first-principles calculations are employed to investigate the potential application of diamanes for high-performance toxic gas sensors. The results show that nine gas molecules (CO, CO2, NO, NO2, NH3, SO2, N2, O2, and H2O) are physisorbed on pristine diamane by weak van der Waals interactions. After introducing H/F defects, diamane can effectively capture specific toxic gases (CO, NO, NO2, and SO2) in the presence of interfering gases (N2, O2, and H2O), suggesting excellent selectivity and anti-interference ability. Orbital hybridization and significant charge redistribution between gas molecules and defective diamane dominate the enhanced adsorbate-substrate interactions. More importantly, the high sensitivity and good reversibility of defective diamane for detecting CO, NO, and SO2 molecules enable its reuse as a superior resistance-type gas sensor. Our calculations provide valuable insights into the potential of defective diamane for detecting toxic gases and shed light on the practical application of novel carbon-based materials in the gas-sensing field.

Synergistic Effect of Doxorubicin and Blue Light Irradiation on the Antitumor Treatment of HepG2 Cells in Liver Cancer.

Doxorubicin (DOX) has been an effective antitumor agent for human liver cancer cells; however, an overdose might lead to major side effects appearing in clinical applications. In this work, we present a strategy of combining DOX and blue light (BL) irradiation for the antitumor treatment of HepG2 cells (one typical human liver cancer cell line). It is demonstrated that synergetic DOX and BL can significantly reduce cell proliferation and increase the apoptotic rate of HepG2 cells in comparison to individual DOX treatment. The additional BL irradiation is further helpful for enhancing the inhibition of cell migration and invasion. Analyses of reactive oxygen species (ROS) level and Western blotting reveal that the strategy results in more ROS accumulation, mitochondrial damage, and the upregulation of proapoptotic protein (Bcl-2) and downregulation of antiapoptotic protein (Bax). In addition to the improved therapeutic effect, the non-contact BL irradiation is greatly helpful for reducing the dosage of DOX, and subsequently reduces the side effects caused by the DOX drug. These findings offer a novel perspective for the therapeutic approach toward liver cancer with high efficiency and reduced side effects.

Oxygen-Terminated Polycrystalline Boron-Doped Diamond Superhydrophobic Surface with Excellent Mechanical and Thermal Stabilities.

Superhydrophobic surfaces are of great interest because of their remarkable properties. Due to its maximal hardness and chemical inertness, diamond film has great potential in fabricating robust superhydrophobic surfaces. In the present study, an oxygen-terminated polycrystalline boron-doped diamond (O-PBDD) superhydrophobic surface with micro/nano-hierarchical porous structures is developed. The preparation method is very simple, requiring only sputtering and dewetting procedures. The former involves sputtering gold and copper particles onto the hydrogen-terminated polycrystalline boron-doped diamond (H-PBDD) to form gold/copper films, whereas the latter involves placing the samples in an atmospheric tube furnace to form hierarchical pores. By controlling the etching parameters, the wettability of the O-PBDD surface can be adjusted from hydrophilic to superhydrophobic, which is significantly different to the normal hydrophilicity feature of O-termination diamonds. The water contact angle of the obtained O-PBDD surface can reach 165 ± 5°, which is higher than the superhydrophobic diamond surfaces that are reported in the literature. In addition, the O-PBDD surface exhibits excellent durability; it can maintain satisfactory superhydrophobicity even after high-pressure, high-temperature, and sandpaper friction tests. This work provides a new research direction for fabricating robust superhydrophobic materials with diamond film.

Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks.

MOTIVATION: Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and biological processes. The knowledge of annotated gene sets is critical for discovering cancer genes but remains to be fully exploited. RESULTS: Here, we present the DIsease-Specific Hypergraph neural network (DISHyper), a hypergraph-based computational method that integrates the knowledge from multiple types of annotated gene sets to predict cancer genes. First, our benchmark results demonstrate that DISHyper outperforms the existing state-of-the-art methods and highlight the advantages of employing hypergraphs for representing annotated gene sets. Second, we validate the accuracy of DISHyper-predicted cancer genes using functional validation results and multiple independent functional genomics data. Third, our model predicts 44 novel cancer genes, and subsequent analysis shows their significant associations with multiple types of cancers. Overall, our study provides a new perspective for discovering cancer genes and reveals previously undiscovered cancer genes. AVAILABILITY AND IMPLEMENTATION: DISHyper is freely available for download at https://github.com/genemine/DISHyper.

Charge Redistribution of Lattice-Mismatched Co─Cu3P Boosting pH-Universal Water/Seawater Hydrogen Evolution.

Practical applications of the hydrogen evolution reaction (HER) rely on the development of highly efficient, stable, and low-cost catalysts. Tuning the electronic structure, morphology, and architecture of catalysts is an important way to realize efficient and stable HER electrocatalysts. Herein, Co-doped Cu3P-based sugar-gourd structures (Co─Cu3P/CF) are prepared on copper foam as active electrocatalysts for hydrogen evolution. This hierarchical structure facilitates fast mass transport during electrocatalysis. Notably, the introduction of Co not only induces a charge redistribution but also leads to lattice-mismatch on the atomic scale, which creates defects and performs as additional active sites. Therefore, Co─Cu3P/CF requires an overpotential of only 81, 111, 185, and 230 mV to reach currents of 50, 100, 500, and 1000 mA cm-2 in alkaline media and remains stable after 10 000 CV cycles in a row and up to 110 h i-t stability tests. In addition, it also shows excellent HER performance in water/seawater electrolytes of different pH values. Experimental and DFT show that the introduction of Co modulates the electronic and energy level structures of the catalyst, optimizes the adsorption and desorption behavior of the intermediate, reduces the water dissociation energy barrier during the reaction, accelerates the Volmer step reaction, and thus improves the HER performance.

Sulfur-Induced Electronic Optimization of N-Doped Carbon with CoP/Co2P Heterostructure by Precursor Design for Rechargeable Zinc-Air Batteries.

Heteroatom doping and heterostructure construction are the key methods to improve the performance of electrocatalysts. However, developing such catalysts remains a challenging task. Herein, we designed two comparable polymers, phytic acid/thiourea polymer (PATP) and phytic acid/urea polymer (PAUP), as precursors, which contain C, N, S/O, and P by microwave heating. To pinpoint how the introduction of sulfur would affect the electronic structure and catalytic activity, these two polymers were physically blended with CoCo-Prussian blue analogue (CoCo-PBA) and further calcination, respectively. The highly dispersed CoP/Co2P-rich interfacial catalysts anchored on the N,S-codoped or N-doped carbon support were successfully prepared (CoP/Co2P@CNS and CoP/Co2P@CN). The prepared CoP/Co2P@CNS catalyst showed good ORR properties (E1/2 = 0.856 V vs RHE) and OER properties (Ej10 = 1.54 V vs RHE), which were superior to the commercial Pt/C and RuO2 catalysts. The reversible oxygen electrode index (ΔE = Ej10 - E1/2) can reach ∼0.684 V. Meanwhile, the rechargeable zinc-air battery assembled with a CoP/Co2P@CNS catalyst as the air cathode also showed excellent performance, with a charge-discharge cycle stability of up to 900 h. DFT calculations further confirm that the introduction of S atoms can affect the electronic structure and enhance the catalytic activity of C and N atoms on carbon support.

CoNiO2/Co3O4 Nanosheets on Boron Doped Diamond for Supercapacitor Electrodes.

Developing novel supercapacitor electrodes with high energy density and good cycle stability has aroused great interest. Herein, the vertically aligned CoNiO2/Co3O4 nanosheet arrays anchored on boron doped diamond (BDD) films are designed and fabricated by a simple one-step electrodeposition method. The CoNiO2/Co3O4/BDD electrode possesses a large specific capacitance (214 mF cm-2) and a long-term capacitance retention (85.9% after 10,000 cycles), which is attributed to the unique two-dimensional nanosheet architecture, high conductivity of CoNiO2/Co3O4 and the wide potential window of diamond. Nanosheet materials with an ultrathin thickness can decrease the diffusion length of ions, increase the contact area with electrolyte, as well as improve active material utilization, which leads to an enhanced electrochemical performance. Additionally, CoNiO2/Co3O4/BDD is fabricated as the positive electrode with activated carbon as the negative electrode, this assembled asymmetric supercapacitor exhibits an energy density of 7.5 W h kg-1 at a power density of 330.5 W kg-1 and capacity retention rate of 97.4% after 10,000 cycles in 6 M KOH. This work would provide insights into the design of advanced electrode materials for high-performance supercapacitors.

Characterised intron retention profiles in muscle tissue of idiopathic inflammatory myopathy subtypes.

OBJECTIVES: Idiopathic inflammatory myopathies (IIMs) are a group of heterogeneous autoimmune diseases. Intron retention (IR) serves as an important post-transcriptional and translational regulatory mechanism. This study aims to identify changes in IR profiles in IIM subtypes, investigating their influence on proteins and their correlations with clinical features. METHODS: RNA sequencing and liquid chromatography-tandem mass spectrometry were performed on muscle tissues obtained from 174 patients with IIM and 19 controls, following QC procedures. GTFtools and iREAD software were used for IR identification. An analysis of differentially expressed IRs (DEIs), exons and proteins was carried out using edgeR or DEP. Functional analysis was performed with clusterProfiler, and SPIRON was used to assess splicing factors. RESULTS: A total of 6783 IRs located in 3111 unique genes were identified in all IIM subtypes compared with controls. IIM subtype-specific DEIs were associated with the pathogenesis of respective IIM subtypes. Splicing factors YBX1 and HSPA2 exhibited the most changes in dermatomyositis and immune-mediated necrotising myopathy. Increased IR was associated with reduced protein expression. Some of the IIM-specific DEIs were correlated with clinical parameters (skin rash, MMT-8 scores and muscle enzymes) and muscle histopathological features (myofiber necrosis, regeneration and inflammation). IRs in IFIH1 and TRIM21 were strongly correlated with anti-MDA5+ antibody, while IRs in SRP14 were associated with anti-SRP+ antibody. CONCLUSION: This study revealed distinct IRs and specific splicing factors associated with IIM subtypes, which might be contributing to the pathogenesis of IIM. We also emphasised the potential impact of IR on protein expression in IIM muscles.

Activation of Ga Liquid Catalyst with Continuously Exposed Active Sites for Electrocatalytic C-N Coupling.

Environmentally friendly electrocatalytic coupling of CO2 and N2 for urea synthesis is a promising strategy. However, it is still facing problems such as low yield as well as low stability. Here, a new carbon-coated liquid alloy catalyst, Ga79Cu11Mo10@C is designed for efficient electrochemical urea synthesis by activating Ga active sites. During the N2 and CO2 co-reduction process, the yield of urea reaches 28.25 mmol h-1 g-1, which is the highest yield reported so far under the same conditions, the Faraday efficiency (FE) is also as high as 60.6 % at -0.4 V vs. RHE. In addition, the catalyst shows excellent stability under 100 h of testing. Comprehensive analyses showed that sequential exposure of a high density of active sites promoted the adsorption and activation of N2 and CO2 for efficient coupling reactions. This coupling reaction occurs through a thermodynamic spontaneous reaction between *N=N* and CO to form a C-N bond. The deformability of the liquid state facilitates catalyst recovery and enhances stability and resistance to poisoning. Moreover, the introduction of Cu and Mo stimulates the Ga active sites, which successfully synthesises the *NCON* intermediate. The reaction energy barrier of the third proton-coupled electron transfer process rate-determining step (RDS) *NHCONH→*NHCONH2 was lowered, ensuring the efficient synthesis of urea.

In Situ, Rapid Synthesis of Carbon-Loaded High Density and Ultrasmall High Entropy Oxide Nanoparticles as Efficient Electrocatalysts.

High entropy materials offer almost unlimited catalytic possibilities due to their variable composition, unique structure, and excellent electrocatalytic performance. However, due to the strong tendency of nanoparticles to coarsen and agglomerate, it is still a challenge to synthesize nanoparticles using simple methods to precisely control the morphology and size of the nanoparticles in large quantities, and their large-scale application is limited by high costs and low yields. Herein, a series of high-entropy oxides (HEOs) nanoparticles with high-density and ultrasmall size (<5 nm) loaded on carbon nanosheets with large quantities are prepared by Joule-heating treatment of gel precursors in a short period of time (≈60 s). Among them, the prepared (FeCoNiRuMn)3O4-x catalyst shows the best electrocatalytic activity for oxygen evolution reaction, with low overpotentials (230 mV @10 mA cm-2, 270 mV @100 mA cm-2), small Tafel slope (39.4 mV dec-1), and excellent stability without significant decay at 100 mA cm-2 after 100 h. The excellent performance of (FeCoNiRuMn)3O4-x can be attributed to the synergistic effect of multiple elements and the inherent structural stability of high entropy systems. This study provides a more comprehensive design idea for the preparation of efficient and stable high entropy catalysts.

Personalized differential expression analysis in triple-negative breast cancer.

Identification of individual-level differentially expressed genes (DEGs) is a pre-step for the analysis of disease-specific biological mechanisms and precision medicine. Previous algorithms cannot balance accuracy and sufficient statistical power. Herein, RankCompV2, designed for identifying population-level DEGs based on relative expression orderings, was adjusted to identify individual-level DEGs. Furthermore, an optimized version of individual-level RankCompV2, named as RankCompV2.1, was designed based on the assumption that the rank positions of genes and relative rank differences of gene pairs would influence the identification of individual-level DEGs. In comparison to other individualized analysis algorithms, RankCompV2.1 performed better on statistical power, computational efficiency, and acquired coequal accuracy in both simulation and real paired cancer-normal data from ten cancer types. Besides, single sample GSEA and Gene Set Variation Analysis analysis showed that pathways enriched with up-regulated and down-regulated genes presented higher and lower enrichment scores, respectively. Furthermore, we identified 16 genes that were universally deregulated in 966 triple-negative breast cancer (TNBC) samples and interacted with Food and Drug Administration (FDA)-approved drugs or antineoplastic agents, indicating notable therapeutic targets for TNBC. In addition, we also identified genes with highly variable deregulation status and used these genes to cluster TNBC samples into three subgroups with different prognoses. The subgroup with the poorest outcome was characterized by down-regulated immune-regulated pathways, signal transduction pathways, and apoptosis-related pathways. Protein-protein interaction network analysis revealed that OAS family genes may be promising drug targets to activate tumor immunity in this subgroup. In conclusion, RankCompV2.1 is capable of identifying individual-level DEGs with high accuracy and statistical power, analyzing mechanisms of carcinogenesis and exploring therapeutic strategy.

Integrated analysis of diverse cancer types reveals a breast cancer-specific serum miRNA biomarker through relative expression orderings analysis.

PURPOSE: Serum microRNA (miRNA) holds great potential as a non-invasive biomarker for diagnosing breast cancer (BrC). However, most diagnostic models rely on the absolute expression levels of miRNAs, which are susceptible to batch effects and challenging for clinical transformation. Furthermore, current studies on liquid biopsy diagnostic biomarkers for BrC mainly focus on distinguishing BrC patients from healthy controls, needing more specificity assessment. METHODS: We collected a large number of miRNA expression data involving 8465 samples from GEO, including 13 different cancer types and non-cancer controls. Based on the relative expression orderings (REOs) of miRNAs within each sample, we applied the greedy, LASSO multiple linear regression, and random forest algorithms to identify a qualitative biomarker specific to BrC by comparing BrC samples to samples of other cancers as controls. RESULTS: We developed a BrC-specific biomarker called 7-miRPairs, consisting of seven miRNA pairs. It demonstrated comparable classification performance in our analyzed machine learning algorithms while requiring fewer miRNA pairs, accurately distinguishing BrC from 12 other cancer types. The diagnostic performance of 7-miRPairs was favorable in the training set (accuracy = 98.47%, specificity = 98.14%, sensitivity = 99.25%), and similar results were obtained in the test set (accuracy = 97.22%, specificity = 96.87%, sensitivity = 98.02%). KEGG pathway enrichment analysis of the 11 miRNAs within the 7-miRPairs revealed significant enrichment of target mRNAs in pathways associated with BrC. CONCLUSION: Our study provides evidence that utilizing serum miRNA pairs can offer significant advantages for BrC-specific diagnosis in clinical practice by directly comparing serum samples with BrC to other cancer types.

Learning Bilateral Cost Volume for Rolling Shutter Temporal Super-Resolution.

Rolling shutter temporal super-resolution (RSSR), which aims to synthesize intermediate global shutter (GS) video frames between two consecutive rolling shutter (RS) frames, has made remarkable progress with the development of deep convolutional neural networks over the past years. Existing methods cascade multiple separated networks to sequentially estimate intermediate motion fields and synthesize target GS frames. Nevertheless, they are typically complex, do not facilitate the interaction of complementary motion and appearance information, and suffer from problems such as pixel aliasing or poor interpretation. In this paper, we derive the uniform bilateral motion fields for RS-aware backward warping, which endows our network a more explicit geometric meaning by injecting spatio-temporal consistency information through time-offset embedding. More importantly, we develop a unified, single-stage RSSR pipeline to recover the latent GS video in a coarse-to-fine manner. It first extracts pyramid features from given inputs, and then refines the bilateral motion fields together with the anchor frame until generating the desired output. With the help of our proposed bilateral cost volume, which uses the anchor frame as a common reference to model the correlation with two RS frames, the gradually refined anchor frames not only facilitate intermediate motion estimation, but also compensate for contextual details, making additional frame synthesis or refinement networks unnecessary. Meanwhile, an asymmetric bilateral motion model built on top of the symmetric bilateral motion model further improves the generality and adaptability, yielding better GS video reconstruction performance. Extensive quantitative and qualitative experiments on synthetic and real data demonstrate that our method achieves new state-of-the-art results.

Artificial intelligence approaches for molecular representation in drug response prediction.

Drug response prediction is essential for drug development and disease treatment. One key question in predicting drug response is the representation of molecules, which has been greatly advanced by artificial intelligence (AI) techniques in recent years. In this review, we first describe different types of representation methods, pinpointing their key principles and discussing their limitations. Thereafter we discuss potential ways how these methods could be further developed. We expect that this review will provide useful guidance for researchers in the community.

Nanowires Framework Supported Porous Lotus-Carbon Anode Boosts Lithium-Ion and Sodium-Ion Batteries.

The novel design of carbon materials with stable nanoarchitecture and optimized electrical properties featuring simultaneous intercalation of lithium ions (Li+ ) and sodium ions (Na+ ) is of great significance for the superb lithium- sodium storage capacities. Biomass-derived carbon materials with affluent porosity have been widely studied as anodes for lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs). However, it remains unexplored to further enhance the stability and utilization of the porous carbon skeleton during cycles. Here, a lotus stems derived porous carbon (LPC) with graphene quantum dots (GQDs) and intrinsic carbon nanowires framework (CNF) is successfully fabricated by a self-template method. The LPC anodes show remarkable Li+ and Na+ storage performance with ultrahigh capacity (738 mA h g-1 for LIBs and 460 mA h g-1 for SIBs at 0.2 C after 300 cycles, 1C≈372 mA h g-1 ) and excellent long-term stability. Structural analysis indicates that the CNFs-supported porous structure and internal GQDs with excellent electrical conductivity contribute significantly to the dominant capacitive storage mechanism in LPC. This work provides new perspectives for developing advanced carbon-based materials for multifunctional batteries with improved stability and utilization of porous carbon frameworks during cycles.

Graph-Based Fusion of Imaging, Genetic and Clinical Data for Degenerative Disease Diagnosis.

Graph learning methods have achieved noteworthy performance in disease diagnosis due to their ability to represent unstructured information such as inter-subject relationships. While it has been shown that imaging, genetic and clinical data are crucial for degenerative disease diagnosis, existing methods rarely consider how best to use their relationships. How best to utilize information from imaging, genetic and clinical data remains a challenging problem. This study proposes a novel graph-based fusion (GBF) approach to meet this challenge. To extract effective imaging-genetic features, we propose an imaging-genetic fusion module which uses an attention mechanism to obtain modality-specific and joint representations within and between imaging and genetic data. Then, considering the effectiveness of clinical information for diagnosing degenerative diseases, we propose a multi-graph fusion module to further fuse imaging-genetic and clinical features, which adopts a learnable graph construction strategy and a graph ensemble method. Experimental results on two benchmarks for degenerative disease diagnosis (Alzheimers Disease Neuroimaging Initiative and Parkinson's Progression Markers Initiative) demonstrate its effectiveness compared to state-of-the-art graph-based methods. Our findings should help guide further development of graph-based models for dealing with imaging, genetic and clinical data.

FEED: a feature selection method based on gene expression decomposition for single cell clustering.

Single-cell clustering is a critical step in biological downstream analysis. The clustering performance could be effectively improved by extracting cell-type-specific genes. The state-of-the-art feature selection methods usually calculate the importance of a single gene without considering the information contained in the gene expression distribution. Moreover, these methods ignore the intrinsic expression patterns of genes and heterogeneity within groups of different mean expression levels. In this work, we present a Feature sElection method based on gene Expression Decomposition (FEED) of scRNA-seq data, which selects informative genes to enhance clustering performance. First, the expression levels of genes are decomposed into multiple Gaussian components. Then, a novel gene correlation calculation method is proposed to measure the relationship between genes from the perspective of distribution. Finally, a permutation-based approach is proposed to determine the threshold of gene importance to obtain marker gene subsets. Compared with state-of-the-art feature selection methods, applying FEED on various scRNA-seq datasets including large datasets followed by different common clustering algorithms results in significant improvements in the accuracy of cell-type identification. The source codes for FEED are freely available at https://github.com/genemine/FEED.