M2-type tumor-associated macrophages upregulated PD-L1 expression in cervical cancer via the PI3K/AKT pathway.

BACKGROUND AND PURPOSE: PD-1/PD-L1 inhibitors have become a promising therapy. However, the response rate is lower than 30% in patients with cervical cancer (CC), which is related to immunosuppressive components in tumor microenvironment (TME). Tumor-associated macrophages (TAMs), as one of the most important immune cells, are involved in the formation of tumor suppressive microenvironment. Therefore, it will provide a theoretical basis for curative effect improvement about the regulatory mechanism of TAMs on PD-L1 expression. METHODS: The clinical data and pathological tissues of CC patients were collected, and the expressions of PD-L1, CD68 and CD163 were detected by immunohistochemistry. Bioinformatics was used to analyze the macrophage subtypes involved in PD-L1 regulation. A co-culture model was established to observe the effects of TAMs on the morphology, migration and invasion function of CC cells, and the regulatory mechanism of TAMs on PD-L1. RESULTS: PD-L1 expression on tumor cells could predict the poor prognosis of patients. And there was a strong correlation between PD-L1 expression with CD163+TAMs infiltration. Similarly, PD-L1 expression was associated with M1/M2-type TAMs infiltration in bioinformatics analysis. The results of cell co-culture showed that M1/M2-type TAMs could upregulate PD-L1 expression, especially M2-type TAMs may elevate the PD-L1 expression via PI3K/AKT pathway. Meanwhile, M1/M2-type TAMs can affect the morphological changes, and enhance migration and invasion abilities of CC cells. CONCLUSIONS: PD-L1 expression in tumor cells can be used as a prognostic factor and is closely related to CD163+TAMs infiltration. In addition, M2-type TAMs can upregulate PD-L1 expression in CC cells through PI3K/AKT pathway, enhance the migration and invasion capabilities, and affect the tumor progression.

Spatiotemporal patterns of leaf nutrients of wild apples in a wild fruit forest plot in the Ili Valley, China.

Malus sieversii, commonly known as wild apples, represents a Tertiary relict plant species and serves as the progenitor of globally cultivated apple varieties. Unfortunately, wild apple populations are facing significant degradation in localized areas due to a myriad of factors. To gain a comprehensive understanding of the nutrient status and spatiotemporal variations of M. sieversii, green leaves were collected in May and July, and the fallen leaves were collected in October. The concentrations of leaf nitrogen (N), phosphorus (P), and potassium (K) were measured, and the stoichiometric ratios as well as nutrient resorption efficiencies were calculated. The study also explored the relative contributions of soil, topographic, and biotic factors to the variation in nutrient traits. The results indicate that as the growing period progressed, the concentrations of N and P in the leaves significantly decreased (P < 0.05), and the concentration of K in October was significantly lower than in May and July. Throughout plant growth, leaf N-P and N-K exhibited hyperallometric relationships, while P-K showed an isometric relationship. Resorption efficiency followed the order of N < P < K (P < 0.05), with all three ratios being less than 1; this indicates that the order of nutrient limitation is K > P > N. The resorption efficiencies were mainly regulated by nutrient concentrations in fallen leaves. A robust spatial dependence was observed in leaf nutrient concentrations during all periods (70.1-97.9% for structural variation), highlighting that structural variation, rather than random factors, dominated the spatial variation. Nutrient resorption efficiencies (NRE, PRE, and KRE) displayed moderate structural variation (30.2-66.8%). The spatial patterns of nutrient traits varied across growth periods, indicating they are influenced by multifactorial elements (in which, soil property showed the highest influence). In conclusion, wild apples manifested differentiated spatiotemporal variability and influencing factors across various leaf nutrient traits. These results provide crucial insights into the spatiotemporal patterns and influencing factors of leaf nutrient traits of M. sieversii at the permanent plot scale for the first time. This work is of great significance for the ecosystem restoration and sustainable management of degrading wild fruit forests.

FastBiCmrMLM: a fast and powerful compressed variance component mixed logistic model for big genomic case-control genome-wide association study.

Large sample datasets have been regarded as the primary basis for innovative discoveries and the solution to missing heritability in genome-wide association studies. However, their computational complexity cannot consider all comprehensive effects and all polygenic backgrounds, which reduces the effectiveness of large datasets. To address these challenges, we included all effects and polygenic backgrounds in a mixed logistic model for binary traits and compressed four variance components into two. The compressed model combined three computational algorithms to develop an innovative method, called FastBiCmrMLM, for large data analysis. These algorithms were tailored to sample size, computational speed, and reduced memory requirements. To mine additional genes, linkage disequilibrium markers were replaced by bin-based haplotypes, which are analyzed by FastBiCmrMLM, named FastBiCmrMLM-Hap. Simulation studies highlighted the superiority of FastBiCmrMLM over GMMAT, SAIGE and fastGWA-GLMM in identifying dominant, small α (allele substitution effect), and rare variants. In the UK Biobank-scale dataset, we demonstrated that FastBiCmrMLM could detect variants as small as 0.03% and with α ≈ 0. In re-analyses of seven diseases in the WTCCC datasets, 29 candidate genes, with both functional and TWAS evidence, around 36 variants identified only by the new methods, strongly validated the new methods. These methods offer a new way to decipher the genetic architecture of binary traits and address the challenges outlined above.

Stable Structure and Fast Ion Diffusion: A Flexible MoO2@Carbon Hollow Nanofiber Film as a Binder-Free Anode for Sodium-Ion Batteries with Superior Kinetics and Excellent Rate Capability.

Designing innovative anode materials that exhibit excellent ion diffusion kinetics, enhanced structural stability, and superior electrical conductivity is imperative for advancing the rapid charge-discharge performance and widespread application of sodium-ion batteries. Hollow-structured materials have received significant attention in electrode design due to their rapid ion diffusion kinetics. Building upon this, we present a high-performance, free-standing MoO2@hollow carbon nanofiber (MoO2@HCNF) electrode, fabricated through facile coaxial electrospinning and subsequent heat treatment. In comparison to MoO2@carbon nanofibers (MoO2@CNFs), the MoO2@HCNF electrode demonstrates superior rate capability, attributed to its larger specific surface area, its higher pseudocapacitance contribution, and the enhanced diffusion kinetics of sodium ions. The discharge capacities of the MoO2@HCNF (MoO2@CNF) electrode at current densities of 0.1, 0.2, 0.5, 1.0, 2.0 and 5.0 A g-1 are 195.55 (155.49), 180.98 (135.20), 163.81 (109.71), 144.05 (90.46), 121.16 (71.21) and 88.90 (44.68) mAh g-1, respectively. Additionally, the diffusion coefficients of sodium ions in the MoO2@HCNFs are 8.74 × 10-12 to 1.37 × 10-12 cm2 s-1, which surpass those of the MoO2@CNFs (6.49 × 10-12 to 9.30 × 10-13 cm2 s-1) during the discharging process. In addition, these prepared electrode materials exhibit outstanding flexibility, which is crucial to the power storage industry and smart wearable devices.

Lattice Oxygen in Photocatalytic Gas‒Solid Reactions: Participator vs. Dominator.

Lattice-oxygen activation has emerged as a popular strategy for optimizing the performance and selectivity of oxide-based thermocatalysis and electrolysis. However, the significance of lattice oxygen in oxide photocatalysts has been ignored, particularly in gas‒solid reactions. Here, using methane oxidation over a Ru1@ZnO single-atom photocatalyst as the prototypical reaction and via 18O isotope labelling techniques, we found that lattice oxygen can directly participate in gas‒solid reactions. Lattice oxygen played a dominant role in the photocatalytic reaction, as determined by estimating the kinetic constants in the initial stage. Furthermore, we discovered that dynamic diffusion between O2 and lattice oxygen proceeded even in the absence of targeted reactants. Finally, single-atom Ru can facilitate the activation of adsorbed O2 and the subsequent regeneration of consumed lattice oxygen, thus ensuring high catalyst activity and stability. The results provide guidance for next-generation oxide photocatalysts with improved activities and selectivities.

Tuna Dark Muscle Feeding Improved the Meat Quality of Holland Mini-Piglets and Modulated the Gut Microbiota.

Pork is one of the most widely produced and consumed meats in the world, and it is also an important source of animal protein. The continuous rise in feed prices has forced the pig industry to consider adding cost-effective alternative feed to pig diets. In this study, we aimed to explore the beneficial effects of tuna dark muscle as a nutritional supplement on the growth performance, serum lipids and antioxidant levels of Holland mini-piglets, as well as on the odor and volatile substances of pork and the gut microbiota. Two-month-old male mini-piglets (n = 24) were fed a control diet or supplemented with either 2% (LD) or 4% (HD) tuna dark muscle for 8 weeks. The use of tuna dark muscle at low and high dosages significantly increased the average daily weight gain, but it showed no significant effect on organ indices or blood lipids. In addition, dark muscle treatment significantly increased the antioxidant capacity, characterized by increased SOD and GSH-Px activities, and it decreased the content of MDA in serum. Moreover, tuna dark muscle feeding shifted the odor of rib muscle and tendon meat away from that of the control group, while similar odor patterns were observed in the longissimus dorsi muscle. Among these volatile substances, hexanal, nonanal, and heptanal increased in response to dietary tuna dark muscle and were regarded as indispensable contributors to the feeding. Furthermore, dietary tuna dark muscle modulated the gut microbiota of the piglets, increasing the abundance of beneficial bacteria such as butyric acid-producing bacteria, and reduced the abundance of harmful bacteria. The feeding strategy reported in this study not only reduces the production costs of pork but also utilizes tuna processing by-products in an environmentally friendly way.

Identification of QTNs, QTN-by-environment interactions, and their candidate genes for salt tolerance related traits in soybean.

BACKGROUND: Salt stress significantly reduces soybean yield. To improve salt tolerance in soybean, it is important to mine the genes associated with salt tolerance traits. RESULTS: Salt tolerance traits of 286 soybean accessions were measured four times between 2009 and 2015. The results were associated with 740,754 single nucleotide polymorphisms (SNPs) to identify quantitative trait nucleotides (QTNs) and QTN-by-environment interactions (QEIs) using three-variance-component multi-locus random-SNP-effect mixed linear model (3VmrMLM). As a result, eight salt tolerance genes (GmCHX1, GsPRX9, Gm5PTase8, GmWRKY, GmCHX20a, GmNHX1, GmSK1, and GmLEA2-1) near 179 significant and 79 suggested QTNs and two salt tolerance genes (GmWRKY49 and GmSK1) near 45 significant and 14 suggested QEIs were associated with salt tolerance index traits in previous studies. Six candidate genes and three gene-by-environment interactions (GEIs) were predicted to be associated with these index traits. Analysis of four salt tolerance related traits under control and salt treatments revealed six genes associated with salt tolerance (GmHDA13, GmPHO1, GmERF5, GmNAC06, GmbZIP132, and GmHsp90s) around 166 QEIs were verified in previous studies. Five candidate GEIs were confirmed to be associated with salt stress by at least one haplotype analysis. The elite molecular modules of seven candidate genes with selection signs were extracted from wild soybean, and these genes could be applied to soybean molecular breeding. Two of these genes, Glyma06g04840 and Glyma07g18150, were confirmed by qRT-PCR and are expected to be key players in responding to salt stress. CONCLUSIONS: Around the QTNs and QEIs identified in this study, 16 known genes, 6 candidate genes, and 8 candidate GEIs were found to be associated with soybean salt tolerance, of which Glyma07g18150 was further confirmed by qRT-PCR.

Microwave-assisted solid-state pretreatment for fabrication of hemp fibres using ethanolamine at low temperature.

Conventional methods faced challenges in pretreating natural cellulose fibres due to their high energy consumption and large wastewater drainage. This research devised an efficient solid-state pretreatment method for pretreating hemp fibres using ethanolamine (ETA) assisted by microwave (MW) heating. This method produced a notable removal rate of lignin (85.4 %) with the highest cellulose content (83.0 %) at a high solid content (30 %) and low temperature (70 °C). Both FT-IR and XRD analyses indicated that the pretreatment did not alter the structure of cellulose within the hemp fibres but increased crystallinity as the CrI increased from 84 % in raw hemp fibre to 89 % in pretreated fibre. As a result, it produced hemp fibres with impressive fineness (4.6 dtex) and breaking strength (3.81 cN/dtex), meeting the requirement of textile fibre. In addition, an improvement in glucose concentration (15.6 %) was observed in enzymatic hydrolysis of the MW pretreated hemp fibres compared to the fibres pretreated without MW. Furthermore, the FT-IR and NMR data confirmed that the amination of lignin occurred even at low temperature, which contributed to the high lignin removal rate. Thus, this study presents a potentially effective energy-saving, and environmentally sustainable solid-state method for pretreating hemp fibres.

Determination of carbohydrate content in kenaf degumming wastewater and converting them to carbon dots.

The traditional textile degumming process produces abundant wastewater, which contains a lot of monosaccharides and oligosaccharides. It is of great economic and environmental significance to utilize these carbohydrates in high value. In this study, high performance liquid chromatography (HPLC) was used to analyze the carbohydrate components in kenaf degumming wastewater, and then the production of C-dots using the wastewater was explored. The results showed that the types and content in the degumming wastewater were monosaccharides (glucose, xylose and arabinose) and oligosaccharides (dextran, xylan and araban). The carbohydrate (mainly glucan and xylan) content in wastewater accounted for 91.16 % of the total carbohydrates weight loss in kenaf degumming process. By using hydrolysis and hydrothermal reaction on kenaf degumming wastewater, blue-green carbon dots (C-dots) with good performance were prepared and successfully applied to anti-counterfeiting printing. In particular, the as-prepared C-dots prepared from kenaf degumming wastewater with urea added (WUC-dots) showed an excitation-dependent photoluminescence (PL) spectrum and quantum yield (QY) of 2.4 % in aqueous solution. The fluorescent code exhibited a clear outline, excitation-tunable color and good stability, showing a great potential for anti-counterfeiting system.

Investigation and characterization of rice dwarfing epidemic caused by southern rice black-streaked dwarf virus in Jiangsu in 2023.

During the field surveys in Jiangsu Province, China, contiguous patches of rice plants with varying degrees of dwarfing, wax-white or dark brown enations at the base of stems, and abnormal heading symptoms were observed in the fields located in Jiangning District in Nanjing City, Jurong County in Zhenjiang City, and Zhangjiagang County in Suzhou City. Through molecular analyses, the presence of southern rice black-streaked dwarf virus was confirmed in symptomatic rice plants. The infections of other rice viruses that cause dwarfing were also ruled out. Additionally, Koch's postulates were fulfilled, further validating SRBSDV as the causal agent for the observed dwarfing disease epidemic. Furthermore, the phylogenetic analyses revealed that the SRBSDV prevalent in Jiangsu in 2023 may originate from multiple regions in Vietnam. Our study has documented the emergence of an SRBSDV epidemic in Jiangsu in 2023, marking the first incidence of southern rice black-streaked dwarf disease in this region.

Transiently Induce RNA Silencing in Plants Using a Tobacco Necrosis Virus A (TNV-A)-Based dsRNA Production System.

Transgenic expression of hairpin RNA or artificial microRNA is widely used for genetic studies in plant science. However, induction of RNA silencing by transgenic method may have a problem when studying essential genes. Here, we provide an in planta transient double-stranded RNA (dsRNA) producing system using a tobacco necrosis virus A (TNV-A)-based replicon for efficiently inducing RNA silencing in plants. In this system, the target sequence is placed between the cauliflower mosaic virus 35S promoter and the 3'-terminal part of viral genomic RNA, while the C-terminal part of TNV-A RNA-dependent RNA polymerase (p82C) is expressed by a different promoter. The endogenous RNA polymerase-synthesized target sequence is recruited by p82C to produce dsRNA to induce RNA silencing.

Pseudo-Data Based Self-Supervised Federated Learning for Classification of Histopathological Images.

Computer-aided diagnosis (CAD) can help pathologists improve diagnostic accuracy together with consistency and repeatability for cancers. However, the CAD models trained with the histopathological images only from a single center (hospital) generally suffer from the generalization problem due to the straining inconsistencies among different centers. In this work, we propose a pseudo-data based self-supervised federated learning (FL) framework, named SSL-FT-BT, to improve both the diagnostic accuracy and generalization of CAD models. Specifically, the pseudo histopathological images are generated from each center, which contain both inherent and specific properties corresponding to the real images in this center, but do not include the privacy information. These pseudo images are then shared in the central server for self-supervised learning (SSL) to pre-train the backbone of global mode. A multi-task SSL is then designed to effectively learn both the center-specific information and common inherent representation according to the data characteristics. Moreover, a novel Barlow Twins based FL (FL-BT) algorithm is proposed to improve the local training for the CAD models in each center by conducting model contrastive learning, which benefits the optimization of the global model in the FL procedure. The experimental results on four public histopathological image datasets indicate the effectiveness of the proposed SSL-FL-BT on both diagnostic accuracy and generalization.

Drying without dying: A genome database for desiccation-tolerant plants and evolution of desiccation tolerance.

Desiccation is typically fatal, but a small number of land plants have evolved vegetative desiccation tolerance (VDT), allowing them to dry without dying through a process called anhydrobiosis. Advances in sequencing technologies have enabled the investigation of genomes for desiccation-tolerant plants over the past decade. However, a dedicated and integrated database for these valuable genomic resources has been lacking. Our prolonged interest in VDT plant genomes motivated us to create the "Drying without Dying" database, which contains a total of 16 VDT-related plant genomes (including 10 mosses) and incorporates 10 genomes that are closely related to VDT plants. The database features bioinformatic tools, such as blast and homologous cluster search, sequence retrieval, Gene Ontology term and metabolic pathway enrichment statistics, expression profiling, co-expression network extraction, and JBrowser exploration for each genome. To demonstrate its utility, we conducted tailored PFAM family statistical analyses, and we discovered that the drought-responsive ABA transporter AWPM-19 family is significantly tandemly duplicated in all bryophytes but rarely so in tracheophytes. Transcriptomic investigations also revealed that response patterns following desiccation diverged between bryophytes and angiosperms. Combined, the analyses provided genomic and transcriptomic evidence supporting a possible divergence and lineage-specific evolution of VDT in plants. The database can be accessed at http://desiccation.novogene.com. We expect this initial release of the "Drying without Dying" plant genome database will facilitate future discovery of VDT genetic resources.

Experimental and Numerical Investigation on the Aerosol Micro-Jet 3D Printing of Flexible Electronic Devices.

In this study, the optimal forming parameters for printing flexible circuits using aerosol jet printing technology are explored through numerical simulation and experiments. The printhead during the deposition process is numerically simulated. By employing the controlled variable method, the process parameters such as gas flow rate, working distance, nozzle diameter, and printing speed are selected to investigate their effects on the morphology of the printed lines. Accordingly, single-factor experiments are designed to validate the printing of flexible circuits on both planar and curved substrates. Laser micro-sintering is utilized to improve the conductivity of the printed lines and ultimately fabricate flexible strain sensors. Under the sheath gas flow rate of 400 sccm, carrier gas flow rate of 100 sccm, working distance of 3 mm, nozzle diameter of 500 µm, and printing speed of 10 mm/s, the optimal morphology of the printed lines is achieved with low linewidth characteristics. The variations in the focal ratio, working distance, nozzle diameter, and printing speed significantly affect the minimum feature line width and morphology of the printed lines.

A robust seizure detection and prediction method with feature selection and spatio-temporal casual neural network model.

Objective.Epilepsy is a fairly common condition that affects the brain and causes frequent seizures. The sudden and recurring epilepsy brings a series of safety hazards to patients, which seriously affects the quality of their life. Therefore, real-time diagnosis of electroencephalogram (EEG) in epilepsy patients is of great significance. However, the conventional methods take in a tremendous amount of features to train the models, resulting in high computation cost and low portability. Our objective is to propose an efficient, light and robust seizure detecting and predicting algorithm.Approach.The algorithm is based on an interpretative feature selection method and spatial-temporal causal neural network (STCNN). The feature selection method eliminates the interference factors between different features and reduces the model size and training difficulties. The STCNN model takes both temporal and spatial information to accurately and dynamically track and diagnose the changing of the features. Considering the differences between medical application scenarios and patients, leave-one-out cross validation (LOOCV) and cross-patient validation (CPV) methods are used to conduct experiments on the dataset collected at the Children's Hospital Boston (CHB-MIT), Siena and Kaggle competition datasets.Main results.In LOOCV-based method, the detection accuracy and prediction sensitivity have been improved. A significant improvement is also achieved in the CPV-based method.Significance.The experimental results show that our proposed algorithm exhibits superior performance and robustness in seizure detection and prediction, which indicates it has higher capability to deal with different and complicated clinical situations.

Combining Multiple Plant Attributes to Reveal Differences in Community Structure in Two Distant Deserts in Central Asia.

International interest is growing in biodiversity conservation and sustainable use in drylands. Desert ecosystems across arid Central Asia are severely affected by global change. Understanding the changes in a plant community is an essential prerequisite to revealing the community assembly mechanism, vegetation conservation, and management. The knowledge of large-scale spatial variation in plant community structure in different Central Asian deserts is still limited. In this study, we selected the Taukum (TD, Kazakhstan) and the Gurbantunggut (GD, China) deserts as the research area, with similar latitudes despite being nearly 1000 km apart. Thirteen and 15 sampling plots were set up and thoroughly investigated. The differences in community structure depending on multiple plant attributes (individual level: plant height, canopy diameter, and plant volume, and community level: plant density, total cover, and total volume) were systematically studied. TD had a better overall environmental status than GD. A total of 113 species were found, with 68 and 74 in TD and GD, respectively. The number of species and plant attributes was unequally distributed across different families and functional groups between deserts. The values of several plant attributes, such as ephemerals, annuals, dicotyledons, and shrubs with assimilative branches in GD, were significantly lower than those in TD. The Motyka indices of six plant attributes (26.18-38.61%) were higher between the two deserts than the species similarity index (20.4%), indicating a more robust convergence for plant functional attributes. The community structures in the two deserts represented by different plant attribute matrices demonstrated irregular differentiation patterns in ordination diagrams. The most variance in community structure was attributed to soil and climatic factors, while geographic factors had the smallest proportion. Consequently, the community structures of the two distant deserts were both different and similar to an extent. This resulted from the long-term impacts of heterogeneous environments within the same region. Our knowledge is further deepened by understanding the variation in community structure in different deserts on a large spatial scale. This therefore provides valuable insights into conserving regional biodiversity in Central Asia.

Plants alter their aboveground and belowground biomass allocation and affect community-level resistance in response to snow cover change in Central Asia, Northwest China.

It is important to elucidate the changing distribution pattern of net primary productivity (NPP) to mechanistically understand the changes in aboveground and belowground ecosystem functions. In water-scarce desert environments, snow provides a crucial supply of water for plant development and the spread of herbaceous species. Yet uncertainty persists regarding how herbaceous plants' NPP allocation responds to variation in snow cover. The goal of this study was to investigate how variation in snow cover in a temperate desert influenced the NPP allocation dynamics of herbaceous species and their resistance to environmental change in terms aboveground and belowground productivity. In the Gurbantunggut Desert, wintertime snow cover depth was adjusted in plots by applying four treatments: snow removal (-S), ambient snow, double snow (+S), and triple snow (+2S). We examined their species richness, aboveground NPP (ANPP), belowground NPP (BNPP), and the resistance of ANPP and BNPP. We found that species diversity of the aboveground community increased significantly with increasing snow cover and decreased significantly Pielou evenness in plots. This resulted in greater ANPP with increasing snow cover; meanwhile, BNPP first increased and then decreased with increasing snow cover. However, this productivity in different soil layers responded differently to changed snow cover. In the 0-10 cm soil layer, productivity first rose and then declined, while it declined linearly in both the 10-20 cm and 20-30 cm soil layers, whereas in the 30-40 cm soil layer it showed an increasing trend. Belowground resistance would increase given that greater snow cover improved the BNPP in deeper soil and maintained the resource provisioning for plant growth, thus improving overall belowground stability. These results can serve as a promising research foundation for future work on how the functioning of desert ecosystems becomes altered due to changes in plant community expansion and, in particular, changes in snow cover driven by global climate change.

Biochemical fulvic acid derived amorphous carbon modified microcrystalline graphite as low-cost anode for potassium-ion storage.

Graphite anode has great potential toward potassium ion storage for abundant reserves, yet it suffers from the large volume expansion and slow diffusion rate. Herein, the low-cost biochemical fulvic acid-derived amorphous carbon (BFAC) is employed to modify the natural microcrystalline graphite (BFAC@MG) by a simple mixed carbonization strategy. The BFAC smooths the split layer and folds on the surface of microcrystalline graphite and builds the heteroatom-doped composite structure, which effectively alleviates the volume expansion caused by K+ electrochemical de-intercalation processes, together with improving electrochemical reaction kinetics. As expected, the optimized BFAC@MG-0.5 exhibits superior potassium-ion storage performance, which delivers a high reversible capacity (623.8 mAh g-1), excellent rate performance (147.8 mAh g-1 at 2 A g-1), and remarkable cycling stability (100.8 mAh g-1 after 1200 cycles). As a practical device application, the potassium-ion capacitors are assembled using the BFAC@MG-0.5 anode and commercial activated carbon cathode, which exhibits a maximum energy density of 126.48 Wh kg-1 and superior cycle stability. Significantly, this work demonstrates the potential of microcrystalline graphite as the host anode material for potassium-ion storage.