Pristine graphene is one of the strongest materials known in the world, and may play important roles in structural and functional materials. In order to utilize the extraordinary mechanical properties in practical engineering structures, graphene should be assembled into macroscopic structures such as graphene-based papers, fibers, foams, etc. However, the mechanical properties of graphene-based materials such as Young’s modulus and strength are 1–2 orders lower than those of pristine monolayer graphene. Many efforts have been made to unveil the multi-scale structure–property relations of graphene-based materials with hierarchical structures spanning the nanoscale to macroscale, and significant achievements have been obtained to improve the mechanical performance of graphene-based materials through composition and structure optimization across multi-scale. This review aims at summarizing the currently theoretical, simulation, and experimental efforts devoted to the multi-scale structure–property relation of graphene-based layer materials including defective monolayer graphene, nacre-like and laminar nanostructures of multilayer graphene, graphene-based papers, fibers, aerogels, and graphene/polymer composites. The mechanisms of mechanical property degradation across the multi-scale are discussed, based on which some multi-scale optimization strategies are presented to further improve the mechanical properties of graphene-based layer materials. We expect that this review can provide useful insights into the continuous improvement of mechanical properties of graphene-based layer materials. Graphene  has trigMonolayer graphene is the smallest building unit of graphene-based materials. In general, graphene can be fabricated by two approaches, that is “top-down” —the nanoscale building blocks of graphene-based materials—can be formed by assembling monolayer graphene in a layer-by-layer manner. According to the continuity of the graphene sheet in GLNs, we classify GLNs into two types, that is, the nacre-like structure and laminar structure. In general, the in-plane mechanical behaviors of the nacre-like structure as well as out-of-plane mechanical behaviors of the laminar structure have been studied. For the nacre-like structure subjected to in-plane tensile load, the in-plane stress in one graphene sheet is transferred to the adjacent graphene sheet via interlayer shearing. Related continuum models ,21,22,23Further assembly of GLNs can form the graphene-based layer materials (GLMs), whose mechanical properties dependent on not only the properties of GLNs, but also multi-scale assembling structures such as the wrinkles, orientation of GLNs, etc. Three kinds of macroscopic graphene assemblies, namely graphene papers (GPs) ,36, grapVarious graphene/polymer nanocomposites have been developed. Thanks to its excellent mechanical properties, graphene is a promising reinforced filler that can significantly enhance the Young’s modulus and strength of composites with only a small amount of graphene. However, the uniform dispersion of graphene in composites with a large graphene content is a large problem, and the weak vdW interface between graphene and polymer matrix also provides the crack initiation source, which hinders the further mechanical improvement in graphene/polymer composites. Therefore, several strategies have been proposed to optimize the interface structures and properties to improve the overall mechanical properties of graphene nanocomposites including Young’s modulus, strength, and toughness ,43. BesiAlthough several reviews have focused on graphene monolayer ,48,49, GThe classical mechanical model may be inappropriate to describe the mechanical properties of the atomic thick 2D materials, thus it is important to develop new theoretical models to accurately predict the structure–property relations of 2D materials to design the mechanical properties of 2D materials. The in-plane Young’s modulus of graphene in a small deformation regime exhibits isotropy credit to the sixfold symmetry atomic structure, but the overall stress–strain relation, strength, and toughness are direction-dependent under finite deformations. For example, the ultimate stress along the armchair direction and zigzag direction has a variation of 11 GPa in terms of Cauchy stress using density functional theory (DFT) simulation , while aAccording to the second law of thermodynamics, defects are inevitably introduced into monolayer graphene in the fabricating process ,48. Qin As aforementioned in D, h0, and G are tensile rigidity of graphene sheet, interlayer distance, and interlayer shear modulus, respectively. When the length of graphene sheets l exceeds 3l0, the TSC model fails to describe the overall mechanical behaviors of nacre-like GLNs due to the tension of graphene sheets and nonuniform interlayer shear stress. Furthermore, two failure modes  and failure of interlayer crosslinks (mode I)), were distinguished. With the increase in l/l0, the tensile strength increases and converges quickly to a saturated value; when l exceeds 5l0, the stiffness also increases but converges at a larger length, and the toughness reaches the maximum value at about l = 3.3l0. Based on this understanding, the ultra-strong graphene fiber with giant graphene sheets was reported by Xu et al. [By considering the tensile deformation of platelets, Liu et al. ,22 develu et al. . l0 to the graphene edges, as shown in The DTS model also revealed that the interlayer shear deformation is mainly located within the distance of λL, where L is the length of the beam and kGA and D0 are the shear and bending rigidity of the beam cross-section, respectively; Dbend is the bending rigidity of monolayer; and n is the number of layer. They further performed MD simulations, finite element simulations, and experiments to validate MTBM. This model captures the intrinsic deformation modes of layered materials to provide an accurate tool to predict and optimize the mechanical properties of layered materials. Furthermore, the failure behaviors of layered materials under bending deformation have also been theoretically studied by Pan et al. [The bending behaviors of layered materials are quite different from traditional bulk materials due to the local curvature dependent bending energy of monolayer graphene and large interlayer shear deformation. Although the Timoshenko beam model can consider the shear deformation, it does not account for monolayer bending energy ,77. To sn et al. . Three fOut-of-plane deformation of pristine graphene can be induced by thermal fluctuation ,82. Due Out-of-plane deformation of graphene can also be induced by defects  with values of 340 N/m and 130 GPa, respectively [2, comparable to that of brittle materials [3 along the armchair direction obtained from DFT simulations [The elastic stiffness ectively . Then, Zectively  found thaterials ,93. Anotulations . Here, wED2 and strength of graphene [ED2, López-Polín et al. [ED2 first increases, and then decreases, leading to an elastic stiffness up to 550 N/m at vacancy density around 0.2%, as shown in ED2 to the suppression of out-of-plane fluctuations by defects, which was not considered in former theories and simulations. Recently, Ruoff and co-authors have shown that ED2 of polycrystalline monolayer graphene decreases to an average value of ~120 N/m with the grain size of 1–2 μm [ED2 of 436 N/m with an average grain size of 20 nm, even higher than that of pristine graphene, as shown in Most of the theoretical and simulated works have revealed that the vacancy defect decreases both the elastic stiffness graphene ,95 becaugraphene , which igraphene ,97. Howen et al.  reportedf 1–2 μm , as showf 1–2 μm . By contf 1–2 μm  achievedIn terms of strength, defective graphene with a vacancy density of 20% still possesses a fracture strength higher than that of most typical engineering materials ,104, as The fracture behavior of monolayer graphene sheets was found to show a brittle-to-ductile transition with vacancy densities of 8–12%, and the toughness was found to dramatically decrease when introducing vacancy defects, but almost kept constant as the vacancy densities ranged from 2 to 20% . On the D to layer number N is predicted in the range of free interlayer sliding D~N to perfectly bonded D~N3. For example, Chen et al. [N (N = 2~6) followed D~N2, indicating that the weak interlayer shear interaction had a substantial stiffening effect for multilayer graphene. In contrast, Shen and Wu [D was proportional to N for N > 5, which can be extended to other kinds of 2D layered materials [The bending rigidity of multilayer graphene has been found to be significantly enhanced by the weak interlayer shearing via nanoindentation and pressurized bubble experiments. It should be noted that the interlayer shear stiffness is three orders of magnitude smaller than that of intralayer stiffness, thus the relation of bending rigidity n et al.  investign and Wu  found thaterials . Wang etaterials ,112 usedaterials  found thIn-plane mechanical properties of multilayer graphene, especially that with nacre-like nanostructures, are also strongly dependent on the interlayer properties. As the graphene sheet is discontinuous in nacre-like nanostructures, the in-plane load is transferred by interlayer shear stress to adjacent graphene sheets. Therefore, the interlayer shear properties play important roles in the overall mechanical properties. Intrinsic vdW interaction between graphene layers is too weak to carry enough in-plane load, thus interlayer crosslinking and nano-structural modification are introduced to regulate the interlayer shear modulus and strength.In general, modulus, strength, and toughness of multilayer graphene can be simultaneously improved by enhancing interlayer properties using typical interlayer bonds, as shown in Since pristine graphene is insoluble and intractable, it is difficult to be shaped in desired structures, or be connected through interlayer interactions. Therefore, chemical modifications are used to generate graphene oxide (GO) and reduced graphene oxide (rGO) , which cMacroscopic graphene assemblies possess hierarchical microstructures that extend from the nanoscale to meter/centimeter scale. The monolayer graphene sheets first stack in a layer-by-layer manner to form the building blocks of GLNs, then GLNs can be assembled to the hierarchically layered structures or nanoporous structures. According to different macroscopic morphologies, these graphene assemblies can be distinguished into fiber (1D), film/paper (2D), and porous (3D) assemblies, respectively. Since the overall properties of materials are decided by their microstructures, the conformations of monolayer and multilayer graphene within graphene-based materials also play fundamental roles in the overall mechanical properties of macroscopic graphene assemblies. Thus, in this section, we first briefly introduce the conformations of GO sheets in solutions, then focus on the microstructures and mechanical properties of the aforementioned three types of graphene assemblies as well as the related fabricating and optimizing methods.2+) as short-range interaction in GO sheets with slight out-of-plane corrugation [Monolayer GO sheets were experimentally processed into various conformations . For example, crumped GO nanosheets can be prepared by rapidly drying the aerosol, possessing a fractal dimension of 2.54 ± 0.04 . Crumplerugation . More rerugation  construcN, N-dimethylformamide (DMF)) and distribute uniformly and isotropically at low GO concentration (<0.02 mg/mL) [More conformations have been observed in GO aqueous dispersions with high GO concentration, as shown in 2 mg/mL) . However2 mg/mL)  at inter2 mg/mL) ,128, whi2 mg/mL) .Graphene/GO fibers have attracted growing interest in the past decade . Xu and Here, it should be noted that most of the graphene fibers fabricated by other methods have lower modulus and strength than that of the wet-spinning method, but have higher breakage strain. For example, Tian et al.  found a The graphene thin films were first fabricated as GO paper using a flow-directed assembly of individual GO sheets by Ruoff and co-authors in 2007 . SubsequStiffening and strengthening strategies involve the crosslinking of adjacent graphene/GO sheets ,149,150 On the other hand, by intentionally introducing micro wrinkles and folds, the flexibility of GPs was significantly improved ,155,156.3 [Graphene porous structures, which are called graphene aerogels (GAs) or graphene foams, were fabricated ,161,162.3 , which i3 , as show3 ), as shoIn general, the stress–strain curves for GAs under compression can be divided into three regimes including the nearly linear elastic regime, stress plateau regime, and stress densification regime, corresponding to bending of the cell walls, buckling of the cell walls, and densification of the cells, respectively. Unlike the stretching of graphene sheets in the paper- or fiber-like assemblies, the bending of cell walls results in an extremely low initial modulus smaller than 1.0 MPa. Furthermore, hysteresis can also be found during cyclic loading of GAs, which is attributed to the discontinuous buckling and breakage of connections among the graphene sheets. Here, the breakage of the internal structure and the irreversible buckling in GAs could also induce the degradation of elasticity, modulus, and strength. To solve these problems, Qiu et al.  proposedDue to the hierarchical porous structures, GAs are probably more promising for multi-functional applications such as elastic and flexible conductors, environmental protection, energy storage, sensors, supercapacitors, and catalyst beds, etc. ,171,172,Composites have attracted lasting interest during the past decades, which are consisted of two or more constituent materials with advanced mechanical properties compared to those of the constituent materials. The mechanical properties of composites are related to their matrix, fillers, and interfaces. Due to the excellent Young’s modulus, strength, thermal, and electrical conductivities, graphene is an ideal nanofiller in high-performance nanocomposites with mechanical properties superior to the polymer matrix. However, among the ever-published papers, the mechanical properties of nanocomposites with a high content of graphene are usually compared with pure graphene assemblies rather than polymers. With this scenario, we first focused on the graphene/polymer nanocomposites with a relatively low content of graphene, whose microstructures and mechanical behaviors compared to the pure polymers are discussed in Composites with nanofillers dispersed in polymer matrix have been widely studied in the past decades. GO is a promising precursor for their rich functional groups, which can provide efficient interactions between GO sheets and polymers. Therefore, GO/polymer composites were first prepared, where GO is further reduced into rGO, and rGO/polymer composites are obtained in the end. In general, these kinds of composites are superior in Young’s modulus and strength compared to the pure polymers. For example, the tensile strength and Young’s modulus of rGO/PVA composites with graphene content of 1.8 vol% fabricated by Zhao et al.  are 1.5 Although the strength of composites with dispersed fillers is enhanced due to the shielding effect of nanofillers to crack propagation perpendicular to the fillers , the disThe weak vdW interaction among GO sheets plays an important role when the GO content rises to a relatively high value, leading to aggregation of the graphene sheets, which limits the mechanical enhancement of the nanocomposites. The aggregation can be partially solved by employing interfacial modification methods ,189. MorKIC with pre-crack perpendicular to the layered structure could be enhanced up to 3.61 times to that of pure epoxy, while the strength exhibited anisotropy due to the anisotropy of the scaffold. Thanks to the continuous structure of graphene networks, composites made by this pre-network strategy also possess superior electric conductivity among graphene-based composites.This kind of nanocomposites can be achieved by forming a 3D graphene scaffold on the template and then infiltrating polymers into the scaffold. With this pre-scaffold-infiltration process, Ji et al.  preparedAs shown in 2+, the tensile strength and toughness of rGO/CMC composites were 4 and 7.3 times larger than those of the pure GO papers, respectively.By mimicking the natural nacre, nanocomposites with high graphene content  were fabricated as nacre-like composites ,179,191.The mechanical properties including modulus, fracture strength, fracture strain, and toughness of GLMs were quite different from monolayer graphene. In this section, we summarize the underlying multi-scale mechanisms to modify the mechanical properties of graphene-based materials to provide the multi-scale optimization routine from nanoscale to macroscale.The mechanical properties of monolayer graphene are sensitive to various in-plane defects and the morphology of the graphene sheet. For example, defects produce residual stress and stress concentration, which lead to a reduction in strength. Graphene morphologies such as wrinkles and crumpling usually leads to a decrease in Young’s modulus. However, the regulation of defect density and arrangement on grain boundaries can substantially suppress out-of-plane deformation, thus efficiently mitigate the loss of Young’s modulus and strength, while increase the failure strain and fracture toughness. Moreover, mechanisms like dislocation shielding and single-atom chain bridging have been demonstrated to improve fracture toughness.The mechanical properties of GLNs are mainly affected by the mechanical properties of individual graphene sheets, the sheet size, and interface properties. For nacre-like nanostructure, their tensile mechanical properties can be improved by increasing sheet size and enhancing the interlayer shear properties, while for the laminar nanostructure, their bending rigidity can also be improved by enhancing the interlayer shear properties. Therefore, interlayer properties play an important role in the mechanical properties of both nacre-like and laminar nanostructures. In order to enhance interlayer shear modulus and strength, different types of crosslinks  have been introduced to replace the original weak vdW interactions. Furthermore, interlayer crosslinks also play an important role in the buckling behaviors of graphene laminar nanostructures.The mechanical properties of graphene-based macroscopic materials including pure graphene assemblies and GO/polymer nanocomposites with high graphene content are strongly affected by their microstructures and nanoscale building blocks. Graphene-based macroscopic materials with well-compacted structures and high-quality graphene sheets were found to possess higher modulus and strength, but less fracture strain than those with wrinkled graphene sheets. Therefore, the additional small molecules introduced as plasticizers can not only smooth wrinkles, but also enhance the interlayer interactions and further enhance the Young’s modulus and strength. For nacre-like GO/polymer nanocomposites, the modulus is comparable to that of pure graphene papers, while toughness and strength have been efficiently enhanced due to the introduction of a polymer as additional interfacial crosslinkers.The mechanical properties of graphene/polymer nanocomposites depend on the polymer matrix, the graphene nanofillers, and the interface between the two components. In the case of low graphene content, by modifying the interface with chemical crosslinkers, Young’s modulus, strength, toughness, and fracture strain of the composites are comprehensively improved compared to the pure polymers. Furthermore, the continuous fracture mechanism and graphene pre-scaffold method were demonstrated to improve the toughness of the composites.In short, from the nanoscale to macroscale, mechanical properties of graphene-related materials can be improved as follows: (1) improvement of modulus can be obtained by using large-size graphene sheets, enhancing interfacial properties, and straightening the wrinkles; (2) improvement of strength can be obtained by precisely engineering defects  and enhancing the interlayer and interfacial properties; while (3) increase in toughness can be obtained by defect engineering and properly managing the interfacial properties with chemical crosslinkers, as summarized in In this review, the multi-scale structures, mechanical behaviors, mechanisms of mechanical degradation as well as the corresponding fabricating methods for graphene-based layered materials are summarized from monolayer graphene to macroscopic graphene assemblies. Multi-scale mechanical optimization strategies have also been proposed based on the mechanical degradation mechanisms. Through our retrospective analysis, we can see that significant efforts have been made to produce the extraordinary properties of monolayer graphene to macroscopic graphene assemblies. However, there is still more space for the improvement in the mechanical properties of graphene-related materials by considering the large gap in the mechanical properties between monolayer graphene and macroscopic graphene assemblies. Here, we suggest a few topics of interest that may help to understand the structure–property relations of graphene-related materials to improve their mechanical properties.At the nanoscale, works have been conducted to understand the effects of several specific defects on the mechanical properties of monolayer graphene. However, defect type and distribution in real materials may be more complicated . Therefore, greater efforts are needed to understand the defect–property relations of 2D materials by machine learning mediated theoretical/numerical methods, based on which defect engineering can be further employed to regulate the mechanical properties. For example, as the Young’s modulus and strength of graphene sheets first decrease and then increase with the increase in grain boundary density, one may obtain the expected mechanical properties by controlling the growth condition of polycrystalline graphene  to modulate the grain sizes of graphene in experiments. On the other hand, based on the experimental observations, free-standing monolayer graphene had massive rippling at finite temperatures and the related theory should be developed to consider the rippling effect on mechanical behaviors of monolayer graphene because materials are usually used under finite temperature. In terms of graphene layered nanostructures, in-plane mechanical behaviors of nacre-like structures and bending behaviors of laminar structures are well studied in two-dimensional models. Further mechanical models should be proposed to describe three-dimensional deformation of GLNs as well as the bending behaviors of nacre-like structures. Moreover, both experimental and theoretical works should also be conducted to study the mechanical behaviors of layered structures under finite deformations, which may play important roles in practical applications such as flexible electronics and biological sensors et al.The mechanical behaviors of macroscopic graphene-related materials are mainly affected by the interfacial properties and microstructures. Previous works have qualitatively explored the effects of interfacial properties, indicating the stronger the interface, the stronger the material. However, more quantitative works involving specific interfacial crosslinkers such as healable and edge crosslinks, interlayer sliding, and the viscoelastic properties of interlayer sliding, are also needed to fully understand the effect of the interface on overall mechanical behaviors. Regarding the microstructures, materials with compact structures are promising for loading bearing, while those with porous structures usually aim toward functional applications such as catalytic et al. As experimentally reported, graphene fibers are usually superior in Young’s modulus and strength to graphene papers. However, the underlying mechanisms are still unclear. Furthermore, the cross-section of GFs usually features core-sheath morphologies, which play important roles on the mechanical behaviors of GFs . Therefo


This study aims to explore the reliability, validity, and feasibility of Clinically Useful Depression Outcome Scale (CUDOS) in screening mixed features in patients diagnosed with mania.A total of 109 patients with (hypo‐) manic episode were recruited. The reliability of Chinese version of CUDOS (CUDOS‐C) were analyzed with Cronbach's alpha and intraclass correlation coefficient (ICC). Spearman correlation coefficient was used to analyze the validity by comparing the correlation between CUDOS‐C and Patient Health Questionnaire‐9 (PHQ‐9), 32‐item Hypomania Checklist (HCL‐32). The score of MINI (hypo‐) manic episode with mixed features—DSM‐5 Module—Chinese version(MINI‐M‐C) ≥ 2 was considered as the gold standard of mixed features, and the receiver operating characteristic (ROC) curve analysis was used to calculate the optimal cut‐off values of CUDOS‐C score.p < .05).The CUDOS‐C score was significantly correlated with PHQ‐9 score , but not with HCL‐32 score.The area under ROC curve was 0.909  for CUDOS‐C identifying mixed features in mania. The optimal cut‐off value was 11 with a sensitivity of 0.854 and a specificity of 0.868. The CUDOS‐C (score ≥ 12) identified 40.4% of the patients with mixed features, which was higher than those diagnosed by clinicians (18.3%) and screened using MINI‐M‐C (37.6%).The Cronbach's alpha value of CUDOS‐C was 0.898, and the ICC of CUDOS‐C test‐retest was 0.880  curve of the Chinese version of the Clinically Useful Depression Outcome Scale (CUDOS‐C) was 0.909  for identifying mixed features in patients with manic episode. The optimal cut‐off value was 11 with a sensitivity of 0.854 and specificity of 0.868. The CUDOS‐C (score ≥ 12) determined 40.4% of the patients with mixed features, whose detection rate was higher than clinicians' diagnoses (18.3%). The results indicate the CUDOS‐C is a reliable and valid self‐administered questionnaire for assessing depressive symptoms and screening patients with mixed mania. At the end of the 19th century, Kraepeline first put forward the concept and definition of mixed state. Subsequently, mixed states have been almost completely neglected for decades under the influence of the idea that the combination of manic and depressive features should not be considered a mood disorder  investigated the manic symptoms in 1380 patients with bipolar depressed episodes, and found that more than two‐thirds of the subjects had concomitant manic symptoms. Still, only 14.8% of the patients met the DSM‐IV criteria for mixed episodes  mania or bipolar depression according to the DSM‐5 diagnostic criteria, and the proportion of patients with MFS in unipolar depression was as high as 24%  manic patients diagnosed by the clinicians using the DSM‐5 criteria was only 18% in China, which was far lower than the reported data  includes three dimensions: depressive symptoms, manic symptoms, and functional impairment  of Hongkou District Mental Health Centre, Shulan (Hangzhou) Hospital, Shenzhen Mental Health Centre, Anhui Mental Health Center, Shanghai Mental Health Center, and Wuxi Mental Health Center from March 2018 to April 2020 . Before the implementation of any evaluation, all participants provided written informed consent.2.2The original version of CUDOS was developed by Zimmerman et al. and had good consistency with other self‐rating scales of depressive symptoms  5.0 was used to rereview the clinical diagnosis and ascertain the (hypo‐) manic episode. The Mini International Neuropsychiatric Interview (Hypo‐) Manic Episode with Mixed Features‐ DSM‐5 Module (MINI‐M) is a supplementary module of MINI and a self‐evaluation tool for screening the mixed features in (hypo‐) manic episode  and32‐item Hypomania Checklist (HCL‐32) were self‐rated questionnaires to assess depressive symptoms and (hypo‐) manic symptoms, respectively. The two questionnaires have been proved to have a good reliability and validity in their Chinese‐translated version  with excellent proficiency in English translated the original document and did preliminary proofreading and editing. Then, the experts who are fluent in both English and Chinese reviewed the translation and provided suggestions for revision. After discussion and consensus, the CUDOS‐M‐C (containing CUDOS‐C) was formed. Later, two professionals  who had never seen this scale before translated the Chinese version back into English  and compared the back translated version with the original for further revisions. The back‐translated version was reviewed and proofread by Professor Zimmerman to confirm that the semantic meaning of the translated manuscript is consistent with that of the original. Finally, other members of the research group evaluated the Chinese version and agreed that all items had a clear meaning and were in accordance with the habits of Chinese language expression, thus forming the final version of CUDOS‐M‐C and CUDOS‐C. The CUDOS‐M‐C and CUDOS‐C only got polished with some minor edits for specific words based on Chinese culture and setting to make it more suitable for the Chinese population. The CUDOS‐C includes 16 items without item added or deleted, and each item is scored from 0 (not present) to 4 (severe).2.4The attending physicians in charge of the patients’ diagnosis and treatment used the DSM‐5 diagnostic criteria to determine whether patients had (hypo‐) manic episode and MFS. The participants were interviewed face to face by trained research assistants (including psychiatrists and psychologists). The research assistants used a self‐designed questionnaire to collect demographic information, and the Chinese version of MINI 5.0 to ascertain the (hypo‐) manic episode of the participants. The participants filled out the self‐evaluation questionnaires of CUDOS‐M‐C (CUDOS‐C), MINI‐M‐C, PHQ‐9, and HCL‐32 by themselves. If any items on any questionnaires are missing, the research assistants would withdraw them on the spot from the study. A part of the participants was retested using CUDOS‐C at the end of the first week.2.5p value < .05 was considered to be statistically significant.All data were analyzed with SPSS version 25.0 statistical analysis software. The categorical data were presented as the number and frequency of observations, and the continuous data were presented as means ± standard deviation (SD) or median  if without normal distribution. The internal consistency of CUDOS‐C was evaluated using Cronbach's α coefficient and item‐total correlation. The intraclass correlation coefficient (ICC) between the scores at the baseline and at the first weekend was calculated to examine the test‐retest reliability of CUDOS‐C. Spearman correlation coefficient was used to analyze the convergent and discriminant validity by comparing the correlation between CUDOS‐C and PHQ‐9, HCL‐32. Setting the score of MINI‐M‐C ≥ 2as the gold standard, the diagnostic validity of CUDOS‐C for screening mixed features was analyzed using the receiver operating characteristic (ROC) curve, and the sensitivity and specificity were evaluated to obtain the optimal cutoff score. All statistical tests were two‐tailed, and a 33.1A total of 112 questionnaires were distributed to the participants, and 109 valid questionnaires were obtained (response rate as 98.1%). Of the 109 participants included, 60 (55.0%) patients were male and 49 (45.0%) were female, and age was 32.00 years. Ninety‐two (84.4%) patients were diagnosed with manic episode and 17 (15.6%) were diagnosed with hypomanic episode. Of all 109 participants, 80 patients (73.4%) were followed up at the end of the first week.3.2p < .05), and the ICC of each item were between 0.393 and 0.883 (p < .05). The results of reliability analysis are shown in Table The Cronbach's alpha value of internal consistency for the CUDOS‐C was 0.898, and the Cronbach's alpha coefficients after deleting each item ranged from 0.877 to 0.906. The corrected item‐total correlations were between0.108 and 0.752 at baseline. The lowest item‐total correlations were for two atypical depressive symptoms [item 4 “increased appetite” (correlation coefficient = 0.108) and item 6 “hypersomnia” (correlation coefficient = 0.425)]. The ICC of CUDOS‐C test‐retest after 1 week in 80 patients was 0.880 , but not with HCL‐32 score at baseline .The scores for CUDOS‐C, PHQ‐9, and HCL‐32 scales at baseline were 9.00, 3.00, and 14.00, respectively. The spearman correlation analysis showed that CUDOS‐C score was significantly correlated with PHQ‐9 score at baseline patients were diagnosed with MFS in manic or hypomanic episode by clinicians according to the DSM‐5 criteria, and 37.6% (41/109) patients were identified as MFS using MINI‐M‐C (score ≥ 2).p < .001) for CUDOS‐C identifying MFS in patients with (hypo‐) manic episode  and Montgomery and Asberg Depression Rating Scale (MADRS) are the most frequently used scales evaluating depressive symptoms. However, they are clinician administered, requiring training and more time to administer reliably and validly, and only measure decrease in sleep and appetite  for identifying depression in patients with type 2 diabetes mellitus and identifying remission in patients with depression , but we did not examine the association with a clinician measure of symptomatology such as HAMD, MADRS and Young Mania Rating Scale. Finally, bipolar disorder is well known to have a high rate of comorbidity. Any co‐occurring psychiatric disorders were not listed as exclusion criteria, and comorbid diagnoses/symptoms were not assessed, which could influence the sample representativeness and findings generalization.5Taken together, the results of this study indicate that CUDOS‐C is a reliable and valid self‐administered questionnaire for assessing depressive symptoms and screening mixed features in bipolar patients with hypomania or mania. If integrated with the manic symptom dimensions of CUDOS‐M‐C, it is useful for effectively measuring concurrent mixed mania and mixed depression. It can easily be incorporated into routine psychiatric evaluations for patients with bipolar disorder and will help clinicians to identify bipolar disorder with mixed features, and prescribe more appropriate treatment for these patients, for example, antidepressants need to be avoided. While the results of this validation study are encouraging, they require replication in large samples with different demographic and clinical characteristics.The authors declare no conflict of interest.


The aim of the study was to evaluate the trend in the incidence of idiopathic pulmonary fibrosis (IPF) in a real-world setting of the Marche region, a region of Central Italy, between 2014 and 2019. This observational prospective study was based on administrative databases of hospital discharges and drug prescriptions. All adult residents in the Marche Region with a first prescription of antifibrotic drugs, or a first hospitalization with a diagnosis of IPF during the study period, were identified as incident cases of IPF. A multiple Poisson regression analysis was used to estimate the IPF incidence trend, adjusted for age, sex, and health conditions. The mean incidence rate was 9.8 cases per 100,000 person-years. A significant increasing trend of 6% per year was observed. The incidence rates were significantly higher in males than females, older subjects, and those with poorer health conditions. To our knowledge, this is the first study evaluating incidences of IPF over a 6-year period in Italy, combining hospital discharge and drug prescription databases. The study highlights that the combined use of two secondary sources is a reliable strategy to accurately identify new cases of IPF when the appropriate disease registry is lacking. Idiopathic pulmonary fibrosis (IPF) is a rare, chronic, and progressive disease, causing an irreversible decline of lung function over time that dramatically impairs the quality of life. It mainly occurs in older male people, and it is characterized by a poor prognosis, with a median survival time ranging between 3 and 5 years . AetioloEpidemiological estimates showed huge geographical variability ,4, with Reasons for worldwide variability in IPF incidence and prevalence include potential differences in genetics and risk factor exposure among countries, as well as methodological issues in case detection, due to lack of uniform definition of IPF in older studies, differences in diagnostic criteria, classification terms, target population, study design, data sources, and data collection methods ,8,9. Most of the latest papers used administrative healthcare databases for an epidemiologic assessment, with the advantage of including very large populations , but with the potential risk of being less accurate in case identification, due to the absence of specific diagnosis codes ,12,13,14Since the approval in clinical practice of antifibrotic drugs (Pirfenidone and Nintedanib) for treatment of mild to moderate IPF, in 2014 and in 2016 (in Europe), respectively, drug prescription healthcare databases can be used in the identification of prevalent and newly diagnosed cases.The epidemiology of IPF in Italy has been recently assessed in a few studies at the regional level (Lombardy and Lazio) based on regional hospital discharge databases ,16 or prIn this observational longitudinal prospective study, the target population included all adult beneficiaries of the National Health Service (NHS) resident in the Marche Region, a region of Central Italy, with about 1.5 million inhabitants. The study period was between 2014 and 2019.Demographic, socio-economic, and health characteristics between the populations of the Marche region and Italy were compared using the indicators calculated in 2019 by the Italian National Institute of Statistics (ISTAT) .IPF prevalent and incident cases were retrieved using healthcare administrative databases of the Health Regional System: (1) Regional Beneficiaries database (RBD) which includes the identification code of the beneficiary, date of birth, date of death, sex, assistance start, and end dates; (2) Hospital Discharges (HD) database reporting information on admission and discharge dates, primary and up to five secondary diagnoses, up to six interventions ; (3) Drug prescriptions (DP) database which contains drug prescriptions  reimbursed by the Nation Health Service (NHS); (4) Outpatient care database (OCD) which reports outpatient specialistic visits and outpatient exams reimbursed by the NHS. The four databases were linked using a deterministic procedure based on the beneficiary’s identification code.Two secondary sources were used in the IPF case identification algorithm, HD, and DP databases.Patients having at least one drug prescription of Pirfenidone (ATC: L04AX05) or Nintedanib (ATC: L01XE31–L01EX09), in the study period, were identified as IPF cases.Using the HD database, IPF cases were identified according to the three definitions previously proposed in the literature ,18 whichTherefore, IPF cases were considered incidents if their first hospitalization, according to the three case definition algorithms, or first drug prescription occurred during 2014–2019. The date of the first health service contact for IPF was used as a proxy of disease diagnosis (index date). Cases residing for less than 3 years before the index date in the Marche region or with hospitalization for IPF or antifibrotic prescription in 2011–2013 were excluded. Patients treated with Nintedanib only in 2014–2019 who had been diagnosed with lung cancer in the 3 years prior to the index date were excluded.A comparison with confirmed IPF cases from the Respiratory Disease Unit, “Azienda Ospedaliero-Universitaria Ospedali Riuniti” of Ancona, was carried out to assess the accuracy of the HD database and DP in identifying the IPF cases, reviewing the clinical charts of 2014–2019 . This hospital is the reference hospital for the Marche region and accounted for 25% of all the 584 first hospital admissions and 27% of the 1055 hospitalizations with the diagnosis of IPF in the study period.Confirmed IPF cases were then matched with IPF cases detected by the two secondary sources, using the beneficiary’s identification code, by one of the co-authors (M.B.) according to the Italian Personal Data Protection Law. In the comparison, the three case definitions were used, and the proportion of incident cases detected by the secondary sources, the true positive rate, on the total cases identified by the clinical charts, was estimated.Continuous variables were summarized by mean values and standard deviations or median values and interquartile range (IQR), according to the shape of data distribution. Absolute and percentage frequencies were used to summarize qualitative variables.The mean 2014–2019 IPF annual prevalence rate per 100,000 residents and 95% confidence interval (95% CI), and the mean and per year IPF incidence rates per 100,000 person-years (py), were estimated by adding the cases identified by the two sources on the total population of the Marche region aged ≥18 years on 1 January of each year between 2014–2019 reported by ISTAT .IPF incidence rates were analysed by sex, age groups  at the disease diagnosis, and health conditions measured by the Multisource Comorbidity Score (MCS) . The MCSA Poisson regression analysis was used to evaluate the overall incidence trend and stratified by age and sex. In presence of an excess of zero values, a zero-inflated Poisson model was used. The annual trend in IPF incidence was estimated using multiple Poisson regression analyses, adjusted for age, sex, and MCS classes. The proportion of residents in each MCS class, by age and sex, was estimated using the database of beneficiaries of the Marche region in 2017. These proportions were applied to the target population in each year of the study period to obtain the offset to be used in the Poisson model. For each Poisson model, the null hypothesis of equidispersion was tested against the alternative of overdispersion [p < 0.05. Statistical analyses were performed using R, version 4.1.0.The significance level for all the analyses was set at One-hundred and forty-two new IPF cases with confirmed diagnosis during 2014–2019 were identified by the clinical charts of the Respiratory Disease Unit of Ancona.The GCD algorithm correctly tracked 121 out of 142 subjects with IPF, with a true positive rate of 85.2% . Among tp < 0.001). No significant overdispersion was observed in each case definition.The incidence rate of IPF in the study period ranged between 7.6 and 12.9 per 100,000 py using the GCD algorithm, between 5.8 and 10.7 per 100,000 py using the BCD algorithm, and from 4.4 to 9.3 per 100,000 py using the NCD algorithm. A significant increasing trend over time was observed for all case definitions  per 100,000 residents and a mean incidence rate of 9.8  per 100,000 py.Of the 766 new cases of IPF, 521 (68.0%) were males, with a median age of 75 years (IQR: 68.3–80); 145 (18.9%), 462 (60.3%), 37 (4.8%), and 122 (15.9%) subjects were identified only by DP, only by HD, first by DP then by HD, first by HD then by DP databases, respectively. The number of new cases detected only by DP source increased during the study period from 5 out of 114 in 2014 to 50 out of 138 in 2019.The median MCS class was 5–14, 319 (41.6%) subjects had an MCS between 0 and 4, and 108 (14.1%) had a score ≥15.The incidence rate in MCS class 5–14 was 7.6  fold higher than class 0–4, and MCS class ≥15 was 1.4  fold higher compared to class 5–14 .To our knowledge, this is the first study providing epidemiologic estimates of IPF over a 6-year period, using both the hospital discharge and the drug prescription databases, in an Italian region of approximately 1.5 million inhabitants, which can be considered representative of the Italian population from a demographic, socio-economic point of view, and for the distribution of the main health indicators. The estimated mean of the annual incidence rates of IPF ranged between a minimum of 7.6 in 2015 and a maximum of 12.8 in 2018 per 100,000 person-years over the study period. These findings confirm that Italy ranks as one of the countries with the highest incidence rates in Europe , as alreOur study also revealed a significant temporal trend in IPF incidence rates of 6% over the period 2014–2019 , more evThe widespread trend of increasing IPF incidence in recent years is likely the result of a reduction in undiagnosed cases, due to an improved awareness among physicians through the development of educational programs, and a real increase in cases, due to the progressive aging of the overall population and the worsening of air pollution .The GCD algorithm, based on the ICD-9 CM 516.3 code applied to the hospital discharge database, showed the highest rate of true positives in evaluating the accuracy of secondary sources. Although the diagnosis of IPF remains a complex procedure involving a multidisciplinary team of specialists, the diagnostic criteria have been continuously refined especially since 2011, providing increasingly accurate recommendations, and lowering IPF misdiagnosis. In addition, new specific treatments for IPF have been introduced in Italy since 2014, so the drug prescriptions database represents a reliable secondary source in the process of IPF case identification. In our study, a remarkable amount of new IPF cases would have been lost (145 out 766) if the drug prescription database had not been considered, whereas 37 would have been detected later by hospitalization; in fact, therapy is indicated in patients with mild/moderate IPF, who can be diagnosed and managed outpatient and who may not be hospitalized in the short term. Therefore, the combined use of these two secondary sources allowed us to estimate the incidence rates over time more accurately.This study has some limitations, related to the type of data sources used that have administrative purposes and are not originally designed to be used in epidemiological studies. Some degree of inaccuracy as to the actual timing of the first IPF diagnosis needs to be considered: new cases are identified through their first IPF hospitalization or their first antifibrotic prescription, both of which may, however, occur several months after an outpatient diagnosis. Furthermore, the procedure used to evaluate the accuracy of the three algorithms used to detect IPF cases from secondary sources was able to estimate the rate of true positives, but not one of the false positives, thus an unknown number of cases of false IPFs may have been included in the study. Patients who did not receive antifibrotics due to the absence of eligibility criteria for therapy and who were not hospitalized during the study period cannot be identified.Another limitation is that the Marche region is a small region of Central Italy, which constitutes about 2.5% of the Italian population; however, it should be emphasized that secondary sources include all the resident beneficiaries of NHS and their access to the healthcare system. In addition, the design of the study and the use of the prescription database in addition to the hospital discharge database allows us to accurately identify the clinical history of each resident. Finally, the Marche region shows demographic, socio-economic, and health profiles similar to those observed for the Italian population. In fact, no significant differences were observed between the Marche region and Italian population in terms of demographic and socio-economic parameters, and for the distribution of main health indicators; therefore, our results can give a picture of the distribution of the IPF disease nationwide.In conclusion, the incidence and temporal trend of IPF observed in our study are consistent with the results of previous Italian and European studies. Moreover, the study highlights that the combined use of hospital discharge and pharmaceutical prescriptions is a reliable strategy to accurately identify new cases of IPF.


Graphene was reported as the first-discovered two-dimensional material, and the thermal decomposition of SiC is a feasible route to prepare graphene films. However, it is difficult to obtain a uniform single-layer graphene avoiding the coexistence of multilayer graphene islands or bare substrate holes, which give rise to the degradation of device performance and becomes an obstacle for the further applications. Here, with the assistance of nitrogen plasma, we successfully obtained high-quality single-layer and bilayer graphene with large-scale and uniform surface via annealing 4H-SiC(0001) wafers. The highly flat surface and ordered terraces of the samples were characterized using in situ scanning tunneling microscopy. The Dirac bands in single-layer and bilayer graphene were measured using angle-resolved photoemission spectroscopy. X-ray photoelectron spectroscopy combined with Raman spectroscopy were used to determine the composition of the samples and to ensure no intercalation or chemical reaction of nitrogen with graphene. Our work has provided an efficient way to obtain the uniform single-layer and bilayer graphene films grown on a semiconductive substrate, which would be an ideal platform for fabricating two-dimensional devices based on graphene. Graphene, an atomically single layer of carbon rospects ,2,3. Grarospects ,5,6. In ects [−1 . The plaects [−1 . Besidesects [−1 . Based oR0Many researches have demonstrated that the thermal decomposition of SiC0001) is a feasible route to prepare manufacturable graphene films  is a fea,12,13,14 surface . Recentld method . As a pld method ,18. Impod method ,20,21. Ed method , it is ste holes . Importate holes ,25,26,27Here, we provide an efficient way for selectively growing high-quality uniform SLG and BLG with the assistance of nitrogen plasma. Combing the scanning tunneling microscopy (STM), reflection high-energy electron diffraction (RHEED), angle-resolved photoemission spectroscopy (ARPES), and X-ray photoemission spectroscopy (XPS), we studied the surface morphology, band structure, and chemical bonding formation of these samples. Remarkably, the SLG and BLG grown under nitrogen plasma show a large-scale uniform surface morphology with regularly ordered terraces. Moreover, the Dirac bands of graphene treated with nitrogen plasma show sharper signals in the ARPRS spectra. These improvements were quantified from the analysis of momentum distribution curves (MDCs) in the ARPES spectra. Last, the results of XPS and ARPES spectra indicate that there is no intercalation or chemical reaction of nitrogen in SLG and BLG after treating in nitrogen plasma, showing the intrinsic characteristics of epitaxial graphene.The samples were grown in a combined plasma-assisted MBE-STM ultra-high vacuum (UHV) system with a base pressure of 3.0 × 10C(0001¯) . The heaThe growth procedure of uniform SLG with assistance of nitrogen plasma is illustrated in  and SLG ,29,30,31 π-state .The next step is critical for obtaining uniform SLG on 4H-SiC(0001). If we simply continue to repeat the pre-flash annealing cycles, BLG islands will appear on the 4H-SiC(0001) surface, forming a mixture of SLG and BLG islands. Therefore, in order to obtain a perfect uniform and pure SLG, the sample was then annealed under nitrogen plasma atmosphere. The nitrogen plasma was produced from a SPECS plasma source with power of 200 W and nitrogen pressure of 1.1 × 10 SLG see .In rate see , we obserate see b. HoweveAs a comparison, the surface morphologies of SLG and BLG prepared by annealing under nitrogen plasma atmosphere after pre-flash annealing are shown in We further explored the effect of nitrogen plasma on the band dispersion of SLG and BLG by using ARPES in ℏ is the reduced Planck constant. The Fermi velocity of SLG untreated with nitrogen plasma is We performed linear fit to the experimental data from energy distribution curves (EDCs) peak position, as shown in The Dirac bands of ARPES spectra in −1) e, while −1) (−1) f after bn plasma g, while sma (−1) h after bIn order to examine the interaction between graphene and nitrogen plasma, we conduct XPS measurements on our samples in terature ,48,49,50terature ,50. NoteIn s report . In addis report . These rs report ; the D ps report ,54, whicWhy is the surface morphology of the sample prepared by flash annealing not highly improved after being annealed in nitrogen plasma atmosphere? Under the UHV condition, the diffusion of Si is not limited, and the rate cannot be well controlled, resulting in the uncontrollable rate of graphene films generation. Similar to the effect of nitrogen and argon in reducing the sublimation rate of Si ,14, the omes SLG ,56. In t SLG see . These iIn summary, we introduced a practical and efficient method for preparing uniform graphene by thermal decomposition of 4H-SiC(0001) with the assistance of nitrogen plasma. With the help of nitrogen plasma, we can precisely control the number of graphene layers. We prepared large-scale uniform and pure SLG and BLG on 4H-SiC(0001) substrate, which have perfect surface morphology with regular step height of approximately 0.75 nm. The ARPES and XPS measurements indicate the high quality of our samples, and there are no nitrogen atoms chemically bonded, intercalated, or doping with graphene. The key to the formation of a uniform surface lies in the equilibrium of the sublimation of Si, at the same time, the nitrogen plasma may have the effect of polishing the protruding islands on the sample surface. Our work provided an efficient way to improve the quality of the epitaxial graphene and offered a platform to epitaxial growth of 2D materials based on graphene.


To evaluate the efficacy and safety of Qingpeng ointment for the treatment of subacute and chronic eczema.  Randomized controlled trials (RCTs) on Qingpeng ointment for subacute and chronic eczema were searched on PubMed, the Cochrane Library, Embase, Web of Science, China National Knowledge Infrastructure, Wanfang Database, Chinese Biomedical Literature Database, and Chinese Science and Technology Periodical Journal from their inception to 30 November 2020. Quality assessment and data analysis were performed by Review Manager 5.3. P < 0.00001), reduce the total symptom score (TSS) , and decrease visual analogue scale (VAS) for pruritus  compared with the placebo. The TER of Qingpeng ointment was similar to that of topical corticosteroid (TCS) , and the TSSs between Qingpeng ointment and medium or low potency TCS were not significantly different . However, Qingpeng ointment was not superior to TCS in reducing VAS score . In addition, Qingpeng ointment combined with TCS performed better than TCS alone in all three outcomes. For safety, nothing but skin irritative reactions occurred in the Qingpeng ointment group, and its incidence of skin irritative reactions was similar to those of the placebo  and TCS . The combined therapy did not increase the risk of skin irritative reactions .  A total of 26 RCTs were included. Qingpeng ointment could significantly improve the total efficacy rate (TER) (RR = 2.60, 95% CI: 2.11 to 3.21,  Qingpeng ointment is an effective and safe treatment for subacute and chronic eczema. It is also an add-on treatment to TCS for eczema. However, due to the suboptimal quality of the included studies, more large-sample and high-quality RCTs are needed to improve the evidence quality. Eczema is a common chronic and recurrent skin disease, which presents with a wide spectrum of skin lesions, such as erythema, papule, vesicle, scale, dry skin, and lichenification. Itch is the most important symptom . In ChinIn many guidelines for the treatment of mild to moderate eczema, topical corticosteroids (TCSs), the standard first-line treatment, are recommended to prevent skin lesions and alleviate itch , 7. HoweOxytropis falcata Bunge, Rheum lhasaense, Aconitum pendulum Busch, Chebulae Fructus (without core), Fructus Terminaliae Billericae, Phyllanthus emblica Linn, Benzoin, Tinospora sinensis (Lour.) Merr., and artificial musk. The herbs comprise multiple therapeutic components. For example, the main chemical ingredients of Oxytropis falcata Bunge, including flavonoids, alkaloids, steroids, and terpenes, show anti-inflammatory, analgesic, and antibacterial effects  OR “dermatitis” [MeSH]) OR (“eczema” [Title/Abstract] OR “dermatitis” [Title/Abstract])) AND (“Qingpeng” [Title/Abstract] OR “Qing Peng” [Title/Abstract]).RCTs published in Chinese or English were included.The patients were diagnosed as subacute eczema or chronic eczema, regardless of age, gender, disease course, and disease severity. The inclusion and exclusion criteria on the types of eczema are listed in The participants in the test group were treated with Qingpeng ointment alone or Qingpeng ointment combined with TCS.The participants in the control group were treated with the placebo or TCS.The primary outcomes were the total efficacy rate (TER) and total symptom score (TSS). TSS was scored based on the severities of lesion morphology, lesion area, and pruritus symptom. In addition, complete cure was defined as the decreased proportion of TSS ≥ 90%; significant improvement was defined as 60% ≤ the decreased proportion of TSS < 89%; moderate improvement was defined as 20% ≤ the decreased proportion of TSS < 59%; no improvement was defined as the decreased proportion of TSS < 20%. TER = (the number of patients with complete cure + the number of patient with significant improvement)/total number of patients × 100%.The second outcomes included visual analogue scale (VAS) for pruritus and adverse events. Among skin adverse events, erythema, itch, pain, tingling, burning, etc. belonged to skin irritative reactions, and skin non-irritative reactions included hyperpigmentation, hypertrichosis, skin infection, atrophy, and telangiectasia.Reviews, animal experiments, case reports, duplicates, unavailable full texts, and studies with inappropriate interventions were excluded.Based on the inclusion and exclusion criteria, the eligible studies were selected by reading the titles, abstracts, and full-texts of studies. The following data were extracted from each included study: the first author's name, year of publication, sample size, age, the interventions of each group, the treatment course, and outcome measures.The methodological quality of the included studies was evaluated based on the Cochrane Collaboration Risk of Bias Tool . The risI2 statistic. A fixed-effects model (I2 < 50%) or a random effects model (I2 ≥ 50%) was used depending on the value of I2. The differences between both groups were considered to be statistically significant when P < 0.05. The publication bias was evaluated by using the funnel plot if the number of included studies was 10 or more.All statistical analyses were performed by the Review Manager 5.3. Dichotomous data were expressed as relative risk (RR) with 95% confidence interval (CI), whereas continuous data were expressed as mean difference (MD) or standardized mean difference (SMD) with 95% CI. The heterogeneity across studies was tested by A total of 501 studies were identified from the four English literature databases and four Chinese literature databases. After removing the duplicates, 181 studies remained for further selection. 113 studies were excluded based on the titles and abstracts, and 42 studies were removed according to the full texts. Ultimately, 26 eligible studies were included in the meta-analysis . A flowcAll studies were published between 2010 and 2019 and were conducted in China. Only one study was published in English , while tAll studies declared randomization, however, only 10 studies provided the details of random sequence generation, including one study with throwing dice , one stuI2 = 25%, P = 0.24), a fix-effect model was employed. The pooled results indicated that Qingpeng ointment significantly increased the TER compared with the placebo , as shown in Seven trials assessed the TER between Qingpeng ointment and the placebo –24. BecaI2 = 12%, P = 0.33), and a fix-effect model was used. The pooled results showed there was no significant difference in TER between Qingpeng ointment and TCS , as shown in Ten trials reported the TER between Qingpeng ointment and TCS , 27–35. I2 = 0%, P = 0.91), a fix-effect model was applied. The pooled results indicated that patients receiving Qingpeng ointment combined with TCS had a significantly higher TER than those receiving TCS alone , as shown in Seven trials compared Qingpeng ointment plus TCS with TCS alone in terms of TER , 40–43. I2 = 97%, P < 0.00001). The pooled results suggested that the TSS of the Qingpeng ointment group decreased more significantly than that of the placebo group , as shown in The TSS of Qingpeng ointment and the placebo was reported in five studies , 22, 23.I2 = 67%, P = 0.02), and a subgroup analysis was conducted based on the potency ranking of TCS. The result revealed that in term of reducing TSS, there was no significant difference between Qingpeng ointment and medium or low potency TCS , however, Qingpeng ointment was inferior to medium-high potency TCS , as shown in Five studies provided available data on the TSS of Qingpeng ointment and TCS , 33–35. I2 = 54%, P = 0.05) was observed, and a subgroup analysis was performed based on the potency ranking of TCS. The result displayed that the combination of Qingpeng ointment and medium or low potency TCS was more effective than medium or low potency TCS alone in terms of reducing TSS . A similar result was also applicable for the Qingpeng ointment combined with medium-high or high potency TCS group , as shown in Six studies mentioned the TSS of Qingpeng ointment plus TCS and TCS alone , 41–43. P < 0.00001), as shown in The data on the VAS score of Qingpeng ointment and the placebo were collected in one study . The resI2 = 58%, P = 0.12), a random-effect model was used. The pooled results showed that Qingpeng ointment was not superior to TCS in relieving pruritus , as shown in Two studies assessed the VAS score of Qingpeng ointment and TCS , 33. TheI2 = 0%, P = 0.90), and a fix-effect model was conducted. The pooled results showed that the combination of Qingpeng ointment and TCS could alleviate itch better than TCS alone , as shown in Three studies compared the VAS score of Qingpeng ointment plus TCS with that of TCS alone , 37, 38.In the studies which reported adverse event, nothing but skin adverse events were observed in each group during the treatment.I2 = 5%, P = 0.38), a fix-effect model was applied. The pooled results showed that Qingpeng ointment did not significantly increase the incidence of skin irritative reactions compared with the placebo , as shown in Among seven studies comparing Qingpeng ointment with the placebo –24, no aI2 = 5%, P = 0.38), and a fix-effect model was used. The pooled results revealed that the risk of skin irritative reactions in the Qingpeng ointment group was similar to that in the TCS group , as shown in Twelve studies comparing Qingpeng ointment with TCS offered available data on adverse events , 34–36. I2 = 0%, P = 0.46), a fix-effect model was applied. The pooled results showed that the combination of Qingpeng ointment and TCS did not increase the incidence of skin irritative reactions compared with TCS alone , as shown in The available data on adverse events were provided in five studies comparing Qingpeng ointment plus TCS with TCS , 41–43. The sensitivity analysis was performed in each meta-analysis, and it found that any individual study could not significantly affect the pooled results, indicating a high stability of the analysis. Publication bias on the TER of Qingpeng ointment and TCS was assessed. The nearly symmetrical funnel plot suggested no obvious publication bias, as shown in As the included studies had the suboptimal methodological quality, such as the absence of blinding and allocation concealment, the level of evidence was moderate for TER and adverse events. On the other hand, due to the suboptimal methodological quality, sample size less than 400, and the significant heterogeneity across studies, the level of evidence was very low to moderate for TSS and VAS for pruritus. The GRADE summary of the included studies is shown in Qingpeng ointment has been widely used for the treatment of subacute and chronic eczema for decades, and a lot of clinical trials have demonstrated its efficacy and safety. However, to date, there is no evidence-based medical proof to support Qingpeng ointment for treating eczema. Therefore, this is the first meta-analysis collecting 26 RCTs to investigate the efficacy and safety of Qingpeng ointment for subacute and chronic eczema.+ T cells and mast cells in the dermis of the lesion but also suppress the production of IL-4 and the mRNA expression of IL-17A in tissue, while the levels of IFN-γ and TNF-α in tissue were increased [The pathogenesis of eczema is complex, including immune dysregulation and epidermal barrier dysfunction . Some anncreased , 46. In ncreased –49. TherIn addition, itch is a remarkable manifestation of eczema, and the treatment of itch is still a great challenge for clinicians and patients. Some animal experiments investigated the antipruritic effect of Qingpeng ointment by using the histamine-induced itch mice model, and the results showed that Qingpeng ointment could significantly decrease thymic stromal lymphopoietin (TSLP) mRNA and IL-4 mRNA levels in the skin and increase the pruritus threshold of histamine phosphate , 51. AnoOn the other hand, many patients with refractory eczema are not satisfied with the effect of TCS alone and are willing to be treated by TCS combined with traditional medicine. In this meta-analysis, compared with TCS alone, Qingpeng ointment combined with TCS could not only remarkably improve the TER but also significantly reduce both TSS and VAS score. In clinical practice, the combination of Qingpeng ointment and TCS could reduce excess application of TCS and avoid the side effects of TCS. Therefore, Qingpeng ointment is an add-on treatment to TCS for treating eczema, and the combination of Qingpeng ointment and TCS might be an optional treatment for patients with refractory eczema.In clinical practice, drug safety is as important as its efficacy. Our study revealed that no serious adverse events but skin irritative reactions were reported in the Qingpeng ointment groups, including erythema, itch, and burning. The symptoms could disappear spontaneously after drug withdrawal. In this meta-analysis, the incidence of skin irritative reactions of Qingpeng ointment was similar to those of the placebo and TCS. Meanwhile, the combined therapy did not increase the risk of skin irritative reactions. In addition, the common side effects of TCS, such as hyperpigmentation and skin thinning, were not observed in the patients treated with Qingpeng ointment. Therefore, Qingpeng ointment is a safe topical medicine. However, some laboratory examinations were not conducted during the treatment in all included studies, such as blood routine test, urine routine test, hepatic function, and renal function. Although no serious adverse event during four weeks of treatment is observed, we need be cautious of the safety of long-term use of Qingpeng ointment, and regular laboratory examinations are needed in future studies to confirm the safety of Qingpeng ointment for other organs, such as liver and kidney.There are some limitations in this meta-analysis. Firstly, the methodological qualities of the included studies were low. Most studies failed to provide the details of allocation concealment, blinding methods, and sequence generation methods, which could increase the risk of bias and degrade the level of evidence. Secondly, all trials were conducted in China. There is a lack of clinical evidence for other races. Finally, eczema is a chronic recurrent skin disease, and the therapeutic goal is to maintain the efficacy of treatment and to reduce the relapse of lesions. However, few trials have a long-term follow-up to observe Qingpeng ointment in decreasing recurrence rate. Therefore, well-designed and large-sample RCTs are warranted to further explore the long-term efficacy and safety of Qingpeng ointment for the treatment of eczema.Our study reveals that Qingpeng ointment is an effective and safe treatment for subacute and chronic eczema. It might be an alternative to medium and low potency TCS for reducing eczema severity, and it is also an add-on treatment to TCS for treating eczema. However, these findings should be interpreted carefully because of the poor methodological quality and limited sample size. More high-quality, large-sample RCTs are needed to provide high-quality evidence of Qingpeng ointment for eczema in the future.


Contextual learning is a critical component of episodic memory and important for living in any environment. Context can be described as the attributes of a location that are not the location itself. This includes a variety of non-spatial information that can be derived from sensory systems  and internal state. In this review, we first address the behavioral underpinnings of contextual memory and the development of context memory theory, with a particular focus on the contextual fear conditioning paradigm as a means of assessing contextual learning and the underlying processes contributing to it. We then present the various neural centers that play roles in contextual learning. We continue with a discussion of the current knowledge of the neural circuitry and physiological processes that underlie contextual representations in the Entorhinal cortex-Hippocampal (EC-HPC) circuit, as the most well studied contributor to contextual memory, focusing on the role of ensemble activity as a representation of context with a description of remapping, and pattern separation and completion in the processing of contextual information. We then discuss other critical regions involved in contextual memory formation and retrieval. We finally consider the engram assembly as an indicator of stored contextual memories and discuss its potential contribution to contextual memory. We have all experienced the recall of a specific memory when we are exposed to a similar situation; the feeling of a warm breeze on a beach might remind you of a summer day from your childhood when you enjoyed swimming with your family and a barbecue. It occurs because the target (the act of swimming with your family in this case), was memorized alongside multiple types of information that occurred at that moment (a warm breeze on the beach in this case). While we may focus on a specific event or person in the moment, many other pieces of information occurring simultaneously around the target become enmeshed in memory formation. Those other information streams can serve as a hint to facilitate recall of the target memory. Together, these are referred to as the “context” . ContextA common test used to assess contextual memory processes is the Pavlovian contextual fear conditioning (CFC) paradigm. An animal (a mouse or rat in most cases) is put into a conditioning chamber  which is designed to signal the delivery of an electrical foot-shock , and learns its specific context as paired with shock. The duration of freezing behavior is measured as an outcome when the animal is reintroduced into the same context vs. a novel context . An animPrevious studies demonstrated that the exposure time to the context chamber itself is crucial for context memory formation . The expBeyond static contextual cues, discrete and salient sensory presentations of CS also become associated with the presentations of US through “cue associated” learning . In audiRichard Hirsh hypothesized that contextual cues have a hierarchical property rather than a universal equivalence . Hirsh’sMany different brain regions are involved in contextual fear memory acquisition, consolidation, and retrieval . In partTaken together, these reports highlight that the EC-HPC formation plays a core role in contextual memory processes with a broader network supporting the expression of contextual memories following their initial formation. In the following sections, we mainly focus on EC-HPC formation as one of best studied contributing brain regions in the processes of contextual memory, and discuss their neuronal circuit connectivity and physiological mechanisms in relation to contextual memory. We describe other relevant brain regions, and how the individual components are involved in CFC as well.The EC-HPC formation have been considered to be crucial for learning and memory . In the Most physiological studies of contextual processing were carried out in the HPC, however, it was more recently that studies of the role of the EC, upstream of HPC, in contextual memory were undertaken. A great deal of the contributing mechanism was unknown. For example, it was unknown whether contextual information was already processed in a subset of EC cells or not, and what the key driver for routing of contextual information to HPC was. Originally, grid cells, which are critical for various aspects of spatial navigation, were “thought to provide a context-independent metric representation of the local environment” . Recentl+ pyramidal cells and Reelin+ stellate cells, with the former projecting to interneurons in stratum lacunosum (SL) of CA1 area and the latter monosynaptically projecting to DG neurons  receptor agonist  has been demonstrated to play a role in contextual fear conditioning, primarily by generating the emotional valence associated with the context-shock pairing. HPC interacts with the BLA via MECVa to encode contextual information during encoding of contextual fear memory. Not only the dorsal HPC (dHPC), but also the ventral CA1 (vHPC) projections to the Basal amygdala, paired with aversive stimuli, contribute to encoding conditioned fear memory . HippocaBeyond the role of the Medial Geniculate Nucleus (MGN) in routing tonal information to the amygdala , the thaThe Retrosplenial cortex (RSC) contributes to spatial navigation, head direction, as well as learning and memory for discrete cues such as auditory or visual stimuli. Lesion studies demonstrate that RSC processing contributes to configuration of contextual representation building (combining of elements). This is particularly interesting considering the previously demonstrated roles of RSC in hippocampal place field remapping . OptogenThe Lateral septum (LS) is downstream from the HPC and has been implicated in various functions such as mood, motivation, anxiety, and spatial behavior, as reviewed in +) neurons project strongly to the HPC, and release dopamine. Optogenetic activation of LC TH+ neurons increased dopamine release into dHPC, promoting novelty associated spatial learning and memory  tyrosine-hydroxylase-expressing  such as c-Fos, Arc (activity-regulated cytoskeleton-associated protein) or Zif268 . German + cells, but not CalB+ cells, in MEC drives contextual information into the HPC, is necessary for the formation and recall of CFC memory. They further showed that the neural input from Reelin+ cells in MEC is necessary for the reactivation of memory engram cells encoding CFC memory in the hippocampal CA3. These results indicate that the hippocampal trisynaptic pathway driven by Reelin+ cells in MEC layer II is crucial for CFC memory.By utilizing IEG promoters to allow activity-dependent labeling of cells, and pairing this technique with optogenetically driven expression of opsins in previously activated cells, T2 through the whole brain in a Fos-TRAP (targeted recombination in active populations) mouse line (Recently, the question of when engram cells are formed during the process of memory acquisition and long-term storage has come into focus. use line . All neuuse line . Engram use line , and optuse line . These r+ cells in MEC layer II via the trisynaptic pathway. This information is then used to form unique ensembles of activity that have the ability to nest within representations of locations. Questions still remain as to the nature of the local processing of contextual data within the HPC, but recent advances in technologies for cell type specific genetic manipulations and for in vivo recording of cellular activity can provide powerful opportunities for investigation of these local circuit mechanisms. For example, the in vivo visualization of calcium transients during behavior, or the stable recording of cellular arrays across multiple brain regions simultaneously that have recently become possible have the potential to yield critical new insights into contextual learning processes as context dependent memory is composed of multiple elements, and simultaneous measurement of physiological neuronal activity could tell us which sub-component is driving the changes in neuronal activities. At the same time, utilizing cell type specific techniques to visually identify neuronal activity in specific subsets of hippocampal neurons could be of great use in further developing our understanding of the local processes that shape contextual encoding.In this review, we have discussed the behavioral neuroscience of contextual fear conditioning in the EC-HPC network. Contextual memory has been found to be a unique part of episodic memory, encoding information about the conditions within a space in such a way as to serve as a hierarchical basis for encoding and indexing memories. Contextual information is fed to the HPC largely by ReelinWM, TK, and SO conceived this project. WM, JY, TK, and SO wrote the manuscript and approved the final manuscript. WM and SO designed and created the figures. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods. Complex patterns exist in various real-world fields and can be simplified into complex networks. Individuals are represented as nodes, and the connections between them are correspondingly transformed to edges in a graph ,2,3. ForMany studies have attempted to incorporate attribute and topology information in the community detection methodologies  beyond tThe main contributions of this work are as follows: (1) We proposed two algorithms OCEA and AOCEA that can be used to detect overlapping communities on attributed networks, which considers the multiple vertex-community memberships and employs a strategy to adjust attribute weights iteratively according to the memberships. (2) A new method for the automatic estimation of the number of communities is proposed to improve the practicability of OCEA. (3) Experimental results on synthetic and real-world datasets validate the effectiveness of OCEA and AOCEA.Related work for Big Data Networks. Researches on community detection in large-scale networks are motivated by the situation that the traditional methods cannot handle the increasing size of networks. ADVNDS  utilizedRelated work on Attributed Networks. From the perspective of processing attribute information, existing algorithms can be classified into five categories . First, Related work on Overlapping Community Detection. The recent overlapping community detection algorithms can be classified into five categories. First, algorithms based on multi-objective evolutionary approach the global optimal solutions by swarm evolution ,31,32. Mk can be evaluated using the density of vertices. Through the evaluation, the automatic OCEA can detect overlapping communities without parameter k and obtain comparable or even better results comparing to the baseline methods.It is still challenging to detect overlapping communities on attribute networks. As introduced above in related work, only CESNA  and CDE The remainder of this paper is organized as follows. G = , where V = {v1, v2, …, nv} is a set with n vertices, E = {|iv, iv∈V} is a set of edges, A = {a1, a2, …, ad} denotes the set of attributes, and X = T is an attribute matrix. Each row of X denotes the binary attribute vector of vertex i with d dimensions. If vertex i has attribute j, ja(iv) = 1. Otherwise, ja(iv) = 0. d-dimensional binary vector associated with the vertex or edge. In this study, we utilize a different formulation called the augmented attribute graph first proposed by [Attributed network can be denoted as posed by  to detecOverlapping community: In a traditional community detection problem, the final partitions do not share any vertices with each other. However, some vertices may be assigned to multiple communities in an overlapping community detection problem. In this paper, we utilize the framework of fuzzy k-medoids [-medoids  to calcuAttributed network: For an attributed network, the final partitions should satisfy two properties: (1) Topology similarity. The vertices belonging to the same community have more connections with each other than the vertices outside the community. (2) Attribute homogeneity. The vertices whose attribute vectors are close to each other have a high probability to be assigned to the same community. The community partitions should embody both the topology similarity and attribute homogeneity.The overlapping community detection problem on attributed networks can be discussed from two aspects:A = {Football, Basketball}, and the set of structure vertices V = {v1, v2, …, v5}. The solid points denote the added set of attribute vertices a =V {av1, av2}, and the hollow points represent the original topology structure of the network. The solid lines denote the original structure edges. The hollow lines are used to connect the attribute and structure vertices. Vertex v4, for example, has two attributes: Basketball and Football. Thus, there are two attribute edges connected to the attribute vertices av1: Basketball and av2: Football. Consequently, the attribute vectors can be transformed into attribute vertices and edges on the augmented attribute graph.In contrast to the traditional formulation of an attributed network, the augmented attribute graph directly transforms the attributes into attribute vertices and adds them to the original graph. iv to structure vertex jv:From structure vertex ipv and jqv corresponding to the pth attribute of vertex iv and the qth attribute of vertex jv, respectively:From attribute vertices ipv to structure vertex jv:From attribute vertex iv to attribute vertex jqv:d is the dimension of attribute vectors. ω denotes the weights of the topology and attribute information, N(iv) denotes the number of neighbors of p and q in Equations (2)–(4) could be equal or not, and From structure vertex Due to the addition of attribute vertices, the structure similarity and attribute homogeneity between vertices can be uniformly represented by the probability of random walks on the augmented attribute graph. Based on this, there are four cases to calculate the transition probability of random walk. The corresponding equations are expressed as follows:l step random walks between each pair of vertices can be obtained by Equation (5).c is the stop probability of the random walk, l is the step length and γ is a power. AP is the transition probability matrix calculated by the Equations (1)–(4). Equation (5) denotes the process of l step random walks on the augmented attribute graph with a stop probability c. Here Based on this, the transition probability of Here, we introduce the proposed OCEA based on augmented attribute graph and its extended version with an estimation of the number of communities. The framework of OCEA is shown as U is updated according to AR. Third, the structural weight ω0 is fixed to 1, and the attribute weights U. The procedure is repeated until the objective function converges within a certain range ε.As shown in ith row of the random walk similarity matrix AR is used as the vector of iv that is denoted as AR(iv). Before the iteration of updating memberships, k initial vertices are selected as the centers of each cluster.Based on the framework of the augmented attribute graph, the OCEA utilizes fuzzy k-medoids  to detecU and the vector j. Further, j. It denotes the average vector of all vertices in cluster j. And j. Thereafter, the center vertices of each cluster are selected as follows:Equations (6) and (7) show the process of updating memberships matrix jc denotes an actual existing vertex that is the center of cluster j determined by Equation (8). It is different from m in Equation (7) and Equation (9) is a parameter and it will be set to 2 in experiments. The Euclidean distance is used to measure the similarity between vertices, after which the vertices are assigned to different communities according to their memberships to the communities.The main objective of the algorithm is to ensure the vertices are close to their corresponding cluster centers. To this end, the objective function is minimized byEach iteration of the update can obtain memberships. The weights of each attribute can be adjusted based on the currently detected communities. The equations of attribute weights adjustment are as follows:Memberships can be observed as the influence of a vertex on a cluster. The high similarity of a certain attribute in a cluster implies that the attribute is an effective feature to detect communities. Subsequently, the weights of the corresponding attributes should be increased, or else they should be decreased.k directly. Most algorithms need to input k as a hyperparameter or directly set k as a fixed value. Based on OCEA, the automatic version of it is proposed using a process for estimating the number of communities k. It will be called AOCEA for brevity in the following.In the case of community detection without ground truth, we cannot obtain the number of communities First, the density of vertex is defined by Equation (12):d. The stop probability c is set to 0.5 to draw the curves.Taking out four egonetworks randomly from the Facebook egonetwork set introduced in The x-axis denotes the vertex density value and the y-axis denotes the number of vertices. Each blue curve denotes the probability distribution curve fitted to each histogram. Each black curve denotes the Gaussian distribution calculated according to the mean and standard deviation of each subnetwork’s vertex dense value, as shown in As we can observe in l step random walk probability changes and influences the similarity between vertices. In general, the vertices in the network gradually gather to several clusters in a distinct trend because of the adjustment of weights.As shown in Function 1. ClusterNumberEstimation.Input: Attribute Augmented Graph AG, random walk length l, stop probability c, number of epochs TOutput: Number of Communities k1:ω0 = ω1 = … = dω = 0, Sd = 0Initialize weights 2:for vertices iv∈V do3:D(iv) by Equation (12) Compute density 4:Sd = Sd + D(iv) 5:end for6:Td = Sd/n7:DV ← {i|D(vi)v ≥ Td, iv∈V}Initialize centers 8:kL = |DV|9:fori = 1, …, T do10:for vertices iv∈V do 11:ijU and cX by Equations (6) and (7)  Update memberships 12:end for 13:form = 1, …, d do 14:mω by Equation (11)  Update weights of attributes 15:end for 16:AR by Equation (5) Compute 17:end for18:forj = 1, …, kL do19:CV ← argmaxvi(ijU) 20:end for21:k = |CV|22:returnkDuring the update of memberships, there may be several clusters that select the same vertex as their centers—that is, there may exist a vertex whose memberships to several clusters are all the highest in the current cluster. Only the vertices with the highest memberships are preserved as centers. This implies that parts of the candidate centers will be eliminated. As shown in As introduced in the previous sections, the update of memberships and the adjustment of weights are the main processes of OCEA, as shown in Algorithm 1.Algorithm 1 OCEAInput: Attribute Augmented Graph AG, random walk length l, stop probability c, convergence parameter ε, overlapping parameter γ, number of communities kOutput: Communities C1, …, kC1:ω0= ω1 = … = dω = 1Initialize weights 2:AR by Equation (5)Compute initial 3:CVSelect the initial centers 4:whilemar > ε do5:for vertices iv∈V do 6:forj = 1, …, kdo  7:Uij by Equation (6)   Update memberships 8:cX by Equation (7)   Update centers 9:end for  10:end for 11:form = 1, …, d do 12:mω by Equation (11)  Update weights of attributes 13:end for 14:AR by Equation (5) Compute 15:tF Compute objective function 16:mar = |t − Ft−1F| 17:t−1 = FtF 18:end while19:for vertices iv∈V do20:imax =μ max 21:forj = 1, …, k do 22:jC ← {i|Uij ≥ γ × μimaxv}  23:end for 24:end for25:return C1, …, kCIt initializes weights, the random walk similarity, and centers first (lines 1–3). Then, the memberships, centers, and weights of attributes are updated iteratively until the convergence of the objective function in a certain range (lines 4–17). Detected communities are obtained using memberships (lines 18–23).ClusterNumberEstimation to obtain an estimated number of communities k. Then, this estimated k is regarded as the input of the OCEA to detect the overlapping communities.Algorithm 2 AOCEAInput: Attribute Augmented Graph AG, random walk length l, stop probability c, convergence parameter ε, overlapping parameter γOutput: Community C1, …, kC1:k = ClusterNumberEstimation Initialize 2:AG, l, c, ε, γ, k)return OCEA  time. Here, k is the true number of communities. Thus, the overall complexity of OCEA and AOCEA is O(n) because k << n. AOCEA has an extra procedure of estimating the number of communities, and its initial estimation of the number of communities is roughly equal to n/2. Therefore, the time complexity of weights adjustment in AOCEA is The time complexity of computing the transition probability is The algorithms were tested on synthetic and real-world attributed networks.http://snap.stanford.edu/data/) (accessed on 28 April 2021) is a set of ten Facebook users’ ego networks. The users are denoted by nodeId’s and each ego network is numbered by the specific nodeId. Vertices denote the users of Facebook and each two pair of vertices within a circle (friends lists) are connected by an edge with a certain probability. The user profiles are encoded into vectors with binary values [We evaluated the performance of proposed algorithms and the baselines on six real-world attributed networks. The Facebook egonetwork set   is an imX and ground truth Y, H*(iX|jY) is defined asab = c = d = Given two covers, detected communities i and j denote the index of the clusters. imX and jmY denote whether there is a vertex m in the cluster i or j, respectively. Subsequently, the entropy H(X|Y) and H(X) can be obtained asIn Equation (13), H(Y|X) and H(Y) can be obtained similarly. ONMI is defined asNext, Four experiments were conducted: runtime, the accuracy of community detection on synthetic or real-world networks, and the accuracy of the estimation of the number of communities.The baseline methods include Inc-cluster, S-cluster, W-cluster , OCEA, aω0 to 1.0 and other weights to 0.0. W-cluster is an algorithm that considers attribute information without weight adjustment. It sets the structural weight to 1.0 and the weight of each attribute to 1/d. S-cluster and W-cluster can be regarded as the variants of Inc-cluster without adjustment of weights. The step length l is set to 5 for all algorithms. The power m in Equations (7) and (9) is set to 2. The ε is set to c is varying from 0 to 1:1 and D2 to compare the accuracy of detected communities. D1 fixes the mixing parameter μ to 0.1 and sets the size of networks from 100 to 700. Meanwhile, D2 fixes the size of the networks to 600 and sets the mixing parameters μ from 0.2 to 0.6.Accuracy of detected communities on synthetic networks: Experiments were conducted on DAccuracy of detected communities on real-world networks: Experiments were conducted on three networks from Facebook egonetwork set and three paper citation networks: Texas, Washington, and Wisconsin. The accuracy of the detected communities was studied and analyzed.Accuracy of estimation on the number of communities: Experiments on Facebook egonetwork set, including all the ten egonetworks, to study the accuracy of the estimation on real-world networks.1 to compare the algorithm runtimes. As the size of networks increasing, we analyze the changing runtime trends.Runtime: Experiments were conducted on DS-cluster only considers a network’s topology information, setting the structural weight The above experiments were conducted on a computer with an Intel i5-6300HQ CPU @ 2.30 GHz, with 8 GB RAM.1 and D2 are shown in The results of ONMI values on synthetic datasets D1 than the other algorithms because they consider the vertex’s memberships. S-cluster detects communities without considering attribute information, and W-cluster does not have the process of attribute weight adjustment. Consequently, the superiority of OCEA and AOCEA reflects the effectiveness of considering attribute information and corresponding adjustment of weights. Besides, the adjustment of weights in OCEA and AOCEA considers the memberships of each vertex to communities. That is, the attribute weights of the vertices in communities are updated according to the memberships. Inc-cluster does not consider the attribute weights to communities in the adjustment of weights.μ, it becomes difficult for the algorithms to uncover the communities with increasingly blurred boundaries, which results in the decrease of the ONMI values. All algorithms could not detect communities correctly when the value of μ reaches 0.6. Although Inc-cluster [μ. S-cluster and W-cluster were affected less than other algorithms because of their inadequate use of attribute information. OCEA had a similar changing trend of the ONMI value compared to Inc-cluster because of the introduction of attribute information. The ONMI value of AOCEA decreased greatly when μ > 0.3 because it included an extra procedure of estimating the number of communities. The ONMI value of AOCEA increased when μ < 0.3 because the estimated number of communities is affected by the update of memberships matrix U. And the boundaries between communities are not blurred when μ < 0.3. The number of communities estimated by AOCEA may not be identical to the true ones when the value of μ is higher than 0.5, which affects its accuracy.As shown in -cluster  exhibiteSix networks were selected to study the accuracy of algorithms on real-world networks. The results are shown in As shown in N are kept invariant because the more important structure and attribute features of the network are invariant. Additionally, as shown in On the whole, as we can observe from Experiments were conducted on all ten egonetworks from Facebook egonetwork set, and the number of vertices on each network was 347, 1045, 792, 755, 547, 227, 59, 159, 170, and 66, respectively. The results of the estimation of the number of communities are shown in As presented in As shown in In this paper, an overlapping community detection algorithm called OCEA—based on an augmented attribute graph—and its extended version with an additional estimation process for the number of communities was proposed. To utilize the attribute information properly, we adopt a strategy of iteratively adjusting attribute weights. For overlapping community detection, fuzzy k-medoids  was empl