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.


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.


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


Lamellar-structured graphene aerogels with vertically aligned and closely stacked high-quality graphene lamellae are fabricated.−1 K−1 at 2.30 vol% of graphene content.The superior thermally conductive capacity of the aerogel endows epoxy with a high through-plane thermal conductivity of 20.0 W mThe nacre-like structure endows the epoxy composite with enhanced fracture toughness.−1 K−1, 100 times of that of epoxy, with a record-high specific thermal conductivity enhancement of 4310%. Furthermore, the lamellar-structured graphene aerogel endows epoxy with a high fracture toughness, ~ 1.71 times of that of epoxy. Although thermally conductive graphene sheets are efficient in enhancing in-plane thermal conductivities of polymers, the resulting nanocomposites usually exhibit low through-plane thermal conductivities, limiting their application as thermal interface materials. Herein, lamellar-structured polyamic acid salt/graphene oxide (PAAS/GO) hybrid aerogels are constructed by bidirectional freezing of PAAS/GO suspension followed by lyophilization. Subsequently, PAAS monomers are polymerized to polyimide (PI), while GO is converted to thermally reduced graphene oxide (RGO) during thermal annealing at 300 °C. Final graphitization at 2800 °C converts PI to graphitized carbon with the inductive effect of RGO, and simultaneously, RGO is thermally reduced and healed to high-quality graphene. Consequently, lamellar-structured graphene aerogels with superior through-plane thermal conduction capacity are fabricated for the first time, and its superior through-plane thermal conduction capacity results from its vertically aligned and closely stacked high-quality graphene lamellae. After vacuum-assisted impregnation with epoxy, the resultant epoxy composite with 2.30 vol% of graphene exhibits an outstanding through-plane thermal conductivity of as high as 20.0 W mThe online version of this article (10.1007/s40820-020-00548-5) contains supplementary material, which is available to authorized users. Therefore, the improvements in thermal conductivity of polymer materials, especially their through-plane thermal conductivity, are highly crucial for their application as thermal interface materials (TIMs) between heaters and heat sinks [−1 K−1) and mechanical properties [−1 K−1 even at the graphene loading of ~ 10 wt% [−1 K−1, higher graphene loading is required, which would seriously deteriorate ductility and toughness of polymers [Although polymers have a wide range of applications in electronic devices, including power electronics, electric motors, and generators due to their lightweight, corrosion resistance, and ease of processing –4, theirat sinks . To achiat sinks , 7, boroat sinks –10, carbat sinks , graphitat sinks , 13, andat sinks –17, are operties . Howeveroperties . The the~ 10 wt% , 21. To polymers .−1 K−1 at a low graphene content of 1.23 vol% [−1 K−1 with its TCE of 8100% and its specific TCE of 1300% [To improve the enhancement efficiency in thermal conductivity, Ruoff et al. fabricated a three-dimensional (3D) graphene foam using a chemical vapor deposition (CVD) method as continuous thermal conduction paths for phase change materials, and its erythritol composite exhibited a high thermal conductivity of 3.44 W mof 1300% .−1 K−1) at low graphene contents .Obviously, because of the effective decreases in both the contact thermal resistance between graphene sheets and the interface thermal resistance between graphene and polymer matrix, preconstruction of a continuous graphene conduction network becomes an efficient approach for improving thermal conductivities of polymer/graphene composites –30. Howe−1 K−1 because of the significantly decreased contact thermal resistance [−1 K−1 [For 3D graphene aerogels used for thermally conductive polymer composites, their skeleton walls can be approximately regarded as ultrathin graphene-based films. The quality of graphene sheets and their dense compaction are crucial for heat conduction along the continuous skeletons of the aerogel. In reality, many graphene aerogels derive from GO sheets, and high temperature annealing is adopted to remove the residual oxygen-containing groups of GO sheets and heal their lattice defects to reduce the phonon scattering –33. But,sistance . Besidessistance . Near-pe [−1 K−1 .−1 K−1 at a low graphene loading of ~ 2.30 vol%, as well as a high TCE of ~ 9915% and a record-high specific TCE of ~ 4310%. Furthermore, the lamellar structure of the LSGA endows its thermally conductive nacre-like epoxy composite with high fracture toughness.Inspired by the ultrahigh thermal conductivity of densely stacked high-quality graphene films, herein, we fabricate lamellar-structured graphene aerogels (LSGAs) with continuous and highly thermally conductive paths by bidirectional freezing of a suspension of polyamic acid salt (PAAS) and graphene oxide (GO), followed by lyophilization, imidization, and graphitization treatments. By regulating polyamic acid (PAA)/GO mass ratios, an optimal lamellar architecture is achieved during the bidirectional freezing process, where PAAS and GO components are expelled by bidirectionally grown ice crystals to form the numerous vertically aligned lamellae. By subsequent imidization treatments at 300 °C, the PAAS monomers are polymerized to polyimide (PI) macromolecules, while the GO is thermally reduced to RGO by partially removing its residual oxygen-containing groups. Finally, graphitization at 2800 °C is adopted to graphitize PI macromolecules to be graphitized carbon with the help of the induced orientation effect of RGO and simultaneously upgrade RGO to be high-quality graphene with negligible lattice defects and large crystal sizes. The resultant lamellar-structured aerogel possesses superior through-plane thermal conduction capacity because of its vertically aligned and closely stacked high-quality graphene lamellae. Besides, the conventional graphene aerogels are proud of their low apparent density , 38, whi1,2-Bisethane as the reactive diluent, methyl hexahydrophthalic anhydride as the curing agent, and 2,4,6-tris(dimethylaminomethyl)phenol as the curing accelerator were purchased from Adamas Reagents (China). Bisphenol-A epoxy resin was supplied by Jiafa Chemicals (China). Sulfuric acid (98%), hydrochloric acid (37%), hydrogen peroxide (30%), and potassium permanganate (99.5%) were supplied by Beijing Chemical Reagents (China). Pyromellitic dianhydride , 4,4′-diaminodiphenyl ether , N,N-dimethylacetamide (DMAc), triethylamine (TEA), and sodium nitrate were purchased from Aladdin (China). Natural graphite flakes (300 meshes) were purchased from Huatai Lubricant & Sealing (China). All chemicals and reagents were used as received without further purification.Graphite oxide was synthesized by oxidizing graphite flakes with a modified Hummers method . GO shee3 or 30 × 50 × 20 mm3) that placed on a copper bridge, and then, one end of the copper bridge was inserted into liquid nitrogen to form a bidirectional temperature gradient. The frozen suspension was freeze-dried in a freeze-dryer  for 72 h. The resultant PAAS/GO hybrid aerogel was designated as PxGy, where x:y is the PAA/GO mass ratio. For comparison, neat PAAS aerogel and GO aerogel were also prepared using the same methodology. Detailed ingredients for the preparation of PAAS/GO hybrid aerogels are provided in Table S1.PAAS was prepared by dissolving PAA and TEA in deionized water under magnetic stirring for 2 h with a mass ratio of 1:0.48. After a GO suspension was added into the PAAS solution, the obtained PAAS/GO suspension was sonicated for 15 min and magnetically stirred for 2 h. The initial PAA/GO mass ratio varied from 9:1 to 3:7 to obtain different PAAS/GO suspensions, but the total concentration of PAAS/GO suspensions is kept at ~ 4 wt%. PAAS/GO hybrid aerogels were prepared by bidirectional freezing and lyophilization  was prepared by conventional freezing the PAAS/GO suspension in liquid nitrogen followed by freeze-drying, imidization, and graphitization. A unidirectionally orientated graphene aerogel (UP6G4-2800) was also fabricated by unidirectional freezing the PAAS/GO suspension followed by freeze-drying, imidization, and graphitization.The graphene/epoxy composites (GEs) were fabricated by vacuum-assisted impregnation followed by thermal curing. Epoxy resin, the reactive diluent, the curing agent, and the curing accelerator were mixed uniformly with the mass ratio of 8:2:9.48:0.0576, and then, the LSGAs were immersed into the mixture under vacuum for 12 h, followed by curing at 80 °C for 4 h and post-curing at 120 °C for 2 h. The resultant composites were designated as GEx, where x varies from 1 to 5, representing the initial mass ratio of PAA to GO from 9:1 to 5:5. To increase the graphene content of the resultant composites, the LSGA (P6G4-2800) was compressed perpendicular to the lamellar surface before epoxy resin was thermally cured. The obtained composites were designated as GE4-y, where y is the compression extents, which can be 30%, 50%, and 70%. As a reference, the IP6G4-2800/epoxy composites and UP6G4-2800/epoxy composites were also prepared using the similar infiltration and curing processes and designated as IGE4 and UGE4, respectively.2 was automatically scanned with a XY stage using a step size of ~ 1 μm, and Raman spectra were recorded at every points. The crystal sizes of LSGAs were calculated with the empirical formula: La(nm) = (2.4 × 10−10)λ4(ID/IG)−1 [ID/IG is the integrated intensity ratio of D band to G band. Thermal conductivities were calculated by k = α × ρ×Cp, where α is the thermal diffusivity, Cp is the specific heat capacity, and ρ is the density. Thermal diffusivities of GEs were measured on a Netzsch LFA467 light flash apparatus from 30 to 80 °C. Specific heat capacities of GEs from 30 to 80 °C were obtained using a TA Q20 differential scanning calorimeter (DSC) at a scanning rate of 10 °C min−1. Densities of GEs were measured by an Mettler Toledo electronic balance with a density determination kit 33,360. Thermogravimetric analysis (TGA) curves were obtained with a TA Q50 thermogravimetric analyzer at a heating rate of ~ 10 °C min−1 in a nitrogen atmosphere. The volume fraction was calculated by: vol% = wt% × (ρcom/ρ*), where wt% is the mass fraction, ρcom is the density of a composite, and ρ* is the true density of graphene (2.25 g cm−3). The heat transfer performances of GEs were recorded by a FLIR E40 infrared camera. High-resolution transmission electron microscopy (HRTEM) images were obtained using a JEM-2100Plus microscope at an operation voltage of ~ 200 kV. Three-point bending tests (span 7.75 mm) were performed on a SUNS UTM4103 tester with a loading rate of 0.05 mm min−1. The loading direction is perpendicular to the lamellar surface of the LSGA. For single-edge notched beam specimens, the epoxy and GEs were cut and polished to beams with dimension of 12 × 2.5 × 2.5 mm3, and the beams were then notched by a diamond blade. The notch was sharpened by a razor blade perpendicular to the lamellar direction. The notch was about half of the thickness of specimens. The initial fracture toughness (KIC) was calculated by Eq.  was calculated by Eq.  is calculated by Eq. . X-ray diffraction (XRD) patterns were recorded using a Rigaku D/Max 2500 X-ray diffractometer at a generator voltage of 40 kV. A Renishaw inVia Raman microscope at an excitation wavelength of 514 nm was used to obtain Raman mapping images. An area of 40 × 40 μmID/IG)−1 , where λFigure −3) in the absence of PAAS presents a completely disordered structure . This is not only because the oxygen-containing groups on the GO sheets are willing to be adsorbed to the surface of the ice crystal, and hence, causing a curved ice crystal during freezing process, the high viscosity of GO suspension at such a high concentration also hinders the unidirectional growth of ice crystal [As expected, the PAAS/GO hybrid aerogels show lamellar and porous structures, and the spacing between adjacent lamellae varies in the range of 20-40 μm Fig. b–f. It i crystal . Fortuna crystal . Consequ−3 as the GO content increases from 10 to 50 wt%  of D band (~ 1350 cm−1) and G band (~ 1580 cm−1) [ID/IG ratios. The blue, green, and red colors correspond to their ID/IG values of 0, ~ 0.2, and ~ 0.4, respectively. Compared to PAA-2800 with an average ID/IG of ~ 0.189 , the graphitization extent of P9G1-2800 is greatly improved with a low average ID/IG of ~ 0.087, although it still presents large green and glaucous areas. Interestingly, as the increase in the GO dosage, the blue area becomes larger, while the green area decreases gradually. The average ID/IG decreases to ~ 0.028 for P5G5-2800, very close to that of GO-2800 (~ 0.026) . Meanwhile, as shown in Fig. ID/IG manifests the stepwise healing of sp2 domains from ~ 87.4 nm of PAA-2800 to ~ 188.9 nm of P9G1-2800, and then to ~ 583.4 nm of P5G5-2800 [The graphene quality in LSGAs is further evaluated with Raman mappings by integrated intensity ratios  . As shown in Fig. Km (W m−1 K−1) and K (W m−1 K−1) are thermal conductivities of epoxy and LSGA/epoxy composites, respectively, and V (vol%) is the volume fraction of fillers. As shown in Fig. −1 K−1 with a relatively high specific TCE of ~ 4750% at the graphene content of ~ 1.23 wt%, because GE4 owns highly efficient thermal conduction paths as well as relatively high filler content.By a vacuum-assisted filtration of LSGAs with epoxy monomer and curing agents followed by thermal curing, the resultant LSGA/epoxy composites still show a nacre-like structure . Due to the anisotropic lamellar structure of LSGAs, their composites exhibit different thermal conductivities in three directions, and the thermal conductivity in Z-direction is higher than those along the other two directions  . The thr−1) Fig. a. Moreov−1 K−1, while that of GE4-70% increases to ~ 20.0 W m−1 K−1, because the lamellar sheets become more compact, while the nacre-like structure of the composites is well retained  [−1 K−1, while that of GE4-70% in Y-direction is only ~ 1.22 W m−1 K−1, which is ascribed to the high interface thermal resistances between graphene and polymer matrix in the conduction path of the Y-direction.To further improve thermal conductivity of the epoxy composites while maintaining their mechanical properties, the graphene content can be increased by slowly compressing the LSGAs perpendicular to the lamellar direction before the epoxy resin is thermally cured. As shown in Fig. . S5d-f) . Note th−1 K−1. As shown in Fig. −1 K−1, much higher than those reported in the literature at similar graphene contents [The influence of temperature on thermal conductivity of the thermosetting epoxy composites is also evaluated Fig. e. When tcontents , 58–61. contents , 59, 62.3 are placed on the same hot stage at 75 °C, and an infrared camera is adopted to record in situ the side temperature variation of the samples  results calculated on the basis of the force–displacement curves. KIC of the IGE4 is ~ 0.64 MPa m1/2, slightly higher than that of epoxy (~ 0.62 MPa m1/2). Fortunately, the lamellar-structured GE4 exhibits an enhanced fracture toughness of ~ 0.88 MPa m1/2, ~ 1.38-fold that of the IGE4 at the same graphene content. Consistent with the force–displacement curves, the KIC decreases gradually to ~ 0.70 MPa m1/2 when the compression extent increases to 70%. As reported previously, when the graphene content is beyond a certain value, the fracture toughness of its epoxy composite would decrease [−1 K−1 as well as a high fracture toughness at a relatively low graphene content. As shown in Fig. KJ) of GE4 gradually increases to ~ 2.06 MPa m1/2 within the American Society for Testing and Materials (ASTM) limit (E1820-13) [J of GE4-70% is lower than that of GE4, it still reaches ~ 1.06 MPa m1/2, which is ~ 1.71-fold that of epoxy.Apart from the high through-plane thermal conductivity, the nacre-like structure also endows the composites with high fracture toughness. The typical force–displacement curves Fig. a show th1820-13) , which iCrack propagation is used to explain the high fracture toughness of GE4-70% Fig. d–g. The −1 K−1 at a low graphene content of ~ 2.30 vol%, with a high TCE of ~ 9915% and a record-high specific TCE of ~ 4310%. In addition, the lamellar-structured graphene aerogel also endows epoxy with an enhanced fracture toughness. Our nacre-like graphene/epoxy composite with high through-plane thermal conductivity and fracture toughness demonstrates an insightful avenue for fabrication of high-performance thermal interface materials.Lamellar-structured PAAS/GO aerogels are fabricated by bidirectional freezing of PAAS/GO suspensions followed by lyophilization and converted to PI/RGO aerogels by thermal treatment at 300 °C, during which PAAS monomers are polymerized to PI macromolecules, while GO is thermally reduced to RGO. The final graphitization at 2800 °C is crucial for obtaining the lamellar-structured high-quality graphene aerogels, during which PI is carbonized and even graphitized to thermally conductive carbon with the inductive effect of RGO, while RGO is simultaneously upgraded to high-quality graphene by thermally removing its residual oxygen-containing groups and healing its lattice defects. By adjusting the initial mass ratio of PAA and GO, an optimal LSGA with superior thermally conductive capacity is obtained because of its continuous network, densely stacked graphene lamellae, and large graphene sizes. Thanks to the excellent compressibility, the lamellar-structured graphene aerogel infiltrated with epoxy monomer and curing agent could be compressed perpendicular to the lamellar direction to adjust the graphene content in the resultant graphene/epoxy composite. The nacre-like anisotropic composite exhibits different thermal conductivities along three directions, and its through-plane thermal conductivity can be as high as ~ 20.0 W mD/IG values of LSGAs; and comparison of thermal conductivities and specific TCE of composites with those reported. (DOC 18656 kb)SEM and AFM images of GO; SEM images of PAAS/GO hybrid aerogels and GO aerogel; SEM images of P6G4-2800; Raman mapping images of PAA-2800 and GO-2800; SEM images of composites; comparison of thermal conductivities of GE4; comparison of thermal conductivities of composites; SEM images and Raman mappings of IP6G4-2800 and UP6G4-2800; XRD patterns of P6G4-2800, IP6G4-2800, and UP6G4-2800; TGA curves of epoxy and its composites; fracture surfaces of epoxy and GE4-70%; detailed ingredients of PAAS/GO hybrid aerogels; filler contents, through-plane thermal conductivities of composites, and average ISupplementary material 2 (MP4 127 kb)Below is the link to the electronic supplementary material.


Staphylococcus aureus (MSSA-PENS). To date, there is relatively little known about the current situation and molecular characteristics of MSSA-PENS in China. In this study, we carried out a laboratory-based multi-region retrospective study to investigate the genomic epidemiology and characterisation of MSSA-PENS isolated from invasive bloodstream infections (BSIs) across 17 provinces. The prevalence of MSSA-PENS isolates increased significantly over the 6-year period, with the proportion increasing from 3.51% in 2014–8.80% in 2019, an average relative increase of 22.14% per year , suggesting that China is experiencing a resurgence of MSSA-PENS. Phylogenetic analysis showed a higher strain diversity occurred; the most frequent clonal complexes (CCs) identified were CC188 (17.14%), CC398 (15.71%) and CC5 (15.71%). Over half of MSSA-PENS strains were pan-susceptible, with erythromycin the most frequent resistance observed. Moreover, 25 isolates were identified as immune evasion cluster negative, including CC15, CC188 and CC1, and 6 strains encoded the Panton-Valentine leucocidin gene. Importantly, virulence assays showed that MSSA-PENS exhibited a level of virulence comparable to that of penicillin-resistant MSSA (MSSA-PENR), indicating that more-sensitive strains should not be mistaken for lacking aggressiveness in vivo. Furthermore, 11 of these isolates were confirmed as blaZ positive but phenotype sensitive, with different amino acid changes in blaZ. Our data support the recommendation to clinicians regarding the usage of penicillin in invasive BSIs caused by MSSA-PENS, which might create a novel opportunity for better antimicrobial stewardship in the future.Many countries have reported increasing rates of penicillin-susceptible methicillin-sensitive  Staphylococcus aureus is an opportunistic pathogen with high capacity to adapt to human hosts and healthcare settings. With the rise of hospital-based medicine, S. aureus quickly became a major cause of health-care-associated infections especially bloodstream infections (BSIs)  × width [W]) for 4 days.Virulence assays were performed as previously described. StrainsA total of 1952 MSSA strains isolated from blood were examined in this study. As shown in S detected among all MSSA isolates from blood cultures identified in the 55 Chinese hospitals was 7.17% (140/1952). Of note, as shown in S increased significantly among the MSSA isolates over the 6-year period, with the proportion increasing from 3.51% in 2014–8.80% in 2019, an average relative increase of 22.14% per year .The proportion of MSSA-PENS isolates from BSIs in patients at 55 hospitals across China, we first undertook a whole-genome phylogenetic analysis. As shown in S isolates, 26 STs were identified, and seven isolates were defined as new singletons. The 26 identified ST types belong to 16 clonal complexes (CCs), with CC188 (17.14%), CC5 (15.71%), CC398 (15.71%), CC8 (9.29%) and CC1 (7.14%) the most frequently detected CCs, comprising >65.0% of all MSSA-PENS isolates. Of note, a higher strain diversity was observed yearly from 2014 to 2019 . We observed that >40% of MSSA-PENS isolates belonged to sporadically occurring clones, such as ST707, ST2315, ST1821 and ST2990.To accurately determine the population structure of MSSA-PENblaZ were observed in this study. Thus, we defined MSSA-PENS harbouring blaZ as MSSA-PENS-blaZpositive(n = 11) and MSSA-PENS that did not harbour blaZ as MSSA-PENS-blaZnegative(n = 129). Among the 11 MSSA-PENS-blaZpositive isolates, most belonged to CC188 , whereas the remaining isolates were CC398 (n = 2), CC7 (n = 1) and CC8 (n = 1). Furthermore, most MSSA-PENS-blaZpositive strains (6/11) were isolated from Zhejiang Province.Of the isolates, 11 strains displaying penicillin sensitivity but harbouring spa type, constituting 15.71% of all strains. All isolates of the t189 type belonged to CC188. MSSA-PENS strains of the second most common spa type, t571, accounted for 10.0% of the isolated strains, 63.64% of which were CC398. Notably, t571 is the spa-type most associated with CC398-MSSA BSIs in Europe  gene and absence of erm[T] gene), as this strain was sensitive to tetracycline and lacked the tet(M) gene. In addition, two CC398 isolates harbouring the PVL gene were found in this study. We identified the two PVL-positive CC398 strains as ST398/t034-carrying/TCY-S/ERY-R/IEC type B and ST398/t1451-carrying/TCY-S/ERY-R/IEC type C, respectively. It is important to emphasize that the human-adapted CC398 MSSA was identified as a predecessor of community-acquired MRSA CC398, which evolved by acquiring SCCmec cassettes imparting methicillin resistance [S strains in other studies [In this study, almost all CC398 isolates belonged to sistance . CC5 is sistance . In addi studies ,41.S-blaZnegative isolates (58.14%). Erythromycin exhibited the poorest antibacterial activity (resistance rate of 32.86%), with most of the erythromycin-resistant isolates belonging to CC5, CC398 and CC59, and the resistance was encoded by the ermB, ermC and ermT genes, as predicted by WGS. Notably, although most of the MSSA-PENS isolates identified in this study were blaZnegative , WGS also confirmed that 11 isolates were blaZpositive with different amino acid changes. As a first-generation cephalosporin, cefazolin is the priority antibiotic that is routinely available to treat invasive MSSA infections [blaZ gene reported early [blaZ. Similarly, we also found a single-nucleotide deletion within the blaR1 gene (c.466delA) in seven isolates that introduced a premature stop codon and suppressed blaR1 expression. Previous study has investigated the effect of different blaZ types and polymorphisms at specific amino acid positions of BlaZ on the CzIE [blaZ and blaR1 genes, respectively. This finding indicates that single-nucleotide deletions might play a significant role in penicillin susceptibility of MSSA. However, the role of nonsynonymous amino acid substitutions in penicillin susceptibility should be investigated further in the future.Pan-susceptibility was identified in over a half of the MSSA-PENfections . Cefazolfections , which hfections . Interesthe CzIE . In addiin vivo. For this purpose, we compared the virulence of MSSA-PENS and MSSA-PENR isolates. Interestingly, MSSA-PENS isolates did not exhibit attenuated virulence. Due to its antimicrobial sensitivity, infections caused by MSSA-PENS isolates are easier to treat than those caused by more-resistant strains. However, based on our study, we propose that the danger posed by MSSA-PENS isolates should not be underestimated, as they can cause infections as severe as those caused by MSSA-PENR isolates.We also investigated whether higher antibiotic sensitivity was associated with less aggressiveness S. aureus infections using broad-spectrum antimicrobials might have led to penicillin becoming a “forgotten” antimicrobial in clinical treatment, and the heavy reliance upon vancomycin has also provided opportunities for the emergence of these sensitive isolates. Alternatively, the emergence of novel successful penicillin-susceptible S. aureus clones may provide another possible explanation for the increasing rate of MSSA-PENS. Continuous investigations should be performed to clarify whether some successful penicillin-susceptible S. aureus clones have emerged, which may be responsible for the increasing rates of MSSA-PENS. Taken together, our integrated epidemiologic analysis and characterisation of the MSSA-PENS isolates indicated that penicillin susceptibility is worthy of research attention and that more-sensitive strains should not be mistakenly considered to lack aggressiveness in vivo. Considering the progressive increase in MSSA in BSIs, our data support the recommendation to clinicians regarding the usage of penicillin in invasive BSIs caused by MSSA-PENS isolates, which might create a novel opportunity for better antimicrobial stewardship in the future.In conclusion, like other developed countries such as Sweden, the USA and Spain, China has also experienced a resurgence of penicillin susceptibility among MSSA strains. In this study, we confirmed increased penicillin susceptibility among MSSA isolates obtained from patients with invasive BSIs. Although variable, in some provinces, more than 10% the MSSA isolates were susceptible to this conventional agent. To date, the reasons for the resurgence of penicillin susceptibility remain unknown. Long-term empiric therapy of Click here for additional data file.