Solomon  C9orf72 that causes the most common genetic form of amyotrophic lateral sclerosis and frontotemporal dementia (C9ALS/FTD). Functional studies in cell and animal models have identified pathogenic mechanisms including repeat-induced RNA toxicity and accumulation of G4C2-derived dipeptide-repeat proteins. The role of TDP-43 dysfunction in C9ALS/FTD, however, remains elusive. We found G4C2-derived dipeptide-repeat protein but not G4C2-RNA accumulation caused TDP-43 proteinopathy that triggered onset and progression of disease in Drosophila models of C9ALS/FTD. Timing and extent of TDP-43 dysfunction was dependent on levels and identity of dipeptide-repeat proteins produced, with poly-GR causing early and poly-GA/poly-GP causing late onset of disease. Accumulating cytosolic, but not insoluble aggregated TDP-43 caused karyopherin-α2/4 (KPNA2/4) pathology, increased levels of dipeptide-repeat proteins and enhanced G4C2-related toxicity. Comparable KPNA4 pathology was observed in both sporadic frontotemporal dementia and C9ALS/FTD patient brains characterized by its nuclear depletion and cytosolic accumulation, irrespective of TDP-43 or dipeptide-repeat protein aggregates. These findings identify a vicious feedback cycle for dipeptide-repeat protein-mediated TDP-43 and subsequent KPNA pathology, which becomes self-sufficient of the initiating trigger and causes C9-related neurodegeneration.Accumulation and aggregation of TDP-43 is a major pathological hallmark of amyotrophic lateral sclerosis and frontotemporal dementia. TDP-43 inclusions also characterize patients with GGGGCC (G4C2) hexanucleotide repeat expansion in  C9orf72) (TARDBP) are a histopathological hallmark of 97% of ALS and 45% of FTD cases  and frontotemporal dementia (FTD) are devastating neurodegenerative diseases for which no cure is available (TDP-43 is an RNA binding protein with two RNA recognition motifs (RRMs), a nuclear localization (NLS) and nuclear export signal, and a C-terminal low complexity domain with prion-like properties that harbours most of the mutations associated with familial forms of ALS and FTD . ImbalanTDP-43 proteinopathy is associated with the majority of C9ALS/FTD cases that are also characterized by RNA foci of G4C2 hexanucleotide repeats and sequestered RNA binding proteins  resulting in a vicious cycle of increasing TDP-43 and KPNA mislocalization and dysfunction, which is crucially also observed in C9ALS/FTD and sporadic FTD post-mortem brain tissue.Here we identify a vicious feedback loop that mediates C9ORF72-related neurodegeneration. Using novel For detailed materials and methods, please see the online Oregon R; 1118w; Actin-Gal4; GMR-Gal4; C155-Gal4Elav; C155Elav;UAS-mCD8-GFP; Gal80ts; Fkh-Gal4; UAS-mCD8::GFP; UAS-hTDP-43; UAS-Q331K-TDP-43 8A,UAS-DsRed2-(G4C2)32, UAS-DsRed2-(G4C2)56, UAS-DsRed2-(G4C2)64, UAS-DsRed2-(G4C2)128B, UAS-DsRed2-(G4C2)8B, UAS-DsRed2-(G4C2)38, UAS-GA8, UAS-GA64, UAS-GR8, UAS-GR64, UAS-PR8, UAS-PR64, UAS-PA8, UAS-PA64. G4C2 constructs were established that contained DsRed2 upstream of either (G4C2)8, (G4C2)38 or (G4C2)56. To generate plasmids containing longer repeat lengths, a further plasmid containing (G4C2)4 repeats within the 3′ UTR was generated. Two copies of this G4C2 plasmid were then differentially digested such that subsequent ligation created a plasmid containing a doubled G4C2 repeat sequence. This strategy was used to generate plasmids containing 8, 16, 32, 64 and 128 G4C2 repeats. The presence and size of G4C2 DNA within the fly genome was confirmed by Southern blotting, which produced expected results for all UAS lines except those containing 128 repeats, which displayed repeat instability (UAS-hTDP-43) and of flies carrying genomic construct ΔNLS-TBPH was carried out as previously described .15 at the C-terminus. Peptide generation and immunization was carried out by Eurogentec. Mouse monoclonal anti-polyGP (clone 2C20) was generated by immunization of mice with the custom-made peptide sequence, GPGPGPGPGPGPGPGPGPGP (GPx10), done by Abmart.To generate rabbit polyclonal anti-polyGP, a custom-made peptide sequence—GPGPGPGPGPGPGPGPGPGPGPGPGPGPGP (GPx15)—was fused to the C-terminus of maltose-binding protein. Two rabbits were immunized with the fusion protein and the resulting serum was purified with GST-fusion proteins containing (GP)Drosophila samples, heads from mated female flies were removed by snap freezing in liquid nitrogen and were then homogenized in RIPA buffer. Total protein concentration was measured using the BCA kit (Thermo Scientific). For detection of insoluble protein, samples were lysed in RIPA buffer and then spun at 16 000g for 10 min, after which the supernatants were separated from the pellets and prepared for western blot. The pellets were washed three times with RIPA buffer then resuspended in urea buffer. For details see the For Dot blots were used to detect DPRs using the protocol described previously (in situ hybridization (FISH) was carried out as described previously , rabbit polyclonal anti-GP , mouse monoclonal anti-GA , anti-GR rat monoclonal , mouse anti-HA clone 6E2 , rabbit anti-TBPH (1:2000)  was carried out following Activity tracking was carried out as previously described . Consent for autopsy, neuropathological assessment and research were obtained and all studies were carried out under the ethical approval of the tissue bank (08/MRE09/38+5). Block taking for histological and immunohistochemical studies and neuropathological assessment for neurodegenerative diseases was performed in accordance with standard criteria. Fifteen cases were used for the western blot experiments, five FTLD-TDP without the 2, 1 mM EGTA, 1 M NaCl, 50 mM imidazole, protease inhibitor cocktail (Roche)]. The homogenate was mixed with 2% SDS-PAGE loading buffer and boiled for 10 min. Samples were centrifuged for 20 min at 13 000 rpm and 4°C. Equal volumes of samples were loaded on 26-well NuPAGE® Novex 10% Bis–Tris pre-cast gels (Invitrogen). Western blots were performed as described  ethane sulphonic acid (MES) (pH 7.4), 0.5 mM MgCl2O2. Sections were counterstained with Harris’ haematoxylin. Immunostaining  was examined and assessed by a consultant neuropathologist (T.H.) blind to diagnosis.For immunohistochemistry and immunofluorescence of post-mortem human brain samples, the protocols were carried out as previously described  test or the Bonferroni-Holm method , mid-aged (Day 20) and older (Day 40) flies. Following mechanical disruption, flies were first tested for their SING response, which, after being overthrown, quantifies their ability to right themselves and climb up the test tube F–J. By DTo rule out that these differences in toxicity were due to potential differences in Gal4/UAS expression levels , flies were generated harbouring two copies of the 64 repeat construct that produce significantly higher levels of DsRed2 protein F. SING aDrosophila TDP-43 homologue  poly-GR64 expression caused an extensive diffuse cytoplasmic accumulation of TBPH  under control of the endogenous TBPH promoter A. ExpresTo test this hypothesis further, we co-expressed ΔNLS-TBPH with 38 and 64 G4C2 repeats and measured the motor behaviour of these flies compared to controls. SING analysis revealed that by Day 5, no significant impairment in the motor behaviour of 64,ΔNLS-TBPH flies was detectable, but exacerbated motor impairment in 38,ΔNLS-TBPH flies at this time point when compared to 38 repeat only flies and controls B. NotablSeveral recent studies reported NCT and NPC deficits in G4C2 RNA and DPR expression models  and pendulin (KPNA2) in flies expressing poly-GA64 or poly-GR64, but not in controls, nor in G4C2 RNA only. Of note, expression of poly-GR64, but not of poly-GA64, also caused nuclear depletion of both importin-α3 and pendulin, whereas expression of poly-GA64 resulted in cytoplasmic inclusions of importin-α3 and pendulin that overlap with poly-GA A and B.Drosophila homologue of Regulator of chromosome condensation 1, a guanine nucleotide exchange factor for Ran GTPase, whereas we detected cytoplasmic dRCC1 inclusions that overlap with poly-GA  in nuclear pore proteins Nup62, Nup153, Nup214 and Nup358, we did not observe any differences between controls, 288RO (G4C2 RNA only), poly-GR64 or poly-GA64 expression in all cases examined (n > 10), despite the presence of DPR inclusions .The results of our experiments signify that DPR-triggered dysfunction of TDP-43 and in turn of KPNAs propagate onset and progression of C9-related neurodegeneration. Consequently, several predictions can be deduced and their validity tested. First and foremost, the reported findings translate into human patients and predict KPNA pathology can be found in C9ALS/FTD but also in sporadic cases devoid of G4C2 repeat expansion. Second, the vicious cycle of DPR-initiated TDP-43 and KPNA pathology predicts that both DPR and TDP-43 inclusions correlate with KPNA pathology, but all three may not be found in the very same cell. To test these predictions, we focused on KPNA4 and examined its expression level and localization in post-mortem human frontal cortex of C9ALS/FTD and sporadic FTD cases with TDP-43 pathology (sFTD-TDP), and compared it to age-matched controls (each Analysis of RIPA and urea protein fractions revealed insoluble aggregates remaining in the wells and lowered expression levels of soluble KPNA4 in both sFTD-TDP and C9ALS/FTD, but not in controls A and B. Drosophila strongly suggest that TDP-43 mislocalization is not just a consequence of defective nuclear import but rather a direct contributor to it.The observed KPNA4 pathology overlapped with immunoreactivity against phosphorylated TDP-43 , arrows.C9orf72-related neurodegeneration (Our findings reported here provide experimental and pathological evidence for a sequence of events in which DPR-triggered TDP-43 accumulation causes KPNA pathology that precedes neration C. TimingA hallmark of TDP-43 proteinopathies is their close correlation between observed clinical phenotypes, degree of neurodegeneration, and regional distribution and severity of TDP-43 pathology . A notabDrosophila (Q331K leads to formation of SDS-stable oligomers, which may start sequestering surrounding proteins, such as importin-α3. These data suggest that wild-type and mutant TDP-43 overexpression can cause a KPNA phenotype in the absence of DPR pathology, resulting in nuclear depletion and cytoplasmic mislocalization of the KPNAs. However, a limitation when considering the human condition is that some of the conclusions of the study derive from results obtained using constructs that are artificially expressed in fruit flies. Nevertheless, our data indicate that G4C2 RNA and/or DPRs act as initiating stressors causing TDP-43 dysfunction that mediates C9-related neurodegeneration, with NCT and NPC deficits occurring later during disease progression.Such a sequence of events is in agreement with the recently discovered NCT and NPC defects in C9ALS/FTD (Drosophila TDP-43, which caused KPNA4 pathology in the absence of NPC pathology (In our early onset models of disease, we observed specific KPNA pathology but found no evidence for alterations of other NCT or NPC components such as RanGAP, RCC1 and NPC proteins such as MAB414 and Nup50. These findings are in agreement with a recent study that did not find any evidence for RanGap, lamin B1 and importin β1 pathology in C9ALS cases in which poly-GR pathology was associated with site of disease and TDP-43 pathology (athology E. Togethathology B, the moThis pathogenic cascade C suggestC9orf72-related neurodegeneration.Taken together our findings establish DPR accumulation as a cause of TDP-43 proteinopathy and suggest a vicious feedback cycle for excess cytosolic TDP-43 by which enhanced DPR levels enhance KPNA dysfunction and TDP-43 mislocalization, thereby becoming self-sufficient of the initiating trigger. Unlike G4C2 RNA foci and the vast majority of DPR inclusions (Supplementary MaterialClick here for additional data file.


CXCL16 is the only ligand for the chemokine receptor CXCR6, which mediates NKT cell survival and accumulation in the liver . Accumulation of PGE2 suppressed anti-tumor immunity through a PTGER4 receptor on CD8 cells, thereby contributing to HCC progression   . Because (PD-L1) , the stu (PD-L1) . Recent  (PD-L1) , 15. On Cxcl16 expression is still unclear. CXCL16 is a small cytokine with a C-X-C motif with an O-glycosylated mucin-like stalk, a transmembrane helix and a cytoplasmic domain with a potential tyrosine phosphorylation site. These features allow CXCL16 to be expressed as a soluble chemokine as well as a cell surface-bound molecule  levels were inversely correlated. Furthermore, mucosal-associated invariant T (MAIT) cells, which are prevalent in human liver, can also express CXCR6 that can bind CXCL16. This evidence suggests that the current study could be translated into clinical practice. cancer. However, comprehensive analysis of human liver tissue is necessary for clinical application considering the differences between humans and mice.This study provided a comprehensive analysis of the relationship among the gut microbiome, the immune system, and liver cancer. However, this research invokes three related questions. First, the mechanism of bile acids regulating molecule . Yoshimomolecule , 9. The  strains . While DBJ wrote the commentary.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Flexible plasmonic devices with electrical tunability are of great interest for diverse applications, such as flexible metamaterials, waveguide transformation optics, and wearable sensors. However, the traditional flexible metal–polymer plasmonic structures suffer from a lack of electrical tunability. Here the first flexible, electrically tunable, and strain‐independent plasmons based on graphene–mica heterostructures are experimentally demonstrated. The resonance frequency, strength, quality factor, electrical tunability, and lifetime of graphene plasmons exhibit no visible change at bending radius down to 1 mm and after 1000 bending cycles at a radius of 3 mm. The plasmon‐enhanced infrared spectroscopy detection of chemicals is also demonstrated to be unaffected in the flexible graphene–mica heterostructures. The results provide the basis for the design of flexible active nanophotonic devices such as plasmonic waveguides, resonators, sensors, and modulators. The mica thin sheet acts as a highly flexible and transparent substrate with atomically flat surface for graphene.2FigureThe working principle of our flexible graphene plasmonic device is illustrated in T = 1 − ETF/TCNP, where TCNP and ETF are the transmission spectra detected at the charge neutral point (CNP) and EF of graphene, respectively.The plasmonic properties of graphene were characterized by Fourier transform infrared microscopy (FTIR). In GNRs, localized plasmon can be directly excited due to the wave‐vector match between external free photons and surface plasmon polaritons via ribbon edge reflection.FigureWe performed a series of flexibility tests of our devices. Q) are the three most important indicators in practical applications because they are strongly associated with the enhancement and confinement of local electric field which are at the heart of sensors and other nano‐optical devices.−1. These values are very small compared to the resonance frequency (≈1600 cm−1) and the full width at half maximum  of the plasmon resonance peak , extinction intensity (Ext.), and quality factor  imaging of the device.FigureTo understand the experimental results, we conduct simulation   varies from 0.5 to 6.5 V), with increasing plasmon intensity. The shallow dips on the spectra, located at the molecular vibrational modes of ion–gel, stem from their interaction to the graphene plasmons.Plasmonic properties were investigated during and after different bending cycles. Even after experiencing 1000 bending cycles, the device was able to effectively excite plasmon and show unaffected electrical tunability. Figure −1 which is only 6.4% of the tunable bandwidth (from 1275 to 1680 cm−1). The extinction intensity of plasmon is proportional to Fermi level due to more carriers involved in the resonance oscillation, as shown in Figure The effects of bending on the electrical tunability of the graphene plasmon are concluded in Figure 0/λp). It is crucial in a wide range of applications from biochemical sensors to deep‐subwavelength metamaterialsp) are extracted following refs. Figure0/λp is as high as 48 with resonant energy at 1300 cm−1 (|EF| = 0.3 eV), which is slightly higher than that of plasmon on SiO2  at same Fermi level and with same ribbon width . The ratio of λ0/λp increases as the Fermi level decreases following the graphene plasmon dispersion relation of E∝EF1/2. The plasmon lifetime can be calculated via t = 2ħ/Γ, where Γ is the FWHM of the resonance peaks extracted from the far field extinction spectra using Fano curve fitting .2 at different Fermi levels with the same ribbon width . The plasmonic lifetime decreases as the resonance energy increases, which relates to the plasmon–phonon coupling.−1 (1168 cm−1) in our mica (SiO2) substrate, which resulted strong plasmon–phonon coupling in our graphene–mica (graphene/SiO2) devices . The plasmon–phonon coupled modes have longer lifetime as they approach the phonon energy since they gain more properties from the phonons.2 substrate was about 100 fs, however, the lifetime of our graphene–mica heterostructure approaches 800 fs at around 1300 cm−1. This is mainly due to the long lifetime of phonons in mica for its 2D crystalline structure which results less damping paths than the SiO2 substrate. Another feature is that the plasmon lifetime of our graphene–mica device is still larger than that of the graphene/SiO2 in the higher frequency range (>1580 cm−1) where the effects of substrate phonon fades. This lower damping may contribute to the atomic flat surface of mica, which introduces less electron–electron scattering.Graphene plasmon has been demonstrated to possess ultrahigh Field localization (wave “shrinkage”) which is defined as how small the supported plasmon wavelengths are compared to the free space wavelength . The λp in our graphene–mica heterostructure is 196 nm, corresponding to a wavelength confinement of ≈57, while the λp of the graphene‐SiO2 device is 220 nm and the confinement of ≈51. This is consistent with our far‐filed experimental observations. The inverse damping ratio γp−1 = Re (qp)/Im (qp) is also calculated by fitting with the method described by Woessner et al.p−1 of graphene on mica is 12.2 which is approximately two times larger than that on SiO2, which is in accord with the lifetime results from the far‐field calculation. This is because graphene on the atomically flat mica surface has reduced electron scattering loss and thus small plasmon damping, similar to the case of the graphene‐BN heterostructure. It is reasonable to deduce that graphene encapsulated between two mica films could realize lower plasmon damping combined with stronger field confinement.We also performed the real‐space imaging of plasmon fields of the flexible graphene–mica devices, which further demonstrated the far‐field spectral results. Mechanically exfoliated graphene is used to obtain better near‐field signal . Figure 2)S + δ(CH2)a and II: δ(CH2)S − δ(CH2)a) as the probe to illustrate the SEIRA function of the flexible devices. The plasmon‐induced molecular vibrational signals are extracted from the extinction spectra with graphene Fermi level of 0.65 eV following the method in Ref.  approaches the modes I and II, and the enhancement largely increases. As the |EF| increases or decreases, the plasmon resonance peak moves away from the modes I and II, and the enhanced vibrational signals decrease. The peak areas of these enhanced vibrational signals are plotted in Figure vMode − vRes| values decrease . These results strongly demonstrate the stability and duration of our flexible graphene–mica heterostructure based plasmonic devices for the SEIRA applications.The plasmon‐induced molecular vibrational signals of the modes I and II are also extracted from the extinction spectra with 3 mm bend radius at different Fermi levels before bending testing, after 500‐cycle and 1000‐cycle bending. The set of spectra after 1000 bending cycles are representatively displayed in Figure 3In conclusion, flexible and electrically tunable plasmonic devices were demonstrated with our graphene–mica heterostructure for the first time. The graphene–mica devices exhibit high flexibility (with bending radius down to 1 mm). High mechanical durability and electrical tunability have also been demonstrated due to the excellent mechanical properties of graphene and mica. Electromagnetic simulation reveals that the bending radius can be down to about 150 nm with negligible effects on the graphene plasmon response since the ultrahigh confinement of graphene introduces weak coupling or damping in the bending structures. Using infrared vibrational modes of ion–gel as probe analyte, the SEIRA function of the flexible devices at different bending conditions are demonstrated, which is unaffected by bending. Our graphene–mica heterostructure strategy provides an excellent platform for photonic devices such as wearable sensors,4Fabrication of Flexible Graphene–Mica Plasmonic Device: Graphene was grown on copper foil via chemical vapor deposition and was then transferred on to mica substrate by a common wet transfer technique. Next, ethyl lactate and about 350 nm thick PMMA solution was spin coated on to a Si substrate and the prepared graphene–mica sheet was fastened onto this Si substrate before the solvent of PMMA was dried because it was supplied as a bonding layer. The sample was baked at 40 °C for 30 min until it was completely dry. Another 80 nm PMMA layer was spin coated on graphene–mica to be a mask and a 30 nm Al film was deposited on it as a conductive layer. GNR arrays were patterned via 100 keV EBL . The sample was soaked in the alkali solution to remove the surface aluminum layer. The exposed PMMA was developed in one‐to‐three methyl isobutyl ketone (MIBK)/ isopropanol for 60 s. Then exposed graphene was etched away using oxygen plasma (5 Pa and 100 W for 2 s). Electrodes (5 nm Cr and 80 nm Au) were made using another EBL process and electron beam evaporation. The ion–gel dielectric material was prepared by our previous work.Characterization of Flexible Graphene Plasmonic Device: The morphology and size of GNRs were characterized by SEM (Hitachi S‐4800) and AFM (Bruker Dimension Icon). The defects of GNRs were confirmed by Raman spectroscopy (Horiba Jobin Yvon LabRAM HR800). Electrical transport measurements were performed using a semiconductor parameter analyzer (Keithley 4200‐SCS) at room temperature in atmosphere. Infrared transmission measurements were performed by a FTIR microscopy (Thermo Fisher Nicolet iN10) with T = 1 − ETF/TCNP, where TCNP and ETF are the transmission spectra detected at the CNP and EF of graphene, respectively. The nanoimaging experiments were performed using a commercial s‐SNOM (Neaspec GmbH), with wavelength‐tunable lasers (900–1000 cm−1).Simulation Methods: Electromagnetic simulations were conducted using a Finite Elements Method with periodic boundary conditions. Graphene was modeled as a 2D surface with complex conductivity from Kubo formula which consisted of interband and intraband transitions; the expression is approximated asω=2πυ, e is electron charge, ℏ is the reduced Planck constant. The relaxation time τ=μEF/evF2, Fwhere v = c/300 is the Fermi velocity and µ = 1000 cm2 V−1 s−1 is the carrier mobility of graphene.FE is the graphene Fermi energy which is 0.5 eV and the ribbons width W of graphene is 60 nm.The authors declare no conflict of interest.SupplementaryClick here for additional data file.


However, the role of Foxc2 and BMP2 in regulating osteo-/odontogenic differentiation and mineralization of stem cells from apical papilla (SCAP) is still uncertain. In this research, overexpression of Foxc2 gene significantly improved the proliferation of SCAP four days and eight days after transfection, but overexpression of both Foxc2 and BMP2 genes significantly inhibited the proliferation of SCAP eight days after transfection. RT-qPCR and western blot results indicated that SCAP-Foxc2-BMP2 significantly upregulated osteo-/odontogenic genes and proteins at most of the time points in SCAP after transfection. Moreover, SCAP-Foxc2-BMP2 formed notably more alkaline phosphatase-positive and alizarin red-positive mineralized nodules than other three group cells sixteen days after transfection. In conclusion, our findings revealed that Foxc2 and BMP2 synergistically promoted osteo-/odontogenic differentiation and mineralization of SCAP  How to effectively regenerate dentin and repair tooth defects is the key problem in dental regenerative medicine . Tissue β) superfamily, is crucial in dental papilla growth and development [Dentin development is the result of interaction between dental epithelium and mesenchyme, and multiple molecular pathways are involved in regulating this process . Bone moelopment , 8. BMP2elopment , 6, 9. Welopment . In our elopment , 6, BMP2Forkhead c2 (Foxc2) is a member of the winged spiral transcription factor family. Foxc2 participates in promoting cell proliferation , 12, ostIn the current research, we constructed Foxc2 and BMP2 gene-modified SCAP by lentivirus transfection technique and then investigated the synergistic effects of Foxc2 and BMP2 in inducing osteogenic and odontogenic differentiation of human SCAP.n = 12) were collected from young patients (18 to 20 years old) in the Oral and Maxillofacial Surgery Department, Sun Yat-sen University. The experiment protocol of the current research was proven by the Ethical Review Committee of Sun Yat-sen University. Freshly extracted third molars were rinsed by PBS, and the apical papillae were isolated by surgical instruments. Sequentially, SCAP were minced and treated with enzymatic digestion according to our previous reports [A few sound mandibular 3rd molars  was applied to analyze the proliferation of those cells on the 1st, 2nd, 4th, and 8th days after transfection.Four group cells were seeded on 96-well plates with initial density of 2 × 10Total RNA was extracted by TRIzol  from the four group cells on the 1st, 4th, 8th, and 16th days after lentiviral transfection. The cDNA synthesis was performed by Revert Aid first strand cDNA synthesis kit . RT-qPCR reactions were conducted by iQ SYBR Green Supermix  and regulated by spectrofluorimetric iQ5 thermal iCycler . The PCR primer sequences were presented in First, proteins were extracted from the four group cells, and then proteins were separated by 12% sodium dodecyl sulfate polyacrylamide gels . The separated proteins were then transferred onto PVDF membranes  at 200 mA for 2 hours. The 5% nonfat milk was used to block the PVDF membranes for 2 hours. Subsequently, the PVDF membranes were incubated with the primary antibodies overnight at 4°C. The primary antibodies include rabbit polyclonal anti-foxc2 , rabbit polyclonal anti-BMP2 , and rabbit polyclonal anti-dentin sialoprotein . After that, the PVDF membranes were incubated with a horseradish peroxidase-conjugated goat anti-rabbit antibody  at 37°C for 2 hours. The rabbit polyclonal anti-GAPDH  was chosen as the control. The chemiluminescence western blotting detection system (EMD Millipore) was used to visualize the resultant bands.4 cells/well. ALP kit  and alizarin red kit  were applied to the cells to visualize ALP expression and mineralized granules on the day 16.The four group cells were seeded on 6-well plates with the density of 5 × 10P value < 0.05.One-way ANOVA test was performed to analyze the differences of the four group cells by SPSS 20.0. Statistical significance was set as The papillae, as showed in P > 0.05).The cells in the four groups showed very high GFP expression percentage four days after transfection . The relP < 0.05). On the 8th day, SCAP-Foxc2-BMP2 displayed the weakest proliferation status (P < 0.05).As showed in P < 0.05) (P < 0.01)  . Western Figures .P < 0.01). The number of mineralized nodules formed by SCAP-BMP2 was significantly more than that formed by SCAP-foxc2 and SCAP-GFP (P < 0.05)  . The numThe mineralized nodules were stained as red . The posin vitro. Moreover, those isolated cells presented typical phenotype of SCAP [Dental apical papilla directly contributes toward the formation of the tooth roots . As a ty of SCAP , which wβ1. Kim et al. [β-catenin pathway. In the current research, the Foxc2 gene-modified SCAP upregulated the expression of osteo-/odontogenic markers, including ALP, OCN, DSPP, and DMP1. Similar phenomenon was also observed in other Foxc2-modified cells, such as bone marrow mesenchymal stem cells [Foxc2 gene, which is necessary for mesenchymal-epithelial interactions during craniofacial development –27, has m et al.  reportedem cells , 30, myoem cells , and ostem cells .in vitro, indicating that Foxc2 gene participated in regulating of mineralization process. Additionally, the mineralization capability of SCAP-Foxc2-BMP2 was significantly stronger than that of the other three groups. Mineralized nodules formed by SCAP-Foxc2-BMP2 cells were about 3 times more than those of SCAP-BMP2, about eleven times more than those of SCAP-Foxc2, and about twenty-two times more than those of SCAP-GFP. These results suggest that Foxc2 and BMP2 synergistically promote osteo-/odontogenic differentiation and mineralization of SCAP.Furthermore, our research presented that lentivirus-mediated Foxc2 gene transfection improved the ability of SCAP to form mineralized nodules in vitro [β1 [A few studies discovered that Foxc2 could stimulate proliferation of some cells. In myoblasts, decreased endogenous Foxc2 reduced cell proliferation activity, while increased expression of Foxc2 promoted cell proliferation . In ostein vitro , and Foxitro [β1 . Consistitro [β1 , 32, ouritro [β1  and meseitro [β1 . Howeveritro [β1 . So, it in vitro. These results have a positive impact on the clinical practice of tooth regeneration. The future study could focus on the interaction between these two genes and the mechanism of their cooperative function on SCAP. Moreover, in vivo studies could further support this discovery.In summary, our research indicated that Foxc2 and BMP2 genes could act synergistically to improve osteo-/odontogenic differentiation and mineralization of SCAP


Human papillomavirus (HPV) is linked to nearly all cases of cervical cancer. Despite available vaccines, a deeper understanding of the immune response to HPV is needed. Human α-defensin 5 (HD5), an innate immune effector peptide, blocks infection of multiple sero-types of HPV, including high-risk HPV16. While a common mechanism of α-defensin anti-viral activity against nonenveloped viruses such as HPV has emerged, there is limited understanding of how α-defensins bind to viral capsids to block infection.We have used cryo-electron microscopy (cryoEM), mass spectrometry (MS) crosslinking and differential lysine modification studies, and molecular dynamics (MD) simulations to probe the interaction of HPV16 pseudovirions (PsVs) with HD5.CryoEM single particle reconstruction did not reveal HD5 density on the capsid surface. Rather, increased density was observed under the capsid shell in the presence of HD5. MS studies indicate that HD5 binds near the L1 and L2 capsid proteins and specifically near the C-terminal region of L1. MD simulations indicate that favorable electrostatic interactions can be formed between HD5 and the L1 C-terminal tail.A model is presented for how HD5 affects HPV16 structure and cell entry. In this model, HD5 binds to disordered regions of L1 and L2 protruding from the icosahedrally ordered capsid. HD5 acts to cement interactions between L1 and L2 and leads to a closer association of the L2/genome core with the L1 capsid. This model provides a structural rationale for our prior observation that HD5 interferes with the separation of L1 from the L2/genome complex during cell entry. Human papillomavirus (HPV) infections are a major health burden. HPV is the most common sexually transmitted infection, causing a range of conditions including genital warts and cancer. While most HPV infections do not cause major health concerns, persistent genital HPV infection is linked to nearly all cases of cervical cancer, in addition to other types of cancers in men and women . We. We74]. We then determined whether positioning HD5 within depressions between L1 pentamers is consistent with our crosslinking mass spectrometry (CX-MS) results. We assumed a maximum distance of 3 nm between Cα atoms of BS3 crosslinked lysine residues . We alsoCX-MS indicates additional crosslinks between L1 (residues K484 and K486) and HD5 . Since tThe mass spectrometry acetylation data is also consistent with the structural model derived from the CX-MS and cryoEM results and MD simulations with an HD5 dimer positioned within a depression between L1 pentamers. There is 1 L1 residue (K467) which displays enhanced acetylation in the presence of HD5. As mentioned above, residue K467 is located within the internal cavity of an L1 pentamer near an observed L2 binding site . We propAlthough there is no high-resolution structural information for L2, it is known that the L2 N-terminal region and various additional segments in the middle of L2 are transiently exposed on the surface of the virion , 68-73. The MD-derived model of the HD5 dimer and the C-terminal 6 residues of L1 is shown in the same orientation with respect to the HPV capsid in all panels of An overview of the differential lysine modification data for an L1 pentamer is shown in not bind homogeneously to the well-ordered region of the L1 capsid. Nevertheless, the cryoEM results do show the effect of HD5 binding in stabilizing the capsid/core interaction with increased core density in the HPV16+HD5 cryoEM structure  would not be expected to inform our model, since it has been shown that L2 is required for DNA packaging in HPV16 , 31. It


We demonstrate a dual-band plasmonic perfect absorber (PA) based on graphene metamaterials. Two absorption peaks (22.5 μm and 74.5 μm) with the maximal absorption of 99.4% and 99.9% have been achieved, respectively. We utilize this perfect absorber as a plasmonic sensor for refractive index (RI) sensing. It has the figure of merit (FOM) of 10.8 and 3.2, and sensitivities of about 5.6 and 17.2 μm/RIU, respectively. Hence, the designed dual-band PA-based RI sensor exhibits good sensing performance in the infrared regime, which offers great potential applications in various biomedical, tunable spectral detecting, environmental monitoring and medical diagnostics. Surface plasmon resonance (SPR) is the interaction between electromagnetic fields and free electrons in metals . With thHowever, at present, many works only operate an a single wavelength, which greatly hinders their application in practice. In many applications, when multiple absorption bands can be monitored, it is convenient to correlate and study structural changes between different molecular regions and is critical to the accurate identification of molecular species ,23,24,25λ1 = 22.5 μm and λ2 = 74.5 μm with high absorption coefficients of 99.9% and 99.4% respectively. The spectral location of the absorption values can be adjusted actively by tuning the graphene’s Fermi level without changing the geometric parameters of the structure. Besides, the resonant wavelengths of two modes are very sensitive to changes of environmental refractive index. It has the figure of merit (FOM) of 10.8 and 3.2, and sensitivities of about 5.6 and 17.2 μm/RIU (refractive index unit) respectively. The PA-based RI sensor has narrower FWHM  than an ordinary absorption sensor, which means the RI sensor has better sensing performance. Hence, the proposed PA-based RI sensor can offer great potential applications in biomedicine, environmental monitoring and medical diagnostics.To this end, we theoretically design a dual-band plasmonic perfect absorber (PA) based on graphene metamaterials in the infrared regime. We obtain two absorption peaks located at x axis. In the FDTD simulation calculations, the accuracy of mesh in the graphene layer along the x (y) and z axes are set to 25 and 0.2 nm. The anti-symmetric and symmetric boundary conditions were respectively adopted in the x and y directions. The boundary conditions of the perfectly matched layer are used in the z direction along the propagation of the incident plane wave.The finite-difference time-domain (FDTD) method is employed to analyze the optical response in the graphene structures. TM-polarization indicates that the direction of the incident electric field is along the n1 = 3.4) layer, a gold mirror, a SiO2 (n2 = 1.97) layer and a periodical graphene pattern tightly stacked to form this structure. Since this structure excites the local surface plasmons, the graphene surface plasmon is confined to the graphene surface—that is to say it does not pass through the SiO2 or even reach the gold surface. What really reaches the gold surface should be part of the incident light. One part of the light in the incident light interacts with the graphene. The other part passes through the graphene to reach the gold surface, which is reflected back by the gold and then lost in the SiO2. We chose the lowermost layer as the most common material, Si [The geometry of the PA-based RI sensor is depicted in rial, Si . Its funRin and Rout are the radius of the inner and outer rings of graphene, respectively. d1, gd, d2, and t represent the thickness of Si, gold mirror, SiO2 and graphene, respectively. In our calculation, the geometrical parameters are set as d1 = 3 μm, dg = 0.45 μm, d2 = 4.17 μm, t = 1 nm, Rin = 0.8 μm, Rout = 1.05 μm, D = 0.2 μm, L = 0.7 μm, G = 0.25 μm, and W = 0.25 μm. The periods in both x and y directions are 2.5 μm. In the whole absorber structure, interspacing of unit elements is 0.4 μm. The total size of each manufactured device is 100 μm.The geometric structure we designed is simple and easy to manufacture. At the same time, considering manufacturing tolerances, we should allow the gap between unit cells to be greater than 100 nm. In each unit cell, the top graphene-pattern composes of a graphene ring and four rectangular graphene bands. 2 spacer and Si substrate are respectively coated on the upper and lower surfaces of Au through thermal evaporation, and then the monolayer graphene is coated on the top of the SiO2 spacer after a chemical vapor deposition (CVD), finally the cavity structures are fabricated on the monolayer graphene by electron beam etching.The designed graphene metamaterials may be realized experimentally by the following procedures : the SiOwp = 1.37 × 1016 rad/s, e¥ = 1, and the damping constant gc = 1.224 × 1014 rad/s [w is the angular frequency of the incident electromagnetic wave. The surface conductivity of signal-layer graphene s can be described by the Kubo formula including interband and intraband transitions [kB, T and EF are the Boltzmann constant, temperature, and Fermi level, respectively. When EF >> kBT, the interband transition dominates and surface conductivity can be simplified as [e, ℏ and t represent the charge of an electron, reduced Planck’s constant, and the relaxation of time, respectively. The relaxation time t can be expressed as [τ is dependent on the electron mobility μ = 1 × 104 cm−2 V−1 s−1, and the Fermi velocity F =ν 1 × 106 ms−1. In this work, Fermi level (EF = 1.0 eV), and the relaxation of time (τ = 1.0 ps) are assumed. Under these conditions, the absorption response can be calculated by using the relation A = 1 − R [R (reflection) approaches zero, A can obtain perfect absorption.The complex dielectric constant of gold at in the infrared regime is described by the Drude model with the plasma frequency 14 rad/s ,29,30,31nsitions . kB, T aified as ,34,35:τ= = 1 − R . When th2 [As predicted from Equation (2), the surface conductivity of graphene can be tuned via manipulating its Fermi energy. At present, low top gate voltage can be used to increase the Fermi energy of graphene. Many gate dielectric materials such as 2D electron gas  indium t2  and ion-2  have bee2 ,44. Ther2 . The sch2 ,47,48.1 = 22.5 μm and λ2 = 74.5 μm, respectively. For Mode A (λ1 = 22.5 μm), the physical origin is related to the quadrupole resonances. For Mode B (λ2 = 74.5 μm), the opposite charges are concentrated on both sides of the ring (left and right sides), which indicates the excitation of dipole resonance in the graphene metamaterials array.For the whole structure, when parameters are set as mentioned above, two resonance responses with reflection and absorption are shown at around 22.5 μm and 74.5 μm in Rout) of graphene from 0.8 μm to 1.05 μm. For Mode A, the absorption peak hardly changes. For Mode B, the maximum absorption decreases and peak wavelength experiences a blue shift as the Rout increases. In L increases, the distance of graphene arrays in the x-direction decreases, resulting in an enhancement in the coupling between them. This is the reason why Mode A experiences red shift and absorption enhancement of when L increases. In D values also affect the absorption and peak wavelength. It can also be seen from D increases from 0.1 to 0.2 μm, the Mode A has experienced a red shift, and the absorption at D = 0.2 μm is maximum. When D increases to 0.25 μm, the positional shift of the peaks is no longer monotonous. This is because when D > 0.25 μm, the spacing between the internal structure and the inner ring is reduced, resulting in coupling enhancement and red shift.To further analyze the performance of dual-band absorber, we first study the effect of different geometric parameters by numerical simulation. D = 0.2 μm, the absorption reaches the maximum. In G increases, the absorption of Mode B hardly changes, while the absorption of Mode A slightly decreases and peak wavelength experiences a blue shift. In other words, the change of G only affects the resonant wavelength of Mode A.When P changed from 2.4 to 2.7 μm. For Mode A, the peak first experiences a blue shift and then undergoes a red shift. The maximum absorption can be achieved when P is equal to 2.4 or 2.5 μm. For Mode B, as the period varies, there is almost no change in absorption, but the absorption peak wavelength has a significant blue shift with the period increases. This is because the grating period is the determinant factor for the resonant frequency when the grating period P matches the period of the plasmonic wave [nic wave . The chanic wave .From EF) and relaxation time (τ). EF values (0.7–1.0 eV). The wavelengths of the absorption peaks have an obvious blue shift as the Fermi level EF increases, but Mode A moves slowly than Mode B. As a whole, high EF has a higher absorption relative to low EF. The physical mechanism is related to the absorption of graphene increases with the increase of graphene Fermi level EF and local electric field, and the enhancement of graphene SPR resonance.However, for many metal absorbers or sensors, once their structures are fixed, their resonant wavelengths will no longer be tunable. Hence, we investigate the absorption spectrum of the absorber with the change of Fermi level , it can be known that the value of τ can be easily adjusted by changing μ (the carrier mobility). There is reference to provide methods for increasing carrier mobility by altering the surrounding environment, such as placing organic molecules on graphene [τ is too large, the absorption will decrease due to most the energy being reflected. Therefore, as shown in τ increases. The absorption peaks can be saturated when EF = 1.0 eV and τ = 1.0 ps. The above result implies that the absorption spectrum can be tuned by changing Fermi level and relaxation time without changing the absorber geometry.graphene . More can and FOM = S/FWHM, respectively [To examine the sensing performance of the proposed dual-band PA, the dependence of the absorption spectrum on different surrounding refraction indices is calculated, with other parameters fixed  and refractive index (RI) sensor based on graphene metamaterials. The numerical results indicate that the absorber can achieve absorptions of 99.4% and 99.9% at 22.5 μm and 74.5 μm, respectively. Specifically, the absorption performance can be modulated by changing the Fermi level and the relaxation time without changing the geometric structure. Also, the PA-based RI sensor is highly dependent on the refractive index and has a large sensing range, which can achieve the sensitivities of around 5.6 and 17.2 μm/RIU, and FOM of around 10.8 and 3.2, respectively. We believe that our study could provide a potential application in biosensing, tunable spectral detecting, environmental monitoring and medical diagnostics.


Community detection is often used to understand the structure of large and complex networks. One of the most popular algorithms for uncovering community structure is the so-called Louvain algorithm. We show that this algorithm has a major defect that largely went unnoticed until now: the Louvain algorithm may yield arbitrarily badly connected communities. In the worst case, communities may even be disconnected, especially when running the algorithm iteratively. In our experimental analysis, we observe that up to 25% of the communities are badly connected and up to 16% are disconnected. To address this problem, we introduce the Leiden algorithm. We prove that the Leiden algorithm yields communities that are guaranteed to be connected. In addition, we prove that, when the Leiden algorithm is applied iteratively, it converges to a partition in which all subsets of all communities are locally optimally assigned. Furthermore, by relying on a fast local move approach, the Leiden algorithm runs faster than the Louvain algorithm. We demonstrate the performance of the Leiden algorithm for several benchmark and real-world networks. We find that the Leiden algorithm is faster than the Louvain algorithm and uncovers better partitions, in addition to providing explicit guarantees. Such a modular structure is usually not known beforehand. Detecting communities in a network is therefore an important problem. One of the best-known methods for community detection is called modularity3. This method tries to maximise the difference between the actual number of edges in a community and the expected number of such edges. We denote by ec the actual number of edges in community c. The expected number of edges can be expressed as Kc is the sum of the degrees of the nodes in community c and m is the total number of edges in the network. This way of defining the expected number of edges is based on the so-called configuration model. Modularity is given by4. Higher resolutions lead to more communities, while lower resolutions lead to fewer communities.In many complex networks, nodes cluster and form relatively dense groups—often called communities5, and consequentially many heuristic algorithms have been proposed, such as hierarchical agglomeration6, extremal optimisation7, simulated annealing8 and spectral9 algorithms. One of the most popular algorithms to optimise modularity is the so-called Louvain algorithm10, named after the location of its authors. It was found to be one of the fastest and best performing algorithms in comparative analyses12, and it is one of the most-cited works in the community detection literature.Optimising modularity is NP-hard13, which overcomes some limitations of modularity. CPM is defined asnc is the number of nodes in community c. The interpretation of the resolution parameter γ is quite straightforward. The parameter functions as a sort of threshold: communities should have a density of at least γ, while the density between communities should be lower than γ. Higher resolutions lead to more communities and lower resolutions lead to fewer communities, similarly to the resolution parameter for modularity.Although originally defined for modularity, the Louvain algorithm can also be used to optimise other quality functions. An alternative quality function is the Constant Potts Model (CPM)14. Communities may even be internally disconnected. To address this important shortcoming, we introduce a new algorithm that is faster, finds better partitions and provides explicit guarantees and bounds. The new algorithm integrates several earlier improvements, incorporating a combination of smart local move15, fast local move17 and random neighbour move18. We prove that the new algorithm is guaranteed to produce partitions in which all communities are internally connected. In addition, we prove that the algorithm converges to an asymptotically stable partition in which all subsets of all communities are locally optimally assigned. The quality of such an asymptotically stable partition provides an upper bound on the quality of an optimal partition. Finally, we demonstrate the excellent performance of the algorithm for several benchmark and real-world networks. To ensure readability of the paper to the broadest possible audience, we have chosen to relegate all technical details to the Supplementary Information. The main ideas of our algorithm are explained in an intuitive way in the main text of the paper. We name our algorithm the Leiden algorithm, after the location of its authors.In this paper, we show that the Louvain algorithm has a major problem, for both modularity and CPM. The algorithm may yield arbitrarily badly connected communities, over and above the well-known issue of the resolution limit10 is very simple and elegant. The algorithm optimises a quality function such as modularity or CPM in two elementary phases: (1) local moving of nodes; and (2) aggregation of the network. In the local moving phase, individual nodes are moved to the community that yields the largest increase in the quality function. In the aggregation phase, an aggregate network is created based on the partition obtained in the local moving phase. Each community in this partition becomes a node in the aggregate network. The two phases are repeated until the quality function cannot be increased further. The Louvain algorithm is illustrated in Fig. The Louvain algorithm15. In particular, in an attempt to find better partitions, multiple consecutive iterations of the algorithm can be performed, using the partition identified in one iteration as starting point for the next iteration.Usually, the Louvain algorithm starts from a singleton partition, in which each node is in its own community. However, it is also possible to start the algorithm from a different partitionWe now show that the Louvain algorithm may find arbitrarily badly connected communities. In particular, we show that Louvain may identify communities that are internally disconnected. That is, one part of such an internally disconnected community can reach another part only through a path going outside the community. Importantly, the problem of disconnected communities is not just a theoretical curiosity. As we will demonstrate in our experimental analysis, the problem occurs frequently in practice when using the Louvain algorithm. Perhaps surprisingly, iterating the algorithm aggravates the problem, even though it does increase the quality function.In the Louvain algorithm, a node may be moved to a different community while it may have acted as a bridge between different components of its old community. Removing such a node from its old community disconnects the old community. One may expect that other nodes in the old community will then also be moved to other communities. However, this is not necessarily the case, as the other nodes may still be sufficiently strongly connected to their community, despite the fact that the community has become disconnected.To elucidate the problem, we consider the example illustrated in Fig. Obviously, this is a worst case example, showing that disconnected communities may be identified by the Louvain algorithm. More subtle problems may occur as well, causing Louvain to find communities that are connected, but only in a very weak sense. Hence, in general, Louvain may find arbitrarily badly connected communities.14. Due to the resolution limit, modularity may cause smaller communities to be clustered into larger communities. In other words, modularity may “hide” smaller communities and may yield communities containing significant substructure. CPM does not suffer from this issue13. Nevertheless, when CPM is used as the quality function, the Louvain algorithm may still find arbitrarily badly connected communities. Hence, the problem of Louvain outlined above is independent from the issue of the resolution limit. In the case of modularity, communities may have significant substructure both because of the resolution limit and because of the shortcomings of Louvain.This problem is different from the well-known issue of the resolution limit of modularityIn fact, although it may seem that the Louvain algorithm does a good job at finding high quality partitions, in its standard form the algorithm provides only one guarantee: the algorithm yields partitions for which it is guaranteed that no communities can be merged. In other words, communities are guaranteed to be well separated. Somewhat stronger guarantees can be obtained by iterating the algorithm, using the partition obtained in one iteration of the algorithm as starting point for the next iteration. When iterating Louvain, the quality of the partitions will keep increasing until the algorithm is unable to make any further improvements. At this point, it is guaranteed that each individual node is optimally assigned. In this iterative scheme, Louvain provides two guarantees: (1) no communities can be merged and (2) no nodes can be moved.Contrary to what might be expected, iterating the Louvain algorithm aggravates the problem of badly connected communities, as we will also see in our experimental analysis. This is not too difficult to explain. After the first iteration of the Louvain algorithm, some partition has been obtained. In the first step of the next iteration, Louvain will again move individual nodes in the network. Some of these nodes may very well act as bridges, similarly to node 0 in the above example. By moving these nodes, Louvain creates badly connected communities. Moreover, Louvain has no mechanism for fixing these communities. Iterating the Louvain algorithm can therefore be seen as a double-edged sword: it improves the partition in some way, but degrades it in another way.20, also in the context of the label propagation algorithm21. However, so far this problem has never been studied for the Louvain algorithm. Moreover, the deeper significance of the problem was not recognised: disconnected communities are merely the most extreme manifestation of the problem of arbitrarily badly connected communities. Trying to fix the problem by simply considering the connected components of communities21 is unsatisfactory because it addresses only the most extreme case and does not resolve the more fundamental problem. We therefore require a more principled solution, which we will introduce in the next section.The problem of disconnected communities has been observed before15, which itself can be seen as an improvement of the Louvain algorithm. The Leiden algorithm also takes advantage of the idea of speeding up the local moving of nodes17 and the idea of moving nodes to random neighbours18. We consider these ideas to represent the most promising directions in which the Louvain algorithm can be improved, even though we recognise that other improvements have been suggested as well22. The Leiden algorithm consists of three phases: (1) local moving of nodes, (2) refinement of the partition and (3) aggregation of the network based on the refined partition, using the non-refined partition to create an initial partition for the aggregate network. The Leiden algorithm is considerably more complex than the Louvain algorithm. Figure We here introduce the Leiden algorithm, which guarantees that communities are well connected. The Leiden algorithm is partly based on the previously introduced smart local move algorithmtion and  aggregatP, just like in the Louvain algorithm. By creating the aggregate network based on P, the Leiden algorithm has more room for identifying high-quality partitions. In fact, by implementing the refinement phase in the right way, several attractive guarantees can be given for partitions produced by the Leiden algorithm.In the Louvain algorithm, an aggregate network is created based on the partition The refined partition 18. The larger the increase in the quality function, the more likely a community is to be selected. The degree of randomness in the selection of a community is determined by a parameter θ > 0. Randomness in the selection of a community allows the partition space to be explored more broadly. Node mergers that cause the quality function to decrease are not considered. This contrasts with optimisation algorithms such as simulated annealing, which do allow the quality function to decrease8. Such algorithms are rather slow, making them ineffective for large networks. Excluding node mergers that decrease the quality function makes the refinement phase more efficient. As we prove in Section In the refinement phase, nodes are not necessarily greedily merged with the community that yields the largest increase in the quality function. Instead, a node may be merged with any community for which the quality function increases. The community with which a node is merged is selected randomly16 and in a slightly different form as “prioritisation”17. The fast local move procedure can be summarised as follows. We start by initialising a queue with all nodes in the network. The nodes are added to the queue in a random order. We then remove the first node from the front of the queue and we determine whether the quality function can be increased by moving this node from its current community to a different one. If we move the node to a different community, we add to the rear of the queue all neighbours of the node that do not belong to the node’s new community and that are not yet in the queue. We keep removing nodes from the front of the queue, possibly moving these nodes to a different community. This continues until the queue is empty. For a full specification of the fast local move procedure, we refer to the pseudo-code of the Leiden algorithm in Algorithm Another important difference between the Leiden algorithm and the Louvain algorithm is the implementation of the local moving phase. Unlike the Louvain algorithm, the Leiden algorithm uses a fast local move procedure in this phase. Louvain keeps visiting all nodes in a network until there are no more node movements that increase the quality function. In doing so, Louvain keeps visiting nodes that cannot be moved to a different community. In the fast local move procedure in the Leiden algorithm, only nodes whose neighbourhood has changed are visited. This is similar to ideas proposed recently as “pruning”We now consider the guarantees provided by the Leiden algorithm. The algorithm is run iteratively, using the partition identified in one iteration as starting point for the next iteration. We can guarantee a number of properties of the partitions found by the Leiden algorithm at various stages of the iterative process. Below we offer an intuitive explanation of these properties. We provide the full definitions of the properties as well as the mathematical proofs in Section All communities are γ-separated.All communities are γ-connected.In these properties, γ refers to the resolution parameter in the quality function that is optimised, which can be either modularity or CPM. The property of γ-separation is also guaranteed by the Louvain algorithm. It states that there are no communities that can be merged. The property of γ-connectivity is a slightly stronger variant of ordinary connectivity. As discussed earlier, the Louvain algorithm does not guarantee connectivity. It therefore does not guarantee γ-connectivity either.An iteration of the Leiden algorithm in which the partition does not change is called a stable iteration. After a stable iteration of the Leiden algorithm, it is guaranteed that:All nodes are locally optimally assigned.All communities are subpartition γ-dense.Node optimality is also guaranteed after a stable iteration of the Louvain algorithm. It means that there are no individual nodes that can be moved to a different community. Subpartition γ-density is not guaranteed by the Louvain algorithm. A community is subpartition γ-dense if it can be partitioned into two parts such that: (1) the two parts are well connected to each other; (2) neither part can be separated from its community; and (3) each part is also subpartition γ-dense itself. Subpartition γ-density does not imply that individual nodes are locally optimally assigned. It only implies that individual nodes are well connected to their community.In the case of the Louvain algorithm, after a stable iteration, all subsequent iterations will be stable as well. Hence, no further improvements can be made after a stable iteration of the Louvain algorithm. This contrasts with the Leiden algorithm. After a stable iteration of the Leiden algorithm, the algorithm may still be able to make further improvements in later iterations. In fact, when we keep iterating the Leiden algorithm, it will converge to a partition for which it is guaranteed that:All communities are uniformly γ-dense.All communities are subset optimal.After each iteration of the Leiden algorithm, it is guaranteed that:A community is uniformly γ-dense if there are no subsets of the community that can be separated from the community. Uniform γ-density means that no matter how a community is partitioned into two parts, the two parts will always be well connected to each other. Furthermore, if all communities in a partition are uniformly γ-dense, the quality of the partition is not too far from optimal, as shown in Section An overview of the various guarantees is presented in Table https://github.com/CWTSLeiden/networkanalysis and deposited at Zenodo23. Additionally, we implemented a Python package, available from https://github.com/vtraag/leidenalg and deposited at Zenodo24). All experiments were run on a computer with 64 Intel Xeon E5-4667v3 2 GHz CPUs and 1 TB internal memory. In all experiments reported here, we used a value of 0.01 for the parameter θ that determines the degree of randomness in the refinement phase of the Leiden algorithm. However, values of θ within a range of roughly  all provide reasonable results, thus allowing for some, but not too much randomness. We use six empirical networks in our analysis. These are the same networks that were also studied in an earlier paper introducing the smart local move algorithm15. Table In the previous section, we showed that the Leiden algorithm guarantees a number of properties of the partitions uncovered at different stages of the algorithm. We also suggested that the Leiden algorithm is faster than the Louvain algorithm, because of the fast local move approach. In this section, we analyse and compare the performance of the two algorithms in practice.  The Leiden algorithm was run until a stable iteration was obtained. When the Leiden algorithm found that a community could be split into multiple subcommunities, we counted the community as badly connected. Note that if Leiden finds subcommunities, splitting up the community is guaranteed to increase modularity. Conversely, if Leiden does not find subcommunities, there is no guarantee that modularity cannot be increased by splitting up the community. Hence, by counting the number of communities that have been split up, we obtained a lower bound on the number of communities that are badly connected. The count of badly connected communities also included disconnected communities. For each network, we repeated the experiment 10 times. We used modularity with a resolution parameter of γ = 1 for the experiments.We study the problem of badly connected communities when using the Louvain algorithm for several empirical networks. For each community in a partition that was uncovered by the Louvain algorithm, we determined whether it is internally connected or not. In addition, to analyse whether a community is badly connected, we ran the Leiden algorithm on the subnetwork consisting of all nodes belonging to the community.  increases. The second iteration of Louvain shows a large increase in the percentage of disconnected communities. In subsequent iterations, the percentage of disconnected communities remains fairly stable. The increase in the percentage of disconnected communities is relatively limited for the Live Journal and Web of Science networks. Other networks show an almost tenfold increase in the percentage of disconnected communities. The percentage of disconnected communities even jumps to 16% for the DBLP network. The percentage of badly connected communities is less affected by the number of iterations of the Louvain algorithm. Presumably, many of the badly connected communities in the first iteration of Louvain become disconnected in the second iteration. Indeed, the percentage of disconnected communities becomes more comparable to the percentage of badly connected communities in later iterations. Nonetheless, some networks still show large differences. For example, after four iterations, the Web UK network has 8% disconnected communities, but twice as many badly connected communities. Even worse, the Amazon network has 5% disconnected communities, but 25% badly connected communities.25. However, if communities are badly connected, this may lead to incorrect attributions of shared functionality. Similarly, in citation networks, such as the Web of Science network, nodes in a community are usually considered to share a common topic27. Again, if communities are badly connected, this may lead to incorrect inferences of topics, which will affect bibliometric analyses relying on the inferred topics. In short, the problem of badly connected communities has important practical consequences.The above results shows that the problem of disconnected and badly connected communities is quite pervasive in practice. Because the percentage of disconnected communities in the first iteration of the Louvain algorithm usually seems to be relatively low, the problem may have escaped attention from users of the algorithm. However, focussing only on disconnected communities masks the more fundamental issue: Louvain finds arbitrarily badly connected communities. The high percentage of badly connected communities attests to this. Besides being pervasive, the problem is also sizeable. In the worst case, almost a quarter of the communities are badly connected. This may have serious consequences for analyses based on the resulting partitions. For example, nodes in a community in biological or neurological networks are often assumed to share similar functions or behaviourThe Leiden algorithm has been specifically designed to address the problem of badly connected communities. Figure 28. We generated benchmark networks in the following way. First, we created a specified number of nodes and we assigned each node to a community. Communities were all of equal size. A community size of 50 nodes was used for the results presented below, but larger community sizes yielded qualitatively similar results. We then created a certain number of edges such that a specified average degree μ and within a community with a probability 1−μ. We applied the Louvain and the Leiden algorithm to exactly the same networks, using the same seed for the random number generator. For both algorithms, 10 iterations were performed. We used the CPM quality function. The value of the resolution parameter was determined based on the so-called mixing parameter μ13. We generated networks with n = 103 to n = 107 nodes. For each set of parameters, we repeated the experiment 10 times. Below, the quality of a partition is reported as H is defined in Eq. , all results in Fig. Figure Analyses based on benchmark networks have only a limited value because these networks are not representative of empirical real-world networks. In particular, benchmark networks have a rather simple structure. Empirical networks show a much richer and more complex structure. We now compare how the Leiden and the Louvain algorithm perform for the six empirical networks listed in Table As can be seen in Fig. As shown in Fig. The quality improvement realised by the Leiden algorithm relative to the Louvain algorithm is larger for empirical networks than for benchmark networks. Hence, the complex structure of empirical networks creates an even stronger need for the use of the Leiden algorithm. Leiden keeps finding better partitions for empirical networks also after the first 10 iterations of the algorithm. This contrasts to benchmark networks, for which Leiden often converges after a few iterations. For empirical networks, it may take quite some time before the Leiden algorithm reaches its first stable iteration. As can be seen in Fig. 10, which is reported to be among the fastest and best performing community detection algorithms12. However, as shown in this paper, the Louvain algorithm has a major shortcoming: the algorithm yields communities that may be arbitrarily badly connected. Communities may even be disconnected.Community detection is an important task in the analysis of complex networks. Finding communities in large networks is far from trivial: algorithms need to be fast, but they also need to provide high-quality results. One of the most widely used algorithms is the Louvain algorithmTo overcome the problem of arbitrarily badly connected communities, we introduced a new algorithm, which we refer to as the Leiden algorithm. This algorithm provides a number of explicit guarantees. In particular, it yields communities that are guaranteed to be connected. Moreover, when the algorithm is applied iteratively, it converges to a partition in which all subsets of all communities are guaranteed to be locally optimally assigned. In practical applications, the Leiden algorithm convincingly outperforms the Louvain algorithm, both in terms of speed and in terms of quality of the results, as shown by the experimental analysis presented in this paper. We conclude that the Leiden algorithm is strongly preferable to the Louvain algorithm.Supplementary InformationLaTeX Supplementary File


In this study, we investigated the directional heating of graphene oxide (GO) dispersion to generate a temperature gradient and form a simulated “ocean current” inside the dispersion so that GO sheets could be aligned in a directional manner and then reduced and self-assembled into anisotropic reduced graphene oxide (rGO) gel. After freeze-drying and varying degrees of vacuum microwave treatment, anisotropic chemically derived graphene aerogels (AGAs) were obtained. Through performance detection and the analysis of the results, it was verified that the AGAs with certain characteristics of “ocean current” were prepared in this experiment, and its axial direction has obvious directional arrangement. After being treated by vacuum microwave for a short time (1 min.), the axial thermal conductivity of the composite materials (AGA-adsorbed paraffin) was observed to be 1.074 W/mK, and the thermal conductivity enhancement efficiency was 995%; as compared with similar thermal conductivity enhancement composites that were found in previous studies, the proposed method in this paper has the advantages of simple processing, high efficiency, and energy conservation. One application of graphene is to assemble two-dimensional 2D) graphene sheets into three-dimensional 3D) graphene aerogels (GAs). These GAs have ultra-high porosity and large specific surface area. When combined with the excellent properties of graphene itself, it has broad prospects in many applications, such as chemical adsorption ,2, energD graphenD graphenGAs is generally prepared by the hydrothermal method  or organHowever, in these studies, the authors prepared the anisotropic GAs by the directional freezing  method, that is, by applying a cold source to the prepared wet gel in a certain face to make the ice crystal grow in a certain direction and then extrude the graphene sheet to arrange along the crystal surface. The process of directional freezing is complicated, and the degree of reduction, speed, and size of crystal growth must be controlled. Sometimes, pre-reduction is required, followed by directional freezing, thawing, and thorough reduction. Otherwise, the binding of graphene sheets will be stronger after a high degree of reduction; this is not conducive to directional freezing, which leads to poor regularity of pores and poor control of pore size. While using 3D printing technology, Zhang et al. prepared graphene aerogels with controllable pores and macro directions. However, the aerogel features only macroscopic anisotropy, while microscopic isotropy, and the graphene sheets inside are still in disordered arrangement, not being conducive to heat transfer .Ocean currents inspired this study, that is, liquid flowing from high-temperature areas to low-temperature areas, prompting the formation of “line flow” inside the liquid. In a graphene oxide (GO) dispersion, the two-dimensional structure of GO sheets, will lead them parallel to the direction of the line flow inside the dispersion . Therefore, after being reduced, the rGO sheets in the self-assembled rGO gel will be parallel to the direction of line flow or at an acute angle. This heat flow method is easy to operate and the reduction and orientation can be completed in one step.GO was prepared by the improved Hummer’s  method. Next, 0.1 g of GO was added into a glass bottle, and 50 ml of 30% ethanol solution was added to obtain the GO dispersion with a concentration of 2 mg/ml. After the dispersion was placed in an ultrasonic bath for 1 h, 500 µL of concentrated ammonia water was added and stirred evenly; then, the glass bottle was placed into the hydrothermal reduction device. In the device, the temperature was programmed to rise from 30 °C to 130 °C within 8 h, and then kept at that temperature for 1 h to obtain wet gel. Next, it was soaked in deionized water for 48 h, during which the water was changed six times. After that, it was frozen at −45 °C for 3 h, and then freeze-dried for 48 h. Finally, it was microwaved under vacuum to obtain AGA. Some AGA samples were immersed into heated and melted paraffin wax , absorbed by vacuum for 30 min., and then taken out and naturally cooled to obtain AGA/paraffin composite material, denoted as PCxWy.Thermogravimetric analysis (TGA) of the AGA samples was performed while using a synchronous thermal analyzer (Mettler Toledo TGA 2). Approximately 5 mg of sample was put into an alumina crucible, and then heated from 50 °C to 300 °C or 1080 °C at a rate of 10 °C/min. under a nitrogen rate of 50 mL/min. −1.Thermal performance analysis of samples were measured by a Differential scanning calorimeter  at a scanning rate of 10 °C minAn ESCALAB 250Xi (Thermo Fisher Scientific) was used for X-ray electron spectroscopy (XPS) detection, with the following parameters: Monochrome Al Ka (hv = 1486.6 eV), power 150 W, 500 µm beam speckle. The binding energy was calibrated at C1s 284.8. Xpspeak41 software was used for peak fitting, with 20% Gaussian and 80% Lorentzian fixed and Shirley background mode being adopted.The microstructures of the AGA samples were analyzed by scanning electron microscopy (SEM) with a Carl Zeiss EVO18 microscope . The sample was placed on conductive adhesive for observation without gold spraying.Raman spectroscopy was carried out by a microscope confocal Raman spectrometer (Horiba Jobin Yvon LabRAM HR800) with a wavelength of 633 nm. The in-plane crystal size of the sample was calculated by the following formula La(nm) =X-ray diffraction (XRD) testing was performed by a Brucker D8 Advance (Germany), with the following conditions: Voltage 40 kV, current 40 mA, step length 0.02°, test speed 0.1 s/step, copper target, and ray wavelength 0.15418 nm.1/2(2θ) are the X-ray diffraction wavelength and the full width at half maximum in radian units, respectively.The average crystal size of the samples was calculated by the Scherrer formula :Lc = 0.8The thermal conductivity of AGA and paraffin composite materials was measured while using the transient hot wire method on a thermal conductivity meter .η is the thermal conductivity enhancement efficiency; mK and K are the thermal conductivities of paraffin and its composite, respectively; and, V is the volume content of AGA in the composite.The enhancement efficiency in thermal conductivity of the paraffin composites can be calculated byIn this study, directional AGA samples were prepared by the heat flow method, their physical and chemical properties were tested, and the thermal conductivity enhancement of the composites was studied after the adsorption of paraffin. wt% O and 8.5 wt% N). TGA testing was carried out to further reveal the thermal stability of the aerogel; the results in The dissociation and content of oxygen-containing groups in the AGA samples were further studied through XPS detection and analysis. 2. The difference in N content without microwave treatment and after 1 min. of treatment might be caused by the difference in local areas of XPS scanning.wt% (C8W0) oxygen content after hydrothermal reduction. These oxygen atoms form covalent bonds with carbon atoms, which increase the graphite lattice along the c-axis, and reduce the diffraction angle of the reduced graphite. However, after microwave treatment, oxygen-containing groups further decompose and leave [The physical properties of the AGA samples were characterized by XRD. From the XRD curves shown in nd leave , which m−1; it was caused by the lattice vibration leaving the center of Brillouin region and it is used to characterize the structural defects of the graphene samples. The vibration peak of sp2 in-plane, the G peak, appeared near 1600 cm−1, which represents a large deviation from 1580 cm−1 of graphene. As can be seen from D/IG does not show a significant decreasing trend. Although a longer microwave treatment time causes a higher reduction degree (lower oxygen content), at the same time more fragments are generated, which leads to an increase in the boundary effect and an increase in the intensity of the D peak [e D peak ,32. In-pe D peak  and averD/IG value indicates that the high boundary effect and defects lead to high phonon scattering and high thermal resistance, which thus reduces the thermal conductivity of AGA. The results and analysis of Raman spectra and XRD indicate that the extension of microwave treatment time does not significantly reduce the ID/IG value, but it does decrease the crystal size La. As a result, the supercooling degree of PC8W1 is relatively low, but there is no significant difference between PC8W5 and PC8W20. Usually, a longer microwave processing time is associated with higher temperature and the annealing effect, and the AGA graphite structure can be better restored, but perhaps in this experiment, the laboratory-made vacuum microwave processing equipment caused microwave energy losses, which resulted in insufficiently high temperatures , and the vacancies and defects of the rGO could not be repaired. Li et al. found that the oxygen-containing groups could be completely removed only after annealing above 1500 °C, upon which the repairs to vacancies and defects could be observed. It may also be that the reduction at a lower temperature, which has the positive effect on thermal conductivity enhancement of sp2 domain (generated by rGO), is weaker than the negative effect of phonon scattering that is caused by boundaries or stacks. Fan et al. also found that the thermal conductivity of high-density AGAs after annealing at 450 °C is lower than that without annealing [The thermal properties of AGA adsorbed paraffin were studied by DSC and thermal conductivity testing. According to the DSC curve in nnealing .wt%) and nitrogen-containing groups remain, although most oxygen-containing groups can be removed by a hydrothermal reduction reaction. The presence of these groups will seriously affect the integrity of the graphite structure, thereby affecting the thermal conductivity effect [As can be seen from the axial-direction temperature-change curves over time in y effect ,35,36. Fy effect .Many studies have reported that graphene enhances thermal conductivity, and many factors that affect the thermal conductivity of graphene composites have also been reported. In addition to the influence of the quality of the graphene itself , the axial thermal conductivity of AGA/paraffin composite material reached 1.074 W/mK and the thermal conductivity enhancement efficiency reached 995% when the content was 0.32 vol%. However, XRD and Raman analysis show that the apparent quality of the rGO does not significantly improve with the extension of microwave time. The average in-plane crystal size La decreases, and the decrease in I