Flaviviruses such as Yellow fever, Dengue, West Nile, and Zika generate disease-linked viral noncoding RNAs called subgenomic flavivirus RNAs. Subgenomic flavivirus RNAs result when the 5′–3′ progression of cellular exoribonuclease Xrn1 is blocked by RNA elements called Xrn1-resistant RNAs located within the viral genome’s 3′-untranslated region that operate without protein co-factors. Here, we show that Xrn1-resistant RNAs can halt diverse exoribonucleases, revealing a mechanism in which they act as general mechanical blocks that ‘brace’ against an enzyme’s surface, presenting an unfolding problem that confounds further enzyme progression. Further, we directly demonstrate that Xrn1-resistant RNAs exist in a diverse set of flaviviruses, including some specific to insects or with no known arthropod vector. These Xrn1-resistant RNAs comprise two secondary structural classes that mirror previously reported phylogenic analysis. Our discoveries have implications for the evolution of exoribonuclease resistance, the use of Xrn1-resistant RNAs in synthetic biology, and the development of new therapies. Subgenomic flavivirus RNAs are generated by a host exoribonuclease and play an important role in virus replication and pathogenesis. Here, the authors show the mechanism by which subgenomic flavivirus RNAs are generated and identify two structurally distinct sfRNA classes in flaviviruses. Infection by mosquito-borne flaviviruses (MBFVs) results in amplification of the genomic RNA (gRNA) and also production of noncoding subgenomic flaviviral RNAs (sfRNAs)8. sfRNAs accumulate to high levels, interacting with many cellular proteins to influence processes such as RNA interference, proper cellular RNA decay, the interferon response, and the process of transmission between mosquito vector and vertebrate host19. sfRNAs have been implicated in cytopathicity in cell culture and in pathogenicity in fetal mice20, thus they are directly related to disease symptoms and are potential therapeutic targets.Flaviviruses are single-stranded, (+)-sense RNA viruses with 10–11 kb-long genomes21. MBFV genomes contain discrete RNA structures in their 3′-untranslated region (UTR) that block the progression of Xrn1. These RNA elements are sufficient to block Xrn1 without the use of accessory proteins, thus they have been assigned the name ‘Xrn1-resistant RNAs’ (xrRNAs)27. xrRNAs halt the enzyme at a defined location such that the viral RNA located downstream of the xrRNAs is protected from degradation. These protected RNAs are sfRNAs, and in some but not all cases multiple xrRNA structures give rise to multiple sfRNA species  the conserved xrRNA structure and sequence could make specific contacts to Xrn1’s surface to prevent helicase activity or block enzyme conformational changes, ii) it could somehow alter the specific catalytic mechanism of Xrn1’s active site, iii) it could use nonspecific physical interactions with the enzyme, iv) it could present a general mechanical unfolding problem, or it could use some combination of these strategies.Xrn1 can unwind and degrade highly structured RNAs such as picornaviral IRES elementsure Fig. , and mod32, direct evidence is lacking for whether other non-arthropod-borne flaviviruses also form sfRNAs. These flaviviruses include members of the no known arthropod vector flaviviruses (NKVFVs) that infect small mammals and bats, and the insect-specific flaviviruses (ISFVs) that have no identified vertebrate host. While the sequences of MBFV xrRNAs contain a number of absolutely conserved nucleotides within a shared three-dimensional fold25, the non-MBFV putative xrRNAs do not have the same sequences as those found in MBFVs29. If these ISFVs and NKVFVs do form sfRNAs, this raises questions of whether they arise from folded RNA structures that function without protein co-factors, and how their structures compare with the MBFV xrRNAs.In addition, although the formation of sfRNA in the MBFVs and tick-borne flaviviruses (TBFVs) is well-establishedHere, we describe studies to address fundamental questions of the mechanism underlying Xrn1 resistance by MBFV xrRNAs and the existence and structure of xrRNAs from other flaviviruses. Using a biochemical approach, we discovered that xrRNAs can halt exoribonucleases unrelated to Xrn1 and that the nature of the interface between the RNA and the enzyme affects where the enzyme halts. This reveals that xrRNAs operate as general mechanical blocks to diverse molecular machines, with implications for the use of these RNAs in diverse contexts. Furthermore, we directly demonstrate that Xrn1 resistance can be achieved by sequences within the 3′-UTR of flavivirus RNAs outside of the MBFVs, but that in some groups the xrRNAs use a secondary structural strategy that differs from that of the MBFVs. By extension, structured exoribonuclease-resistant RNAs may be widespread, possibly conferring novel pathways for RNA maturation or regulation of mRNA decay.KUN) as a representative xrRNA and challenged it with pure bacterial RNase J1, a 5′–3′ exoribonuclease unrelated to Xrn1  of Xrn1’s halt site. Because these exoribonucleases have narrow entrance tunnels to their active sites that only allow single-stranded RNA to enter, the difference in halt sites likely correlates with the distance between the active site and the surface of each enzyme35. In contrast to RNase J1, the halt site of Dxo1 was somewhat poorly defined, with the enzyme appearing to ‘stutter’ as it interacts with the xrRNA , by infecting C6/36 cells. Northern blot analysis with a probe to the 3′-end of the viral RNA revealed a robust band consistent with the production of sfRNA, and reverse transcription showed the location in the 3′-UTR Fig. . To deteExamination of these secondary structures show that several tertiary structure interactions critical for maintaining the three-dimensional structure of the MBFV xrRNAs can also form in the CFAV xrRNAs Fig. . Specifi+•G-C base triple, which is isosteric with the U•A-U and thus can structurally replace it. To test this, we generated mutant versions of the xrRNA and tested them for Xrn1 resistance in vitro , resistance was recovered; this provides strong evidence for the existence of a base triple , reveals a sequence that matches the xrRNA patterns of the MBFVs25, but many other members do not have this sequence pattern. To examine sfRNA formation in these divergent NKVFVs, we infected BHK-21J cells with Modoc virus (MODV), Montana myotis leukoencephalitis virus (MMLV), Apoi virus (ApoiV), and Rio Bravo Virus (RBV); these represent various groups of NKVFVs. Northern blot analysis with probes to the 3′-ends of the viruses revealed the presence of sfRNAs in all  is the primary determinant of resistance; rather than thermodynamic stability, resistance is conferred by a specific RNA topology that the enzyme encounters as it approaches from the 5′ side, and this structure presents a specific barrier to progression.Combined with previous data, the mechanism that emerges is one in which xrRNAs resist progression of exoribonucleases using a specific three-dimensional RNA fold that braces against the surface of the enzyme and creates a general mechanical block to continued enzyme movement. Several existing pieces of evidence fit this mechanistic model. First, although the MBFV xrRNAs block Xrn1 approaching from the 5′ side, the viral RNA-dependent RNA polymerase can pass through from the 3′ side, as can the reverse transcriptase used in our experiments. Although the nature of these enzymes’ helicase activity may differ from Xrn1’s, this observation is consistent with resistance owing to an encounter with the 5′ side of the structure. Also, while the global structural stability of the xrRNA may play a role in resistance, Xrn1 passes through many very stable structured RNAs, suggesting thermodynamic stability is not the sole source of resistance18. Likewise, versions of Xrn1 capable of overcoming xrRNAs are unlikely to arise through a specific mutation within a species, eliminating one host-centered mechanism of evolving resistance to the virus. The ability to block diverse exoribonucleases also confers the potential to use processive 5′–3′ exoribonucleases other than Xrn1 to produce sfRNAs. Indeed, Xrn1 knockdown experiments in Zika virus suggested the presence of redundancy in the 5′–3′ decay machinery that could be exploited by xrRNAs that block diverse exoribonucleases27.The mechanism supported by the data has several implications for the function of these xrRNAs in flavivirus infection. The ability of the MBFV xrRNAs to resist diverse exoribonucleases may assist them in operating in a wide range of hosts and vectors. In other words, sequence differences in Xrn1 from different species are unlikely to result in an enzyme that is able to degrade through an xrRNA. Although viral tropism is dependent on many factors, at least the xrRNA is likely to be functional in a very wide range of species37. In addition, the distance (in terms of sequence length) between the sequences involved in a putative long-range base pairing interaction is longer than is typical for the MBFVs25. Likewise, the halt point for the exoribonuclease in the tested NKVFV and TBFV xrRNAs lies within a helical element and only 5–6 nucleotides upstream of the junction. This is potentially significant because as the enzyme approaches, it will unwind these helical elements, with the effect of further increasing the amount of single-stranded RNA between the structure and the long-range base pairing. Furthermore, at the moment the enzyme halts, the 5–6 nucleotides between the junction and the halt point will be single-stranded and inside the enzyme, suggesting a different RNA structure-based mechanism of halting Xrn1. Although it is possible that these divergent secondary structures and sequences may possess three-dimensional folds or topologies similar to those from the MBFV, it is premature to make this prediction; it is just as possible that they use a different tertiary structure and strategy to block Xrn1. These observations suggest there are at least two secondary structural classes of xrRNAs within the flavivirus genus: the well-characterized class typified by the MBFVs and ISFVs (Class 1) and the class found in the TBFV and some of the NKVFVs (Class 2)  Fig. .Flaviviridae family, the hepatitis C virus (a hepacivirus) and bovine viral diarrhea virus (a pestivirus) have been shown to block Xrn1 progression past a point proximal to the 5′-end of the viral genomic RNA, resulting in dysregulation of host RNA decay pathways40. However, Xrn1 resistance does not appear to be as robust as in the MBFVs and serves a somewhat different purpose in that it does not result in accumulation of a noncoding RNA that alters multiple host pathways during infection. Also, there is no evidence of a three-dimensional RNA structure that is similar to the xrRNAs found in the MBFV 3′-UTRs. Nonetheless, the presence of the ability to resist Xrn1 by these members of the Flaviviridae family outside the flavivirus genus show how useful this ability may be for viruses, inviting speculation that this function may exist throughout the viral RNA world. Indeed, exoribonuclease-resistant RNA sequences have been reported in some plant-infecting viruses that show no sequence similarity to the flavivirus xrRNAs41.It is also worth considering that outside the flavivirus genus but within the Flaviviridae and their use of different arthropod vectors  structural features. Consistent with this, MODV and MMLV are members of the NKVFVs that are overall more closely related to TBFVs, and their xrRNAs match that class. Hence, the point of divergence of Class 1 from Class 2 xrRNAs appears to be near the point where MBFVs and TBFVs phylogenetically diverge and where the diverse NKVFVs lay. We hesitate to make strong conclusions based on these observations; nonetheless it will be interesting to see if future identification of any arthropod vectors for these various NKVFVs shows that the class of xrRNAs correlates with the vector; that is, if Class 1 xrRNAs are exclusive to mosquitoes and Class 2 xrRNAs are exclusive to ticks.It is informative to examine the diverse RNA structure-based ways to halt Xrn1 in light of the phylogenetic relationship of the ors Fig. . In the Within the areas of RNA-based nanotechnology, synthetic biology, and RNA-based therapies, viral RNAs such as xrRNAs can provide elegant building blocks for diverse applications. One can imagine a wide range of applications where it would be advantageous to control the degradation of specific RNA species. Given that the flavivirus xrRNAs are active against a range of exoribonucleases from different species, they might also be utilized as tools within a range of organisms to protect specific RNAs from 5′–3′ decay. The widespread presence of xrRNAs in the flaviviruses, the diversity of sequences and structures capable of mechanically blocking different exoribonucleases, and the apparent usefulness of this strategy for viral infection suggests that RNA structures capable of interfering with exoribonuclease activity may be more widespread than is currently appreciated.22. The RNase J1 expression vector was a kind gift of Ciaran Condon, and the Dxo1 expression vector was a kind gift of Liang Tong. RNase J1 was expressed in BL21 (DE3) cells in LB containing kanamycin and chloramphenicol at 30 °C. Expression was induced with 0.1 mM IPTG at OD600 = 0.6 and protein was expressed at 30 °C for 3 h. The cells were harvested by centrifugation at 5488×g for 10 minutes and the cell pellets were stored at −80 °C until further use. Dxo1 was expressed in Rosetta (DE3) cells in LB with kanamycin. The cells were grown at 37 °C to OD600 = 0.6–0.9. Expression was induced with 0.25 mM IPTG, the temperature was lowered to 16 °C and culture continued overnight before harvesting and storing similar to RNase J1 (above). Cell pellets were lysed by sonication for 2 minutes processing time and centrifuged at 31,000×g for 30 minutes to clear the lysate. The protein was purified using Ni-NTA resin (Thermo) in a gravity flow column followed by size exclusion chromatography with either a Superdex 75 or Superdex 200 column. The final product was stored in buffer containing 20 mM Tris pH 7.3, 300 mM NaCl, 1 mM DTT or 2 mM BME, and 10% glycerol (1 mM EDTA was added to the RNase J1 sample) at −80 °C. The purity was assessed by SDS-PAGE and Coomassie staining.The RppH and Xrn1 enzymes were expressed and purified as describedEscherichia coli (DH5α) and their sequences verified. To generate DNA template for transcription reactions, the desired region of the plasmid was amplified by PCR, then used in 5 mL in vitro transcription reactions and purified as previously described22.Plasmids encoding the RNA sequences of interest under control of a T7 promoter were generated using standard molecular biology techniques. In brief, gene fragments (gBlocks) synthesized by Integrated DNA Technologies (IDT) were ligated into pUC19 vector (NEB) between the BamHI and EcoRI sites. Resultant plasmids were amplified in 2, 1 mM DTT. This solution was incubated at 85 °C for 3 min, followed by 20 °C for 5 min, then held at 4 °C. Then, 0.8 µg of His-Xrn1 and 0.08 µg of His-BdRppH were added and the mixture was incubated at 37 °C for 2 h. The resulting RNA products were analyzed by denaturing PAGE22.The exoribonuclease resistance assays were carried out by first folding 1–2 µg of 3′-UTR RNA and 1 µg of the 24-mer control RNA in a buffer composed of 50 mM Tris, pH 7.9, 100 mM NaCl, 10 mM MgCl22. The RT products were resolved on a 12% denaturing polyacrylamide sequencing gel. The gels were visualized by phosphorimaging using a Typhoon 9400 Imager (Molecular Dynamics) and visualized with ImageQuant software.Exoribonuclease halt sites were mapped by gel purifying His-Xrn1 digested 3′-UTR flavivirus RNA using the same protocol as mentioned above but at a larger scale . Then RT-PCR was performed using unique 5′-end-labeled primers for each flavivirus and GoScript Reverse Transcriptase (Promega)42. In brief, RNAs were transcribed as described above, and purified using Agencourt AMPure XP magnetic beads. To anneal the RNA, it was heated to 90 °C for 2 minutes, transferred to ice for 2 minutes, and then placed at room temperature. RNA was modified via exposure to 24 mg/mL NMIA for 30 minutes before halting the reaction with an acid quench solution 42. Modified RNAs were then reverse transcribed at 42 °C for 45 minutes with a universal fluorescently labeled primer (IDT) (/5–6FAM/ AAAAAAAAAAAAAAAAAAAAGTTGTTGTTGTTGTTTCTTT). Labeled cDNA products were eluted in HiDi formamide and Gene Scan ROX 350 (Thermo) for capillary electrophoresis analysis on an Applied Biosystems 3500 XL Genetic Analyzer42. All reactions were performed in triplicate, as were “no modification” control reactions and ddNTP ladders. Analysis of capillary electrophoresis data was performed using the HiTRACE MATLAB toolkit (MathWorks) and 1D analysis pipeline developed by the Das and Yoon labs46. For further information, the website and tutorial for these programs and protocols can be found at: https://hitrace.github.io/HiTRACE/.We utilized the one-dimensional (1D) chemical probing method described by Cordero et al. to conduct our SHAPE experiments and analysis49. Infections were performed as previously described48. Total RNA was isolated with Trizol (Invitrogen) at 30 h.p.i. from BHK-21J cells infected with MODV, APOIV, MMLV, or RBV , or at 36 h.p.i. from CFAV-infected C6/36 cells .The origin and culture conditions of the BHK-21J  and C6/36 (ATCC CRL-1660) cells have been described2O and 5 μg was used for RT-PCR to amplify the Xrn1 stalling site-containing region of either MODV (nts. 10247–10343) or MMLV (nts. 10265–10367). Primers for these reactions can be found in Supplementary Table 50  was dissolved in 30 ml H50. Approximately 1.5 × 106 transfected BHK-21J cells were seeded in a 35 mm plate. Total RNA was isolated from the transfected cells at 8 h post-electroporation using Trizol (Invitrogen). For Northern blotting, 7.5–10 μg of total RNA isolated from either infected or electroporated cells was denatured using formaldehyde and separated on a formaldehyde-containing 1.5% agarose gel and blotted to Hybond-N+ 51. The blots were hybridized using 32P-labeled oligonucleotides as probes (Supplementary Table BHK-21J cells were transfected with 5 μg of in vitro transcribed Sinrep5 recombinant RNAs as describedSequences were aligned using CLUSTALW though the online server. Accession numbers of sequences are contained in the appropriate figure legends.All data are available from the authors upon request.Supplementary InformationPeer Review File


The World Health Organization (WHO) international standard (IS) for hepatitis B virus (HBV) DNA for use in nucleic acid amplification assays was characterized by determining the complete genome sequence, which was assigned genotype A. This information will aid the design, development, and evaluation of HBV DNA amplification assays. Hepatitis B virus (HBV) remains a major public health problem worldwide. An estimated 350 million people worldwide have chronic HBV infection of which approximately 600,000 people die annually either from acute infection or cirrhosis and hepatocellular carcinoma caused by chronic infection . Direct The HBV IS was prepared from a dilution of the Eurohep reference R1  virus stock held at NIBSC and also used for all previous standards. Following viral DNA extraction (Qiagen), PCR-amplification reactions were performed with HotStarTaqTM master mix . Four independent sets of amplification and sequencing primers were applied as previously described by Chook et al. . CombinaAfter visualization on a 1% agarose gel, each amplicon was diluted in water before being used as a template for sequencing using a combination of PCR primers and suitable internal sequencing primers. Each sequencing reaction contained 4-μl cleaned PCR product, 0.5 μm primer, 3 μl reaction mix  amplified using the cycling profile of 25 cycles of 96°C 30 s; 50°C 15 s; 60°C 4 min as recommended by the manufacturer’s protocol. Amplified products were precipitated with ethanol prior to running on an ABI 3130XL sequencer in accordance with the manufacturer’s instructions. Sequence was generated in both directions, assembled and analyzed using Geneious v7 software, and a consensus sequence generated.This is the first report of the complete genome sequence of the WHO HBV DNA IS. The standard is used in development and evaluation of genome amplification assays for HBV DNA quantification, which provide important clinical data for management of HBV-infected patients. The complete consensus genome sequence reported here, derived from a viral DNA template, will support genome amplification assay development used in the clinical management of HBV-infected individuals and molecular epidemiological studies.KY003230.The complete genome sequence of the WHO IS for HBV DNA reported here as a consensus has been deposited in GenBank under the accession number


In this scenario, the 2DEG is proposed to undergo a spontaneous phase separation and breaks up into locally superconducting puddles in a metallic matrix. However, as the inhomogeneous superconducting 2DEG is only illative, this proposal still lacks the direct experimental demonstration. Here, we artificially construct superconducting puddles-2DEG hybrid system by depositing tin nanoislands array on single crystalline monolayer graphene, where the two quantum critical behaviors are reproduced. Through the finite-size scaling analysis on magnetoresistivity, we show that the two quantum critical behaviors result from the intra-island and inter-island phase coherence, respectively, which are further illustrated by the phase diagram. This work provides a platform to study superconducting quantum phase transitions in a 2D system and helps to integrate superconducting devices into semiconductor technology.Two magnetic-field-induced quantum critical behaviors were recently discovered in two dimensional electron gas (2DEG) at LaTiO Two quantum critical behaviors appear in a two dimensional electron gas (2DEG) but its origin remains to be attested. Here, Sun et al. construct superconducting puddles-2DEG hybrid system by depositing tin nano-islands array on monolayer graphene where the two quantum critical behaviors are reproduced, suggesting the formation of inhomogeneous superconducting 2DEG. The QPT occurs at zero temperature as a result of the change of the Hamiltonian under the non-thermal parameters, like disorder, the magnetic field or the doping7. The quantum criticality arising from a continuous QPT can govern the phase diagram up to very high temperature and be described by the theory of finite-size scaling (FSS). The conventional perpendicular magnetic field tuned QPT is always single in the amorphous or granular 2D superconductors13. While recent experiments instead showed superconductor to insulator phase transitions (SIT) tuned by the magnetic field corresponding to two QPTs at LaTiO3/SrTiO3 interface14. Previous theoretical studies suggest that the QPT, even for multiple QPTs, may be induced by inhomogeneities of superconductivity spontaneously developed near the transition in two dimensions16. Thus, the two QPTs emerged at LaTiO3/SrTiO3 interface were also assumed to be related to the inhomogeneous superconductivity, in which one QPT corresponded to the local ordering of superconducting puddles formed by high-mobility carriers (HMCs) and the other QPT was due to the coherence between superconducting puddles coupled by 2DEG14. However, since the existence of superconducting puddles and 2DEG at LaTiO3/SrTiO3 interface could not be visually observed, the physical mechanism underlying multiple QPTs in 2D superconductors is still under debate. To elucidate the origin of the observed multiple QPTs, a visualized platform to reproduce the system consisting of the illative superconducting puddles-2DEG system at LaTiO3/SrTiO3 interface is essentially demanded.Novel quantum phenomena, like high temperature superconductivityH–T phase diagram further verifies the presence of two QPTs in graphene-tin nanoislands array hybrid.Here, we artificially fabricated the inhomogeneous 2D superconductor consisting of superconducting tin nanoislands and single crystalline monolayer graphene, in which an array of disordered superconducting tin nanoislands can visually mimic the suspected superconducting puddles and single crystalline monolayer graphene provides two dimensional electron gas (2DEG), as schematically illustrated in Fig. 18  and STEM-energy dispersive x-ray spectroscopy (STEM-EDS) also suggest that tin nanoislands are discontinuously formed on graphene/Ge (110) substrate  wafer was chosen as the starting platform for the synthesis of single crystalline monolayer graphene10 wafer tem Fig. . The sinate Fig. . To avoiate Fig.  was immeate Fig.  and tranRs) of the device array at zero magnetic field firstly exhibits a semiconductor-like resistivity behavior (dR/dT<0) where the sheet resistance increases as the temperature decreases  of the global superconductivity as a function of θ at 2 K is demonstrated in Fig. Hc2 is defined as the magnetic field where the sheet resistance becomes 50% of the normal state resistance as shown in Supplementary Figure Hc2 observed at θ = 90° can be well fitted by the 2D Tinkham model  curves of graphene-tin nanoislands array from 1.87 to 4 K in the log-log scale. A power-law dependence of α increases monotonically with decreasing temperature  type transition in 2D superconductor31. The BKT type transition is usually utilized to interpret the unbound vortex–antivortex pairing into bound vortex–antivortex process at low temperature in 2D superconductor, and V–I curves always meet V = I3 at the BKT transition temperature (TBKT)31. Here the TBKT equals to 2.42 K at α = 3, as indicated by the blue solid line in the inset of Fig. 20.The upper critical field  curves at 0 < H < 3 kOe, where the R(T) curve at each magnetic field is separated by the superconducting (dR/dT > 0) and weakly localizing metallic (dR/dT < 0) regions at certain temperature Tpeak (marked with arrows). As the magnetic field increases, the Tpeak shifts to a lower temperature and ends at the T-independent resistance region as previously observed in the LaSrx2−CuxO4 thin film1. The T-independent resistance region is indicated by the critical line , which is the signature of the continuous QPT. In Fig. Tpeak behaviors (pink and black arrows) suggest the presence of two distinct continuous QPTs.The measurements conducted above confirm that graphene-tin nanoislands array system behaves as a true 2D superconductor since the feature characteristics of 2D superconductivity are obviously observed. The superconductivity in 2D system can be easily tuned into a weakly localizing metal state by the perpendicular magnetic fieldR/dT changes as the magnetic field varies and dR/dT can approach to zero for certain magnetic field. For high temperature critical region (HTCR), the critical resistance Rc and the critical magnetic field H* are Rc1 = 988.4 Ω and H1* = 1.75 kOe, respectively. While, Rc2 = 1044.4 Ω and H2* = 2.58 kOe are obtained in low temperature critical region (LTCR). It is rather interesting that both critical resistances Rc1 = 988.4 Ω and Rc2 = 1044.4 Ω are significantly lower than the quantum resistance for Cooper pairs RQ = h/4e2=6.45 kΩ32. The early experiments on InOx film showed that the disorder of the system had significant influence on the critical resistance13. For the strong disordered materials, the critical resistance approaches RQ as predicted by the dirty boson model, while the weak disorder system undergoes the QPT from the superconducting state to an metallic phase with Rc < RQ13. Thus, our graphene-tin nanoislands array system (Rc1/RQ = 0.15 & Rc2/RQ = 0.16) agrees with the weak disorder picture13 due to the weak electronic disorder of single crystalline monolayer graphene.To clarify the nature of two QPTs, we performed the detailed measurements nearby the critical lines as shown in Fig. 7. The FSS states that the Rs(H) near the continuous QPT obeys the relationship H is the magnetic field, Hc is the critical field, Rc is critical resistance and F is an arbitrary function with F(0) = 132. The parameter v is the correlation length exponent, z is the dynamical scaling exponent and ξ and the temporal correlation length ξτ in ξ ~ δv− and ξτ ~ ξz in the vicinity of the continuous QPT. We thus performed the FFS analysis and re-plotted the same sheet resistance data displayed in Fig. H in Fig. Rs(H) curves exactly converge at the crossing points  and , respectively  = 1 at the lowest temperature T0. The temperature dependence of the parameter t is the formula: t = (T/T0)zv−1/. The product zv is determined by plotting t dependence of T on a log-log scale with the straight line’s slope equaling to the −zv. The T versus t plot reveals the exponent product value zv = 0.63 D XY model7. The v = 2/3 is expected in clean (2 + 1)D XY regime36, which frequently describes the QPT induced by magnetic field in metallic superconducting films, such as in the amorphous Bi film11 or ultrathin Be film37. Thus the critical behavior in HTCR (zv = 0.63) reflects that the QPT in metallic tin nanoislands belongs to the clean (2 + 1)D XY model. In the clean (2 + 1)D XY model, the external magnetic field causes a frustration in the phase coupling36, suggesting that the QPT is driven by phase fluctuations at intra-tin nanoislands. In the superconducting LaTiO3/SrTiO3 interface, the average size of assumed superconducting puddle Ld is determined by the critical magnetic field as described in Φ0 is the quantum flux38. We thus obtain Ld = 118 nm according toFor HTCR, to yield the best collapse, the method described in ref..63 Fig.  inset. Uves Fig.  can be szv = 3.85, which suggests that v = 3.85 and z = 1 as discussed above. The exponent product 3/SrTiO3 interface14, amorphous InOx13 and amorphous MoGe9, which is the consequence of the QPT in 2D dirty regimes39. As shown in Fig. 40. Thus, the exponent v = 3.85 is related to the QPT of 2D dirty regimes and attributed to the QPT of inter-tin nanoislands superconductivity.The magnetoresistivity curves in LTCR Fig.  can be s41. However, the pervious study on the electrostatically gated graphene-tin nanoislands hybrid system only exhibits single QPT19, which is different from two QPTs induced by the magnetic field in our work. The possible reason is that the electrostatic gating can alter the Fermi level of graphene and the consequent carrier density42, but can not tune the carrier density of metallic tin nanoislands40, therefore, only single QPT emerges corresponding to the inter-tin islands physics. As the magnetic field can induce the vortex matter in both the intra-tin and inter-tin nanoisland superconductors, two QPTs can be observed in the graphene-metallic tin nanoislands hybrid system accordingly, other than single QPT emerging during the electrostatic gating.It is worth noting that the electrostatic gating can also induce the QPT in 2D superconductor by changing the carrier densityH1* and H2*, respectively, the resistance scaling behavior at H1* holds only down to 2 K instead of approaching to the lowest temperature, in contrast to the H2*. To account for the quantum critical behaviors of H1* at lowest temperature, the H–T phase diagram has been constructed in Fig. Tpeak data are extracted from the Fig. ρ/dT > 0) and the weakly localizing metal region (dρ/dT < 0). The scaling regions for the critical field H1* and H2* are also shown in Fig. H1* is about 2–3 K, which is slightly less than the tin bulk superconducting transition temperature Tc = 3.7 K and larger than the zero resistance temperature (∼1 K), while the scaling region corresponding to H2* is around 0.05 ~ 0.2 K, which is approaching to the lowest temperature (50 mK) in present experiment. By analyzing the resistance data in the Arrhenius plot5  wafer. This hybrid system can visually reproduce the superconducting puddles-2DEG system assumed at oxide interface, and the expected two QPTs induced by the magnetic field reappear as identified through FSS analysis on magnetoresistivity. The constructed 2, 99.9999% purity) to make the chamber to reach the atmospheric pressure. During the growth process, a certain proportion gas mixture of methane , hydrogen , argon  was flowed in the chamber at 910 oC for 300 min. Finally, the chamber was rapidly cooled down to room temperature under the flowing gas mixture of H2 and Ar.Single crystalline wafer-scale monolayer graphene was synthesized on the intrinsic Ge (110) wafer by chemical vapor deposition (CVD). The Ge(110) wafer was loaded in a 4-inch CVD furnace. Afterwards, the chamber was evacuated to high vacuum. Then, the chamber was filled with the gas mixture of argon  and hydrogen  with RTESP AFM tips. Raman spectra measurement (HORIBA Jobin Yvon HR800) was carried out with the laser wavelength 514 nm and a spot size of 1 μm.R–T curve of Hall device was measured in physical property measurement system with He3-He4 dilution refrigerator  with a base temperature of 50 mK.To avoid the possible contamination of the graphene surface by chemical polymer resist, we fabricated the device using the stencil mask which could maintain the intrinsic property of graphene. To exclude the possible shunt effect from the substrate, 4 inch intrinsic Ge (110) wafer  was chosen as the starting platform for the synthesis of graphene and the fabrication of Hall bar devices. The Hall bar device fabrication process started with the 10 nm Ti/100 nm Au electrode pattern, which was deposited utilizing the stencil mask to form ohmic contact with graphene. To define the graphene channel, the graphene except the channel graphene was etched using oxygen plasma aligned with the stencil mask. Finally, the 10 nm thick tin film was deposited on the sample by electron beam evaporation to complete the fabrication of hall bar device. The data that support the findings of this study are available from the corresponding author on reasonable request.Supplementary Information


In situ chemical monitoring of cell wall modifications performed by synchrotron infrared spectroscopy during enzymatic hydrolysis demonstrated that the boosting effect of the AA9 LPMO was dependent on the cellular type indicating contrasted recalcitrance levels in plant tissues. Our study provides a useful strategy for investigating enzyme dynamics and activity in plant cell wall to improve enzymatic cocktails aimed at expanding lignocelluloses biorefinery.Lignocellulosic biomass bioconversion is hampered by the structural and chemical complexity of the network created by cellulose, hemicellulose and lignin. Biological conversion of lignocellulose involves synergistic action of a large array of enzymes including the recently discovered lytic polysaccharide monooxygenases (LPMOs) that perform oxidative cleavage of cellulose. Using  In addition, cell walls display high variability depending on genetic and environmental factors, as well as plant tissue and cell types4. Understanding plant cell wall complexity during lignocellulosic bioconversion is therefore important to identify critical features impacting hydrolysis, for optimising pretreatments of biomass5 and enzyme cocktails6.Plant cell walls constitute the largest renewable source of biomass on Earth that can supply environmental benefits for the production of fuel, chemicals and materials. They are composed by lignocellulose made from three main polymers  assembling as a network whose structural and chemical complexity hampers hydrolysis of cellulose by enzymes and micro-organisms8. Combined approach using multiple microscopic techniques including scanning electron microscopy, atomic force microscopy, light microscopy, immunocytochemistry and microspectrometry can be used to monitor microstructural and topochemical heterogeneity of plant cell walls and their recalcitrance at tissue, cell and subcellular levels15. In addition, spatial and temporal imaging of enzymes distribution within lignocellulose substrates can be carried out by means of immunoprobe or fluorescent labelled protein18 to study accessibility at the molecular scale20. However real time imaging of cell wall microstructure and enzyme distribution during bioconversion still remains challenging.Investigation of the dynamics of lignocellulose hydrolysis requires physicochemical characterization and multiscale visualization22. To overcome the recalcitrance of plant polysaccharides, filamentous fungi and bacteria secrete lytic polysaccharide monooxygenases (LPMOs) that perform oxidative cleavage of glycoside bonds25. In industrial processes, addition of LPMOs to cellulolytic cocktails leads to the reduction of the enzyme loading required for efficient saccharification of lignocellulosic biomass26. These powerful enzymes are copper-containing enzymes classified within the auxiliary activity (AA) class of the CAZy database (www.cazy.org27) in AA9-AA11 and AA13 families. AA9 LPMOs are mostly active on cellulose but some have been shown to act on xyloglucan and glucomannan28. Despite their recognized boosting effect on biomass hydrolysis, AA9 LPMOs activity has been essentially investigated on model substrates with only sparse studies focusing on the insoluble fraction of the substrate that show their disruptive action at the surface of cellulosic fibers31. To our knowledge, no studies have investigated the impact of LPMOs on lignocellulose substrate at the plant tissue and cell wall scales.Enzymatic degradation of cellulose and hemicelluloses involves several types of enzymes, namely glycoside hydrolases that work synergisticallyTrichoderma reesei cellulose-active cocktail (Celluclast®) were tested in combination with a fungal AA9 LPMO from Podospora anserina (PaLPMO9E) bearing a family 1 carbohydrate- binding module and acting on cellulose only32. Taking advantage of the high tuneability, brilliance and stability of the synchrotron light source, deep-UV autofluorescence of the enzymes and of the cell walls were used to track the enzyme localization and to follow real-time cell wall modifications at early stages of enzymatic degradation. Synchrotron Fourier Transformed InfraRed (FTIR) microspectrometry enabled to follow PaLPMO9E-mediated chemical changes of the cell walls. To this end appropriate miscanthus samples were prepared and experimental design was set-up to allowing real-time monitoring changes of selected tissues and cells during the action of enzymes under hydrated conditions and optimal temperature.In this study, we have followed the LPMO action on miscanthus as a model substrate using dynamical imaging. Enzymes from a commercial 30, they have never been tested on miscanthus, which is a candidate perennial bioenergy crop producing high biomass with low input and environmental impact35. The ability of PaLPMO9E to improve saccharification of pretreated miscanthus was first evaluated on steam-exploded miscanthus. Indeed, steam explosion is currently among the most effective pretreatment for second generation biofuels production and can be scaled-up industrially38. Steam explosion consists in application of steam at a high pressure followed by rapid decompression, inducing destructuration of lignocellulose and tissue disruption with lignin recondensation  was applied to small portions of miscanthus stem samples to preserve cell and tissue organization. Although this reagent may induce different cell wall structure compared to steam explosion, it is widely used as delignification agent in paper industry for example42. Regarding chemical composition, chlorite pretreatment induced a strong decrease of the lignin content of miscanthus sample (from 26% to 5% dry matter) in comparison to untreated sample  Regiion Fig. .Figure 1® only permitted a limited conversion of miscanthus, which was slightly enhanced by the addition of PaLPMO9E  decreased in agreement with compositional data . When incubated with enzymes, the spectra of the chlorite-treated miscanthus showed decreasing intensity of several vibration bands assigned to lignocellulosic polysaccharides (cellulose and hemicellulose) as illustrated by the corresponding spectral regions recorded for sclerenchyma and parenchyma . Intensity of these two bands was expressed relatively to the intensity at the beginning of hydrolysis  and tracheids (10%) suggesting structural important diffferences in the recalcitrance of chlorite-treated miscanthus cells. Such heterogeneity was still observed after 80 min incubation with PaLPMO9E. Thus combining Celluclast® and PaLPMO9E during hydrolysis induced a sharper decrease in the absorption for both bands compared to Celluclast® alone, especially for sclerenchyma. Faster and more severe degradation of cellulose was thus achieved on chlorite-treated miscanthus in the presence of PaLPMO9E, and this enhancement depended on the cell type with sclerenchyma degradation being more affected compared to parenchyma. This result can be explained by the structural heterogeneity of corresponding cell walls 43. Indeed delignification can impact on cellulose structure and removes some hemicelluloses and pectins. Interestingly, cellulose crystallinity was shown to increase after chlorite delignification42. One can suggest that altered crystallinity of cellulose in delignified sclerenchyma would explain the stronger boosting effect of PaLPMO9E on enzymatic degradation of sclerenchyma.As for visible light and fluorescence imaging, real time FTIR characterization of miscanthus samples was achieved in a microfluidic device that allowed following structural changes of the cell walls in the presence of enzymes Fig. . More spyma Fig. . Compare. ® Fig. , additiotra Fig. . This inPaLPMO9E on enzymatic saccharification of miscanthus indicates a synergistic and boosting effect of PaLPMO9E with cellulases on pretreated miscanthus. Experimental design setup at synchrotron allowed dynamic imaging of enzyme action working in situ under optimal conditions  and the corresponding changes in cell structure by real-time chemical monitoring. For the first time, the LPMO effect was found to depend on the tissue and the cell types in agreement with the heterogeneous structure and biological recalcitrance of cell walls. This approach could be extended to other bioenergy crops showing stem tissue heterogeneity like maize, wheat as well as deeper understanding of the synergic action of enzymes in lignocellulose saccharification. In addition real-time monitoring of enzyme action on lignocellulose using dedicated microscopic tools47 should provide complementary information at mesoscopic and molecular scales to improve efficiency of pretreatments and enzyme cocktail.In conclusion, multimodal evaluation of the effect of Miscanthus x giganteus grown at INRA Estrées-Mons (France) and harvested at “dry” stage (February). Delignification pretreatment was performed on stem fragments (10 mm length × 3 mm thickness) using sodium chlorite-acetic acid as previously described20. Samples were washed several times with water until pH of the washings was neutral then embedded in polyethylene glycol to get serial 40 µm and 16 µm thickness sections for fluorescence imaging and FTIR microspectrometry respectively. Before using miscanthus samples, PEG was removed from the sections by washing with water (3 × 5 min). Another set of samples was dried into air forced oven at 40 °C and grinded prior to chemical analysis.Steam exploded miscanthus was provided by IFPEN (France). For synchrotron experiments, plant material was collected from the bottom stem region of 20.Lignin content was quantified by spectroscopy using acetyl bromide. Polysaccharides composition was determined by quantifying sugar monomer released from sulfuric acid hydrolysisPaLPMO9E was produced and purified as previously described32. Supplementation assays were carried out in sodium acetate buffer  in a final volume of 1 mL at 0.5% consistency (weight dry matter per volume). Enzymatic treatments were performed in 2-mL tubes incubated at 50 °C and 850 rpm in a rotary shaker . PaLPMO9E was added to the substrate at a concentration of 4 mg.g−1 dry matter in presence of 1 mM ascorbic acid for 72 hours, followed by addition of 12 filter paper units of commercial cellulases from Celluclast® 1.5 L (Sigma-Aldrich), per gram of dry matter. After hydrolysis, samples were boiled for 10 min to stop the enzymatic reaction. After centrifugation , glucose was quantified using the glucose RTU kit , following the manufacturer’s instructions, and the DNS reagent using glucose calibration curve48. Assays were performed in triplicate. Oxidized cellobio-oligosaccharides were detected using HPAEC (high performance anionic-exchange chromatography chromatography)28. Previous study on the characterization of PaLPMOE with different control reactions without enzyme indicated that ascorbate alone did not induce any modification of the substrate32.Saccharification assays were carried out on several pretreated miscanthus samples. ® 1.5 L, alone or in combination with PaLPMO9E. Control experiments without enzymes were performed using the same buffer.For microscopic visualization of the dynamic changes at cell level, higher enzyme loadings (10 times more) were chosen to enable experiments within a reasonable timeframe of 2–3 hours. The reaction was performed in acetate buffer pH 4.8 (50 mM acetate containing 1 mM ascorbate) at 45 °C using the cellulase cocktail Celluclast49 using two filters: i) emission 327–353 nm to image enzyme (tryptophan autofluorescence)45, ii) 420–480 nm to image cell walls (phenolic components fluorescence). These conditions were selected by comparing images obtained at different excitation/emission filters. Miscanthus sections (40 µm thickness) were mounted between 2 quartz cover slides (160 µm in thickness) using a spacer to provide a 25 µL sealed chamber containing 22 µL enzymes–buffer solution ion Fig. . Reactio−1 resolution with a 12 × 12 µm² aperture. A background spectrum was recorded in water at the beginning of the experiment. The infrared region 1200–900 cm−1 was analyzed with baseline correction to monitor the extent of cell wall degradation. Peak intensities were measured at 1060 cm−1 and at 1107 cm−1 with a baseline between 1300 cm−1 and 950 cm−1.The data were acquired on the SMIS beamline in the SOLEIL synchrotron which exploits the edge and bending radiations from a bending magnet. The data were recorded on a Continuum XL microscope  equipped with a MCT-A detector. As for DISCO imaging, the incubation was performed at 45 °C thanks to the use of a temperature-controlled microfluidic device. Miscanthus sections (16 µm thickness) were mounted in a home-made microfluidic device between two ZnS windows using a 25 µm thickness ETFE spacer  to make a chamber of 12 µL volume Fig. . Time laAll experiments were performed as triplicates. Values in graphs and tables were presented as means with standard error of the means.SUPPLEMENTARY INFORMATION


Apple dwarfing rootstocks cause earlier shoot termination and reduced root and shoot mass. To identify physiological factors responsible for rootstock-induced growth restriction, we compared vascular-enriched gene expression between two dwarfing rootstocks (‘M27’ and ‘M9’) and the vigorous rootstock ‘M793’ using RNA sequencing and quantitative reverse transcriptase PCR. Differentially expressed genes common to both dwarfing rootstocks belonged to five main biological processes: (1) primary metabolism, (2) cell wall synthesis and modification, (3) secondary metabolism, (4) hormone signalling and response and (5) redox homeostasis. Genes promoting the biosynthesis of amino acids, lipids and cell walls were downregulated in dwarfing rootstocks, whereas genes promoting the breakdown of these compounds were upregulated. The only exception to this trend was the upregulation of starch synthesis genes in dwarfing rootstocks. Non-structural carbohydrate analysis demonstrated that starch concentrations in ‘M9’ roots, stems and grafted ‘Royal Gala’ (‘RG’) scions were double that of equivalent tissues from ‘RG’ homo-grafted trees (‘RG’/‘RG’). Fructose and glucose concentrations were much lower in all three tissues of the ‘RG’/‘M9’ trees. Together, these data indicate that dwarfing rootstocks are in a state of sugar depletion and reduced cellular activity despite having large starch reserves. Another significant finding was the over-accumulation of flavonoids and the downregulation of auxin influx transporters MdAUX1 and MdLAX2 in dwarfing rootstocks. We propose that both factors reduce polar auxin transport. The results of this study contribute novel information about the physiological state of dwarfing rootstocks. Despite their utility and a century of research, the underlying mechanism of rootstock-induced dwarfing is still unknown. The effects of dwarfing rootstocks are apparent within the first year after grafting and include reduced root growth,11 The ‘Malling’ series have been used to breed new rootstocks that confer a range of vigour control, including the strongly dwarfing ‘M27’. Rootstock-induced dwarfing is conferred by two major quantitative trait loci.12–15 Most of the known dwarfing rootstocks carry markers linked to one or both loci, suggesting that there is one source of dwarfing rootstocks and they reduce scion vigour by the same underlying physiological mechanism.15The most widely used and best-characterised dwarfing apple rootstock is ‘Malling 9’ (‘M9’), which was first phenotyped in the early twentieth century.16 Stem segments of ‘M9’ transport radiolabelled IAA (auxin) at a lower rate than that of vigorous genotypes.18 Levels of free IAA and Zeatin (cytokinin) in cambial sap are lower in ‘M9’ than vigorous rootstocks.19 Other studies have shown an inverse relationship between the rate of IAA diffusion and xylem concentration of cytokinin.2 Van Hooijdonk et al.20 demonstrated that application of the polar auxin inhibitor 1-N-naphthylphthalamic acid to the stem of an invigorating rootstock had the same effect on scion growth as that of the ‘M9’ rootstock. Application of cytokinin and 1-N-naphthylphthalamic acid restored the number of secondary shoots to levels typical of scions on vigorous rootstocks. Likewise, application of cytokinin to scions grafted onto ‘M9’ increased the number of secondary shoots.20 Other hormones may also have a role in rootstock-induced dwarfing. The concentration of gibberellic acid in the xylem is lower in ‘M9’ relative to vigorous rootstocks.21 Application of gibberellic acid to 1-N-naphthylphthalamic acid-treated vigorous rootstocks or scions on ‘M9’ increased node number of both the primary axis and secondary shoots.20 Abscisic acid (ABA) concentrations are higher in dwarfing than in vigorous rootstocks.23Many of the hypotheses to explain how dwarfing rootstocks affect scion growth include altered levels, transport and signalling of hormones between scion and rootstock. Perhaps, the best-supported model proposes that dwarfing rootstocks reduce basipetal auxin transport, thereby limiting root growth and the amount of root-synthesised cytokinin supplied to the scion.24–26 Vasculature at the graft junction between ‘M.9’ and the scion is disorganised, suggesting auxin accumulation in the region, but no evidence of graft incompatibility was found.26 Some researchers have suggested that a smaller root system and reduced amount of conducting tissues observed in dwarfing rootstocks were limiting the transport of water and metabolites to the scion.28 The effect of root restriction on shoot growth is the basis of bonsai cultivation and has been shown to have the same effect on apple scion growth as that of an dwarfing rootstock.5 The rate of hydraulic conductivity is lower in roots and stems of dwarfing rootstocks;29 however, this does not take into effect the smaller ‘M9’ root mass.30Anatomical factors have also been implicated as causal factors in rootstock-induced dwarfing. The roots and stems of dwarfing rootstocks have a higher proportion of bark, which consists of phloem and cortex, and a reduction in the number and diameter of xylem cells relative to that of vigorous rootstocks.28 demonstrated that scions grafted directly onto dwarfing roots were reduced in vigour and concluded that the roots alone were able to exert some effect, although the presence of a dwarfing stem enhanced this effect. Other researchers have shown that a segment of stem, or ‘interstock’, of dwarfing tissue inserted between vigorous roots and scion reduces scion vigour, with larger stem segments having a greater effect.32 Bark implants from dwarfing genotypes or even an inverted ring of bark from a vigorous genotype have been shown to dwarf the scion, providing strong evidence that the mechanism of rootstock-induced dwarfing is likely to involve the vasculature and/or bark.33 Hormones, soluble sugars, metabolites and nitrogen are translocated via the vascular system and have all been implicated in rootstock-induced dwarfing. Phenols affect many biological processes including auxin levels, and are concentrated in the bark of apple trees.16The relative importance of the root versus the stem in rootstock-induced dwarfing has been the subject of numerous studies. Beakbane and Rogers35 While it is useful to identify scion responses to dwarfing rootstocks, the aim of our study was to identify differences in physiological states between dwarfing and vigorous rootstocks that could influence scion growth. We included the strongly dwarfing ‘M27’ in our study to identify processes that were common to two different dwarfing rootstocks.To identify biological processes and regulatory networks that are involved with rootstock-induced dwarfing, we compared the transcriptomes of vascular-enriched tissue from dwarfing and vigorous rootstocks. Previous studies to compare apple rootstock effects on gene expression have focused on gene expression in the scion.Malus × domestica Borkh) trees were grown in 2012–2013 for RNA collections and dry weight measurements. In a second experiment, grafted trees were grown in 2014–2015 for non-structural carbohydrate and metabolic analysis. For RNA collections and dry weight measurements, ‘Royal Gala’ (‘RG’) scions were cleft-grafted at a height of 35 cm onto 1-year-old rootstock stools of ‘M793’, ‘M9’ (clone ‘NZ9’) and ‘M27’ rootstocks  in August (winter). In early October, scions were de-budded to a single vegetative bud and received no further pruning. Grafted trees were planted into 50 l bags containing growing medium . Irrigation was scheduled daily for 30 min (min) at dawn and dusk using an automated time controller. For the carbohydrate and metabolomics analysis, ‘RG’ scions were grafted onto either ‘M9’ or ‘RG’ rootstocks and grown as described above.Grafted apple . The outer bark was removed with a razor blade and vascular-enriched tissue was collected from the rootstock stem 2–10 cm below the graft junction and immediately snap-frozen in liquid nitrogen. Tissue was harvested between 4 and 5 h after sunrise for all time points. Total RNA was isolated as previously described.36 The quality and concentration of the RNA samples were assessed using a Fragment Analyzer . Only samples with a RNA integrity number value of 8 or higher were further analysed by sequencing or quantitative reverse transcriptase PCR (qRT-PCR).Vascular-enriched tissue was collected for RNA purification in November of 2012, and January and March of 2013. These collection dates correspond to 60, 120 and 180 days after scion bud break (DABB), respectively. For each time point, four to six trees of each rootstock genotype were selected for uniform scion growth to minimise any effects because of differential tree size. Previous work has shown that the scion bud type affects both scion and rootstock growth;37 Quality score analysis was performed using fastqc (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) both before and after trimming. Trimmed reads with a minimum length of 30 bp and an average quality score greater than 20 were mapped to the Apple Genome V1.0-predicted coding DNA sequence (CDS) sequences  using bowtie2 v2.2.5  were selected using the DEseq 2 package42 in BioConductor. Significant DEGs were selected using an adjusted P value of <0.05 and |log2 fold change|>1. Significantly over-represented gene categories in the DEGs were identified using Fisher’s exact test and visualised in Pageman using MapMan 3.5.0 and mappings for Malus domestica.43 Metabolic pathways were visualised using Kyoto Encyclopedia of Genes and Genomes (KEGG) gene ontologies.44 Arabidopsis orthologues were determined by BLAST against the TAIR database. Chromosome and position were determined by an in-house database. Venn diagrams were generated by Venny 1.0 (http://bioinfogp.cnb.csic.es/tools/venny/index.html).RNA from the 60 DABB (November) collection was sent to Axeq/Macrogen for library preparation and sequencing using an Illumina Hiseq 2000  instrument. RNA from six individuals of each genotype was made into separate libraries. The 18 libraries were run as a multiplexed sample on one lane to produce 100 nucleotide paired end sequence reads. The first 13 bases of all RNA sequencing (RNAseq) reads were trimmed using an in-house perl script. Adapters were removed using fastq-mcf from the ea-utils package.2.5 ref.  using th45 All qRT-PCR results are shown as expression relative to actin, except for MDP0000264875. Primer efficiencies and relative expression were calculated using the Roche 480 Light Cycler software (version SW1.5).First strand complementary DNA was synthesised from 1.0 μg total RNA using oligo dT primer and Primescript Reverse Transcriptase . qRT-PCR was performed with KAPA Sybrfast qRT-PCR mastermix on a Roche 480 Light Cycler . For the qRT-PCR reactions, 2 μL complementary DNA (1:20 dilution) was used as a template in a reaction volume of 7 μL. For each analysis, there were four to six biological replicates of each genotype and four technical replicates of each sample. Complementary DNAs were loaded into a 384-well plate by a Biomeck liquid handling robot  to minimise pipetting errors. PCR cycles are as follows: initial denaturation at 95 °C for 5 min, followed by 45 cycles of 94 °C for 10 s, 55 °C for 15 s, 72 °C for 10 s and a final melt curve analysis to determine whether a single product was amplified. Primers were designed by Primer 3 to span an intron (if possible) and to amplify products of 100–120 base pairs . For eacAfter tissue was collected for RNA extractions , four to six trees of each rootstock genotype were harvested, severed at the graft junction, and then oven-dried at 60 °C to a constant mass before weighing. Dry weights of scion include scion budwood, primary axis, sylleptic shoots and leaves, while dry weights of rootstock includes roots and rootstock stem. One-way analysis of variance analysis and graphing were performed with OriginLab 8.5 .et al.46 A subsample of the supernatant was taken and dried using a centrifugal concentrator; samples were then re-dissolved in ultrapure water. The sugars were analysed using DIONEX ICS-5000 Reagent-Free IC  system with a CarboPac MA1 column with electrochemical detection.After extension growth had ceased (250 DABB), final architectural measurements were made of six ‘RG’/‘M9’ and six ‘RG’/‘RG’ grafted trees. A segment of stem tissue was collected from 20 cm above the graft junction (scion), 5 cm below the graft junction (rootstock stem) and roots. Tissue was snap-frozen in liquid nitrogen, dried in a freeze-dryer, and then ground to a fine powder. A 0.05 g subsample was extracted with 80% ethanol with Adonitol added as the internal standard and then incubated for 1 h at 60 °C. Extracted samples were centrifuged and the supernatant decanted off. The residue was re-suspended in 80% ethanol re-spun and supernatants combined. The insoluble residue was transferred into Erlenmeyer flasks and analysed for starch as per Smith −1. The solvents were A=0.2% formic acid and B=100% acetronitrile. The solvent gradient was: 10% A 90% B 0–0.5 min; linear gradient to 50% A 50% B, 0.5–12 min; linear gradient to 5% A 95% B, 12–15 min; composition held at 5% A 95% B, 15–17 min; linear gradient to 10% A, 90% B, 17–17.2 min; to return to the initial conditions before another sample injection at 20 min. The injection volume for samples and standards was 1 μL. The micrOTOF QII parameters for polyphenolic analysis were: temperature 225 °C; drying N2 flow 6 Lmin−1; nebuliser N2 1.5 bar, endplate offset −500 V, mass range 100–1500 Da, acquired were acquired at 5 scans per s. Negative ion electrospray was used with a capillary voltage of +3500 V. Post-acquisition internal mass calibration used sodium formate clusters with the sodium formate delivered by a syringe pump at the start of each chromatographic analysis.The ‘RG’ and ‘M9’ stem samples used for analysis by liquid chromatography–high resolution accurate mass–mass spectrometry (LC–HRAM–MS) were the same as those used for the carbohydrate analysis. There were six biological replicates and each sample was run in duplicate. The LC–HRAM–MS system was composed of a Dionex Ultimate 3000 Rapid Separation LC and a micrOTOF QII high resolution mass spectrometer  fitted with an electrospray ion source. Metabolite separation by LC was achieved using a Luna Omega Polar 2.1×100 mm, 1.6 μm  maintained at 40 °C. The flow was 350 μL mint-tests using ProfileAnalysis. Metabolites that showed a |fold change|>1.2 between ‘M9’ and ‘RG’, and a P value<0.05, were selected for further analysis. The chemical identity of each metabolite was based on accurate mass and chemical formula calculations and authentic standards when available.The molecular features present in each sample were found using the find-molecular-feature algorithm in the DataAnalysis . The find-molecular-feature process combines mass spectral signals that are related to each other (isotope clusters and molecular adducts) into single molecular features. The molecular features for each analysis were combined into a single data table using ProfileAnalysis . The intensity values were normalised by 'the sum of all intensities in an analysis'. To assess the distribution of metabolites in the ‘RG’ and ‘M9’ sample sets, the data table was analysed using both principal component analysis and 3 All trees terminated extension growth by 250 DABB. No significant difference was detected in either scion or rootstock dry weight between any of the rootstock treatments until 300 DABB  and slowing of scion growth (180 DABB). Read counts of 10 063 621 to 17 934 007 million were obtained. Using an adjusted analysis . We reasanalysis .43 This revealed functional categories that were over-represented in both upregulated and downregulated DEGs  were upregulated in dwarfing rootstocks, whereas starch breakdown genes MdBeta-amylase (MdBAM7) were downregulated (MdSUS genes were downregulated in dwarfing genotypes (MdSS2 genes (MDP0000842179 and MDP0000283839) were expressed more highly in ‘M27’ and ‘M9’ from 60 to 120 DABB, but were at similar levels to ‘M793’ at 180 DABB, towards the end of the growing season (MdSUS4 (MDP0000252802) expression was higher in ‘M793’ throughout the year, with the greatest difference at 120 DABB, the middle of summer . ‘RG’/‘RG’ trees developed 60% more nodes than the ‘RG’/‘M9’ trees, largely due to homo-grafted trees having threefold more sylleptic shoots than the ‘RG’/‘M9’ . For each tissue type, the starch concentration was twice as high in ‘RG’/‘M9’ relative to ‘RG’/‘RG’ . Sorbitootstocks . Sucroseotstocks . The carRG’/‘M9’ .MdCellulose synthase A , which encodes a key enzyme in secondary cell wall synthesis, was expressed higher in ‘M793’ throughout the year  encodes a glycoside hydrolase involved with the degradation and reorganisation of the cell polysaccharides.47MdBXL7 was expressed much higher in the dwarfing rootstocks throughout the year , which encodes a key enzyme in lignin biosynthesis, was expressed at much lower levels in the dwarfing rootstocks throughout the year  were upregulated in dwarfing rootstocks  and two Small Auxin Upregulated (SAUR) genes (MDP0000737171 and MDP0000148780) were downregulated in dwarfing rootstocks. A Grechen Hagen 3.6 gene , involved with auxin homeostasis, was upregulated. BRI-associated receptor kinase , part of the brassinosteroid signalling pathway, was highly upregulated in both dwarfing rootstocks, although there was no evidence of altered expression of brassinosteroid response genes. Likewise, the genes encoding ethylene signal transduction proteins were upregulated in dwarfing rootstocks, but no changes in ethylene response genes were detected. Both copies of the cytokinin signalling histidine kinase 3  were downregulated, whereas the apple homologue of Cytokinin oxidase 7  was upregulated in dwarfing rootstocks. Gibberellin and jasmonic acid synthesis genes were downregulated. An oxidoreductase that promotes the synthesis of strigolactone was upregulated.Most of the DEGs involved with hormone synthesis and response were downregulated in dwarfing rootstocks . AbscisiMdAUXIN RESISTANT 1  over the year. At 60 DABB, MdAUX1 was expressed at similar, low levels in all rootstocks. However, during the period of maximum growth from 120 to180 DABB, MdAUX1 expression levels in ‘M793’ rose to nearly twice that of the dwarfing rootstocks’ (MdSAUR32 (MDP0000367919) expression was reduced in dwarfing rootstocks throughout the year  of ‘M9’ grafted onto seedling roots retained high concentrations of starch in the roots and reduced growth of scion, whereas interstocks of an invigorating genotype depleted all starch from ‘M9’ roots and led to vigorous growth of the scion. Higher starch concentrations in citrus roots have also been correlated with reduced scion growth.59 On the basis of these observations and our findings, we propose that apple dwarfing rootstocks are impaired in sensing and/or maintaining the balance between starch reserves, cellulose and hexose sugars for glycolysis and cell metabolism. Because of this defect, dwarfing rootstocks act as ‘super sinks’, holding excess starch reserves at the expense of both root and scion growth.Colbyet al.60 described molecular signatures of carbon starvation and oxidative stress in apple buds from heavily flowering trees with a high propensity for biennial bearing. They suggest that competition for carbohydrates between developing fruit (strong sinks) and nearby apical buds (weaker sinks) leads to a local carbon depletion and reduced cellular activity in the vegetative meristems, thus blocking the onset of floral development.Competition between sink tissues is a well-documented phenomenon in fruiting trees. Guitton 1 Reduced auxin transport in ‘M9’ was finally proven late last century,18 but few advances have been made since towards identifying the underlying mechanism responsible. The results of our RNAseq analysis indicated that the phenylpropanoid pathway showed flux towards flavonoid biosynthesis and away from lignin biosynthesis in dwarfing rootstocks. LC–HRAM-–MS data confirmed the over-accumulation of flavonoids in ‘M9’ relative to ‘RG’. Both genetic and metabolomic analyses have provided strong evidence that flavonoids inhibit polar auxin transport.61–68 Plants that over-accumulate flavonoids have reduced auxin transport and dwarfed phenotypes,68 and those that are blocked in flavonoid synthesis or glycosylation (CHS) have increased auxin transport and also show abnormal root and shoot phenotypes.62–66 Chalcone synthase catalyses the first committed step in flavonoid biosynthesis. Silencing of the apple CHS gene completely removed many of the same flavonoid compounds that over-accumulated in ‘M9’.66 Future research is needed to explore the role of flavonoids in rootstock-induced dwarfing. The reduced expression of MdAUX1 and MdLAX2 observed in the dwarfing rootstocks may also contribute to reduced polar auxin transport.Researchers have long speculated that rootstock-induced dwarfing involved reduced auxin transport.69 Both have been implicated in the mode of rootstock-induced dwarfing.16 Downregulation of SAUR genes in dwarfing rootstocks indicates a reduced auxin response. Expression of MdAUX1 and the auxin conjugating MdGH3.6 was variable over the season, which likely reflects the fact that auxin signalling is tightly regulated by feedback, feedforward and cross-talk with other signalling pathways. Cytokinin signal transduction genes were downregulated, which is consistent with previous reports of lower cytokinin concentrations in ‘M9’ and ‘M27’ xylem sap.70MdCKX7, which encodes a cytokinin-degrading enzyme, was upregulated in dwarfing rootstocks. Overexpression of CKX genes in Arabidopsis and tobacco results in slow-growing, dwarfed shoots and a reduction in soluble sugars.71–73 Transgenic manipulation of cytokinin activity has implicated cytokinin in regulating sink strength in storage organs.73–75Auxin and cytokinin act antagonistically to regulate root and shoot growth, the outgrowth of axillary meristems and the synthesis and transport of one another.22 In contrast, we found that multiple ABA biosynthesis and response genes were downregulated in dwarfing rootstocks. Glucose increases expression of the ABA biosynthetic genes (ABA1-ABA3), which were also identified as glucose insensitive (gin) mutants.76–78 Numerous studies have demonstrated a clear connection between sugar-sensing pathways and hormone metabolism and signalling.79–82 Sugar promotes auxin biosynthesis83–85 and polar auxin transport.87 Glucose and auxin transcriptionally regulate many of the same genes and appear to act synergistically in plant development.88 Cytokinin and glucose also share many transcriptional targets.89 The gin2 mutant, which is defective in glucose sensing, is hypersensitive to cytokinin and insensitive to auxin.75 It is unclear whether the reduced glucose concentrations in dwarfing rootstocks are the cause or result of altered hormone levels or signalling, but changes to either would have a profound impact on growth and development of roots and shoots.The xylem sap of ‘M9’ and ‘M27’ has higher ABA concentrations than that of vigorous rootstocks.Malus roots that had been grafted to different scion genotypes also found that hormone signal transduction and sugar metabolism genes were highly represented in the DEGs.90 The authors concluded that the scion genotype can affect root phenotype by altering sugar metabolism, and auxin and cytokinin signalling. Our results indicate that the rootstock genotype influences scion growth by the same signalling pathways.A recent publication comparing the transcriptomes of genetically identical 11 and while both contain the dwarfing loci Dw1 and Dw2, the non-dwarfing alleles of these loci, as well as many other unlinked loci differ between these two genotypes.12–15 Harrison et al.15 have reported that the specific allelic combinations at Dw1, Dw2 and a region of LG13 influence the expression of rootstock-induced dwarfing. Allelic variation at these or other loci could be responsible for the differences in gene expression observed between ‘M9’ and ‘M27’. We could not identify any obvious candidate genes that would affect carbohydrate metabolism or flavonoid biosynthesis within either the Dw1 or Dw2 mapping intervals. Further research is needed to identify the genetic basis for the transcriptional changes identified in this study.The aim of this study was to identify DEGs common to two different dwarfing rootstocks. However, it is worth noting that there were a large number of DEGs that were unique to ‘M9’ or ‘M27’, and even DEGs that were common to both did not always show the same degree of differential expression. ‘M9’ is a parent of ‘M27’,MdAUX1 and MdLAX2 expression contribute to the reduced auxin transport observed in dwarfing rootstocks.The most consistent manifestation of apple dwarfing rootstocks is earlier termination of primary axis and sylleptic shoot growth and reduced biomass accumulation in both roots and shoots. This generally manifests within the first year of growth after grafting and becomes more pronounced with successive growth seasons and fruiting. Our results indicate that dwarfing rootstocks do not respond to sugar depletion and eventually run out of carbon to support growth in both roots and scion. By holding excess starch reserves, apple dwarfing rootstocks would be unable to provide carbon to the scion in early spring, before the scion becomes a source of fixed carbon. Once the tree began fruiting, the developing fruit would create strong carbon sinks, further increasing competition for carbon by vegetative meristems. On the basis of our findings, we propose that excess flavonoids and reduced


Refer to Schwarz et al. [2] for a detailed description of the production of the organosolv residue and for further information on the raw material used for lignin extraction. The dataset is made publicly available and can be useful for extended lignin research and critical analyzes.This article presents experimental data of organosolv lignin from Poacea grass and structural changes after compounding and injection molding as presented in the research article “Effects of high-lignin-loading on thermal, mechanical, and morphological properties of bioplastic composites”  Specifications TableValue of the data●Poaceae grass and can be compared with other related studies.The data are convenient to examine the structural characteristics of organosolv lignin from herbaceous plants such as ●The data establish a link between lignin content in bioplastic composites and load-dependent molecular weight changes.●These data allow other researchers to extend the characterization of lignin in highly-filled composites.1Poaceae grass are shown in Morphological characteristics of lignin from Raw spectral data of the starting lignin are shown in 22.1A lignin-rich fraction was obtained by organosolvation of a grass silage press cake (PC) batch, described earlier by Schwarz et al. 2.2Lignin purity analysis was conducted according to NREL standard methods 2.3−1, spectral resolution: 2 cm−1, total scans: 128, background: KBr.FT-IR analysis was performed to examine the starting lignin. Direct transmittance was measured by using the KBr pellet technique with a lignin concentration of 0.3 wt% in 300 mg KBr. The following parameters were used: spectral range: 400–4000 cm2.4Mean lignin particle size and particle size distribution were evaluated using a scanning electron microscope operated at 10 kV and by image analysis.2.5Spectral data were obtained according to a previously reported procedure and data on different functional groups present in lignin were obtained from integration of the spectra and calculated as described herein 2.63 as the eluent. Lithium nitrate  was added to minimize association effects. All samples were made up at 0.1% (w/v) in 0.075 M DMSO/LiNO3. Pullulan polymer standards (PSS) ranging from 180 to 708.000 g mol−1 were used for calibration.GPC was used to examine the mass average molecular weight and molecular weight distribution of isolated (starting) and processed lignin. For the determination of molecular weight changes following thermoplastic processing, lignin was Soxhlet-extracted for 24 h from ground composites using DMSO and lyophilized. The measurements were performed at 50 °C using 0.075 M DMSO/LiNO


The existence of such neuronal avalanches has been confirmed by several studies both in vitro and in vivo, among different species and across multiple spatial scales. However, despite the prevalence of scale free activity, still very little is known concerning whether and how the scale-free nature of cortical activity is altered during external stimulation. To address this question, we performed in vivo two-photon population calcium imaging of layer 2/3 neurons in primary visual cortex of behaving mice during visual stimulation and conducted statistical analyses on the inferred spike trains. Our investigation for each mouse and condition revealed power law distributed neuronal avalanches, and irregular spiking individual neurons. Importantly, both the avalanche and the spike train properties remained largely unchanged for different stimuli, while the cross-correlation structure varied with stimuli. Our results establish that microcircuits in the visual cortex operate near the critical regime, while rearranging functional connectivity in response to varying sensory inputs.Mounting evidence supports the hypothesis that the cortex operates near a critical state, defined as the transition point between order  and disorder . This criticality is manifested by power law distribution of the size and duration of spontaneous cascades of activity, which are referred as  How does the activity of individual neurons and neuronal circuits give rise to knowledge representation, computation, and cognition? This fundamental question in neuroscience  is deeplThe self-organized criticality hypothesis for the nervous system ,12 contiHere, we addressed this question by imaging the activity from populations of layer 2/3 neurons in primary visual cortex of awake and behaving mice during visual stimulation. We analyzed the spike trains with respect to (i) the statistics of the spatiotemporal cascades of activity  , (ii) thThis study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The protocol was approved by the Committee on the Ethics of Animal Experiments of the Columbia University (Permit Number: AC-AAAD0720). All surgery was performed under isoflurane anesthesia, and all efforts were made to minimize suffering. This study represents an independent analysis of experiments described previously . In brieAll animals were monitored daily, including weekend days, according to an internal schedule. During daily monitoring if an animal was extremely jumpy to touch, vocalized while undisturbed in cage, was unable to groom, or showed lethargy, ruffled fur, or signs of abnormal nursing behavior, we further determined distress or pain by comparing weight loss to other age and sex matched animals. A couple of animals became ill after surgery, were euthanized, and were not included in this study. All mice used in this study were healthy throughout the experiment. Infection at site of surgery confounds experimental results making any data acquired invalid. Thus, animals with infection were euthanized to prevent further pain. A couple of mice became ill and were euthanized. They were not included in the study. No animals died prior to the experimental endpoint. Daily monitoring of every injected animal was carried out by our laboratory, including weekend days, according to an internal schedule. In addition, weight loss compared to baseline pre-surgery was monitored to assess pain and distress. Animals were weighted twice weekly and euthanized if weight loss was greater than 20%. Animals were euthanized by carbon dioxide to prevent further pain. The secondary method of euthanasia was cervical dislocation.On the imaging day, mice were anesthetized with isoflurane and the craniotomy, marked previously, was completed for dye injection. For bulk loading of cortical neurons Oregon Green Bapta-1 AM (Molecular Probes) was first dissolved in 4 μL of freshly prepared DMSO containing 20% Pluronic F-127 (Molecular Probes) and then further diluted in 35 μL of dye buffer [150 mM NaCl, 2.5 mM KCl, and 10 mM Hepes (pH 7.4) . SulforhVisual stimuli were generated using the MATLAB (MathWorks) Psychophysics Toolbox  and dispThe raw images were processed to correct brain motion artifacts using the enhanced correlation coefficient image alignment algorithm  or a hidS, i.e., the integrated network activity between threshold crossings, and its duration D, i.e., the time between threshold crossings  The maximum avalanche size Smax was taken as the largest observed avalanche size. (ii) The exponent τ was estimated for three values of the minimum avalanche size Smin ranging from 1 to 3 and the corresponding Kolmogorov-Smirnov (KS) values were obtained. (iii) The minimum avalanche size Smin and the corresponding exponent τ yielding the smallest KS value were chosen. (iv) When Smax reduced by 1 until the condition Using maximum likelihood estimation methods, we fitted a truncated power law S = Smin(1 − r)τ − 1)−1/ with the same exponent, number of avalanches, minimum avalanche size, and maximum avalanche size, as estimated from the experimental avalanche distribution. Specifically, using the inverse method, the surrogate distributions were generated according to n method .KS statistics. The p value was calculated as the fraction of the surrogate distributions with KS values smaller than the KS value of the corresponding experimental avalanche distribution. We took the significance level to be 0.05, i.e., for p < 0.05 the power law hypothesis was rejected, whereas for p ≥ 0.05 the power law hypothesis was not rejected. This hypothesis testing procedure is illustrated, by plotting the experimental avalanche distribution over a gray band that delineates the 5 to 95 percentiles of the surrogate distributions.The deviation between the simulated surrogate distributions and a perfect power law was quantified with the The uncertainty of the exponent estimation was computed using bootstrap method. After estimating the exponent from the experimental avalanche distribution, we resampled actual avalanches (with replacement) 1000 times and then fitted the resampled data to a power law and estimated the exponent. The standard deviation of re-estimated exponents provided an estimate of the uncertainty in the exponent estimation from the experimental avalanche distribution.S > ~ Dβ, we estimated the fitted β from the experimental data using linear regression. We then compared the fitted β to the predicted β, where the predicted β = (α − 1)/(τ − 1) was obtained from the size and duration exponents derived from pure power law estimation [To test whether average avalanche size scales with duration according to < timation .Cross-correlation coefficients were obtained by calculating the zero-lag pairwise Pearson correlation coefficients of the thresholded inferred spike probability for a given stimulus condition using the Matlab corrcoef routine. The resulting matrix of cross-correlation coefficients for grating stimulation was clustered using hierarchical clustering with maximum or complete-linkage clustering (Matlab dendrogram routine). Neurons were rearranged accordingly to visualize the clusters in the cross-correlation matrix during grating stimulation. The clusters seen for grating stimulation indicate groups of neurons with similar orientation selectivity. Subsequently, the cross-correlation matrices for ongoing activity and natural movie stimulation were plotted without rearranging the neurons. This display shows the reorganization of the cross-correlation when varying the stimulus condition.To record neuronal population spiking at cellular resolution within cortical microcircuits, we performed two-photon population calcium imaging of layer 2/3 neurons in primary visual cortex of head-fixed awake and behaving mice during three conditions of visual stimulation: dark screen, moving grating, and naturalistic movie . RecordiS, i.e., the number of spikes, quantified by the area under the curve between threshold crossings, and its duration D, i.e., the time between threshold crossings ~Sτ− (P(D)~Dα− ~S−τ –S3 Figs P(D)~D−α –S6 Figs.P(D)~D−α . AvalancP(D)~D−α  Figs.Importantly, despite the different spatiotemporal structure of the three stimulus conditions, recorded population activities typically resulted in power-law avalanche distributions , indicatIn addition, the exponent values tended to be smaller for anesthetized mice compared to awake mice . This obConcerning the potential impact of the stimulus condition on the exponent values, the data were less conclusive . While fS > ~ Dβ. Second, the exponent β is not independent, but rather depends on the exponents τ and α according to β = (α − 1)/(τ − 1). Our experiments confirmed both these predictions from the scaling relation for all stimulus conditions. Average avalanche size scaled with duration < S > ~ Dβ according to a power law (τ and α provided a good prediction β = (α − 1)/(τ − 1) of the fitted β , defined as the ratio of the standard deviation and the mean of the inter spike intervals (ISI) of individual spike trains, provides qualitatively different measures to test the criticality hypothesis. Irregular spiking with a CVISI distribution with peak above 1 is predicted to be an emergent property of a neural network operating near criticality [ISI distribution peaked above CVISI values of 1 , movie (yellow), and ongoing (blue). The solid dots denote the avalanches included for fitting to a truncated power law; open circles denote avalanches that were excluded in the fitting procedure see . P-value(PDF)Click here for additional data file.S2 FigThe violet dots denote the avalanches (based on recorded spike trains) that were included for fitting to a truncated power law; pale green dots denote avalanches that were excluded in the fitting procedure see . Shuffli(PDF)Click here for additional data file.S3 Figp < 0.05 the power law hypothesis was rejected, whereas for p ≥ 0.05 the power law hypothesis was not rejected.For visual comparison, the gray shading indicates the range (5–95%) of expected probabilities for the truncated power law with the same exponent as estimated from the experimental data and with the same number of samples see . P-value(PDF)Click here for additional data file.S4 Figp < 0.05 the power law hypothesis was rejected, whereas for p ≥ 0.05 the power law hypothesis was not rejected.Probability density functions for avalanche durations for each mouse and for the three stimulus conditions: grating (red), movie (yellow), and ongoing (blue). The solid dots denote the avalanches included for fitting to a truncated power law; open circles denote avalanches that were excluded in the fitting procedure see . P-value(PDF)Click here for additional data file.S5 FigThe violet dots denote the avalanches (based on recorded spike trains) that were included for fitting to a truncated power law; pale green dots denote avalanches that were excluded in the fitting procedure see . Shuffli(PDF)Click here for additional data file.S6 Figp < 0.05 the power law hypothesis was rejected, whereas for p ≥ 0.05 the power law hypothesis was not rejected.For visual comparison, the gray shading indicates the range (5–95%) of expected probabilities for the truncated power law with the same exponent as estimated from the experimental data and with the same number of samples see . P-value(PDF)Click here for additional data file.S7 FigS > ~ Dβ between average avalanche size and duration as predicted by criticality theory. The fitted exponent β is derived from the linear regression line (yellow dashes). The predicted line (red) is derived from the predicted exponent β = (α − 1)/(τ − 1). Solid purple dots were included in the exponent estimation, open circles were not  the size is plotted  vs the duration for each mouse. For each avalanche duration the average avalanche size (cyan dots) is plotted. The linear relationship on logarithmic axes reveals a power law relationship <  not see .(PDF)Click here for additional data file.S8 FigS > ~ Dβ between average avalanche size and duration as predicted by criticality theory. The fitted exponent β is derived from the linear regression line (yellow dashes). The predicted line (red) is derived from the predicted exponent β = (α − 1)/(τ − 1). Solid purple dots were included in the exponent estimation, open circles were not  the size is plotted  vs the duration for each mouse. For each avalanche duration the average avalanche size (cyan dots) is plotted. The linear relationship on logarithmic axes reveals a power law relationship <  not see .(PDF)Click here for additional data file.S9 FigS > ~ Dβ between average avalanche size and duration as predicted by criticality theory. The fitted exponent β is derived from the linear regression line (yellow dashes). The predicted line (red) is derived from the predicted exponent β = (α − 1)/(τ − 1). Solid purple dots were included in the exponent estimation, open circles were not  the size is plotted  vs the duration for each mouse. For each avalanche duration the average avalanche size (cyan dots) is plotted. The linear relationship on logarithmic axes reveals a power law relationship <  not see .(PDF)Click here for additional data file.S10 FigISI) distributions for the recorded neurons for 10 mice and three stimulus conditions. All CV distributions are widely distributed with most neurons having a CVISI larger than 1.The ISI coefficient of variation (CV(PDF)Click here for additional data file.S11 FigThe distributions of the zero-lag pairwise Pearson cross-correlation coefficients of the thresholded inferred spike probabilities for all mice and for the three stimulus conditions. Significance testing was obtained by comparing with uncorrelated spike trains of the same mean rate and adopting a p-value threshold of 0.1.(PDF)Click here for additional data file.S12 FigTOP ROW: The cross-correlation coefficient matrix for the grating stimulus for each mouse, with the matrix clustered using a hierarchical clustering algorithm see . A subse(PDF)Click here for additional data file.