Estimates of the crust composition are derived from average sediment, while crustal growth is ascribed to amalgamation of differentiated magmatic rocks at continental margins. Here we show that chemical weathering of Ni- and Cr-rich, undifferentiated ultramafic rock equivalent to ~1.3 wt% of today’s continental crust compensates for low Ni and Cr in formation models of the continental crust. Ultramafic rock weathering produces a residual that is enriched in Ni and also silica. In the light of potentially large volumes of ultramafic rock and high atmospheric CO The concentration of Ni and Cr of the continental crust cannot be explained by formation models involving differentiated magmatic rocks. Here, the authors show that hydrothermal alteration and chemical weathering of ultramafic rock compensates for the low Ni and Cr concentrations of island arc-type magmatic rocks. Models of continental crust formation involve partial melting of mantle peridotite2 and have developed from amalgamation of island arc-type magmatic rocks of andesite composition5, magmatic recycling of chemically weathered material6, to geodynamically more complex mechanisms involving melting of mantle metasomatites8, reactions with overlying peridotite10 and differentiation of subducted material via buoyancy-driven delamination–relamination processes12. Model complexity has progressively increased to account for the mismatch between experimentally derived data, the composition of juvenile island-arc type andesitic melts and the composition of the continental crust13. Despite the general consensus among the different estimates of the continental crust composition, the concentrations of Ni  and Cr  do not reconcile with the average andesitic composition of 30 ppm Ni and 55 ppm Cr19  by secondary serpentine phases (Mg3Si2O5(OH)4) and magnetite (Fe3O4)  and awaruite  as reaction by-product phases is controlled by fluid pH, fO2,aq and aSiO2,aq, that are dependent on temperature and rock composition. Hydrothermal ultramafic rock alteration by CO2-enriched fluid in the continental crust  promotes precipitation of additional magnesite (MgCO3) and dolomite (CaMg(CO3)2). Carbonate precipitation increases aSiO2,aq and commonly stabilises additional secondary silicate minerals with Si/(Mg+Fe) higher than in the precursor olivine and/or serpentine , soapstone  and listvenite (quartz+magnesite/dolomite)25. Isobaric cooling at constant aCO2,aq will result in a similar reaction path with additional clay mineral formation at low temperature and high aCO2,aq O4) Fig. . Concomiine Fig. . Along a32, and produce sharp reaction interfaces with the fresh interior  and chromite are present as minor matrix constituents. Magnesite veins and patches increase in abundance towards the talc-carbonate alteration zone in the shallower part of the drill core. Talc-carbonate alteration of serpentinite is massive and resulted in the formation of coarse-grained magnesite in a matrix composed of talc, minor chlorite and magnetite, consistent with the assemblages of soapstone altered peridotite elsewhere , near Yakabindie Station from the Western Australian Yilgarn Cratonere Fig. 25. Cr-spved Fig. , whereas2 (1.18 ± 0.04)20  and high Ni and Cr (1200–3800 ppm) concentrations and a slightly elevated MgO/SiOMgO Fig. . Elevateδ26Mg) is independent of earlier magmatic processes and Mg fractionates into 24Mg-enriched magnesite and 26Mg-enriched talc during hydrothermal peridotite carbonation37. Thus, bulk rock concentrations of Mg in combination with its isotope ratios provide constraints on elemental mass-changes related to stabilisation and breakdown of individual mineral phases. Deviation of the Mg isotope ratios of talc-carbonate altered samples to values lower than in the precursor serpentinite at near constant SiO2/MgO is consistent with addition of small amounts of 24Mg during serpentinite carbonation  thus provide unique natural laboratories to investigate the effect of erosion of weathered peridotite on bulk sediment composition.Estimates of the crustal composition are based on large sets of sediment samples that integrate over the composition of large provenance areas32. The Solund basin is one of five continental supra-detachment fault basins situated along the coast of south-western Norway. Caledonian nappes containing the Solund-Stavfjord ophiolite constitute the depositional basement of the Solund basin and are separated from the underlying Western Gneiss region by the Nordfjord-Sogn detachment zone. The Lower Devonian depositional age of the Solund basin has been constrained by fish and plant fossils, at which time Scandinavia was in a near-equatorial position, promoting tropical weathering of the exposed ophiolite40. The basin infill is dominated by conglomerate with subordinate sandstone layers that are distinctly more abundant in the central part of the basin. The conglomerate layers contain between 1 and 20 vol% peridotite boulders that show an alteration-related zonation with peridotite cores, intermediate talc zones and red rims composed of a quartz+hematite ± calcite assemblage24 that are similar to those observed for weathering of talc-carbonate rock at the SGC, Western Australia. Alteration involved a strong depletion in bulk rock Mg and formation of a quartz-hematite-dominated weathering assemblage resembling jasper in the outer layer, while the cores often retain the original peridotite. Some former peridotite boulders have been entirely silicified and/or carbonated and can only be classified as former peridotite based on their high Cr and Ni contents.Samples used to investigate the influence of peridotite in the sediment provenance area on bulk sediment composition were collected from the Solund Devonian sedimentary basin and from a Devonian conglomerate-dominated sedimentary sequence in the periphery of the Feragen ultramafic bodySandstone layers interbedded with conglomerate are abundant, display a distinctive red colour and comprise a mixture of ultramafic, mafic and felsic provenance Fig. . Mafic c17 except that it is distinctly enriched in Ni, Cr and light REE (LREE) (Supplementary Table 2 content is 63 ± 3.76 wt% and the molar Mg# (Mg/Mg+Fe) is 0.35. Chondrite-normalised REE patterns are steep from La to Gd, flat for the heavy REE and show a slight negative Eu anomaly , south eastern Norway, allow for detailed sampling and textural analysis of mineral replacement interfaces, whereas in the SGC this is complicated by the large scale of the drill core and lack of spatial context. The circular FUB (~4.1 km in diameter) is one of several ultramafic intrusions in the Cambro-Silurian felsic basement of the Trondheim basin and composed of interlayered dunite and harzburgiteces Fig. . Weather43. Analysis of serpentinisation textures reveals that Ni in olivine partitions into secondary Fe-Ni alloy (awaruite) or Ni-sulphide phases (e.g. heazlewoodite), depending on the sulphur fugacity during serpentinisation  47 constrain the mass of peridotite required to balance the andesite model to 1.28 wt% of today’s continental crust.The composition of sediments used to define the average composition of the upper continental crust is controlled by rock composition in the provenance area and element solubility in surface water. Sediment concentrations of elements with low solubility in aqueous fluid, such as Ni and Cr2 concentrations during the Archean49 and resulting lower pH of surface waters have likely prevented precipitation of Ni50 during weathering of komatiite. Hence, Archean weathering and solute transport in the surface runoff can be assumed to be distinct from the Phanerozoic.Assuming that the Ni budget of the Phanerozoic continental crust is at steady-state with input from weathering of volcanic arcs and ultramafic rock and output due to sediment transport into subduction zone trenches and dissolved Ni in the continental runoff, the mass-balance provides constraints on the ultramafic rock weathering flux. The steady-state assumption can only apply for the Phanerozoic as during the Precambrian large volumes of Ni-rich komatiite were continuously being added to the crust. Furthermore, elevated atmospheric COJUMW) required to balance Ni in the in- and output fluxes from the continental crust based on the sediment transport and riverine discharge rates and their Ni concentrations together with the Ni concentration in the average andesitic crust relative to the weathering flux from volcanic arcs (JArc)45. The weathering flux from continental volcanic arcs is unknown but constraints can be derived from rates of arc magmatic addition and bulk sediment transport to the oceans. The current sediment removal rate from the continents is ~0.5 km3 yr−141 while the estimated average arc accretion rate is ~1.1 km3 yr−153. Even though these estimates are subject to uncertainty, the weathering flux from arc magmatic rocks that contributes to continental sediments must be smaller than arc accretion rates to allow for growth or maintenance of the continental crust. It is therefore probable that the accumulation rate of sediment derived from weathering of volcanic arcs is similar to the total continental sediment output or smaller. For volcanic arc weathering rates of 0.5 km3 yr−1 and 0.25 km3 yr−1, the mass-balance indicates an ultramafic weathering flux of 6.18 ± 4.46 × 1013 g yr−1 and 7.68 ± 5.40 × 1013 g yr−1, respectively . The presence of several similarly large ophiolite complexes28 implies that our estimate of the global ultramafic weathering flux is consistent with independently constrained peridotite weathering rates in nature and corroborates the importance of ultramafic rock weathering for the Ni and Cr budget of the continental crust. The mass-balance also indicates that Cr accumulates on the continents relative to Ni or that Ni is preferentially transported to the oceans relative to Cr. This is reflected in estimates of the upper continental crust composition and Ni and Cr concentrations in banded iron formations46  on the crustal Co budget is small relative to the contribution of volcanic arc weathering and that Co is effectively removed from the continents at volcanic arc weathering rates below 0.4 km3 yr−1  indicates average volcanic arc concentrations of 27.7 ± 44.2 ppm Ni , 68.7 ± 137 ppm Cr (n = 9783) and 18.5 ± 12.8 ppm Co (n = 6310). These average concentrations are similar to those of average andesite5 indicating that the average andesite composition is a valid proxy for the different lithologies present in volcanic arcs. Nevertheless, the presented mass-balance represents a maximum estimate for the amount of ultramafic rock weathering contributing to continental sediments, as additional Ni is provided by weathering of continental intraplate volcanic rocks including e.g. flood basalt. The compilation of compositional data for continental flood basalt55 indicates average concentrations of 98 ± 125 ppm Ni and 203 ± 297 ppm Cr . These concentrations are slightly higher than in average andesite, the bulk, and upper continental crust but significantly lower than that of peridotite. As continental weathering of mafic rocks is somewhat slower than that of ultramafic rocks56 and the overall contribution of continental intraplate volcanic rocks to crustal growth is presumably small (0.1 km3 yr−151), we conclude that Ni, Cr and Co addition from weathering of intraplate volcanic rocks can only be a fraction of that derived from ultramafic rock and volcanic arc weathering.Even though we use the composition of average andesite3 yr−1, SiO2 liberation from altered ultramafic rock is approximately 2.78 ± 2.0 × 1013 g yr−1 that is equivalent to 3.4% of the total SiO2 input to the continental crust in our mass-balance calculation  assemblages are common in Archaean greenstone belts  approximately 41 times higher than Phanerozoic seawater (~9 nm) resulting from chemical weathering of the proto-continental crust with a large proportion of Ni-rich peridotite46. Archean hydrothermal alteration and weathering has likely been facilitated by elevated atmospheric pCO2 and heat flux61 resulting in low pH surface waters and increased dissolution kinetics. While this particular geochemical environment would diminish the formation of Fe-hydroxide phases and low-temperature carbonates that sequester Ni from the continental runoff, it favours precipitation of crystalline and amorphous silica phases. If the production of silica-enriched weathering residuals on the continents scales with the seawater Ni concentration, chemical weathering of ultramafic and mafic rocks must have been an important driver towards the compositionally evolved felsic continental crust prior to the onset of plate tectonics.The presence of silcrete and listvenite in altered ultramafic terranes provides direct evidence that both chemical weathering and hydrothermal carbonation favour generation of silica-enriched product phases extending to pure quartz, which is eventually added to the continental crust as sediment. High bulk rock Ni concentrations in quartz-bearing ultramafic weathering residuals and hydrothermally altered peridotite indicates that Ni can be used as a conservative tracer of fluid-driven silica release from ultramafic rock. Based on the calculated ultramafic rock weathering flux at volcanic arc weathering of 0.5 km17, whereas average andesitic crust has 30 ppm Ni5 and average ultramafic rock has 1324 ± 888 ppm Ni (n = 881)47. To compensate for the low Ni concentration in andesite that contributes the bulk mass to the continental crust, Ni addition via weathering of peridotite equivalent to ~1.28 wt% of the bulk continental crust is required. The calculated mass is based on the continental crust volume of 7.18 × 109 km35 and andesite density of 2.65 g cm−3 and represents a minimum estimate as Ni is continuously removed from the continental crust by the sediment runoff and river discharge.The average composition of the continental crust is based on the composition of continental sediments. Weathering of two principal source rocks contribute to the sedimentary Ni budget, low-Ni differentiated magmatic rock (andesite) and high-Ni ultramafic rock. The ultramafic source comprises komatiite flows, ultramafic intrusions and tectonically obducted oceanic lithosphere (ophiolites). The upper continental crust Ni concentration has been constrained to 47 ppm63, the lack of evidence for significant Mg concentration changes of the Phanerozoic continental weathering flux64 and the uniform composition of post-Archaean terrigenous clastic sediments65. We can therefore assume that the Phanerozoic Ni budget of the continental crust is at steady state with input from weathering of accretionary volcanic arcs and ultramafic rocks and output as dissolved and particulate matter in the continental runoff45. The steady-state assumption cannot be made for the Archean due to the continuous addition of Ni-rich komatiite66. We can therefore define the continental mass-balance for Ni as:J represents the fluxes for each of the inputs and outputs and CNi their Ni concentrations. Subscripts UMW, Arc, GloSS and River denote ultramafic rock weathering, weathering of volcanic arc-derived average andesite, global subducting sediments and the continental runoff. Based on the steady-state assumption, the ultramafic rock weathering flux can be calculated using:JGloSS) and its Ni concentration , and the average Ni concentration of the continental runoff and the global discharge are 0.801 ± 0.08 ppb and 3.74 ± 0.37 × 1019 g yr−1, respectively45. For our calculation we use 5. This concentration is consistent with the average Ni concentration of the global compilation 55 of volcanic arc rocks with bulk rock SiO2 concentrations above 45 wt%, which accounts for the presence of mafic rocks in volcanic arcs. The volume to mass conversion in our model is based on the andesite density of 2.65 g cm−3. Calculated uncertainties for the ultramafic weathering and element fluxes are based on propagation of individual uncertainties of each flux and concentration value. We use published uncertainties for the flux and element concentrations of the global subducting sediment (GloSS)41, estimate a 10% uncertainty for the global riverine discharge and its concentrations45 and the concentrations in average andesite5. The uncertainty in all calculated average concentrations for ultramafic and volcanic arc rocks is the 1σ standard deviation and represents the overall spread of the data population55.The contribution of ultramafic rock weathering to the composition of the continental crust can be further explored by constraining the weathering flux required to maintain its Ni budget. By restricting our flux calculation to the Phanerozoic, the mass-balance simplifies because of the relatively even occurrence of ophiolite complexes throughout the Phanerozoic67. The intensity data was corrected for Time Dependent Intensity (TDI) loss (or gain) using a self–calibrated correction for Si Kα1, Na Kα1, Ti Kα1, K Kα1, Fe Kα1. Interference corrections were applied to Fe for interference by Mn, and to Mn for interference by Cr68. Results are the average of three points and detection limits ranged from 0.006 wt% for Si Kα1 to 0.008 wt% for Al Kα1 to 0.009 wt% for Na Kα1 to 0.012 wt% for Ti Kα1 to 0.028 wt% for Ni Kα1. Analytical sensitivity (at the 99% confidence level) ranged from 0.279% relative for Si Kα1 to 0.981% relative for Al Kα1 to 10.056% relative for Cr Kα1 to 52.104% relative for Na Kα1 to 392.577% relative for Ti Kα1. Oxygen was calculated by cation stoichiometry and included in the matrix correction. The elemental maps were processed using Probe Software’s CalcImage application and Matlab. The matrix correction method was ZAF and the mass absorption coefficients data set was LINEMU Henke  < 10 KeV/CITZMU > 1 0KeV. The ZAF algorithm utilised was Armstrong/Love Scott69.Quantitative elemental maps and mineral compositions were acquired on a JEOL 8530F electron probe micro analyser (EPMA) housed at the Centre for Microscopy, Characterisation and Analysis (CMCA), The University of Western Australia (UWA). The EPMA is equipped with five tunable wavelength dispersive spectrometers. Operating conditions were 40 degrees takeoff angle, and a beam energy of 15 keV. The beam current was 20 nA for calibration and map acquisition. The beam diameter was 2 µm. Dwell time was 40 msec per pixel with a pixel dimension of 2 × 2 µm. Elements were acquired using analysing crystals LiFH for Ti Kα1, Cr Kα1, Mn Kα1, LiF for Fe Kα1, Ni Kα1, PETJ for Ca Kα1, K Kα1, and TAP for Mg Kα1, Si Kα1, Al Kα1, Na Kα1. The standards were an assortment of synthetic and natural minerals and metals. The counting time was 20 s on peak for all elements, and Mean Atomic Number background corrects were used throughout70, which are implemented in the CAMECA PAP‐program.Qualitative elemental maps were acquired using a Cameca SX100 electron microprobe . Analyses were carried out using an acceleration voltage of 15 kV, a beam current of 10 nA, and a counting time of 10 s for major and minor elements. Na and K were analysed first. Standardisation was done with synthetic oxides and natural minerals. Matrix corrections follow described procedures71. Magnesium separation (~20 µg) of bulk digests took place in 10 ml Bio-Rad Poly-prep columns and >99% of the Mg loaded into the columns was recovered at the end of the procedure. The cation/Mg ratio of all samples prior to isotope analysis was <0.001 as determined by ICP–MS analysis. Magnesium isotopic compositions were measured using a Thermo-Finnigan ‘Neptune’ Multi Collector ICP–MS at Géosciences Environnement Toulouse (GET), France. All solutions were prepared in 0.32 m HNO3 and introduced into the argon plasma using a standard spray chamber. Solution concentrations were typically ~600 ppb and gave intensities of ~12 V, with total procedural blanks generally having a negligible contribution of <3 mV. Sample-standard bracketing was used to correct instrumental mass fractionation effects and all data are presented as δxMg with respect to DSM3 international reference material (δxMg = ((xMg/24Mg)sample/(xMg/24Mg)DSM3−1) × 1000), where x refers to the Mg mass of interest. All sample analyses were run in triplicate with the mean value presented in Supplementary Table δ26Mg analyses was assessed by replicate analyses of Mg reference standards reported in Supplementary Table 74.The magnesium isotopic composition of the whole rock samples was determined after chromatographic separation following the protocol described in Mavromatis et al.2 and FeO were performed by Actlabs Laboratories Ltd., using the lithium metaborate/tetraborate fusion ICP Whole Rock and the trace element ICP/MS packages.Whole-rock geochemical analyses including CO2 sample gas are measured by infrared absorption after decomposing 0.2 g of sample material in a resistance furnace in a pure nitrogen environment at 1000 °C, using an ELTRA CW–800 (www.actlabs.com).Samples are mixed with a flux of lithium metaborate and lithium tetraborate and fused in an induction furnace. The melt is immediately poured into a solution of 5% nitric acid containing an internal standard, and mixed continuously until completely dissolved (~30 min). The samples are run for major oxides and selected trace elements on a combination simultaneous/sequential Thermo Jarrell–Ash ENVIRO II ICP or a Varian Vista 735 ICP. Calibration is performed using seven prepared USGS and CANMET certified reference materials. One of the seven standards is used during the analysis for every group of ten samples. FeO is determined through titration, using a cold acid digestion of ammonium metavanadate, and hydrofluoric acid in an open system. Ferrous ammonium sulphate is added after digestion and potassium dichromate is the titrating agent. Weight fractions of dry COThe data used to support the findings of this study are available from the corresponding author upon request.Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2


For negative supercoiling, this regime lies between bubble-dominated and plectoneme-dominated phases, and explains the broad transition between the two observed in experiment. Tip bubbles cause localisation of plectonemes within thermodynamically weaker AT-rich sequences, and can greatly suppress plectoneme diffusion by a pinning mechanism. They occur for supercoiling densities and forces that are typically encountered for DNA  Biological information is not only stored in the digital chemical sequence of DNA, but also in the mechanical properties of its strands, which can inuence biochemical processes involving its readout4678910111213510151617181920Here, we study the interplay between plectonemes and bubbles using a recently developed model, oxDNA22in vivo. Torsion is quantified using the length-independent superhelical density σ; σ = +(−)1 for one full positive (negative) imposed turn per pitch length. We study a 600 base-pair (bp) duplex using an “average-base” parameterisation derived at 500 mM NaCl22First, we test oxDNA by calculating canonical “hat-curves” for strand extension over a range of applied torsions and forces similar to those used in single molecule assays4678910111213L  does not change appreciably as σ is increased from zero until a buckling transition occurs to a writhed plectonemic structure at σ = bσ(F). As σ is increased beyond bσ(F), the overall extension decreases linearly with σ because the extra writhe is stored in the growing plectoneme. For σ → −σ. For σ causes the formation of bubbles, leading to little or no shortening of the strand. In all simulations, we observed at most one plectoneme, as is expected at high salt concentration and short strand length1291113We now address the unresolved question of how the system transitions between the plectoneme-dominated and bubble-dominated regimes. Instead of a simple transition between the two types of conformations, we observe an intermediate “tip-bubble” regime dominated by states with a co-localised bubble/plectoneme pair. This novel regime can be seen in the population diagram L along the force. For negative supercoiling the tip bubble is also able to absorb some of the negative twist, allowing the plectoneme to shrink further. The latter is the reason why, although tip bubble formation is seen for positive and negative supercoiling (σ.At the plectoneme tip, the duplex axis inverts its direction. For low forces, this is achieved by homogeneous bending. However, when curvature becomes larger an alternative is for bending to be localised at a kink defect involving a few broken base pairs, as recognised in studies on DNA bendingrcoiling  the tranFor negative supercoiling and small forces around 1.0–1.5 pN, end-loop kinks are stable against further bubble growth because larger bubbles lead to a smaller contraction in plectoneme size per disrupted base pair than the initial kink, as shown in It is instructive to compare this tip-bubble scenario to what might be expected if bubbles and plectonemes did not co-localize. The free-energy landscape would then be bi-stable, with plectoneme and bubble states separated by a substantial free-energy barrier due to the significant nucleation costs of both591314Deff exhibits a marked further decrease as the fraction of time that the system is pinned increases. A reduction in plectoneme diffusion with force for positive supercoiling was observed in Ref. To further quantify the dynamics of plectonemes we calculated their diffusion coefficients (see also σ = −0.06 and F = 1.27 pN using a sequence-dependent parametrisation of oxDNA for a fully random sequence with a GC content of 49%, and a block-random sequence containing five 120 bp stretches with a GC content alternating between 70% and 30% (average GC content of 52%; see σ → −σ symmetry at lower forces than the average-base model does (see Bubble formation is highly sequence dependent5σ found in vivoIn conclusion, we predict a conformational regime for DNA where plectonemes and denaturation bubbles are co-localised. This behaviour follows from the basic physics of a semi-flexible polymer with inherent chirality combined with nucleated denaturation and plectoneme formation. These are generic features of DNA under a range of conditions, so our results are likely to be qualitatively robust. One consequence of the tip-bubble state is a broad coexistence regime, which has already been observed experimentally13148 time steps. During simulations, σ was fixed to values in the range −0.1 ≤ σ ≤ +0.1 by trapping the strand ends. Torsionally relaxed states were identified by demanding vanishing torque Γ on the traps. A force F with 0.25 pN ≤ F ≤ 7.9 pN was applied to the molecule ends; further details of the simulations are given in Section I of the Simulations were performed using an Andersen-like thermostatSupplementary Movie: Plectoneme dynamicsSupplementary Information


Acid catalysis by hydronium ions is ubiquitous in aqueous-phase organic reactions. Here we show that hydronium ion catalysis, exemplified by intramolecular dehydration of cyclohexanol, is markedly influenced by steric constraints, yielding turnover rates that increase by up to two orders of magnitude in tight confines relative to an aqueous solution of a Brønsted acid. The higher activities in zeolites BEA and FAU than in water are caused by more positive activation entropies that more than offset higher activation enthalpies. The higher activity in zeolite MFI with pores smaller than BEA and FAU is caused by a lower activation enthalpy in the tighter confines that more than offsets a less positive activation entropy. Molecularly sized pores significantly enhance the association between hydronium ions and alcohols in a steric environment resembling the constraints in pockets of enzymes stabilizing active sites. The rates of acid-catalysed reactions vary in constrained environments. Here the authors show that molecularly sized pores greatly promote aqueous phase alcohol dehydration by enhancing the association between substrate and hydronium ions, and even by lowering the free energy barrier. Understanding and maximizing the rates of acid-catalysed dehydration of alcohols is key to many synthetic strategies1234578998102O)n·H3O+) the prevalent form of Brønsted acid sites (BAS). However, the structure–function relations of hydronium ions generated at solid surfaces are complex3In the presence of high concentrations of water, nearly complete proton transfer occurs from acidic hydroxyls at the solid surface to a cluster of water molecules14151617181920We report here on the fundamentals of the impact of steric confines on hydronium ions and the kinetic and thermodynamic consequences for catalysis, using aqueous-phase dehydration of cyclohexanol as a prototypical example. We show that properly sized confines can be exploited to both maximize the substrate–hydronium ion association and lower the intrinsic free energy of activation for alcohol dehydration. The rate of hydronium ion-catalysed elimination reactions can vary by up to two orders of magnitude with changing size of the confines, relative to aqueous-phase acid solutions, pointing to the possibility of designing catalysts that can achieve enzyme-like rates for acid-catalysed reactions under mild conditions.3PW12O40, H4SiW12O40, HClO4 and H3PO4, <0.1 M) showed identical rates when normalized to the concentration of hydronium ions (abbreviated as H3O+aq), that is, all these acids had identical turnover frequencies. The H3O+aq concentration in pure liquid water was too low to catalyse the reaction at detectable rates at these temperatures.The initial rates on solid acids  decreased with increasing pore dimensions  and saturation uptakes (qmax) were derived using a single-site Langmuir adsorption model to fit the measured isotherms (Hads°) were derived from the Van't Hoff plot of Kads°. The values are in excellent agreement with those determined from aqueous-phase calorimetry (Sads°) showed the opposite trend, growing more positive with increasing pore size. The more negative enthalpy and entropy of adsorption observed for MFI than larger-pore zeolites are mainly a consequence of more favourable dispersive interactions between the MFI framework and the aliphatic portion of the alcohol. Remarkably, an endothermic, entropy-driven process was observed for the adsorption on zeolite FAU  became moderately lower at higher temperatures for all zeolites . These upper-bound estimates correspond to ∼20 water molecules per unit cell for both MFI  and BEA  samples, and ∼70 water molecules per unit cell for FAU . At this level of pore hydration, hydronium ions form locally in the zeolite pore paired with the AlO4– T-site, as shown by X-ray absorption spectroscopy−3 at 0.32 M).The saturation uptake . With the extrapolated Kads°  was estimated to be high under reaction conditions  than the saturation uptake (0.40–1.26 mmol g−1 at reaction temperatures), we conclude that these hydronium ions are all associated with cyclohexanol under reaction conditions, in line with the zero-order dependence on cyclohexanol concentration  were used together with ormation . The depormation . The rep218O as solvent (18O incorporation (10–32%) into cyclohexanol. With the SN2 path for 16O–18O exchange between water and secondary/tertiary alcohols also ruled out2829303118O incorporation would be the recombination between H218O and an intermediate that is formed upon the C–O bond cleavage and precedes the Cβ–H bond cleavage transition state (TS). Therefore, we conclude that dehydration of cyclohexanol in aqueous phase proceeds along the E1 path. We surmise that this positively charged carbenium ion intermediate is loosely solvated by intrazeolitic water moleculesThe results of reactions with unlabelled cyclohexanol and H solvent  and the  solvent  allow usation 10–% into cyβ–H bond breaking step being kinetically relevant. Above we concluded that the hydronium ion catalysed dehydration of cyclohexanol occurs via an E1 path with the C6H11OH]a is the concentration of the association complex formed between cyclohexanol and hydronium ion that depends on the alcohol–hydronium ion association equilibrium constant  and the initial concentration of cyclohexanol. For dilute homogeneous acids ([H3O+aq]=3–4 × 10−3 M), KL,a was determined to be ∼40 and weakly temperature dependent (433–473 K)K1) for cyclohexanol in water was determined to be the highest (1 M−1) at room temperature and then decreased with temperatureK1 (protonation of cyclohexanol) was estimated to be <0.2 M−1. Thus, for catalysis by homogeneous acids, Here, [C6H11OH]a inside the zeolite pore does not depend on the alcohol concentration in solution. Since protonation of cyclohexanol by the hydronium ion cluster in zeolites is thermodynamically unfavourable according to density functional theory (DFT) calculations  should be smaller than or comparable to, but cannot be considerably greater than, k-2 (Cα–O bond recombination) and that the free energy of TS3 is higher than or similar to that of TS2 (in situ nuclear magnetic resonance (NMR) spectroscopic data for this reaction in zeolite HBEA that showed that k−2 is more than a factor of two greater than k3  in reaching the kinetically relevant TS  for all aqueous-phase dehydration catalyses. The differences in ΔG°‡ , derived from kinetic measurements and the transition state theory formalism, are compiled in  in ΔG°‡  indicateus acids .H°‡ and ΔS°‡ increased with increasing pore size, most prominently from MFI to BEA. This can be qualitatively attributed to the increased van der Waals contacts for the larger-sized TS (loosely bound carbenium ion and water) relative to the adsorbed alcohol with the internal walls of smaller pores. However, it is important to mention that the observed activation enthalpies and entropies may include contributions from changes in the organization of the reactant molecule itself and water molecules before and after reactionFor zeolite-catalysed dehydration, both ΔIn conclusion, the catalytic activity of hydronium ion is drastically altered by the constraints and the local organization of solvents and substrate molecules induced by its environment. The compensation between enthalpy and entropy induced by the nanoscopic environment results in a ranking of free energy of activation that perfectly explains the measured activities and allows predictions of activities for other Brønsted acids and alcohol substrates. These findings show that a molecular environment can be tailored to dramatically enhance reaction pathways for acid catalysis in water, opening new opportunities for designing catalysts for selective conversions, utilizing confinement principles in analogy to enzymatic systems.−1 to 723 K in a 100 ml min−1 flow of dry air for 6 h) and stored in closed containers kept away from volatile organics. Specifically, HZSM-5 (Si/Al=45) and HBEA (Si/Al=75) were provided by Clariant AG, while HFAU  was obtained from Zeolyst International. CHA was synthesized according to the reported procedure2 (Aerosil-90), amorphous silica alumina , phosphotungstic acid (H3PW) and tungstosilicic acid (H4SiW) were purchased in hydrate form from Sigma–Aldrich. Commercial γ-Al2O3 and boehmite products from Sasol (Condea) were also tested for the title reaction. More details about the chemicals and catalyst characterization protocols are provided in the Commercial zeolites were obtained in the H-form, activated once . The uptake was determined by the change in the bulk concentration, volume of the solution and mass of the solid sample. Heat of cyclohexanol adsorption from aqueous solutions into MFI, BEA and FAU zeolites was determined by aqueous-phase calorimetry using a Setaram Calvet C80 calorimeter with reversal mixing cells. The lower compartment was loaded with 0.03 g zeolite dispersed in 0.8 ml water, while the upper compartment was loaded with 0.2 ml of the cyclohexanol solution. The reference cell was loaded with liquids with identical compositions but without zeolite.Aqueous-phase dehydration reactions were performed in a 300 ml Hastelloy Parr reactor, a gradientless batch reactor. Preliminary tests showed that the zeolite samples (protected from contaminants) could be used without reactivation procedures. For reactions catalysed by solid acids, the external diffusion limitation  was precluded in preliminary experiments varying the stirring speed (400–700 r.p.m.) and catalyst loading (50–300 mg).2 or 6 bar N2 (for heteropoly acids due to their reducibility at elevated temperatures) at room temperature and heated up while being stirred vigorously (∼700 r.p.m.). The reaction time was based on the point when the set temperature was reached (∼10–15 min). At the end of a reaction, the reactor was cooled with an ice/water mixture to at least 277 K. The reaction mixture of cyclohexene (oil phase) and cyclohexanol-containing aqueous phase was extracted using dichloromethane . The combined DCM phase was dried over Na2SO4 and an aliquot of the solution was analysed on an Agilent 7890A gas chromatograph (GC) equipped with a HP-5MS 25 m × 0.25 μm (i.d.) column, coupled with Agilent 5975C MS. For quantification, 1,3-dimethoxybenzene was used as the standard. The carbon balance was typically at 85–90%.In a typical experiment, 80 ml 0.33 M aqueous cyclohexanol solution and a specific amount of catalyst  were loaded in the reactor. The closed reactor was then pressurized with 40 bar H−1 according to aqueous-phase calorimetric measurements). Consequently, the uptake of base titrants was always complete, as verified by the analysis of the post-extraction DCM phase by GC–MS (splitless injection).For titration experiments, different quantities of aqueous solutions (0.03 M) of pyridine or 2,6-lutidine were added to the starting reaction mixture, and then rates were determined, using the protocol described above, on these pretitrated samples. Pyridine and 2,6-lutidine adsorb much more strongly than cyclohexanol on acid sites (differences in heat of adsorption >20 kJ mol18O-labelled water and nonlabelled cyclohexanol (0.3 M) were carried out in a ∼2 ml stirred batch reactor constructed from a stainless steel ‘tee' (HiP), while ensuring similar solution-to-headspace ratios (0.3–0.4) as in the Parr reactor. The recovered post-reaction mixture was extracted with DCM , dried over Na2SO4 and analysed with GC–MS. For the analysis of 18O content in recovered alcohol, it is useful to consider that the dominant MS fragment ion of unlabelled cyclohexanol is m/z 57, the [C3H5O]+ ion, while the m/z 59 is present with 1% of the ion intensity of m/z 57. Thus, the intensity ratio between the two fragment ions can be used to quantify the extent of 18O incorporation into cyclohexanol that would lead to a growth of the m/z 59 peak.Rates of dehydration were measured in the Parr reactor for perdeuterated cyclohexanol , using operation protocols described above. Experiments using All data are available within the article and its  files anHow to cite this article: Shi, H. et al. Tailoring nanoscopic confines to maximize catalytic activity of hydronium ions. Nat. Commun.8, 15442 doi: 10.1038/ncomms15442 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary figures, supplementary tables, supplementary notes, supplementary methods and supplementary references.


Localizing facial landmarks is a popular topic in the field of face analysis. However, problems arose in practical applications such as handling pose variations and partial occlusions while maintaining moderate training model size and computational efficiency still challenges current solutions. In this paper, we present a global shape reconstruction method for locating extra facial landmarks comparing to facial landmarks used in the training phase. In the proposed method, the reduced configuration of facial landmarks is first decomposed into corresponding sparse coefficients. Then explicit face shape correlations are exploited to regress between sparse coefficients of different facial landmark configurations. Finally extra facial landmarks are reconstructed by combining the pretrained shape dictionary and the approximation of sparse coefficients. By applying the proposed method, both the training time and the model size of a class of methods which stack local evidences as an appearance descriptor can be scaled down with only a minor compromise in detection accuracy. Extensive experiments prove that the proposed method is feasible and is able to reconstruct extra facial landmarks even under very asymmetrical face poses. Facial landmark localization is the first and a crucial step for many face analysis tasks such as face recognition , cartoonFor the last ten years remarkable progress has been made in the field of facial landmark localization , 8. AmonHowever, facial landmark localization still meets great challenges in practical applications, such as handling pose variations and partial occlusion while maintaining moderate training model size and computational efficiency. In SDM and its improved methods, the dimension of regression matrix in each stage keeps the same with the stacked feature descriptors, where the magnification of dimension is relevant to the number of feature points. This substantially limits the use of feature descriptors with high dimension. In this type of methods, solving such high dimensional regression matrices occupies a large memory and the computation of both training and testing is not very efficient.In this paper, we consider the possibility of only training a subset of available facial landmarks while in predicting stage the “missing” landmarks are restored by the inner shape correlation as if they were trained before. In this way, both the training time and the model size can be scaled down by only compromising a minor detection accuracy. Following this idea we propose a global shape reconstruction (GSR) method for predicting extra facial landmarks with limited models; see The main contributions of this paper are the following: (1) we exploit sparse shape constraints for nonlinear restoring extra facial landmarks comparing to landmarks used for training. Specifically, we propose two strategies, named constrained global shape reconstruction with Procrustes analysis (C-GSR-PA) and simple global shape reconstruction with Procrustes analysis (S-GSR-PA), respectively, to utilize the facial landmarks correlation in landmark reconstruction procedure. (2) We study the performance between model compression and alignment error in extensive experiments. As mentioned above, using the proposed method existing works can train models on less landmarks while predicting equivalent results, but this may introduce corresponding alignment error during reconstruction. The experimental comparison results and conclusions are given in the end.Facial landmark localization methods can be roughly divided into two categories : generatThe active shape model  and actiIn contrast to generative methods, discriminative methods such as deep network based methods and cascade regression based methods have attracted much attentions in recent years. In the deep network domain, Sun et al.  first haAs discussed in ESR  and dualIn this section, we present the proposed method for extra facial landmark localization in detail. First we give a brief review of the popular SDM framework. Then the training process on reduced facial landmarks is described. Finally, extra facial landmarks are predicted via proposed sparse shape constraints. An approximate sparse shape reconstruction method is also presented for reconstructing just a small number of extra facial landmarks.S of a face image is here defined by a vector of facial landmarks coordinates in ℝN2 shape space; that is, S =  ∈ ℝN2, where N is the number of landmarks and xn is the 2D coordinates of the nth landmark. Given an image I and an initial shape S0, SDM progressively refines the face shape by estimating a shape increment ΔS, which is added up to the shape estimate of previous stage. This can be formulated asRt[·] is the stage regressor. In the case of SDM, Rt[·] is generalized into a linear regressor Rt. ϕt(·) is a nonlinear feature mapping function that stacks shape-indexed feature descriptors into a single vector and T is the total number of regression stages. Since the feature mapping function ϕt(·) is related to the previous shape estimate, the output of the function is also referred to shape-indexed features. In the training stage, the stage regressors  are learned sequentially.The shape Previous works mostly focus on designing a robust feature mapping function or a specific form of stage regressors . PracticJ labeled face images I = {Ij}j=1J, along with their ground truth locations of facial landmarks S = {Sj}j=1J, where Sj is in the ℝN2shape space, K facial landmarks are selected out of the original N landmarks, which means Sj =  and makes Sj in the ℝK2 shape space. Let ϕt(·) extract shape-indexed features on the newly formed shape Sj. The SDM method seeks an optimal Rt at each stage, which minimizes the following fitting cost:Sjt−1 is the shape estimate of jth image at t − 1 stage. Then the shape estimate of the next stage is updated by Sjt = Sjt−1 + Rtϕt. Equation (Given a set of SK2 ∈ ℝK2 → SN2 ∈ ℝN2  (N > K). Although the restoring problem is ill-posed, the sparse shape representation demonstrates robustness in regularizing this problem. To be more precise, let DN2 ∈ ℝN×M2 denote an overcomplete shape dictionary containing M exemplar shapes (M > 2N). That is, any face shape SN2 ∈ ℝN2 can be reconstructed as SN2 = DN2γ, where γ ∈ ℝM is a coefficient vector with a few nonzero entries. Since the dictionary DN2 is overcomplete, the above equation is underdetermined and has a unique solution if γ is sparsest. As for the shape of reduced facial landmarks SK2, a similar sparse decompose equation can be formulated as SK2 = DK2γ. The coefficient vector of sparse decomposition is reused in extra landmarks restoration. To summarize, for every input shape SK2 a corresponding sparse coefficient vector is solved by using the dictionary DK2. The shape dictionary DN2 will be combined to the coefficient vector to generate the desired output SN2. In the following we propose two strategies to train the dual dictionaries DK2 and DN2.Motivated by sparse shape constraint proposed in DSC-CR , we adopSN2 is collected for training the dictionary DN2. The dictionary DN2 can be obtained by minimizing the following equation:k-singular value decomposition (K-SVD) [l0-norm problem in  realized in LIBLINEAR toolbox [SN2 = DN2ΥN2 ≈ DN2HΥK2, as illustrated in We call this a constrained strategy since the sparse coefficient vector is reused in a regressive manner. A number of face shapes  (K-SVD)  and orth (K-SVD)  to solveequation:argminD2equation:argminD2ated fromargminΥ2 toolbox  to solveequation:argminH ΥK2 are considered as the rough approximation of the coefficients ΥN2. Then extra facial landmarks can be calculated from SN2 = DN2ΥN2 ≈ DN2ΥK2, when K → N, as illustrated in To further simplify the C-GSR method, an approximation is made as follows: after calculating all the sparse coefficient vectors in C-GSR, the sparse coefficients Experiments are conducted on two datasets, namely, LFPW-68 and HELEN-68.LFPW-68. The labeled face parts in the wild (LFPW) dataset [ dataset  include  dataset . And thiHELEN-68. The HELEN dataset [ dataset  contains dataset  to test ∗400 pixels and flipped together with the landmark coordinates to enlarge the training data size. To achieve a better generalization ability, 10 initial shapes are randomly sampled for each training and testing image. The cost in the SVR is set to 0.005. For the SDM method, 5 stages are used in the cascade regression. Histogram of gradient [As an overall setting, every training image is cropped by detecting a face bounding box and then resized to 400gradient  is adoptK-SVD is utilized to train a compact shape dictionary. A grid search for two parameters is performed. The size M of shape dictionary is varied from 200 to 800 while keeping the shape sparsity constant. Then the shape sparsity α is varied from 5 to 35 while fixing the shape dictionary size. The comparing results are plotted in K-SVD has an optimal value and is not the larger the better. α and β.In this section, we evaluated the reconstruction error of  with resN = 68. Here experiments are conducted on ground truth shapes since we only intend to find out the best model simplification strategy and not to test generalization ability. It will be tested in latter sections.In this section, we study the reconstruction performance under different extra facial landmarks reconstruction strategies. Two strategies of C-GSR and S-GSR with the Procrustes analysis are denoted with a “-PA” postfix. Experiments are all carried out on LFPW-68 points testing dataset; that is, K. The size of K is varied from 22 to 68. We reconstruct extra facial landmarks SK2 ∈ ℝK2 → SN2 ∈ ℝN2  (N ≥ K) by using proposed strategies. It shows in K = N = 68, which means no extra landmarks are actually reconstructed. In this case, the error is introduced in the process of coefficient solving and shape synthesis. With the size of K growing, all the reconstruction error curves have a decrease trend. The error curve of S-GSR is obviously steeper than the error curve of C-GSR, indicating that the size of K has a great influence on the S-GSR method. Comparing S-GSR with S-GSR-PA, it can be found that the Procrustes analysis makes a difference when K is small. But it gradually plays a less role in rectifying reconstruction shifts when K grows larger. From the enlarged view in K is smaller than 62 points, the C-GSR-PA achieves a far stable performance than S-GSR-PA. This is due to the rough approximation from sparse coefficients ΥK2 to ΥN2 in S-GSR-PA. But the S-GSR-PA shows a comparable performance when K exceeds 62 points.In K is relatively small. But if K is large enough, a few extra facial landmarks can be restored accurately by S-GSR-PA. To clarify the robustness of proposed method, we also illustrate the reconstruction results when half of the face landmarks are removed. This task is challenging and practical because large pose variations often exist in the wild images. It shows in columns (d) and (f) that the proposed strategies can effectively explore inner shape constraints and reconstruct the other half face landmarks even under a very asymmetry case, which is impossible for a typical linear interpolation algorithm.In order to get an intuitive understanding of the proposed method, we give a visualization of five different landmark configurations in In this section, we evaluate the performance of the proposed method in four aspects, namely, the normalized alignment error, training time cost, testing time cost, and training model size, on two facial datasets. We first train models as described in In From For a better understanding, we also present the detection results with predicted landmarks labeled as green dots in As discussed in Sections In this paper, we present a global shape reconstruction method for locating extra facial landmarks comparing to facial landmarks used in the training phase. By applying the proposed method, both the training time and the model size of a class of methods can be scaled down with only a minor compromise in detection accuracy. Specifically, we propose a constrained strategy (C-GSR-PA) which exploits the sparse coefficients constraints in face shape correlations. Extensive experiments show that it is able to reconstruct up to three times facial landmarks even under very asymmetrical face poses. We also propose a simplified strategy (S-GSR-PA) which shows comparable performance when reconstructing a few facial landmarks. It does not need to train a regression matrix in advance and require a less computation resource. It has potential in refining a small number of unreliable predictions.


Forests cover 30% of the terrestrial Earth surface and are a major component of the global carbon (C) cycle. Humans have doubled the amount of global reactive nitrogen (N), increasing deposition of N onto forests worldwide. However, other global changes—especially climate change and elevated atmospheric carbon dioxide concentrations—are increasing demand for N, the element limiting primary productivity in temperate forests, which could be reducing N availability. To determine the long-term, integrated effects of global changes on forest N cycling, we measured stable N isotopes in wood, a proxy for N supply relative to demand, on large spatial and temporal scales across the continental U.S.A. Here, we show that forest N availability has generally declined across much of the U.S. since at least 1850 C.E. with cool, wet forests demonstrating the greatest declines. Across sites, recent trajectories of N availability were independent of recent atmospheric N deposition rates, implying a minor role for modern N deposition on the trajectory of N status of North American forests. Our results demonstrate that current trends of global changes are likely to be consistent with forest oligotrophication into the foreseeable future, further constraining forest C fixation and potentially storage. These increases in N deposition, along with changes observed in some ecosystems2, have led to the suggestion that a planetary boundary has been crossed, leading to wide-spread eutrophication that will destabilize the Earth system and human society4. However, other global change factors, such as elevated CO2 can stimulate plant productivity and reduce N availability, despite increased deposition5. Ongoing environmental changes such as altered precipitation6, drought, and warming7 are also likely to directly affect N cycling although the total magnitude and direction of change are unknown. Therefore whether N availability has been increasing or decreasing—and over which spatial and temporal scales—is an open question. In support of the declining N hypothesis, observations of leaf N concentrations in European forests have shown multi-decadal declines8, forests of the eastern U.S. have demonstrated increased demand for N relative to supply9, and streamwater nitrate export has been declining for decades at sites in the U.S.10 and Europe11. Despite these indications, there is no coherent picture of the long-term changes in N availability of forested ecosystems at broad scales.N deposition rates to forests are generally elevated over pre-Industrial levels due to widespread increases in supplies of reactive N from human activities, potentially increasing forest N availability13, current and future trajectories of forest N cycling are difficult to predict. Hence, there is considerable uncertainty about future forest productivity because it depends on availability of the limiting nutrient, N. As a critical step toward reconstructing historic trajectories in N availability for North American forests, we measured the isotopic composition of N stored in wood of living trees from a variety of forest types and climatic conditions. The stable N isotopic composition (δ15N) of plant tissue tends to increase with increasing N availability14 as sites with high N availability generally lose a high proportion of their N through fractionating loss pathways15. The δ15N of N acquired by plants is recorded in wood, which serves as a long-term, stable record of N availability experienced by trees16. Here, we assembled data from published literature and acquired new data on trajectories of δ15N in tree rings to allow for reconstruction of N availability for 49 forest sites across the continental United States , with an average first year of 1888 C.E. All sites had trees with wood dated from 1970 to 2005. A large portion of the climate space in North American forests is represented by our sampled sites, which range in mean total annual precipitation (MAP) by over 2400 mm, mean annual temperature (MAT) by 19 °C and N deposition by 14.5 kg ha−1 y−1 . An average wood δ15N value was then calculated for each site for each decade as well as the average year within the decadal range that the wood was sampled. When averaged across all sites, the composite curve reveals declining wood δ15N values over nearly the entire period of analysis. The cumulative change in wood δ15N from 1850 to 2010 was a decline of 1.41 ± 0.34‰, with 1.13 ± 0.34‰ of that occurring since the 1930s. These values are equivalent to an annual rate of change of −0.009‰ y−1and −0.014‰ y−1, respectively. This pattern did not change significantly when calculated using only the 45 sites that predate 1930 ; the cumulative change in wood δ15N for these sites over the entire record was equivalent to a decline in wood δ15N of 1.45 ± 0.34‰.Trajectories of wood δrve Fig. . Data fo15N can be analyzed in more detail over the past few decades because the time period covered by all sites begins in 1970 . Cooler forests declined in δ15N at a faster rate than warmer forests . The lack of a significant effect of N deposition occurred whether rates of change in wood δ15N were calculated beginning in 1960 or 1970 (P > 0.12 for both).The geographic patterns of declining wood δ95‰ Fig. . Althoug15N trajectories. Both mean basal area increment (BAI) and the slope of BAI over time (both since 1970) were regressed against the residuals of the model of modern wood δ15N. Mean BAI since 1970 varied among sites by over 3700 mm2 y−1 . Individual trees also varied greatly in their trajectory of BAI over time, with the central 95% of the values ranging from −82 mm2 y−2 to 52 mm2 y−2. After accounting for climate and N deposition, there was no influence of wood production or trajectories in wood production on the change in wood δ15N over time. Neither sites with greater BAI nor sites that were increasing in BAI the most were declining in δ15N more than others (P > 0.37 for both). Likewise, the age of trees at each site did not influence the rate or magnitude of declines in wood δ15N (P = 0.69). A parallel mixed-effects regression model examined relationships between wood δ15N and predictors for individual trees and indicated no significant differences in the declines in wood δ15N between angiosperms and gymnosperms . Sites were selected to be geographically distributed within the contiguous United States, represent a natural or semi-natural area, contain over 50% forested canopy cover, and span a wide range of climates. Tree selection at each site targeted individuals in canopy positions with straight stems, from species representative of the overall stand composition. We therefore sampled multiple tree species at sites with mixed species assemblages. Cores that were damaged, appeared to be stained from fungal colonization, or contained fewer than 40 consecutive annual rings were excluded from further consideration.15N data at super-annual timescales.Following sampling, cores were dried for at least 72 h at 70 °C, then sanded using 400 grit sandpaper. After sanding, each core was scanned at 1600 dpi using an Epson Expression 1000 XL scanner . From these high-resolution images, the widths of all individual rings were quantified using Cybis CooRecorder v7.8 and CDendro v7.8 . Although we performed tests of ring-width coherence for all trees within a site, some dating imprecision likely remains with these chronologies, and therefore we selected analytical and statistical approaches that allowed for integration of wood δ15N, generally selecting the longest series for analysis. First, increment cores were sectioned by hand with a razor blade along ring boundaries until the sample approximated a target weight of 30 mg. Ultimately, individual rings were divided or aggregated into 25–30 mg segments, providing enough N for isotopic analysis while limiting the carbon content and the risk of incomplete combustion. On average, each sample represents 1.6 years of wood. Not all samples of wood were analyzed for δ15N; we selected enough samples from each core to provide at least two measurements of wood δ15N per decade. No chemical pretreatment of wood samples was performed as none of the reported pretreatment protocols consistently identify and remove labile N components in wood  in per mil format (‰) relative to AIR. Each lab used a two-point normalization curve with internal standards calibrated against international standards.After partitioning, wood tissue was wrapped in pressed tin capsules and analyzed at one of six labs: the University of California Davis Stable Isotope Facility using a PDZ Europa ANCA-GSL elemental analyzer interfaced to a PDZ Europa 20–20 isotope ratio mass spectrometer , the University of New Mexico Stable Isotope Laboratory using a a Costech 4010 elemental analyzer  connected to a ThermoFinnigan Delta V isotope ratio mass spectrometer , the Central Appalachians Stable Isotope Facility at the University of Maryland Center for Environmental Science, the Stable Isotope Mass Spectrometry Laboratory at Kansas State University, the University of Florida, and the W. M. Keck Paleoenvironmental and Environmental Stable Isotope Laboratory at the University of Kansas. All elemental analyzers employed traps of MgClOde novo here, additional wood δ15N data were complied from the published literature (three sites) and unpublished data (seven sites). The criteria for inclusion of these data included the following requirements: (1) δ15N data must be available for a minimum of three trees per site, (2) the δ15N chronologies must extend at least 40 years, and (3) data must have been collected since 2005.In addition to the 39 sites collected http://nadp.sws.uiuc.edu/committees/tdep/tdepmaps/. Although climate variables and N deposition levels are dynamic during the duration of the tree-ring record, including the period of analysis 1970-present, we decided to use a summary of the most accurate data for predictor variables for this time period. The assumption with this approach is that across space, relative values of climate variables and N deposition have remained similar. For the time periods for which we have N deposition data and climate data, that assumption is supported.For each site, mean annual temperature (MAT) and mean total annual precipitation (MAP) were obtained from the PRISM Climate Group (http://prism.oregonstate.edu) incorporating interpolated data averaged from 1981–2010. Mean annual atmospheric N deposition was obtained from the National Atmospheric Deposition Program National Trends Network (NTN) monitoring stations. Total N deposition (wet + dry) averaged from 2000–2013 for each site was extracted from deposition maps provided by the NADP at 15N from 1970 to the end of the record was calculated for each core at a site using a simple linear regression. Site-level mean slopes were then calculated and relationships with log-transformed MAP, MAT, and N deposition were assessed with a multiple regression. No outliers were detected in the regression model, based on a Bonferroni Outlier Test (P > 0.05). Residual wood δ15N from this model was regressed against mean basal area increment (BAI) since 1970 and the linear slope of BAI over time since 1970. BAI was calculated from the inside of the core outwards based on ring-width measurements. Basal Area Increment (BAIt) was calculated for each year as follows:rt = radius of outside of ring, and r0 = 0.To assess recent patterns of N cycling in forests, the slope of wood δ15N over time, a random effects model that paralleled the multiple regression model was assessed using the slopes from individual trees of wood δ15N since 1970, but included the species’ taxonomic group (gymnosperm vs. angiosperm) and site identity as a random effect in addition to MAT, MAP, and N deposition. There were not enough samples of any tree species to allow for species-level analysis. Twelve of the 49 sites had more than one tree taxonomic group (gymnosperms and angiosperms). Our study design did not target tree taxonomic group, rather we focused on sampling representative trees at each site.To determine whether there were differences between angiosperms and gymnosperms in their trajectory of wood δ15N, the average wood δ15N value from 1970–2015 was calculated for each increment core. A constant that was opposite in sign to this average was then added to each wood δ15N value for the increment core. This standardizes the average wood δ15N value from 1970–2015 = 0‰ and all individual values to represent an anomaly from this standardized mean. Data for all cores were then averaged at the decadal scale for each site and a second decadal-scale average generated across all sites. A loess curve was then fit to these data and curves representing the 95% confidence interval for the predicted fit was calculated. This is a common approach used to synthesize spatio-temporal data39.To assess the long-term patterns of wood δAll data analyses were conducted in R v. 3.2.5. Data for this manuscript are available at the Dryad Digital Repository.Supplementary Information


Scaffolded DNA origami has proven to be a powerful and efficient technique to fabricate functional nanomachines by programming the folding of a single-stranded DNA template strand into three-dimensional (3D) nanostructures, designed to be precisely motion-controlled. Although two-dimensional (2D) imaging of DNA nanomachines using transmission electron microscopy and atomic force microscopy suggested these nanomachines are dynamic in 3D, geometric analysis based on 2D imaging was insufficient to uncover the exact motion in 3D. Here we use the individual-particle electron tomography method and reconstruct 129 density maps from 129 individual DNA origami Bennett linkage mechanisms at ~ 6–14 nm resolution. The statistical analyses of these conformations lead to understanding the 3D structural dynamics of Bennett linkage mechanisms. Moreover, our effort provides experimental verification of a theoretical kinematics model of DNA origami, which can be used as feedback to improve the design and control of motion via optimized DNA sequences and routing. Scaffolded DNA origami by folding single-stranded DNA into three-dimensional nanostructures holds promise for building functional nanomachines, yet their dynamic structures remain largely unknown. Here, Lei et al. address this issue using individual-particle electron tomography at 6–14 nm resolution. The folding occurs in a molecular self-assembly process utilizing Watson–Crick DNA base pairing.The development of functional nanomachines paves the way for the advancement of novel devices and processes. Nanomachines have demonstrated a potential to revolutionize medicine, and reduce resource consumption and environmental pollution in manufacturing processes. They also enable new manufacturing processes to assemble objects, such as biomolecules, gold nanoparticles, quantum dots, and polymers, in a precise and efficient way. Scaffolded DNA origami2,3 have demonstrated that the nanotechnology can be used to design and fabricate DNA origami mechanisms (DOM) with multiple degrees of freedom and complex 2D and 3D motion. DOM can exhibit motions, which can be actuated using DNA strands as an input. This actuation can be reversed using DNA strand displacement4, providing precise motion control. To advance the analysis and future design of dynamic DOM, a thorough understanding of the 3D structure and motion of DOM is required.Recently, Castro and colleagues2 used transmission electron microscopy (TEM) to image DOM deposited on a surface and obtained geometric parameters, including lengths and angles. As these measurements were performed only on two-dimensional (2D) projections, they are inadequate for complete validation of 3D structures. Furthermore, although single-particle 3D reconstruction methods using TEM can achieve atomic resolution, it requires image averaging over many homogeneous structures. This is unsuitable for dynamic mechanisms that adopt many different conformations. For these two reasons, individual-particle electron tomography (IPET) was chosen to study the 3D structures of each individual DOM (without averaging from different DOM molecules). The IPET reconstruction method requires no pre-given initial model, class averaging, or lattice, but can tolerate small tilt errors and large-scale image distortion to achieve an intermediate resolution of 3D reconstruction (1–2 nm for negative staining samples and 3–5 nm for cryo-electron microscopy (cryo-EM) samples) via decreasing the reconstruction image size5. In the IPET iterative refinement process, automatically generated dynamic low-pass filters and soft masks were sequentially used. The IPET 3D reconstruction approach has been used to study the 3D structure and dynamics of DNA-nanogold conjugations6, very low density human lipoproteins7, immunoglobulin-G1 antibodies8, and many other macromolecules9–12.In earlier works, Castro and colleagues5 to reconstruct the 3D structure from a tilt series of each individual DOM. By comparing the conformations revealed from each 3D reconstruction, this approach enables us to understand how the underlying design is related to the structural mobility and motion of DNA origami-based machines.In this study, we used the IPET method15 cryo-EM, and cryo-positive staining electron microscopy method (cryo-PS).16Samples of DNA origami Bennett linkages Fig.  were pre−1. The representative particles showed a “quadrilateral” shape . The representative particle images  showed a poor agreement with the kinematics model20  was relatively low compared with the resolutions reported for rigid DNA origami21 (~ 1 nm) and similar to our earlier reported ~ 5.0 nm resolution for very-low-density lipoprotein (using cryo-EM and IPET)7, these maps still provided enough information for us to obtain 129 conformations of Bennett linkage. The 129 conformations of Bennett linkages are the first observed dynamic DNA origami in 3D. These structures are all “quadrilateral” shaped but with various conformations ranging from a square-like open conformation to a compacted conformation.Although the 3D structure of rigid DNA origami has been obtained at ~ 1 nm resolution using cryo-EM and single-particle reconstruction methods23. To reduce the effects of the missing wedge, the missing wedge data was estimated via computational algorithms reported by numerous groups26. For example, a computational approach to fill the missing data in 2D electron crystallography was first reported by Agard and Stroud in the 1980s27. Recently, a simple Fourier angular filter to effectively suppress the ray artifacts in the single-axis tilting projection acquisition scheme was reported by Kovacik et al.24. A statistical reconstruction method, sequential maximum a posteriori expectation maximization was used to compensate the missing-wedge effects by Ruotsalainen et al.28. An iterative compressed-sensing optimized non-uniform fast Fourier transform reconstruction (ICON) for missing-wedge restoration was proposed by Sun et al.25. Moreover, a generalized Fourier iterative reconstruction algorithm (GENFIRE) proposed by Miao and colleagues26 showed a certain capability in reducing the missing wedge artifact to achieve a 3D structure with more isotropic resolution. All those approaches benefited the 3D reconstruction by reducing the missing wedge effects and providing a relatively isotropic resolution 3D density map. In our IPET, a similar missing wedge filling algorithm was also included for 3D reconstruction correction.Notably, the IPET 3D density maps were reconstructed from the tilt series with tilt angle range of ± 45°. The limited tilt angle range was responsible for missing wedge of information in Fourier space. As a result, the final 3D reconstruction often contains certain artifacts, such as elongation, blurring, and distracting causticsIn the theoretical model, the conformation of Bennett linkages was characterized by the geometrical constraint from joints that connect adjacent arms of the linkage Fig. . In ordeα and β angles from kinematic analysis agree with the measurement when both α and β are large , the linkages are unexpectedly flexible and have different conformations from the design when the linkages are more “closed” . Based on these results and visualization of Bennett linkage model, we proposed potential approaches for improving the control of Bennett linkage conformation. The approach includes redesigning the DNA sequences near the joint that forms the angle α  of diluted sample was then placed on an ultra-thin carbon-coated 200-mesh copper grid , which had been glow discharged for 15 s. After 1 min incubation, the excessive solution on grid was blotted with filter paper. The grid was then washed with water and stained with 1% (w/v) uranyl formate before air-drying with nitrogen. The cryo-PS specimens were prepared following the procedure described by Zhang et al.16. Briefly, an aliquot (~ 4 μl) of DNA origami Bennett linkage sample (concentration ~ 4 nM) was placed on a glow-discharged lacey carbon film-coated copper grid  for ~ 1 min. The grid was then washed twice by 1% (w/v) uranyl formate. Instead of air-drying in the last step, the samples were flash-frozen in liquid ethane at ~ 90% humidity and 4 °C with a Leica EM GP rapid-plunging device  after being blotted with filter paper. The flash-frozen grids were transferred into liquid nitrogen for storage. The cryo-EM specimens were prepared similarly to the cryo-PS specimen, but were not washed by uranyl formate before flash-frozen in liquid nitrogen as described before.16The negative-staining specimens of DNA origami Bennett linkage were prepared using OpNS protocol− Å−2 at a magnification of 50 kx (each pixel of the micrographs corresponds to 0.24 nm in specimen), or a dose of ~ 60–90 e− Å−2 under a magnification of 125 kx (each pixel of the micrographs corresponds to 0.096 nm in specimens) using a Gatan UltraScan 4 K × 4 K charge-coupled device (CCD). The acquired micrographs were Gaussian high-pass filtered to 150 nm and low-pass filtered to 1 nm after the X-ray speckles were removed.OpNS samples were imaged using a Zeiss Libra 120 Plus TEM (Carl Zeiss NTS) operated at 120 kV high tension with a 20 eV energy filter. The untilt micrographs were acquired at Scherzer defocus and with the dose in the range of ~ 10–25 e29 and a Gatan K2 Summit direct electron detection camera at ~ 1 μm defocus and a magnification of 19 kx. At this magnification, the pixel size of the micrographs is 0.185 nm. The electron dose per tilt series is ~50 e− Å−2. On Zeiss Libra 120 Plus TEM (operated at 120 kV), the tilt series were collected from − 45 ° to + 45 ° at 1.5 ° increment using Gatan tomography software and Gatan UltraScan 4 K × 4 K CCD and fully mechanically controlled automated ET software30 at ~ 1.5 μm defocus and a magnification of 50 kx. At this magnification the pixel size of the micrographs is 0.24 nm. The electron dose per tilt series is from ~ 40 to ~ 180 e− Å−2.The cryo tilt series were collected by using a FEI Tecnai TF20 TEM (five series) and a Zeiss Libra 120 Plus TEM (15 series). On the FEI Tecnai TF20 TEM (operated at 200 kV), the tilt series were collected from − 48 ° to + 48 ° at 1.5 ° increment using UCSF tomography software package31 software package. CTF of tilt series collected on the FEI Tecnai TF20 TEM was corrected using TomoCTF32. The tilt series of the particles in square windows of ~ 95 nm × 95 nm were semi-automatically tracked, windowed using IPET software5, and finally binned by two times to reduce computation time in subsequent reconstruction.All tilt series were initially aligned using the IMOD5, a tilt series containing a single DNA origami Bennett linkage particle was extracted from the full-size tilt series. This allows us to perform “focused” 3D reconstruction, such that the reconstruction is less sensitive to image distortion, tilt-axis variation with respect to tilt angle, and tilt angle offset. In this process, an ab initio 3D density map was directly back-projected in Fourier space and served as the initial model. The refinement was then iteratively invoked to translationally align each tilted particle image to the computed projection. During the refinement, automatically generated Gaussian low-pass filters, soft-boundary circular masks and particle-shaped soft-boundary masks were sequentially applied to the tilt images and references to increase the alignment accuracy5. An improved model was then reconstructed based upon the refined alignment at the end of each refinement. The 3D map was then reconstructed by back-projection of filtered and masked particle tilt series. The back projection was performed in Fourier space without weighting. Post 3D reconstruction processing was conducted using our newly developed interactive algorithm to estimate the data in the missing wedge. The resolution was determined based on the FSC that was calculated based upon two reconstructions that were generated using odd and even halves of aligned particle tilt series. It is noteworthy that the generation of the FSC curve did not take into account the missing wedge, as the missing data were estimated in our approach. The frequency at which the FSC curve first falls to a value of 0.5 was used to represent the resolution of the IPET 3D density map. We used the 0.5 FSC criterion, because this method, used for determining the resolution of single particle reconstruction33, gave a lower resolution than other criteria. All of the IPET density maps in the presented figures were low-pass filtered to 8 nm.In the pipeline of IPET reconstruction19 was used to build an initial Bennett linkage model. This model was used to show the flexibility in linkage by flexibly docking it into reconstruction. During this process, each domain (arm) of DNA origami Bennett linkage model was separately translated and rotated to get its best fit to target position in the density map with a minimal overlap among domains. As a result, the achieved conformation of DNA origami Bennett linkage had the same domain structure as initial structure, but differed in the relative position and orientation.Although the resolution (~ 10 nm) of IPET 3D reconstructions of DNA origami Bennett linkage is not sufficient to determine the secondary structure of each individual Bennett linkage, it is sufficient to shed some light on the domain orientations and positions. This information is quite useful in revealing the structural heterogeneous and dynamics. CanDoTo determine the conformational flexibility of DNA origami Bennett linkage, all Bennett linkage conformations were aligned based on their centers using visual molecular dynamics (VMD). The internal angle and dihedral angles of Bennett linkage were then measured and statistically analyzed to explore the structural dynamics of Bennett linkage.To measure the internal angles between the arms of Bennett linkage, four vectors representing the orientations of arms were included. The angles between vectors were then directly measured as the internal angles. Moreover, based on the four vectors, we also calculated the normal directions of planes formed by two adjacent arms. The angles between normal directions were then measured to determine the dihedral angles between planes. The obtained internal angles and dihedral angles, and their corresponding histograms were plotted and fitted with a sixth-degree polynomial function using MATLAB. These histograms represent the conformational space of Bennett linkage, thus can be used to explore the conformational flexibility of Bennett linkage.The data sets generated during and/or analyses during the current study are available from the corresponding author on reasonable request.The datasets generated during and/or analyses during the current study are available from the corresponding author on reasonable request. The TEM 3D density maps of 129 DNA origami Bennett linkage are available from the EM data bank (from EMD-7155 to EMD-7285). Detail of these 129 density maps and their corresponding EMDB deposition number is listed in Supplementary Table Supplementary InformationPeer Review File


The surface charge and chemistry of a protein determines its ability to facilitate biomimetic mineralisation. via a process called biomimetic mineralisation. Herein we show that the surface charge and chemistry of a protein determines its ability to seed MOF growth. We demonstrate that chemical modification of amino acids on the protein surface is an effective method for systematically controlling biomimetic mineralisation by zeolitic imidazolate framework-8 (ZIF-8). Reaction of surface lysine residues with succinic (or acetic) anhydride facilitates biomimetic mineralisation by increasing the surface negative charge, whereas reaction of surface carboxylate moieties with ethylenediamine affords a more positively charged protein and hinders the process. Moreover, computational studies confirm that the surface electrostatic potential of a protein is a good indicator of its ability to induce biomimetic mineralisation. This study highlights the important role played by protein surface chemistry in encapsulation and outlines a general method for facilitating the biomimetic mineralisation of proteins.The durability of enzymes in harsh conditions can be enhanced by encapsulation within metal–organic frameworks (MOFs)  ZIF-8 is porous (BET surface area ca. 1200 m2 g–1), stable in a wide range of organic solvents and can be synthesised in neat water.The most studied MOF for biomimetic mineralisation has been zeolitic imidazolate framework-8 (ZIF-8),A detailed understanding of the chemistry at the interface of the MOF and the biomacromolecule is necessary to develop this burgeoning area. A first step towards this aim is to ascertain how the surface chemistry of the protein influences the biomineralisation process. Preliminary data showed that MOF crystallisation was facilitated by the capacity of the biomacromolecule to attract and concentrate metal cations and ligands; however, empirical data was only provided for a composite made with a single protein, bovine serum albumin (BSA).–1 of protein dissolved in a solution composed of a 1 : 4 : 278 molar ratio of Zn2+ : mIM : H2O). These standard conditions were chosen because: (1) they have previously been shown to give rise to rapid (within seconds) biomimetic mineralisation;Ka 3.7, and glutamate, pKa 4.3) which will be deprotonated, and thus negatively charged under the basic reaction process.ca. 7) which conversely possess a larger percentage of basic amino acids .2+ ions that engenders biomimetic mineralisation.To determine the main features of the protein chemistry that induce ZIF-8 encapsulation, we screened a series of structurally distinct proteins under identical biomimetic mineralisation conditions  and pI of a protein, both of which we have shown to be good discriminators of a protein's ability to seed ZIF-8 formation, can be determined from theory.From the peptide sequence and acid-base equilibria we calculated the pI for all the proteins studied and reproduce the trend in the experimental results shown in Finally, we have computed the electrostatic potential around each of the proteins studied experimentally by solving the Poisson–Boltzmann equation, from which we can approximate the protein zeta potential -N′-ethylcarbodiimide hydrochloride (EDC·HCl) were purchased from Sigma-Aldrich, zinc acetate dihydrate from VWR Chemicals, succinic anhydride from BDH, acetic anhydride from Chem Supply, and ethylene diamine (EDA) from Merck. The water used was ultra pure Milli-Q (MQ) with resistivity of 18 MΩ cm–1 (Merck Millipore purification system). All other buffers and solvents were purchased from commercial sources and used without further purification.All proteins were purchased from Sigma-Aldrich unless otherwise stated Table S1. Each of2  was mixed with a solution of mIM  containing the protein (2 mg). The reaction mixture was left for 16 hours undisturbed, and collected by centrifugation at 4000 rpm. The pellet was washed with water twice, followed by ethanol and air dried at ambient temperature and pressure.Zn(OAc)–1 in MQ water.The method for the succinylation and acetylation of proteins was adapted from literature procedures.The method for the chemical amination of proteins was adapted from a literature procedure.λ = 1.5418 Å) radiation source. Samples were mounted in 0.5 mm glass capillaries and data collected for between 2θ of 2° to 52.94° with Phi rotation at 20 rotations per min at 1 second exposure per step at 5001 steps. The data were then converted into xye format and background-subtracted using WinPlotr 2000 software.49Powder X-ray diffraction (PXRD) data were collected on a Bruker D8-Advanced X-ray powder diffractometer  using a Cu Kα (Scanning electron microscopy (SEM) images were collected using a Philips XL30 field emission scanning electron microscope (FESEM). Samples were dry loaded onto an adhesive carbon tab and sputter coated with 5 nm platinum thin film.UV/Visible (UV/Vis) spectra were recorded at 30 °C on an Agilent Cary 60 UV/Vis spectrophotometer. Samples were diluted to 4 mL prior to each measurement.Cp): 0.887, Dispersant dielectric constant 78.5, f(Ka): 1.5 (Smoluchowski approximation).Zeta potential measurements were recorded on a Malvern Zetasizer nano using a disposable folded cell capillary (DTS1070). Protein samples were dissolved in a HmIM solution  or MQ water (≈pH 7) with measurements recorded with the following parameters; Dispersant RI: 1.33, viscosity  or treated as glutamate residues with respect to their charge and pKa (succinylation). See Fig. S1The pI of surface-modified proteins was calculated using Biopython and assuming 100% efficiency of modification reactions on all target residues. For the amination reaction, any aspartate or glutamate residues were treated as lysine residues with respect to their charge and pCrystal structures were obtained from the Protein Data Bankψ(r), at position r. In the expression, ρ(r) is the (fixed) charge density of the solute (protein), ε(r) is the spatially varying dielectric permittivity, which is different in the protein and in the solution, and κ(r) is the Debye screening parameter given bye is the elementary charge, kB is the Boltzmann constant, T is temperature, ε0 is the vacuum permittivity, εr is the relative permittivity of water (80), and ci0 and zi are the bulk concentration and valency of ions of type i, respectively. For all our calculations the Debye length (κ–1) was 8.86 Å. The efficiency of the linearised Poisson–Boltzmann equation makes it more amenable to high-throughput computational screening than solving the full nonlinear equation and comparison of the calculated average surface potentials with experimental zeta potentials at pH 7 and pH 11 ) using the Boltzmann equation,The concentration of ions of type c0+ = 0.04 M and c0– = 0.08 M, respectively, and were assumed to be independent of pH. The cation and anion valencies were z+ = +2 and z– = –1, respectively. The zinc ion enhancement (X(r)) was calculated from the ion concentration as .To match the experimental conditions, the bulk concentrations of the cations (zinc) and anions (acetate) in solution were taken to be The Authors confirm that there are no conflicts to declare.Supplementary informationClick here for additional data file.