Hum Brain Mapp 38:1208–1223, 2017. © 2016 Wiley Periodicals, Inc.Neuroimaging studies have reported structural and physiological differences that could help understand the causes and development of Autism Spectrum Disorder (ASD). Many of them rely on multisite designs, with the recruitment of larger samples increasing statistical power. However, recent large‐scale studies have put some findings into question, considering the results to be strongly dependent on the database used, and demonstrating the substantial heterogeneity within this clinically defined category. One major source of variance may be the acquisition of the data in multiple centres. In this work we analysed the differences found in the multisite, multi‐modal neuroimaging database from the UK Medical Research Council Autism Imaging Multicentre Study (MRC AIMS) in terms of both diagnosis and acquisition sites. Since the dissimilarities between sites were higher than between diagnostic groups, we developed a technique called Significance Weighted Principal Component Analysis (SWPCA) to reduce the undesired intensity variance due to acquisition site and to increase the statistical power in detecting group differences. After eliminating site‐related variance, statistically significant group differences were found, including Broca's area and the temporo‐parietal junction. However, discriminative power was not sufficient to classify diagnostic groups, yielding accuracies results close to random. Our work supports recent claims that ASD is a highly heterogeneous condition that is difficult to globally characterize by neuroimaging, and therefore different (and more homogenous) subgroups should be defined to obtain a deeper understanding of ASD.  AALAdvanced Automated LabellingAAMActive Appearance ModelsABIDEAutism Brain Imaging Data ExchangeADNIAlzheimer's Disease Neuroimaging InitiativeADOSAutism Diagnostic Observation ScheduleANOVAAnalysis of varianceASDAutism Spectrum DisorderCBMComponent‐based morphometryCSFCerebrospinal fluidGMGrey matterIRInversion RecoveryMNIMontreal Neurological InstituteMRIMagnetic resonance imagingPCAPrincipal component analysisSBMSource based morphometrySPGRSpoilt gradient recallSSPFSteady state free processionSVCSupport vector classifierSVDSingular value decompositionSWPCASignificance‐weighted principal component analysisVBMVoxel‐based‐morphometryWASIWechsler abbreviated scale of intelligenceWMWhite matterAutism Spectrum Disorder (ASD) is a neurodevelopmental syndrome characterized by social and communication impairment as well as restricted, repetitive patterns of behaviour, interests or activities. The delimitation of both functionally and structurally affected areas in the brain in such an etiologically and neurobiologically heterogeneous condition has long been a major concern :pc is the statistical significance of the c‐th component with respect to the acquisition site and pth is the statistical threshold for significance; that is, pth=0.05. This weighting ensured that most of the components of variance that are not related to the acquisition site are kept unchanged, while at the same time it strongly reduces the influence of components with P‐values less than the threshold.The computation of each This procedure is illustrated in Figure To validate the effects of the SWPCA algorithm on the inter‐site variance, experiments were undertaken to assess the reduction of the undesired site variance in the original datasets, and its impact on the between‐group signal. Two kind of analysis were performed: a characterization of voxel‐wise differences, and a classification analysis.t‐test on the smoothed maps from each modality. SWPCA is included (when needed) in this pipeline as a plug in, after the smoothing and before the computation of the test. Permutation testing assessed the significance of the relationship between the tested and target variables. A max‐type procedure was used to obtain family‐wise, whole‐brain corrected P‐values [Freedman and Lane, Z‐maps for visual inspection comparable to those obtained in VBM, by selecting component loadings W, scaling them to unit standard deviation and weighting their contribution to the final map with their statistical significance, computed using the same permutation inference as in VBM.Voxel‐wise differences between groups were characterized using voxel‐based‐morphometry (VBM) [Ashburner and Friston, A classification analysis was undertaken using a common classification pipeline [Khedher et al., The classification was validated using stratified 10‐fold cross‐validation Kohavi, . In brieExperiment 1: To demonstrate the ability of the SWPCA algorithm to reduce undesired effects due to acquisition site, the PCA + SVC pipeline was applied to the datasets labelled by acquisition site. Classification accuracy was compared to datasets with and without SWPCA. VBM was then applied to identify the spatial location of the between‐site differences. This was undertaken on the whole database (ALL), and subgroups containing only ASD or CTL participants.Experiment 2: The discrimination ability of each modality, acquired at different sites was assessed by classification performance of individuals from London (LON) and Cambridge (CAM) was separately assessed, using group (ASD and CTL) as the labels.Experiment 3: To assess the impact of SWPCA on the datasets when characterizing the differences between ASD and CTL groups, the classification pipeline comprising PCA + SVC, as well as VBM and CBM, were applied to all participants with group as the labels.For each modality independently, the following experiments were performed:The first experiment was to demonstrate the ability of SWPCA to reduce the intensity variance related to acquisition site. To do so, we first performed a VBM analysis in all five modalities  separately, with the uncorrected (without applying SWPCA) and the corrected (after applying SWPCA) maps, using the acquisition site as labels.t‐map of significant  GM and WM between‐site differences. The biggest reductions in variance were found in qT1 and synT1 maps, where high variability between acquisition sites, especially in the right hemisphere, was substantially reduced after the application of SWPCA. The reduction in the qT2, GM and WM maps was smaller, although noticeable.To illustrate where the sources of variance of the acquisition sites are located, Figure To quantify the impact of this variance reduction on the between‐groups effects, the classification analysis was undertaken. Higher accuracy values imply that the maps contain site‐related patterns that were significant, whereas accuracy close to 0.5 indicates that the site‐related variance was low. The test was applied to ALL, and also to the ASD and CTL subgroups. The classification results are presented in Table Performance results indicate clear advantages of using SWPCA, in particular in the case of qT1 and synT1 which were associated with strong site‐dependent variance. These results are also consistent with the reduction of significant between‐group areas observed in Figure The between‐site differences were smaller for GM and WM maps, possibly due their reduced sensitivity. Since fractional occupancy values are abstract, unitless values derived from each image they are less influenced by the acquisition site effects. For qT2 maps, the site‐related differences were greater for the CTL participants than ASD where, according to the classification accuracy, they were nearly indistinguishable. Acquisition site differences were therefore noticeably reduced in the CTL and ALL databases, but not in the ASD.In this second experiment, accuracy, sensitivity and specificity in the between‐group comparison were recorded for images acquired from each site. This is an estimation of the discrimination ability of the different modalities without the influence of the site effects; Table Finally, group differences were characterised with and without applying site‐effects reduction via SWPCA to the five modalities.t‐maps of significant  qT1, qT2, synT1, GM and WM between‐group differences, using ALL, with the GM + WM mask, before and after applying SWPCA, so that the reduction of site‐related variability can be observed. Some of the highlighted areas after applying SWPCA are inconsistent across modalities, with spurious peaks and noise, including a large area around the ventricles in the qT1 and synT1 modalities related to some abnormal participants that will be discussed later. However, there were some areas that were consistent across modalities. Significant areas found across at least 4 of the 5 modalities correspond to the Advanced Automated Labelling (AAL) [Tzourio‐Mazoyer et al., z = 60); (B) the pars opercularis of the left inferior frontal gyrus, Brodmann areas 44; (C) the pars triangularis of the left inferior frontal gyrus, Brodmann areas 45; (D) the posterior part of the left middle temporal gyrus (z = 24); CSF filled spaces on the margins of the ventricles ; and the left crus I of cerebellar hemisphere (z = −26).Whole‐brain VBM analysis was performed on the corrected and uncorrected maps from each modality. Figure Z|>2.57 (corresponding to P < 0.01), were superimposed on the MNI template, and are depicted in Figure z = −6, 4, 14, 24), the right middle temporal gyrus (plane z = 14) and the left crus I of cerebellar hemisphere (plane z = −26). However, none of these regions were repeated over more than two of the modalities, except for the large areas around ventricles that were caused by abnormalities in three participants, which will be discussed later.The complementary CBM (Section 2.4) analysis was performed on the most significant components. The resulting regions, statistically thresholded with |Performance results for the classification analysis applied to ALL are shown in Table Brain anatomical and functional differences between ASD participants and controls have been explored by a number of previous studies [Di Martino et al., The technical and logistical drawbacks of multicentre studies are widely documented, including participant recruitment procedures [Pearlson, There are two major types of site effects, regardless of their source: geometric distortions and intensity inhomogeneities. In this work, we focused on the latter, since much of the geometric distortion has been eliminated during acquisition (see section “Image Database”), and the DARTEL normalization and registration acts as a homogenizing step, reducing both between‐site and between‐subject geometric differences, substantially reducing the impact of the site‐related geometric differences.Regarding intensity correction, in the MRC AIMS database used in this study [Ecker et al., In this work, the approach we have taken is to perform a multivariate decomposition of each dataset into a number of components that explain different portions of variance. The following step was to identify the components of variance that are due to multi‐site acquisition and reduce them. Decomposition was completed using PCA and then, to identify which of the components were linked to acquisition site, we performed an ANOVA on the component scores. Finally, using the weighting function defined in Sec. 2.3.3, we reconstructed the original signal reducing the undesired variance, in what we called Significance Weighted PCA (SWPCA). The method has proven its ability in reducing undesired variance, quantifiable by means of the accuracy obtained in a site vs. site classification. In this case, SWPCA reduced the accuracy from >0.8 to approximately ∼0.5, a random classifier, suggesting that most site‐related variance was eliminated.A simpler approach such as applying a voxel‐by‐voxel ANOVA would also be useful to reduce the acquisition site effects [Suckling et al., Y along with the data X as inputs to partition the influence of these into components. However, the most significant difference is the underlying model. Whilst SWPCA estimates the principal components blindly using their variance, which is what we aim to reduce, and performs an ANOVA afterwards, PLS uses the categorical variable in the computation of the covariance matrix and then estimates the components.There exist a number of similar multivariate methods that model the influence of categorical variables, such as the well‐known Partial Least Squares (PLS) algorithm [Vinzi et al., On the other hand, SVA, used for gene expression studies [Leek and Storey, t‐value at the left side (containing Wernicke's area) and positive t‐value at the right side.Focusing on the VBM results, after performing the site‐effects removal by SWPCA significant between‐group differences were noted in five areas: (A) the right superior frontal gyrus; (B) the pars opercularis of the left inferior frontal gyrus; (C) the pars triangularis of the left inferior frontal gyrus; (D) the posterior part of the left middle temporal gyrus; and (E) the left crus I of cerebellar hemisphere. The first three regions are within Brodmann areas 6, 44 and 45. However, when examining the projection of the region D onto the MNI template . After examining the database, two individuals had appreciable structural abnormalities in the form of abnormal ventricle size and cerebellar atrophy, as can be seen in Figure t‐maps, we can assume that most of the structural differences in ASD are so subtle that the influence of just one or two images can impact on the final results. This, along with the poor performance of the classification pipeline presented in Section 3, dramatically reduces the significance of the aforementioned t‐maps. Therefore, the existing evidence leads to the conclusion that ASD presents as either undetectable structural differences or, more likely, with such heterogeneous differences that are difficult to establish a common pattern even after reducing the variance introduced by acquisition site.After observing the influence of these participants on the computation of the It may be the case that cohorts of individuals examined at different sites are somehow systematically biased towards a specific type of patient (in ways that we cannot see simply based on phenotypic information), then site‐related intensity variability is also enriched with important variability about nested autism subgroups. So with any technique trying to remove the site‐related inhomogeneity, the subgroup information could also be removed. Together, the evidence supports the claim that defining meaningful subgroups based on different measures, such as genetic profiling, clinical co‐morbidities or sensory sensitivities, is the most urgent next step for ASD research [Haar et al., In this work, a novel method called Significance Weighted PCA (SWPCA) is proposed. The method comprises a principal component extraction, the characterization of their statistical significance according to a categorical variable  and the reconstruction of the images using a weighted approach. This approach was tested with an ASD database and demonstrated its ability for reducing inter‐site variance, which we have characterized with a site vs. site classification of the images. A priori, the images yielded >0.8 of accuracy, in contrast to a ∼0.5 accuracy between groups, suggesting that the images contained many differences depending on acquisition site. After minimizing site‐related variance, statistically significant group differences were found for example in Broca's area and the temporoparietal junction. However, their discriminative power was not sufficient to classify diagnostic groups, yielding accuracy results close to random. Our work supports recent claims that ASD is a highly heterogeneous syndrome/diagnostic category that is difficult to characterize globally using neuroimaging and therefore different (and more homogeneous) subgroups should be defined using imaging and perhaps other biological measures to obtain a deeper understanding of ASD.


Following the last glacial maximum (LGM), the demise of continental ice sheets induced crustal rebound in tectonically stable regions of North America and Scandinavia that is still ongoing. Unlike the ice sheets, the Alpine ice cap developed in an orogen where the measured uplift is potentially attributed to tectonic shortening, lithospheric delamination and unloading due to deglaciation and erosion. Here we show that ∼90% of the geodetically measured rock uplift in the Alps can be explained by the Earth’s viscoelastic response to LGM deglaciation. We modelled rock uplift by reconstructing the Alpine ice cap, while accounting for postglacial erosion, sediment deposition and spatial variations in lithospheric rigidity. Clusters of excessive uplift in the Rhône Valley and in the Eastern Alps delineate regions potentially affected by mantle processes, crustal heterogeneity and active tectonics. Our study shows that even small LGM ice caps can dominate present-day rock uplift in tectonically active regions. For half a century, the cause for recent uplift of the European Alps has been debated. Here, the authors show that ∼90% of the geodetically measured rock uplift in the Alps can be explained by the Earth’s viscoelastic response to ice melting after the Last Glacial Maximum. Recent vertical movements of the Earth’s crust are mostly due to tectonic deformation along plate boundaries, volcanism and changes in crustal loading from water, ice and sediments6–107 years, whereas others, such as postglacial rebound, occur relatively rapidly (∼103 years). New approaches to modelling orogen-scale sediment storageFor half a century, the cause for recent uplift of the European Alps has been debated. Possible drivers of uplift include postglacial reboundMountain building in the European Alps is due to the convergence of Africa and Eurasia beginning in the Mesozoic with continental collision culminating in the Eo-Oligocene18−1 across the Eastern Alps  stations indicate ongoing crustal convergence of 1–2 mm yrern Alps  that is n Alps 15.In this study, we re-evaluate the effect of GIA on the present-day rock uplift in the Alps while accounting for postglacial erosion, sediment deposition and variations in lithospheric strength. We show that most of the postglacially eroded material was trapped within the mountain belt and did not contribute to erosional unloading as previously suggested3, which is ∼20% higher than the previous estimate of 24 km3, and thus reasonably consistent when considering the differences in spatial resolution. According to our estimate, the total volume of valley-filling sediments is 1,800±202 km3. This volume is a minimum bound on postglacial erosion; yet it is likely only a small underestimate because a large fraction of all postglacially eroded material was trapped in a closed system of overdeepened valleys and marginal lake basins3 of material eroded after deglaciation. Assuming mean densities of 2,000 kg m−3 for unconsolidated sediments and 2,700 kg m−3 for bedrock, we computed a total eroded mass of ∼4 × 103 Gt. Evenly distributed over the Alps  this volume corresponds to a rock column of 11.9±1.3 m. With the assumption that the valley fills have formed following ice retreat ∼17 kyr before the present (BP) , our modelled ice volume is only ∼8% larger , which has negligible effects on our final results. The total reconstructed ice mass is 62 × 103 Gt, which is ∼16 times the mass of the postglacially eroded sediments.Based on mapped ice extent and thickness indicators, such as terminal moraines3233The LGM Alpine ice cap started growing before 30 kyr BP and reached its maximum ∼21 kyr BP, followed by rapid deglaciation with ∼80% ice loss over the course of 3 kyr ref. . This ch19–1021 Pa s, we calculated the corresponding uplift rates associated with deglaciation, which we assume to have occurred at 17±2 kyr BP . To account for the isostatic component related to erosional unloading, we calculated the lithospheric response to the estimated erosion/deposition for average EETs of 10–70 km, assuming that the erosional mass redistribution is compensated by the mantle at a steady rate. We finally compared the combined uplift signal of GIA and erosional unloading with measurements obtained from precise levelling3839Due to the relatively small dimensions of the Alpine ice cap, the upper mantle viscosity determines isostatic uplift and relaxation rates. For a range of mantle viscosities of 1020 Pa s, we are able to reproduce virtually all of the geodetically measured uplift with ∼90% of this uplift caused by deglaciation and only ∼10% by erosional unloading , but higher than that for a region with recent crustal thinning  was much larger than postglacially eroded sediments (∼4 × 103 Gt) and suggest the dominance of ice melting over erosional unloading in contributing to the total recent uplift rate. Furthermore, erosion rate estimates based on our valley-fill volumes exceed long-term rates based on thermochronology , but leads to systematically lower modelled uplift rates in the northwestern periphery of the Alps (−1 and contributes <0.2 mm yr−1 of rock upliftThe most sensitive parameter in our modelling that is not well constrained is the average EET. A low average EET does not change the best-fit viscosity by much (1.7–2.2 × 10the Alps . Active −1) that exceed the combined signal of GIA and erosional unloading by up to 1.5 mm yr−1  and 1:50,000 . To calculate the eroded rock column for each cell since the LGM, we adopted the proposed relation between erosion rate (E) and mean local relief  of E=1.4 × 10−6R1.8 −1 −1 ref.  and a sp)−1 ref. . To achi)−1 ref. . This adWf, for every 10 model time steps (∼30 days) using the two-dimensional elastic thin-plate equation:Because the lithospheric deflection due to glacier growth has a direct effect on the slope and elevation of the ice surface and hence on the ice flow and mass balance, we reconstructed the ice cap for uniform EETs of 20, 30, 40 and 50 km, respectively. We calculated the flexural isostatic adjustment, Df=YEET3/12(1−ν) denotes the flexural rigidity, where EET is the effective elastic thickness of the lithosphere, Y=100 GPa is Young’s modulus and ν=0.25 is the Poisson ratio. L=ρigH−ρagWf is the vertical load where ρi=917 kg m−3 is the density of ice, H is the ice thickness in each model cell and ρa=3,300 kg m−3 is the density of the compensating asthenosphere. The variation in total ice volume and maximum ice thickness due to an increase of the EET from 20 to 50 km is <5%. To further investigate the effect of a laterally heterogeneous lithosphere, we introduced variations in EET3 km3 more ice, which is small when compared with the total ice volume of 68 × 103 km3. The larger ice volume can be attributed to thicker ice (up to 160 m) in the foreland lobes.Here EET of the lithosphere exerts a primary control on the plate’s flexural rigidity, which in turn determines the magnitude and pattern of the isostatic response. Reflecting the long-term and often complex history of the continental plate, the EET depends mostly on the combined effects of rheological and thermal heterogeneity. In this study we use EET estimatesu, which we calculated with an exponential decay model:The viscoelastic decay of the lithospheric deflection after removal of the surface load results in uplift at a rate W0 is the equilibrium deflection, τ is a characteristic timescale of relaxation and t is the time since unloadingwhere μ is the mantle viscosity, ρ=3,300 kg m−3 is the mantle density, g is the gravitational acceleration and λ=320 km is the wavelength of the load, which we calculated from the average transverse extent of the LGM ice cap. We used uplift rates determined by precise levelling in Switzerland38−1 (Z) and 0.8±0.15 mm yr−1 (L) in the global reference frame IGb08 , with larger EETs leading to higher viscosities .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figures 1-7, Supplementary Tables 1-2 and Supplementary References.


This is in agreement with reports of large hail on the ground and with hydrometeor classification derived from ground‐based polarimetric radars observations. This work confirms that multiple scattering generated by large ice hydrometeors in deep convection is relevant for airborne radar systems already at Ku band. A fortiori, multiple scattering will play a pivotal role in such conditions also for Ku band spaceborne radars .Due to the large natural variability of its microphysical properties, the characterization of solid precipitation is a longstanding problem. Since in situ observations are unavailable in severe convective systems, innovative remote sensing retrievals are needed to extend our understanding of such systems. This study presents a novel technique able to retrieve the density, mass, and effective diameter of graupel and hail in severe convection through the combination of airborne microwave remote sensing instruments. The retrieval is applied to measure solid precipitation properties within two convective cells observed on 23–24 May 2014 over North Carolina during the IPHEx campaign by the NASA ER‐2 instrument suite. Between 30 and 40 degrees of freedom of signal are associated with the measurements, which is insufficient to provide full microphysics profiling. The measurements have the largest impact on the retrieval of ice particle sizes, followed by ice water contents. Ice densities are mainly driven by a priori assumptions, though low relative errors in ice densities suggest that in extensive regions of the convective system, only particles with densities larger than 0.4 g/cm Novel technique to retrieve microphysics of graupel and hail in severe convection via the combination of microwave instrumentsObservations compatible with high‐density ice in agreement with ground reports and with polarimetric radar hydrometeor classificationMultiple scattering generated by large ice hydrometeors in deep convection is relevant for airborne radar systems already at Ku band Despite its importance, there is a dearth of observations of convective processes. On the other hand modeling of convective clouds is becoming increasingly sophisticated: regional‐scale numerical weather prediction models are now run at convection‐resolving scales, i.e., at horizontal resolutions of the order of 1 km , , and , respectively. The PIAs that fulfill such requirements and that correspond to the minimum absolute change of δPIA are selected as set of estimated PIAs. Finally, an 11‐bin running average is performed to smooth the PIA field. Estimates of PIAs and corresponding errors are plotted later on in Figure Second, the set of PIAs derived from the first step is then passed through a quality control procedure. If more than one PIA is available , PIAs are recalibrated according to a consistency procedure among the different channels. Each PIA is perturbed by the same amount, 3Rodgers, Delanoë and Hogan, Munchak and Kummerow, Grecu et al., Battaglia et al., Mace et al., In order to get an insight into the microphysical structure of the two convective cells a microphysics retrieval algorithm has been developed. The methodology is based on an optimal estimation approach Rodgers, , which hx (length n) from a measurement vector y (length m) which is characterized by a measurement error ε, i.e., y = F(x) + ε, where F represents a forward operator mapping state into measured variables. The retrieval problem is equivalent to minimizing the following cost function: Sε is the measurement error covariance matrix and xa is the a priori value of x and Sa the associated covariance matrix. Note the two contributions to the cost function coming from the measurements and from the a priori. A normalized cost function for measurements, CFmeasn, obtained by dividing CFmeas by the length of the vector y, will be used in the following sections. The a priori term is used to partially constrain the problem and to prevent nonphysical values from being retrieved .The general inverse problem consists in retrieving an unknown state vector Rodgers, Ji is the Jacobian of the forward operator evaluated at the i iteration of the state vector, xi, andThe solution can be found by Newtonian iterations based on the Gauss‐Newton method Rodgers, : (4)xi+3.1ρice). The PSDs are assumed to be exponential for the ice particles and Γ functions with μ = 3 for rain )0.65 is used [Iguchi et al., For this specific profile it is clear that uncertainties are intrinsically large in the bottom part of the atmosphere (below 6 km). In that region only the X band radar is providing useful observations. This is demonstrated in Figure v et al.  and Meneand Kozu  at 10°C,Dm reaching 8 mm, Figure 3, see Figure A , which A = ∂xretrieved/∂xtrue. An example for the profile shown in Figure dof. In this case dof is equal to 34, i.e., much smaller than the number of unknowns. Thus, our measurements are not providing enough information content on all our retrieved variables. For ideal inversions, A would correspond to the unit matrix. Here the matrix A is peaked along the main diagonal, but there are areas significantly different from zero in some of the diagonal terms for blocks outside the main diagonal. This is expected because of the interdependence of the retrieval of the different quantities, e.g., of Dmi, ice water content, and ρi. If we restrict our focus to the blocks along the main diagonal, the rows of A, here referred to as the “reduced averaging kernel rows,” do typically peak at the right level. Their areas can be considered to be a rough estimate of the fraction of the retrieval that comes from the observations [Pounder et al., Dmr. This means that all values plotted in Figure Dmi and IWC, especially at high altitudes.The averaging kernel provides the sensitivity of the retrieval to the true state Rodgers [Hi is the height corresponding to the retrieved variable and the sums are extended only to the “reduced averaging kernel elements.” This quantity provides an idea about the effective spatial resolution of the retrieval. For instance, the profiles shown in Figure Following Rodgers , but takRodgers [WAi≡∑jAijRodgers [WAi≡∑jAij43 within the two cells with integrated ice water paths reaching 25 and 15 kg/m2 in the west and east cell, respectively; (b) Dmi are above 10 mm both in the west and in the east cell; (c) very large ice densities are retrieved inside the cells with small relative errors in the solution ensemble  g/m3 with integrated rain (cloud) water paths reaching 24 (10) kg/m2 and 20 (8) kg/m2 in the west and east cells, respectively. All these features are strong evidence of the presence of hail within the cells, as confirmed by the ground‐based radar hydrometeor classification   are found above 13 km in both cells. Ice PSDs with mass‐weighted mean diameters in that range are the most efficient scatterers at W band and are capable of producing W band attenuation in excess of 5 dB/km/ in the west (east) cell  and thuBattaglia et al., Battaglia et al., The retrieved results can be forward modeled to compute single and multiple scattering reflectivities; thus, they can be exploited to assess the importance of MS for the different channels. The levels where the MS enhancement exceeds 6 dB for the four radar frequencies are plotted in Figure Dmi of the order of 0.5–3 mm in the east cell. Incidentally, the mean Doppler velocities appear very noisy in correspondence to the areas characterized by strong MS enhancement, even if the signal is significantly above noise (not shown). This is connected to a decorrelation effect of MS, which is known to broaden the Doppler spectrum [e.g., Battaglia et al., As expected, Ka and W bands are heavily affected by MS, but with distinct behaviors in the two cells: While in the west cell the Ka and W signals behave similarly with a penetration down to only approximately 9 km, in the east cell the W band signal is penetrating only down to 10–11 km, while the Ka reaches below 6 km. This is mirrored by the behavior of the BTs for the 37 and 85.5 GHz channels and, as explained before, is believed to be linked to the presence of a large amount of dense ice particles with 4.1Dmi  in severe convective systems remains a thorny problem. This study presents a methodology for retrieving hydrometeor profiles of ice, rain, and cloud from a unique and unprecedented suite of active and passive microwave airborne observations, which provide top‐down information that are very complementary to more conventional ground‐based measurements.In deep convective systems multiwavelength airborne radar observations are the result of a complex interplay between attenuation, non‐Rayleigh, and MS effects. For a correct interpretation of the radar signal all these effects must be properly accounted for in the radar forward modeling. The large impact of attenuation and MS at the higher frequencies caused by high‐density ice at high altitudes significantly reduces the profiling capabilities of a multifrequency suite of radars when going deep into convective cores. For the North Carolina 23–24 May ER‐2 observations, the four‐frequency profiling system is effectively reduced to a single‐frequency (X band) one at the center of the convective cores in proximity to the surface. In trailing stratiform precipitation, on the other hand, the full suite of radars may be effective down to the surface.Optimal estimation algorithms are powerful tools for understanding deep convective systems by shedding light onto vertical profiles of cloud microphysics . Because of their complexity, a variety of a priori assumptions (like those related to the maximum density of ice particles in the profile or the height of the melting region) must be explored; the solution space can then be significantly narrowed down by selecting only those solutions that are best matched with the measurements. In this ensemble approach the spread of the best converging solutions provides a measure of the accuracy of the retrieved variables.For the convective scenes here considered between 30 and 40 degrees of freedom of signal are associated to the current suite of measurements and are insufficient to provide full microphysics profiling. For the ice segment of the cloud, measurements have the largest impact onto the retrieval of characteristic sizes, followed by equivalent water contents. Ice densities are more driven by the a priori assumptions. Still, uncertainties in ice density are relatively small in extensive regions of the convective system, an indication that only certain densities can explain the observations.In the core of convective cells, one‐way PIA can reach 20 dB at X band. This suggests values larger than 40 dB at Ku band. Part of this attenuation can be attributed to dense ice particles aloft, which are characterized by very high single‐scattering albedo and thus produce a very efficient source of MS. MS is playing an important role in affecting the signal at radar frequencies above the X band and must be accounted for in order to correctly interpret the observations. This has strong implications for satellite retrievals. For instance, the estimates of precipitation as derived from the Tropical Rainfall Measuring Mission Precipitation Radar (and similarly for the GPM Ku band radar) signal by current algorithms (which do not account for MS) must be taken with extreme caution in presence of dense, ice‐laden convective cells.The retrieval, based on optimal estimation, is applied to two convective cells observed on 23–24 May 2014 over North Carolina during the NASA IPHEx campaign. The key findings of our study can be summarized as following.This work provides a first step toward remote sensing microphysical characterization of deep convective clouds by a multifrequency suite of active and passive microwave instrument observations. Future work will aim (1) at refining the retrieval algorithm along the following guidelines: (a) better definition of a priori ensemble profiles ; (b) introduction of correlations between different microphysical parameters; (c) inclusion of melting hydrometeors and of more sophisticated scattering properties for nonspherical ice crystals at the high‐frequency channels; and (d) extending the observation vectors to Doppler and dual‐Doppler variables; (2) at quantifying the information content of the different measurements ; and (3) at deriving a climatology of convective cloud microphysics by applying the algorithm to the relevant cases observed with the ER‐2 suite of instruments and by including ground‐based remote sensing observations into the measurement vector. This could then be exploited in better understanding ice processes and in improving the microphysics in spaceborne algorithms.Supporting Information S1Click here for additional data file.


Odorrana schmackeriTrypsin Inhibitor (OSTI), with a canonical Cys6–Cys16 disulfide bridge, was isolated and identified in piebald odorous frog (O. schmackeri) skin secretion. A synthetic replicate of OSTI-exhibited trypsin inhibitory activity with a Ki value of 0.3 ± 0.04 nM and also a tryptase inhibitory effect with a Ki of 2.5 ± 0.6 μM. This is the first time that this property has been reported for a peptide originating from amphibian sources. In addition, substituting lysine (K) with phenylalanine (F) at the presumed P1 position, completely abrogated the trypsin and tryptase inhibition, but produced a strong chymotrypsin inhibition with a Ki of 1.0 ± 0.1 μM. Thus, the specificity of this peptidic protease inhibitor could be optimized through modifying the amino acid residue at the presumed P1 position and this novel native OSTI, along with its analogue, [Phe9]-OSTI, have expanded the potential drug discovery and development pipeline directed towards alleviation of serine protease-mediated pathologies.Frog skin secretions contain complex peptidomes and peptidic protease inhibitors that are one of the biologically and structurally described groups of components. In the present study, by use of molecular ‘shotgun’ cloning and LC MS/MS fractionation sequencing, a novel Bowman–Birk-type heptadecapeptide (AALKGCWTKSIPPKPCF-amide), named  Inhibitors of serine proteinases are present in multiple forms in numerous tissues of animals and plants as well as in microorganisms, where they function by binding to their cognate enzymes in a substrate-like manner, forming stable complexes. Due to the fact that serine proteases perform some fundamental physiological roles such as peptide hormone release and blood coagulation, they are pathogenic factors in a variety of diseases which include pulmonary emphysema and cancers .The Bowman–Birk-type inhibitor (BBI) family is a typical canonical serine protease inhibitor family, which was originally found in the seeds of leguminous (dicots) and gramineous (monocot) plants ,3. BBI p3 group, it mediates hydrophobic interaction between the enzyme and inhibitor -OSTI exerted distinct chymotrypsin inhibition effects. Therefore, OSTI serves as a potential peptide template for the generation and design of candidate therapeutics for the treatment of gastrointestinal, dermatological and cardiovascular disorders.Until now, BBI peptides have been rarely reported from amphibian sources and our research group reported a novel C-terminally amidated BBI octadecapeptide from  in 2008 . In 2014atouchii . All wer peptide . Here, wO. schmackeri  were captured during expeditions in Fujian Province in the People’s Republic of China. All frogs were adults and secretion harvesting was performed in the field after which frogs were released. Skin secretion was obtained from the dorsal skin using gentle transdermal electrical stimulation -OSTI analogue were successfully synthesized by standard solid-phase Fmoc chemistry using a Protein Technologies PS3™ automated peptide synthesizer. Following cleavage from the resin, deprotection and oxidative disulfide bond formation were performed.Following confirmation of the primary structure of the novel cloned cDNA-encoded peptide, wild-type OSTI and its 0 and [I] are total enzyme (trypsin) and peptides (inhibitors) concentrations respectively.Ki value was calculated from Ki(app) using KM, for substrate hydrolysis in the absence of OSTI.This kinetic treatment was used for the inhibition of trypsin by OSTI. Since the inhibition studies were performed in the presence of competing substrate, the true 50 from a plot of % inhibition against inhibitor concentration [I]. The data points were fitted to the resulting curve, by non-linear regression analysis, using GraphPad Prism. The IC50 values were then converted into Ki values using the For modest binding inhibition, we first calculated the IC9]-OSTI. In inhibition studies with both these proteases, their respective substrates were used at a fixed concentration [S] of 50 μM. The Michaelis constant KM for tryptase against Boc-Phe-Ser-Arg-NHMec was determined to be 227.2 μM, while KM for chymotrypsin against Succinyl–Ala–Ala–Pro–Phe–NHMec was determined to be 38.4 μM. The determination of these KM values was carried out using identical buffer and temperature conditions (37°C) as those employed in the inhibition studies with the two synthetic amphibian peptides.This kinetic treatment was applied to the inhibition of tryptase by OSTI and chymotrypsin by  = θ/(10 × c × l), ‘c’ refers to the molar concentration of the sample (mol/l) and the term ‘l’ is the cuvette path length (cm). DichroWeb webserver (http://dichroweb.cryst.bbk.ac.uk/html/home.shtml) was used to estimate the α-helix and β-sheet content  in deg.cm content –18.O. schmackeri, were fractionated by rpHPLC using a C5 column  PS3™ automated peptide synthesizer. Following cleavage from the synthesis resin, impurities were removed from the synthetic replicates by rpHPLC and the molecular masses of the purified major products were confirmed by MALDI-TOF MS .When the primary structure of OSTI had been unequivocally established, the peptide and its Phe1  with time (t) is described by The synthetic replicate of OSTI was tested quantifiably for inhibitory activity against trypsin. The ‘progress curves’ for the hydrolysis of the fluorogenic substrate in the presence of competing concentrations of OSTI are shown in Vs is the final steady-state velocity, Vo is the initial velocity and kobs is the apparent first order rate constant for the transition from initial to final steady state and d is a displacement term that reflects the concentration of product formed at t0. These progress curves indicate that the interaction of OSTI with trypsin follows a two-step complexing mechanism -OSTI were estimated from these progress curves and these were compared with the initial rate (Vo) of product formation in the absence of [Phe9]-OSTI. From these, the % inhibition obtained at each concentration of [Phe9]-OSTI studied was calculated and the data plotted  to calculate a Ki value for the peptide, using Ki of 1.0 ± 0.1 μM for the inhibition of chymotrypsin.The synthetic replicate of -OSTI presented a mixture conformation of α-helix (9%), β-sheet (36%) and random coil (55%).The CD spectra of OSTI and -OSTI, confirmed this differentiation. The mechanism of action of protease inhibitors is achieved through cognate enzyme binding in a substrate-like manner and the specificity is determined by whether the P1 position residue can fit into the S1 pocket of proteases. In the S1 pocket of trypsin, there is a negatively charged aspartic acid residue that prefers long and positively charged side-chain residues and in the S1 pocket of chymotrypsin, there is a serine residue that prefers a deep hydrophobic pocket, so long uncharged side-chained amino acids like phenylalanine, are optimal. Besides simple amino acid effects, the precise mechanisms of inhibitor interactions with proteases must be associated with their steric conformation, which is why not all the arginine and lysine residues containing sequences have the ability to inhibit trypsin and phenylalnine containing peptides to inhibit chymotrypsin. Their secondary structure, including the conserved disulfide bridge, is essential for bioactivity. There are reports that the core disulfide loop of the vast majority of BBIs forms a short type VIb β-turn motif and it is extremely well ordered for a short peptide. While our CD data revealed that OSTI and [Phe9]-OSTI did not display canonical β-turn motif in aqueous solution and only OSTI formed low percentage of β-sheet secondary structure in 50% TFE-water solution, this might be the consequence of a solution-dependent inherent property of the peptides. In addition, OSTI is highly analogous to another peptide HV-BBI, which we have previously demonstrated to be an excellent trypsin inhibitor [9]-OSTI remain to be further investigated by NMR under more complex environments, this could shed some light on exactly how they inhibit proteases in a competitive and/or non-competitive manner -OSTI analogue that showed effective chymotrypsin inhibition ability would probably inhibit chymase as well. Despite that, other factors should be taken into consideration, for instance the kinetic data based on inhibition of chymase by soybean BBI have identified a non-competitive mechanism through a Lineweaver–Burk double-reciprocal plot -OSTI does not meet the criteria to be a chymase inhibitory peptide and a [Leu9]-OSTI might have been a better choice. All these results and predictions might be useful for development of therapeutics for the treatment of allergic inflammatory reactions.Tryptase and chymase are atypical serine proteases that are synthesized, stored and released by mast cells. Due to their close involvement in the pathogenesis of inflammation and some other diseases, there have been reports of BBIs possibly having utility in the treatment of such conditions. In the present study, we tested the activity of OSTI in a tryptase inhibition assay and found a relatively fast association rate constant of 8.36 μM. These data were not in agreement with the conclusion put forward by Ware et al. , whose scal plot . Also, tractions ,26. As hRana (Odorrana) genus and their primary structure-based modifications can produce a series of analogues that exhibit substantial bioactivities, such as canonical serine protease inhibition, myotropic activity, anticarcinogenic activity or even antimicrobial activity. Perhaps the most interesting discovery in this project is the strong tryptase inhibition activity of OSTI, as numerous biological and immunological investigations have implicated tryptase as a regulator in the pathology of a variety of allergic and inflammatory conditions including rhinitis, conjunctivitis and most notably asthma, and thus, identification of a potent and selective tryptase inhibitor could provide avenues for novel drug development. Future systematic structure/activity studies may represent a way forward for generation of peptides with desirable therapeutic endpoints for a variety of diseases.Skin secretion BBIs are believed to be widespread in species of frogs of the This research did not involve any human participants.


Escherichia coli results in multisite Tau phosphorylation including also naturally occurring sites which were not previously considered in the context of 14-3-3 binding. Tau protein co-expressed with PKA displays tight functional interaction with 14-3-3 isoforms of a different type. Upon triple co-expression with 14-3-3 and PKA, Tau protein could be co-purified with 14-3-3 and demonstrates complex which is similar to that formed in vitro between individual 14-3-3 and pTau obtained from dual co-expression. Although used in this study for the specific case of the previously known 14-3-3/pTau interaction, our co-expression system may be useful to study of other selected 14-3-3/phosphotarget interactions and for validations of 14-3-3 complexes identified by other methods.Abundant regulatory 14-3-3 proteins have an extremely wide interactome and coordinate multiple cellular events via interaction with specifically phosphorylated partner proteins. Notwithstanding the key role of 14-3-3/phosphotarget interactions in many physiological and pathological processes, they are dramatically underexplored. Here, we focused on the 14-3-3 interaction with human Tau protein associated with the development of several neurodegenerative disorders, including Alzheimer’s and Parkinson’s diseases. Among many known phosphorylation sites within Tau, protein kinase A (PKA) phosphorylates several key residues of Tau and induces its tight interaction with 14-3-3 proteins. However, the stoichiometry and mechanism of 14-3-3 interaction with phosphorylated Tau (pTau) are not clearly elucidated. In this work, we describe a simple bacterial co-expression system aimed to facilitate biochemical and structural studies on the 14-3-3/pTau interaction. We show that dual co-expression of human fetal Tau with PKA in  T. T35]. Tous work . For in ing IMAC . Thermosing IMAC .http://www.uniprot.org/) molar extinction coefficients at 280 nm equal to 7450 M-1 cm-1 for Tau, 31860 M-1 cm-1 for 14-3-3γ, 27390 M-1 cm-1 for 14-3-3ζ, 28880 M-1 cm-1 for 14-3-3ε, 25900 M-1 cm-1 for 14-3-3σ∆C.Protein concentration was determined spectrophotometrically on a NanoPhotometer P330 (Implen) using calculated in ProtParam  in a total volume of 110 μl for 2 h at 30°C and then analyzed by gel-electrophoresis under non-denaturing conditions at pH 8.6 [coex (0.2 mg/ml) was performed by thermosensitive alkaline phosphatase FastAP (ThermoFisher Scientific) in a buffer recommended by manufacturer, but devoid of bovine serum albumin , for 1.5 h at 37°C and then analyzed by SDS gel-electrophoresis [To reveal any undesired consequences of phosphorylation, four purified isoforms of human 14-3-3 (14 μM per monomer) were incubated with large amounts of PKA (2 μM) in buffer P  in buffer A for 30 min at 30°C and then analyzed by gel-electrophoresis  under non-denaturing conditions at pH 8.6 in 15% polyacrylamide gels [To test the ability of pTauide gels . For comcoex (15 μM), or their mixture at indicated protein concentrations were pre-incubated for 20 min at 37°C and then subjected to size-exclusion chromatography on a Superdex200 Increase 10/300 column  operated at room temperature and a flow rate of 1.2 ml/min. The profiles were followed by absorbance at 227 nm since at 280 nm the extinction coefficient of Tau is aproximetly 5 times lower than that of 14-3-3.Alternatively, 14-3-3ζ (10 μM), pTauIn order to create a flexible and convenient co-expression system for studies on 14-3-3/phospho-Tau interaction and probably other targets of 14-3-3, all three components  had to be carefully designed and cloned in different vectors having compatible origins of replication and antibiotic resistance.2-3(pS/pT)X(P/G) for 14-3-3 binding [prepares targets for 14-3-3 binding. Moreover, the well-established expression of the soluble catalytically active subunit of PKA in Escherichia coli [in vitro [in vitro and in cells can be different, probably, because of the crowding effects of cellular environment. To our knowledge, triple co-expression of a target, PKA, and 14-3-3 has not been tested yet.Often protein kinase A (PKA) works in pair with 14-3-3 since it phosphorylates target proteins at (R/K)RX(S/T) sequences (where X is any amino acid) , which ahia coli ,36, makehia coli  or protehia coli . Importain vitro ,41, PKA in vitro , suggestE. coli in the soluble form. Tau protein meets all these criteria as it is (i) a “good” substrate for PKA [For our co-expression system, we considered an established 14-3-3 partner that could be readily phosphorylated by PKA and produced in  for PKA , (ii) a  for PKA ,28–30,4358W, ζ isoform numbering) located in the dimerization interface of 14-3-3 and, expectedly favor dimer dissociation [Not only 14-3-3 targets but some 14-3-3 isoforms itself can be phosphorylated by PKA. This mainly involves a semiconservative Ser58 . Currenociation ,18,46 buociation ,48, for ociation ,18,46.in vitro phosphorylation of four 14-3-3 isoforms  at a large PKA amount  cells by compatible plasmids encoding Tau and PKA or transformation of competent BL21(DE3) cells already carrying PKA plasmid see , left rephoresis . Interesin vitro, namely Ser156, Ser235, Ser267, Ser320, Ser327 [6-3C-Tau sequence can be covered and the N-terminus of the bacterially expressed Tau was intact. At the same time, in all the samples we failed to confirm the presence of last 3 amino acids QGL, most likely indicating intracellular proteolytic degradation of the expressed Tau. Still, the electrophoretic behavior suggested high purity and absence of contaminating proteolytic fragments usually present in all preparations of Tau obtained by classical methods [coex is heavily phosphorylated involving at least 7 residues that we could validate by MS/MS (pSTE), Thr187 (site RLQpTAP), and Ser320 (site RHLpSNV) (pTAP) was demonstrated by using Rho-dependent kinase (ROCK) [in vitro [Tau protein is highly phosphorylated in the brain of patients with different tauopathies, and phosphorylation patterns can be highly complicated ,25,27. A, Ser327 , however, Ser327 ,42. It i, Ser327 . Taking  methods . Notewor methods . Mass-spby MS/MS . Phosphoby MS/MS . We coulby MS/MS , we showRHLpSNV)  that were (ROCK) , a crucie (ROCK) , howeverin vitro . Moreovecoex is phosphorylated on not only several already known, but also on novel potential 14-3-3-binding sites that substantiated further utilization of pTaucoex for studies of its interaction with 14-3-3. At present, the hierarchy and interrelations between these multiple 14-3-3-binding sites are not well understood and lie before us to explore. The ability of a particular 14-3-3-binding site  to be phosphorylated by several kinases may illustrate the necessity that its interaction with 14-3-3 is controlled by different intracellular stimuli, however, a particular enzyme phosphorylating it may not be crucial for structural studies of a corresponding 14-3-3/target complex as its phosphorylation per se in this case is more important.In summary, we established that pTaucoex was confirmed by its ability to interact with 14-3-3. Purified pTaucoex was mixed with 14-3-3ζ at two ratios and then analyzed by native gel-electrophoresis  cells expressing His6-tagged PKA and His6-3C-Tau were transformed with CDF-14-3-3σΔC plasmid to get simultaneous expression of Tau, PKA, and 14-3-3  was treated with the His-tagged 3C protease ; “IMAC2”in vitro phosphorylation and complex assembly, (iii) easy means to obtain 14-3-3/phosphotarget (stoichiometric) complexes to be directly used for biochemical and structural biology studies. The flexibility of the system is underlined by the fact that any its element  can be altered and further upgraded to improve performance. The system is currently tailored for utilization of only 14-3-3 isoforms naturally resistant to PKA phosphorylation-induced dimer dissociation, however, mutations of other 14-3-3 isoforms blocking site-specific phosphorylation should help in overcoming this limitation. The system described currently uses PKA to phosphorylate a 14-3-3 partner. While other protein kinases may also be used, it may be challenging to (co-)express them in a soluble, correctly folded form in bacteria. Nevertheless, it is possible to engineer protein partner’s phosphorylatable sites to facilitate their phosphorylation by PKA, provided that particular positions are known to be phosphorylated (by any kinase) and recognized by 14-3-3 members in vivo. Successful examples of such engineering to enforce PKA phosphorylation of a certain site can be found in literature [in bacteria should be checked for each particular partner and, although somewhat difficult to control, may be steered by engineering  of undesired site(s). Still, this can be turned to an advantage of the possibility to separately analyze the role for particular sites in 14-3-3 binding. The introduced co-expression system seems promising for validation of newly identified (by other methods) 14-3-3 partners in both low and high-throughput format, provided that the protocol for co-purification of 14-3-3 complexes is suitably adjusted for selected partners.This study is devoted to the co-expression system for efficient and convenient production of phosphorylated Tau protein competent for 14-3-3 binding and, most importantly, for simultaneous expression of Tau, phosphorylating kinase (PKA), and 14-3-3 for biochemical and/or structural characterization. By applying this co-expression system to the well-established 14-3-3/pTau interaction ,18,28–30terature . As a li


The Coulomb interaction among massless Dirac fermions in graphene is unscreened around the isotropic Dirac points, causing a logarithmic velocity renormalization and a cone reshaping. In less symmetric Dirac materials possessing anisotropic cones with tilted axes, the Coulomb interaction can provide still more exotic phenomena, which have not been experimentally unveiled yet. Here, using site-selective nuclear magnetic resonance, we find a non-uniform cone reshaping accompanied by a bandwidth reduction and an emergent ferrimagnetism in tilted Dirac cones that appear on the verge of charge ordering in an organic compound. Our theoretical analyses based on the renormalization-group approach and the Hubbard model show that these observations are the direct consequences of the long-range and short-range parts of the Coulomb interaction, respectively. The cone reshaping and the bandwidth renormalization, as well as the magnetic behaviour revealed here, can be ubiquitous and vital for many Dirac materials. Strong electronic correlations are expected to generate novel behaviour in materials possessing exotic massless electrons with a relativistic nature. Here, the authors demonstrate the reshaping of tilted Dirac cones and ferrimagnetism associated with Coulombic interactions in a layered organic compound. EF. Triggered by the studies in two-dimensional (2D) graphene4578910111213141516EF, the long-range (LR) part of the Coulomb potential V(q) (q: wave vector) is unscreened owing to the vanishing density of states (DOS) at EF. Consequently, the LR character of the interaction (V(q)∝1/|q|) is preserved at low energy, which couples to the fermionic excitations and induces a logarithmic correction to the Fermi velocity vF and associated physical quantities78111213Dirac materialsensional D graphenk-points emerge more generally in a broad class of materials11819α-(BEDT-TTF)2I3 (α-I3) (BEDT-TTF=bis(ethylenedithio)-tetrathiafulvalene) (kD) in the 2D first Brillouin zone  , which huin zone 202122232α-I3 in terms of the Coulomb interaction problems is that, within the pressure–temperature (P–T) phase diagram  electron correlations in this 3/4-filled system40P above a critical value of PC≈1.2 GPa  and present experimental evidence for interaction effects of massless DFs. Employing site-selective nuclear magnetic resonance (NMR), we uncover three distinct interaction phenomena induced by the electron–electron Coulomb interaction. First, NMR-shift measurements in conjunction with renormalization-group (RG) analyses reveal a T-driven cone reshaping around each of the DPs due to the LR part of the Coulomb interaction. Because of this reshaping, tilted cones become effectively isotropic at low energies. Second, quantitative RG analyses establish that the best fit to the data inevitably requires a strong bandwidth reduction inherent to the SR electron correlation, as often discussed in strongly correlated materials. Finally, an anomalous ferrimagnetic spin polarization is observed, which is accounted for by the onsite Coulomb repulsion, as revealed by a simulation based on the Hubbard model presented here. These experimental and theoretical investigations demonstrate that α-I3 under P is an intrinsically interacting 2D massless DF system, in which both the LR and SR parts of the Coulomb interaction strongly influence the electronic behaviours.In this article, we focus on the 2D massless DF phase in α-I3 is as follows. The crystal structure of α-I3 has a 2D unit cell with four molecular sites , B and C), each of which constitutes a sublattice in the crystalline ab-plane , B and C around the DP at kD, q= is defined as q=k−kD and ζ=± is the band index  have a large weight on the B-site wavefunction, whereas the Bloch states with small vF (on the opposite side of the cones in the gentle slope) have a large weight on the C-site wavefunction  and holes (ζ=−). Hence, the site-selective NMR in α-I3 works as an effectively q-resolved probe of 2D DFs thermally excited around each of DPs. Indeed, the electronic excitations in the steep and gentle parts of the Dirac cones can be almost independently probed by Our strategy to investigate tilted Dirac cones in ab-plane . The foulane , B and C. The two 13C nuclei (spin I=1/2) introduced at the centre of BEDT-TTF (ET) molecules A and A' are not distinguished hereafter). j-site total shift for a given T and a field-angle a axis; σj(T and P dependence), while the susceptibility EF) are vanishingly small at low temperatures, the total shift Sj at the lowest T (=3 K in the present experiment) is expected to provide the chemical shift term σj. Thus, subtracting σj excitations across the gapless point at EF and is proportional to the kBT average of the j-site DOS around EF: T=300 K, T-independent features down to T≈200 K, followed by a rapid decrease with a clear difference in the size of the susceptibility between non-equivalent sites, Dj(E) at high energies (inset I of Dj(E) at low energies around EF (inset II of 2425EF) and linear energy dependence is suggested around the band-crossing point at EF. Note that Dj(E) around the gapless point (E=EF=0) is given by a q summation of the site-spectral weight E for the band ζ (q-dependence of DC(E)>DA(E)>DB(E) at low energy. This is in excellent agreement with the observed relation T≈200 K  model given in ref. EF located around the gapless point.see also , and fin weight   at a give band ζ . Then, tac cones  is expec T≈200 K  and is tT≈150 K does not comply with the expectation of TB calculations, which leads to T at low temperatures25T), as shown in T, dT exhibits a peak at B, and at A and C, and then drops continuously to zero at all sites towards low temperatures. These features are in striking contrast to the TB calculationT increases on cooling but saturates at low T  around EF (=0) in an energy range of D(E), is proportional to the inverse square of vF in 2D massless DF systems for the non-interacting case vF. Thus, the observed peak structure in dT strongly indicates that a T-driven renormalization of vF grows below T≈EF in charge-neutral massless DF systems and is known to cause a logarithmic correction to vF either driven by tuning carrier densities17891011129B  and v= are velocities reflecting the tilt and anisotropy of the cone, respectively . At the one-loop level, the self-energy correction leads to a renormalization of v but does not affect w0  is measured from kD, l=ln(Λ/q) is the momentum scale, Λ (=0.667 Å−1) is a momentum cutoff of the size of the inverse lattice constantF(gϕ) has the form ɛ is the dielectric constant. where vTB)q=Λ, we have calculated the RG correction effects on Dj(E)  Here, we note that the temperature is used as an explicit scale parameter in the calculation of u is introduced to adjust the velocities at q=Λ such that v′=uvTB. The two parameters in the calculation, , are then optimized from a least-square fit to the experimental susceptibilities. Good agreements are obtained in the fit especially at the site A and C ≈.  is shown around the gapless point at EF. A strong suppression from the E-linear DOS is seen at low energies due to the renormalization. kD), where the colours indicate the magnitude of the site-spectral weight, u (≈0.35) is used. The fact that we have u<1 indicates a reduction of the initial velocities or of the hopping amplitudes between the adjacent lattice sites. In conventional strongly correlated materials, the SR part of the Coulomb interaction is well known to induce this sort of hopping (or bandwidth) renormalization due to the frequency dependence of the self-energyu-reduction effect in the initial velocities occurs because of this self-energy correction due to the SR Coulomb interaction, which is not considered implicitly in the original RG calculation.Assuming the four velocities given by the effective TB calculation (ects on   Dj(E) (F  (Dj(E)  and the   (Dj(E) .  as indicated by thin arrows in δ around the DP, vF DFs, dominantly probed by the site B, while it is small for the small-vF DFs, having a large weight on the site C (and A). Hence, the renormalization starts from a higher T in T) than in T) (and T)), producing the observed energy scale difference ΔEB>EA,C, consistent with the previous RG calculation of Isobe et al.EF because of the non-uniform velocity renormalization around each DP, as reflected in vx/vy≈1) in α-I3 (ref. w0|<<|v| around the DP due to the RG flow (w0) to be effectively negligible near EF.Remarkably, the calculation well reproduces the observed difference in the thermal energy scale of the e band ζ . Namely,-I3 ref. , and we  RG flow , leadingu-reduction effect) due to the SR repulsion between electrons as well as the T-driven logarithmic renormalization of vF by the LR part of the Coulomb interaction. Nevertheless, we note that, at low temperatures, the agreement is less satisfactory for the site B compared with the other sites  in the experiment is appreciably stronger and more complex than what is predicted by the RG calculation (T) exhibits an anomalous sign change at T≈60 K and an upturn with a negative slope below T≈40 K . For the RPA calculation, we have considered both the inter-band and intra-band contributions to the spin susceptibility with a wave vector Q=0 . B. Using U=0.14 eV, the RPA calculation (in particular the inter-band term) clearly reproduces the observed negative spin susceptibility for site B   coupling between sites A and B (A' and B), a large energy gain is expected due to the kinetic energy of electrons, which favours the observed pattern of the ferrimagnetic polarization.The temperature dependence of culation . Indeed,inset of , while tperiment , indicater sites . To furtculation  in good culation . These fa B) see . Densitya B) see , and X-rU=0.14 eV, is chosen smaller than what would be typically used (U≈0.4 eV)  refs , 26. Thiα-I3, which develop systematically at different temperature scales , as summarised in u-reduction of vF) occurs due to the SR Coulomb interaction which appears to exist from room temperature down to lowest T. At temperatures T≤EF), a T-driven logarithmic renormalization of vF and the resultant non-uniform reshaping of the Dirac cones appear, due to the LR part of the Coulomb interaction. With further decreasing T, a ferrimagnetic spin polarization shows up in the unit cell because of the onsite Coulomb repulsion between electrons.So far, we have demonstrated three distinct Coulomb-interaction phenomena in the 2D massless DF phase of α-I3 should affect other physical observables at low temperature or at low magnetic field. As the cones become effectively isotropic around each of DPs  structure in grapheneH⊥) normal to the 2D plane, the massless DFs in α-I3 exhibit the LL spectrum, ζ=± distinguishes the electron and hole bands crossing at the gapless point, n  is the LL index, lB=(ℏ/eH⊥)1/2 is the magnetic length and EF locates at the gapless point in α-I3 and the n=0 LL is half-filled, one may be able to detect, for instance, the dipole transition n=0→n=1 in the absorption line at the energy vx, vy) with a logarithmic correction may appear in ΔE10 at low field; vx (≈vy) will increase by a factor of four by changing ln(Λ/q) from 1 to 5 (2)3/4 increases by a factor of two (from ∼0.5 to ∼1.0). One would expect to see this change in ΔE10 at low temperatures where the LL broadening, mainly caused by the thermal scattering of carriers in the n=0 LL  makes the system flow to a weak coupling regime at low energies (or at low T in the case of a T-driven RG flow as in our experiment)T electron correlation effects, if any, would be induced by the SR part of the Coulomb interaction, as usually the case in conventional strongly correlated materials. In fact, the insulating phase possessing charge order222631323334353637383940P–T phase diagram in α-I3 , having tilted Dirac cones and gapless points fixed at EF. We found that the LR part of the Coulomb interaction, which is unscreened around the gapless DPs, causes a T-driven renormalization of the velocity and induces a non-uniform reshaping of tilted Dirac cones. Quantitative analyses of the cone reshaping based on the RG approach further necessitates a large bandwidth reduction due to the SR electron correlation. Moreover, we showed that the onsite Coulomb repulsion gives rise to a ferrimagnetic spin polarization as unveiled by the numerical calculations using the Hubbard model. These findings can be distinguished from the case of weakly interacting 2D massless DFs in graphene with vertical Dirac cones and are consistent with the emergent correlated phase on the verge of the massless DF phase in the P–T phase diagram. Continuing this study to the vicinity of the phase transition at PC (≈1.2 GPa) would be of particular interest, which may connect the physics of the massless DFs and conventional strongly correlated materials.To conclude, our NMR measurements combined with theoretical calculations have demonstrated three less DFs  in pressα-(BEDT-TTF)2I3 (α-I3) (ref. 3 were synthesized from 13C-enriched BEDT-TTF (ET) molecules using the conventional electrochemical method. To perform 13C-NMR measurements, the central carbon atoms of BEDT-TTF molecules connected by a double bond were 99% enriched by carbon-13 (13C) isotopes  ref.  with theinset of  with a nP=2.3 GPa was applied to the sample using a BeCu/NiCrAl clamp-type pressure cell, with the Daphne 7373 oil as a pressure medium. At this pressure, the oil locates close to the liquid–solid phase transition point at room temperature61T≈50 °C) during the pressurization. With decreasing temperature from T=300–3 K, a pressure reduction of ΔP∼0.1 GPa occurs inside the cell61T we measured (3 K) is, however, substantially higher than the transition pressure to the charge-ordered phase at T≈0  and were converted into the NMR spectra via Fourier transformation. The resonance frequency of the natural abundance 13C nuclei in TMS (tetramethylsilane (CH3)4Si) was used as the origin of the NMR shift. We note that the present work is targeted at lower T and higher P compared with the earlier NMR studies65T 2D massless DF phase emerging at P>PC on the phase diagram (2227282930ab-plane . To get  diagram 22272829313C-NMR spectra in this compound are given elsewhere3713C-NMR spectra of α-I3 show large temperature dependence (13C lines that consist of two doublets (from the B and the C molecules) and one quartet (from the A (=A') molecule)  and molecules B and C, reflecting the fishbone-like arrangement of ET molecules in the crystalline ab-plane  . The douab-plane . Thus, bα-I3, the T-dependence curve of the j-site total shift T dependence of ° showing a prominent decrease with decreasing T  towards lower T  and assumed this fitted curve to be the chemical shift value at each angle Kj and B and C  at low T (≤In s at ±kD  are fixet of  in . This is EF ref. . Our expbelow T≈  and a nej=A(1), A'(2), B(3) and C(4)  . , A'(2), B(3) and C(4)], whose Fourier transformed matrix elements are given byare within this range and are thus acceptable. Using these potentials, the Hamiltonian of the effective TB model in ref. with the kinetic termsab initio calculations such that the resultant electronic bands become compatible with experimental observations in this system. For this, we employed the hopping integrals reported by the first-principle density-functional calculation at T=8 K  is fixed in α-I3 due to the stoichiometry, the low-energy continuum model is shown to be given by the generalized Weyl Hamiltonian20232535Around the band-crossing DPs, where the Fermi energy w0= and v= are effective velocities describing the tilt and the anisotropy of the Dirac cone, respectively; σ0 is the 2 × 2 unit matrix;  are the Pauli matrices; and q= is the 2D wave vector measured from the DP at kD. Note that the twofold valley degeneracy associated to the two DPs at ±kD (kD).  The Hamiltonian  superposition of the highest occupied molecular orbital which describes the electronic states in the vicinity of one of the DPs. Here, s at ±kD  will be iltonian  is definj=A(1), A'(2), B(3) and C(4) represents the different sites (see Eζ(q) (ζ=±) and the eigenstates where ites see . Diagonaee Eζ(q)  in termsϕq=vyqy/vxqx. We note that the two states q, as in the two-band model of the graphene Dirac cone, whereas the four states q-dependent site-spectral weight by taking a projection of where tan  ,  ref.   site  site . As the inset of , the FerA(A'), B and C around the DP. Namely, the site B predominantly reflects the large-vF electrons in the steep slope of the cone (ϕ≈π); the site C mostly probes the small-vF electrons in the gentle slope (ϕ≈0); and the site A(A') probes the entire electronic states on average around the DP , for the different sites—DC>DA(A′)>DB   is defined as the q summation of The most striking consequence of this anisotropy, in conjunction with e DP see . This as ζ) ref. —where DjVC is the 2D unit-cell volume in the conducting ab-plane. The contrasting features of the B and C site-spectral weights around the DP provide unique opportunities to probe the excitations of large-vF Dirac electrons (in the steep slope) and small-vF Dirac electrons (in the gentle slope) separately in terms of a site-selective local measurement such as NMR.Here, et al.w0== and v==, because both the primitive vectors and the reciprocal lattice vectors are set to have a unit length in their notation. To recover the ordinary physical unit (length/time), one has to multiply the velocities either by a/ℏ or b/ℏ, using the values of the lattice constants a and b at the current pressure (2.3 GPa), where ℏ is the Planck constant divided by 2π. By linearly extrapolating the X-ray diffraction data obtained at 1.76 GPa  is introduced in the RG theory, which is of the size of the averaged inverse lattice constant, Λ=2π/L with L=(a+b)/2=9.425 Å at 2.3 GPa. For the initial values of the velocities at the cutoff momentum q=Λ  are renormalized following Λ/q (where q is measured from the DP), whereas w0= are not renormalized .To check this hypothesis, we have performed a RG calculation based on the generalized Weyl Hamiltonian  and trie and vTB , as discrmalized . We notev, the eigenenergy with the RG correction becomes (with the band index ζ=±)Reflecting the renormalization of j are respectively given by the expressionsCorrespondingly, the local DOS and the electron-spin susceptibility for the site VC=88.086 Å2 is the 2D unit-cell volume in the conducting ab-plane estimated at 2.3 GPa, N=4 is the number of fermion species , EZ=gμBH is the electron Zeeman energy and f(E) is the Fermi distribution. The integration with respect to q in q=Λ). Note that T plays the role of the flow parameter in T-driven RG flow).where j-site DOS (j-site electron-spin susceptibility (u (≤1), in the calculations which is defined by the expressionsIn site DOS  and the tibility  based ontibility  and equatij between the lattice site i and j due to the SR part of the Coulomb interactionu.This parameter reflects a suppression of the velocity or a reduction of the hopping amplitude vx and vy. The dielectric constant ɛ affects the power of the flow function v=v(Λ/q) . It is clearly seen that a prominent suppression of the DOS develops around E=EF (=0) by reducing the dielectric constant ɛ (which corresponds to an increase in the coupling gϕ at the cutoff momentum q=Λ). The reduction of the parameter u, on the other hand, leads to an enhancement of the DOS because the DOS is linked to the inverse square of the velocitieskBT-average of the DOS near EF (=0), the suppression of the DOS for small ɛ causes a reduction of the susceptibility at low temperatures  , while tthe flow . Supplemeratures . The enh fashion .T-driven RG-flow fit to the experimental spin susceptibilities for the site j=A(A') and C (u−') and C  based onTi is the experimental temperature points , and Ti) and T=Ti, respectively. Note that the variance is defined only for the results on the site A(A') and C, since the results on the site B has less good agreement with the RG-calculation (see B at low temperatures (inset of u andj=A(A'), B and C together with the experimental data in B, the agreement with the calculations and the experiments is pretty good for small values of u and ɛ. The variance Var= with little ɛ dependence up to ɛ≈101.5 = as the optimal parameters hereafter.  The small value of u=0.35(<1) is in agreement with the aforementioned reduction of the hopping amplitude due to the SR repulsionB.)where tion see . This isinset of —which is ɛ≈101.5  and incrv= with w0= staying constant in vx, vy≫|wx0|, wy0 is realized for a large value of the momentum scale Λ/q (≫1) . This means that the tilting term (w0) becomes effectively negligible with respect to the anisotropy term (v) at low energy in the generalized Weyl Hamiltonian =5 in −1 in terms of Λ=0.667 Å−1). These points together suggest that the anisotropy of the Dirac cone becomes very small near the DP and the cone is practically isotropic at low energy, as one can see in the calculated cone in Lastly, it may be worthwhile to mention that the flow of the velocities iltonian . MoreoveT-dependence of A(A') and C  and C  when theU, by extending earlier studyTo understand the origin of the observed j=A(1), A'(2), B(3) and C(4) in the unit cell α with the spin σ, Nu.c. is the total number of the unit cell, and tiαjβ: is the nearest-neighbour and next nearest-neighbour hopping amplitude between the lattice site  and   see .  see  lacks thtiα:jβ at finite temperatures are estimated from the hopping integrals given by the first-principle calculationsp=a1−a4'} The amplitude =a1−a4'} , and at in combination with the interpolation formula given in ref. 50Within the mean-field approximation, the diagonalization of the Hubbard Hamiltonian  yieldsδij is a vector connecting the nearest-neighbour lattice sites i and j, Eσ1>Eσ2>Eσ3>Eσ4), diησ(k) is the corresponding eigenvector, f(E) is the Fermi distribution and μ is the chemical potential. Note that the average electron number σ is omitted hereafter.where we define ij)-element is given byWe introduce the bare site-spin susceptibility matrix, in terms of a form factoriδ (δ>0) is an infinitesimally small imaginary part. Within the RPA approach, the spin fluctuations are estimated using the expression50where j-site-spin susceptibility (for Q=0 and ω=0) is given by(where Q=0), respectively. The intra-band RPA susceptibility is then defined byNow, we decompose the bare susceptibility and the inter-band contribution to the total RPA susceptibility is expressed asU=0.14 eV in comparison to the non-interacting case (U=0) for the site j=A(A'), B and C. It is seen that the intra-band susceptibility U becomes always larger than the case for U=0 at all temperature and all site j. The inter-band contribution, on the other hand, is found to provide a site-dependent correction to the susceptibility (A(A') and C, whereas the contribution is negative on the site B. The negative inter-band contribution on the site B  feel a positive field, as schematically illustrated in In tibility . Namely,easing U . By takiinset of . These rU=0, the total RPA susceptibility U=0.14 eV, and the observed spin susceptibility (A(A') and C above the peak temperature (T≈50 K), whereas the calculation does not agree with the experiment at all on the site B. At low temperatures, on the other hand, the agreement between the calculation and experiment becomes worse even for the site A(A') and C. That is, in the low temperature limit, the calculations show a saturation both for U=0 and the finite U, while the experiment exhibits a monotonic decrease towards zero . The vF-renormalization results in a super-linear temperature dependence in the spin susceptibility To have an overall comparison of the RPA calculations with the experiment, the first derivative of the susceptibility is analysed for the case ibility   for U=0.tibility , as depirds zero . We beliibility  , which cFinally, we comment on the comparison of the experiment with an orthodox RPA fitting. This is done by assuming a simplified RPA (s-RPA) expression for the spin susceptibility, which is defined byUj is an adjustable parameter reflecting the onsite Hubbard interaction. (Note that Q=0 and ω=0 with the Hubbard interaction U in Uj.) UA(A′)=0.6, UB=1.3 and UC=0.4 (in eV). It is clearly seen that the observed nonlinear temperature dependence of the susceptibility cannot be reproduced by the s-RPA fit at all sites. In particular, there is an unphysical divergence in the calculation, which is linked to the large Uj values used in the calculation that are too large compared with the typical values applicable to this system24263135404148where The data that support the findings of this study are available from M.H. upon requests.How to cite this article: Hirata, M. et al. Observation of an anisotropic Dirac cone reshaping and ferrimagnetic spin polarization in an organic conductor. Nat. Commun. 7:12666 doi: 10.1038/ncomms12666 (2016).Supplementary Figures 1-14, Supplementary Discussion and Supplementary References


Climate warming affects the development and distribution of sea ice, but at present the evidence of polar ecosystem feedbacks on climate through changes in the atmosphere is sparse. By means of synergistic atmospheric and oceanic measurements in the Southern Ocean near Antarctica, we present evidence that the microbiota of sea ice and sea ice-influenced ocean are a previously unknown significant source of atmospheric organic nitrogen, including low molecular weight alkyl-amines. Given the keystone role of nitrogen compounds in aerosol formation, growth and neutralization, our findings call for greater chemical and source diversity in the modelling efforts linking the marine ecosystem to aerosol-mediated climate effects in the Southern Ocean. Climate warming-derived changes in sea ice extent2, thickness and texture are expected to exert feedback effects on climate through changes in surface albedo and emission of climate-active substances, such as those forming cloud-seeding aerosols4. An accurate projection of climate dynamics in the Southern Ocean, therefore, requires a sound assessment of aerosol precursor emissions from the sea ice region ecosystem. Here we report results of a study conducted during a research cruise across the Subantarctic and Antarctic waters of the Atlantic sector of the Southern Ocean in summer 2015, where low troposphere aerosols were characterised, source apportioned and compared with concurrent measurements in sea water and sea ice.Antarctic sea ice covers between 1% (summer) and 5% (winter) of the global ocean, and influences an even larger area, the sea ice region. It is a vast biome composed of multiple habitats such as the upper and lower ice surfaces, snow cover, brine channels, melt ponds, ice openings and ice floes of all sizes, and the surrounding sea water, all hosting rich microbial communities5, which are formed via oxidation of dimethylsulphide (DMS), another volatile of planktonic origin6. In fact, both methanesulphonic acid (MSA) and non-sea-salt sulphate (nss-SO4) were also enhanced in the ice-influenced air masses  and “sea-ice” (SI) source regions according to the characteristics of the area overflown  also revealed the ubiquitous occurrence of organic nitrogen (ON) compounds and their enhancement under sea ice influence  in real time, and found an enhancement of almost 3 fold (189 cm−3 versus 69 cm−3) in SI air relative to OW air  and as much as 0.3–1% of ON, which was almost undetectable from seawater  further showed an 8-fold enhancement of ON in ice-influenced air masses. Moreover, of all ON-containing particles analysed, 11% were internally mixed with sea salt. Whilst this confirms a major secondary aerosol pathway for aerosol ON formation, it also shows an unneglectable contribution of sea-spray-associated ON. The surface ocean is rich in colloidal and particulate organic matter including proteinaceous gels1. Chlorophyll a concentrations in the sea ice samples (8–48 µg L−1) were higher than those found in surrounding seawater (0.3–2 µg L−1) and within the range previously found in similar rafted sea ice12. Ice algae were largely dominated by diatoms of the genera Fragillariopsis and Nitzschia, commonly found in polar sea ice and surrounding waters13. Brine channels are also known to harbour abundant organic matter1 that remains largely uncharacterized. Consistent with previous reports15, we measured concentrations of carbon-rich exopolymeric particulate matter in the ice which were ten times higher than in seawater samples. However, neither these C-rich particles nor chlorophyll a concentrations in sea ice samples were positively correlated to endogenous and sprayed ON  as chemical sunscreens for protection from UV radiation17. MAA have a distinct light absorption pattern that allows relative quantification18. MAA in our ice samples were proportional to the ON contents in the sprayed aerosols  for osmoregulation and cryoprotection24. Release of DMSP from ice enhances the emission of its volatile breakdown product, DMS, through microbial action24. Besides this degradation of organic material released from brine channels, sea ice promotes formation of nitrogen and sulphur volatiles in a more indirect way, by triggering oceanic phytoplankton blooms along the seasonally receding ice edge1. In the Weddell / Scotia Sea region of our study, the border of sea ice influence in ice-free waters was defined by the position of the Southern Boundary of the Antarctic Circumpolar Current (SBACC)25, around 60°S  cruise was conducted on board de RV Hesperides in the regions of Antarctic Peninsula, South Orkney and South Georgia Islands from 2 January to 11 February 2015.Particles in the range 1–3 nm were measured every 2 minutes with a Particle Size Magnifier . Particle size distributions in the 10–500 nm size range were measured with a Scanning Mobility Particle Sizer  at 5 minute resolution.Ambient aerosol was sampled by means of a multistage impactor and a high volume sampler in parallel, from the ship upper deck. The Berner impactor , mounting Tedlar foils as sampling substrates, collected particles on five stages with 50% particle upper cut off points at 0.14, 0.42, 1.2, 3.5 and 10 μm aerodynamic diameter (Dp), at a flow rate of 80 LPM. The high volume sampler  collected ambient aerosol particles with Dp < 1 μm on pre-washed and pre-baked quartz-fibre filters, at a controlled flow of 500 LPM. Sampling was allowed only when the samplers were upwind the ship exhaust with a relative wind speed threshold of 5 m s-1. Due to the necessity of collecting sufficient amounts of samples for detailed chemical analyses, sampling time was of the order of ~50 h for each sample. Samples were stored at −20 °C until the chemical analyses.32 and low molecular weight alkyl-amines 5. An IonPac CS16 3 × 250 mm Dionex separation column with gradient MSA elution and an IonPac AS11 2 × 250 mm Dionex separation column with gradient KOH elution were deployed for cations and anions, respectively. The water soluble organic carbon (WSOC) and water soluble total nitrogen (WSTN) content of the samples were quantified using a TOC-TN thermal combustion analyser 33. The water soluble organic nitrogen (WSON) was calculated as the difference between the WSTN and the inorganic nitrogen deriving from ammonium, nitrate and nitrite ions, as detailed elsewhere34. All the chemical species, with the exception of WSOC, were determined from the Berner impactor samples. WSOC concentration was determined from the high volume samples in order to improve accuracy. WSOC measurements are less sensitive than Ion Chromatography ones, therefore dividing the sample over the different stages of the impactor caused an enhancement of the measurement uncertainty, because of the presence of stages with WSOC concentration below or very close to the detection limit. This is not the case with the high volume sampler, which collects all the submicron aerosol mass in only one filter. Comparison of the results for ionic species measured on both substrates, demonstrate consistency between the two samplers.Aerosol samples were extracted in MilliQ water by sonication (30 min). Extracts were analysed by ion chromatography for the quantification of water soluble inorganic ions, organic acids et al.35. HR-ToF AMS was routinely calibrated according to the methods described by Jimenez et al.36. ON AMS is defined as total ON containing m/z fragments detected online by HR-ToF-AMS. The aerosol time-of-flight mass spectrometer  collects bipolar mass spectra of individual aerosol particles. Ambient aerosol is focused into a narrow particle beam for sizes between 0.1 and 1.5 μm.The size resolved non-refractory chemical composition of submicron aerosol particles was measured with an Aerodyne High Resolution Time of Flight Aerosol Mass Spectrometer . The instrument was described by DeCarlo 37 (SP-2) and metals by ATOFMS38.Black Carbon was measured by Single Particle Soot Photometer39 available at IFREMER. This same analysis allowed assigning air mass trajectories to the aerosol samples collected on filters. Of a total of 8 filter samples, three (PE24–28–06) had spent most of the time (79%) over open water, and three (PE09–13–18) had spent 73% of the time over sea ice and the sea ice marginal zone  and Maritime Polar - mP - (from open water regions). Simple calculations were made to assign each 5-days trajectory to the types of surface cover overflown. The Polar Stereographic map classified each of 1024 × 1024 24 km grid cells as land, sea, ice and, from this information, the percentage of time each trajectory spent over each surface type, and particularly over sea ice, could be calculated. A similar calculation was repeated but using daily maps of sea ice percentage concentration measured on a 12.5 km grid. The percentages assigned to each trajectory step allow a ‘spectrum’ of sea ice concentration from 0 to 100% (5% width) to be calculated, as shown in Supplementary Figure 40. The procedure follows closely the one described by Chang et al.41. In the present work, five-day back trajectories (HYSPLIT4 with GDAS data)42 were calculated four times a day during the cruise period, with arrival position 100 m above the ship. Organic nitrogen AMS data, filtered for black carbon (BC) < 1 ng m−3 to avoid contaminations from the ship, were averaged over 6 hours periods . The southern hemisphere was divided into 2.5° latitude x 7.5° longitude grid cells. The conditional probability that the air passing through the ij-th cell had a high aerosol concentration when arriving to the ship position is given by the ratio nij/mij, where nij is the number of trajectories with segment endpoints in cell ij, and mij is the subclass (mij < nij) of trajectories connected to organic nitrogen concentrations above a threshold defining “high concentrations” .To identify the potential source areas of the observed ON-rich aerosol, potential source contribution function (PSCF) analysis was appliedWeighting factors were applied to the grid cells, according to the following scheme:−1. The entrained air formed bubbles that, upon bursting, produced sea spray aerosol, as reported in O’Dowd et al.43. Particle-free compressed air was blown into the tank headspace (120 ml min−1), which had outlet ports leading to an Aerosol Chemical Speciation Monitor (ACSM) and a Scanning Mobility Particle Sizer (SMPS) for aerosol size distributions (10–500 nm). In parallel, bubble-bursting aerosol generation experiments with the same samples were performed using a square glass tank (10 L) that was filled with about 3 L of either seawater or melted sea ice, sealed with a stainless steel lid and continuously flushed with particle-free air (11 L min−1) as described elsewhere44. Sprayed aerosol size and composition was monitored with a HR-ToF-AMS and an ATOFMS.An airtight high grade stainless steel tank (200 L) was half filled with a constant flow of seawater pumped from a depth of 4 m by the flow-through pumping system of the ship. Water was dropped from the top of the tank as a plunging jet at a flow rate of 20 L minSeawater salinity and temperature were recorded continuously via the flow-through thermosalinograph SBE 21 SeaCAT. The water current velocity was measured with the Shipboard Acoustic Doppler Current Profiler (SADCP) “Ocean Surveyor” at 75 khz. Seawater samples were collected from a depth of 4 m using either the uppermost Niskin bottle of the CTD rosette or the ship’s flow-through underway pumping system.et al.25. Gradients in the continuous record of seawater, salinity , about 0.1 ma concentrations were determined by filtration on glass fibre filters followed by extraction with 90% acetone at 4 °C in the dark for 24 hours. Fluorescence of extracts was measured with a calibrated Turner Designs fluorometer. No phaeophytin corrections were applied. Samples were also taken and fixed with formalin-hexamine 1% for quantitative phytoplankton determination by the inverted microscope method45.Surface ocean and melted ice chlorophyll 46.Samples (2 ml) were fixed with glutaraldehyde , refrigerated, quick frozen in liquid nitrogen and stored at −80 °C. In the lab, they were stained with SYBR Green I and counted with FACSCalibur flow cytometer with a blue laser (488 nm). Only high-fluorescence viral populations, considered to infect eukaryotes, were used in this study47.Aliquots of melted sea ice were frozen at −20 °C until analysis of nitrate, nitrite and ammonium with an automated segmented flow analyser. Organic nitrogen was determined upon digestion (120 °C for 30 min) with alkaline persulfate and subtraction of inorganic nitrogen concentrations48.CDOM absorption spectra were measured by spectrophotometry on board, and the peak intensity of absorption at 330 nm was extracted from the baseline absorption as a measure of mycosporine-like aminoacid (MAA) concentration. FDOM excitation/emission matrices were also analysed on board using a Perkin Elmer LS55 luminescence spectrometer equipped with a xenon discharge lamp equivalent to 20 kW for a duration of 8-µs. Excitation at 280 nm and emission at 350 nm  was taken as indicator of protein-like substances49. Samples (160–450 mL) were filtered through 25 mm diameter 0.4 µm pore size polycarbonate filters (DHI). The filters were stained with 500 µL of pre-calibrated (with a xanthan gum solution) Alcian Blue  for 5 s and rinsed with MilliQ water. They were soaked in 80% sulphuric acid for 3 h and the absorbance of the extract was determined at 787 nm in a Varian Cary spectrophotometer. Duplicates were taken for each sample. Results were corrected for blanks consisting of unused filters stained with Alcian blue.TEP were analysed following the colorimetric methodFor TOC, 30 mL samples of sea water or melted sea ice were collected in acid-cleaned polycarbonate bottles, and stored in the dark at −20 °C until analysis. They were analysed in triplicate with a Shimadzu TOC-LCSV, with MilliQ water as a blank, potassium hydrogen phthalate as the calibration standard, and deep Sargasso Sea water as the reference. For POC, 250–2000 mL of seawater were filtered through pre-combusted  25 mm glass fiber filters, frozen at −20 °C and analysed with a Perkin Elmer 2400 CHN analyzer.50. A polydimethylsiloxane/divinylbenzene fibre was exposed in the headspace above the water sample, and the extraction performed for 2.5 h at 60 °C. The methylamines were then resolved and detected using a GC with NP detector. Calibrations were performed using external matrix-matched standard solutions containing mono-, di- and trimethylamine in the range 0.13–13.3 nM. DMS was determined, along with other volatiles, with a purge and trap GC with MS detector. Samples of 25 ml of seawater pre-screened through 200 µm were filtered on-line through GF-F, injected and sparged with He for 12 min. Calibration was performed with DMS solutions generated by dissolution and hydrolysis of solid DMSP in high purity water.To determine methyl-amine concentrations, 850 mL seawater samples were gravity filtered through 47 mm GF/F filters into 1 L high density polyethylene (HDPE) bottles containing HCl , and stored at 4 °C until analysis. The seawater was saturated with NaCl and adjusted to pH 13.4 with sodium hydroxide. Methylamines were extracted from the water sample by solid phase microextraction approach51. DMSPt concentrations were calculated by subtraction of the endogenous DMS. Calibration as for DMS. For glycine betaine and choline, 10 mL of sample was gravity filtered onto 47 mm GF/Fs. Filters were snap frozen and stored at −80 °C until analysis. Glycine betaine and choline were extracted from filters into 15 mL 12:5:1 methanol:chloroform:water containing internal standard (d11-glycine betaine) by soaking (1 hr). Solutions were clarified by centrifugation and analysed by LC/MS52. To calculate the proportion of POC contributed by DMSP or the N-osmolytes, their concentration in nmol L−1 was multiplied by 5 to account for 5 C atoms per molecule, and divided by 1000 and by the POC concentration in µmolC L−1.For DMSPt  analysis, two pellets of NaOH were added to 30 mL of sample for hydrolysis to DMS, for at least 24 h at room temperature in the dark. Aliquots of 0.1 to 1 mL were injected into a purge flask with high purity water, sparged for 4–6 min. and analysed for evolved DMS with a purge and trap GC system with FP detectorSupplementary Information