Parisa Bayat

Charlotte Rambaud

Bernard Priem

Matthieu Bourderioux

Mélanie Bilong

Salomé Poyer

Manuela Pastoriza-Gallego

Abdelghani Oukhaled

Jérôme Mathé
email: jerome.mathe@univ-evry.fr

Régis Daniel
email: regis.daniel@univ-evry.fr

Comprehensive structural assignment of glycosaminoglycan oligo-and polysaccharides by protein nanopore

Glycosaminoglycans are highly anionic functional polysaccharides with information content in their structure that plays a major role in the communication between the cell and the extracellular environment. The study presented here reports the label-free detection and analysis of glycosaminoglycan molecules at the single molecule level using sensing by biological nanopore, thus addressing the need to decipher structural information in oligo-and polysaccharide sequences, which remains a major challenge for glycoscience. We demonstrate that a wild-type aerolysin nanopore can detect and characterize glycosaminoglycan oligosaccharides with various sulfate patterns, osidic bonds and epimers of uronic acid residues. Size discrimination of tetrato icosasaccharides from heparin, chondroitin sulfate and dermatan sulfate was investigated and we show that different contents and distributions of sulfate groups can be detected. Remarkably, differences in α/β anomerization and 1,4/1,3 osidic linkages can also be detected in heparosan and hyaluronic acid, as well as the subtle difference between the glucuronic/iduronic epimers in chondroitin and dermatan sulfate. Although, at this stage, discrimination of each of the constituent units of GAGs is not yet achieved at the single-molecule level, the resolution reached in this study is an essential step toward this ultimate goal.

Glycosaminoglycans (GAGs) are highly anionic linear polysaccharides expressed on the cell surface and in the extracellular matrix, which have prominent roles in a variety of physiological and pathological processes, through the binding of numerous proteins (e.g. chemokines, growth factors) [START_REF] Daniel | Glycosaminoglycanomics: where we are[END_REF][START_REF] Lepedda | Glycosaminoglycans: from vascular physiology to tissue engineering applications[END_REF] . Given the central biological functions of GAGs and considering them as promising pharmacological targets, there is a strong demand for methods capable of reading the GAGs oligosaccharide sequences, especially those that are specifically involved in pathological situations. While such tools are widely available for other classes of biopolymers (DNA, proteins), efficient sequencing methods for GAGs based on routine analytical tools are still lacking. This is due to the remarkable molecular complexity of GAGs. Indeed, a puzzling feature of GAGs such as heparan sulfate (HS) and chondroitin sulfate (CS) is that they are regioselectively modified at the polysaccharide level through epimerization, N-and O-sulfation, and deacetylation, thus leading to complex patterns that are finely tuned for specific interactions [START_REF] Kjellén | Specificity of glycosaminoglycan-protein interactions[END_REF] . These two emblematic groups of GAGs HS/Heparin (HP) and CS/dermatan sulfate (DS), are composed of alternating residues of hexosamine and uronic acid (UA) forming a disaccharide repeating building block, linked α- (1,4) in HS/HP and β- (1,3) in CS (Fig. 1). Hexosamine motif is an N-acetyl-D-galactosamine (GalNAc) in CS, and Nacetyl-D-glucosamine (GlcNAc) in HS/HP in which the N-acetyl group could be further deacetylated and sulfated (GlcNS) (Fig. 1). Uronic acid is present under two epimer forms, namely D-glucuronic (GlcA) and L-iduronic acid (IdoA), both of which are found in HS/HP whereas CS contains only GlcA. In addition to these isomeric differences, HS/HP and CS exhibit a considerable variability of sulfation: in addition to sulfation of the amine group in HS/HP, hydroxyl groups can also be sulfated at the position C2 of uronic acid and the position C3 and/or C6 of GlcN and C4 and/or C6 of GalN.

However, the resulting huge combinations of regioselective modifications within disaccharide (degree of monosaccharide polymerization of two, dp2) elementary units lead to an extraordinary structural complexity that conventional bio-structural methods are facing. Despite great progress in structural characterization of GAGs over the past two decades, particularly by mass spectrometry [START_REF] Pepi | Developments in mass spectrometry for glycosaminoglycan analysis: a review[END_REF] , the need for GAG sequencing methods is still not fulfilled [START_REF] Compagnon | Lasers and ion mobility: new additions to the glycosaminoglycanomics toolkit[END_REF][START_REF] Hook | High sensitivity analysis of nanogram quantities of glycosaminoglycans using ToF-SIMS[END_REF] . In this context, the recent development of nanopore-based methods to decipher the information encoded in linear biopolymers such as DNA [START_REF] Jain | The Oxford Nanopore MinION: delivery of nanopore sequencing to the genomics community[END_REF] and proteins [START_REF] Ouldali | Electrical recognition of the twenty proteinogenic amino acids using an aerolysin nanopore[END_REF][START_REF] Bakshloo | Nanopore-based protein identification[END_REF] could also bring a decisive breakthrough in glycosciences [START_REF] Song | Analysis of the glycosaminoglycan chains of proteoglycans[END_REF][START_REF] Hagan | Chemically tailoring nanopores for singlemolecule sensing and glycomics[END_REF] . Nanopore analysis remains comparatively much less applied to carbohydrates, especially bioactive polysaccharides such as GAGs. Few studies have been reported on nanopore detection of GAGs, using either solid-state nanopore fabricated from conventional nanofabrication materials such as silicon nitride (SiNx) [START_REF] Im | Single molecule identification and quantification of glycosaminoglycans using solid-state nanopores[END_REF] or nanoscopic protein channels. Large solid-state nanopores can easily accommodate polysaccharides, and have recently been exploited for the detection of the unsulfated GAG hyaluronan [START_REF] Rivas | Label-free analysis of physiological hyaluronan size distribution with a solid-state nanopore sensor[END_REF][START_REF] Fasanello | Hyaluronic acid synthesis, degradation, and crosslinking in equine osteoarthritis: TNF-α-TSG-6-mediated HC-HA formation[END_REF] , sulfated GAGs heparin and oversulfated chondroitin sulfate contaminant in a pharmaceutical anticoagulant preparation [START_REF] Karawdeniya | Surveying silicon nitride nanopores for glycomics and heparin quality assurance[END_REF][START_REF] Ma | Biosensors and Bioelectronics Single conical track-etched nanopore for a free-label detection of OSCS contaminants in heparin[END_REF] , and of heparan sulfate [START_REF] Xia | Synthetic heparan sulfate standards and machine learning facilitate the development of solid-state nanopore analysis[END_REF] . Nanoscopic solid-state nanopores and biological nanopores can achieve high molecular resolution given their dimension of the same scale as many single molecules [START_REF] Kennedy | Reading the primary structure of a protein with 0.07 nm 3 resolution using a subnanometre-diameter pore[END_REF][START_REF] Piguet | Identification of single amino acid differences in uniformly charged homopolymeric peptides with aerolysin nanopore[END_REF] . Moreover, given the importance of pore length and internal structure in discrimination power, their ability to be tailored with angstrom precision through surface functionalization [START_REF] Chen | Atomic layer deposition to fine-tune the surface properties and diameters of fabricated nanopores[END_REF][START_REF] Yusko | Controlling protein translocation through nanopores with bio-inspired fluid walls[END_REF] or protein engineering techniques [START_REF] Boersma | Continuous stochastic detection of amino acid enantiomers with a protein nanopore[END_REF] , respectively, makes them attractive high-resolution sensors. To date, only a few reports have described the use of protein nanopores for the detection of GAGs with oligosaccharide-level resolution [START_REF] Fennouri | Comparative biosensing of glycosaminoglycan hyaluronic acid oligo-and polysaccharides using aerolysin and α-hemolysin nanopores[END_REF] . We previously reported the sensitive detection of hyaluronan (HA) oligosaccharides by protein nanopores and their ability to discriminate these unsulfated oligosaccharides according to their size [START_REF] Fennouri | Single molecule detection of glycosaminoglycan hyaluronic Acid oligosaccharides and depolymerization enzyme activity using a protein nanopore[END_REF] , thus fueling the concept of nanopore-based single-molecule mass spectrometry [START_REF] Baaken | Nanopore-based single-molecule mass spectrometry on a lipid membrane microarray[END_REF][START_REF] Robertson | Single-molecule mass spectrometry in solution using a solitary nanopore[END_REF] . It allowed real-time recording of oligosaccharide formation upon enzyme depolymerization of HA [START_REF] Fennouri | Kinetics of enzymatic degradation of high molecular weight polysaccharides through a nanopore: experiments and data-modeling[END_REF] .

In the study herein, we address a much greater structural complexity by targeting the analysis of sulfated oligosaccharides from HS/ HP and CS/DS using the aerolysin nanopore (AeL) (Fig. 2a) to probe not only the oligomer length but also fine structural determinants such as stereochemistry of osidic linkage, monomer composition and sulfate distribution along the oligosaccharide chain. Since interactions with partner proteins rely on the specific recognition of these structural determinants, in particular the specific arrangement of sulfate groups in given GAG sequences, the so-called sulfation code, it is of importance to be able to determine them within these sequences, and in this Fig. 1 | Constituent disaccharide units of the different GAGs studied: heparin (HP), heparosan (HN), hyaluronic acid (HA), dermatan sulfate (DS), and chondroitin sulfate (CS). The structural determinants studied are represented by different colors for the different types of monosaccharide units (D-Glc (dark blue), D-Gal (green), or uronic acid (black)), the type of glycosidic linkage within each disaccharide repeating unit (α-1,4 or β-1,4) and also between the disaccharide units (β-1,4 or β-1,3), the epimer position of the carboxylate group in uronic acid (encircled carboxylic acid at up (D-GlcA) or down (L-IdoA)) and also the presence or absence of sulfate groups.

respect the reported nanopore strategy paves the way for this deciphering [START_REF] Da Costa | Sulfation of glycosaminoglycans and its implications in human health and disorders[END_REF] .

Results

Investigating size discrimination

Figure 2a illustrates the organization of the translocation device through the aerolysin nanopore without attempting to respect the scale of the individual players (GAGs and the nanopore). The nanopore assays were carried out in 25 mM HEPES buffer, pH 7.5, containing 1 mM EDTA and 4 M LiCl. Under these experimental conditions and applying a voltage of +60 mV (cathode in the cis compartment), an ionic open pore current (I 0 ) of 58 ± 3 pA was observed (Fig. 2b), allowing the reliable detection of GAG oligosaccharides translocated through the AeL nanopore. More details on the experimental conditions are provided at Supplementary Information (Supplementary Note 1, and Supplementary Figs. 1 and2). Size discrimination of sulfated GAG oligosaccharides using the AeL nanopore under these experimental conditions was first studied on compounds derived from heparin, a relatively homogeneous GAG because it is almost completely sulfated and consists of repeating disaccharide units [-4-glucosamine NS,6S-α1,4iduronic acid,2S-β1-]. Introduction of HP oligosaccharides varying in size from dp4 to dp20 into the cis compartment generated transient current blockades corresponding to translocation events that were easily distinguishable from noise (Fig. 2b). The choice of even oligosaccharides is determined by the enzymatic mode of production of the oligosaccharides, which exploits the activity of enzymes lyase and hydrolase degrading polysaccharide substrates into even oligosaccharides. The data shows an increase in both the current blockade (Fig. 2c) and the average translocation time (Fig. 2c,e) as a function of the polymerization degree, in agreement with our previous study on non-sulfated HA oligosaccharides [START_REF] Fennouri | Single molecule detection of glycosaminoglycan hyaluronic Acid oligosaccharides and depolymerization enzyme activity using a protein nanopore[END_REF] , thus showing the capability of AeL nanopore to detect highly sulfated oligosaccharides of different lengths.

Normalized blockade current (ratio of blockade current (I b ) to open pore current (I 0 ), (I b /I 0 )) decreases from dp4 to dp10, and then barely decreases (about 9 % change in blockade current) above dp10, indicating that the pore is almost completely occupied by dp10 and therefore further increase in oligosaccharide size does not significantly change the depth of the events. For lower oligosaccharide sizes, the relative increase of the blocked current is mostly due to the low pass filter employed in the acquisition setup [START_REF] Merstorf | DNA unzipping and protein unfolding using nanopores[END_REF] . The calculated blockade durations for dp4, dp6, dp10, dp16 and dp20 are 19 ± 10 µs, 23 ± 15 µs, 86 ± 10 µs, 230 ± 15 µs and 391 ± 17 µs, respectively. An almost linear increase of the translocation time with the size of the oligosaccharides is observed, despite some deviation from linearity for the smallest oligosaccharides HP dp4 and dp6. We have previously reported the linear dependence of translocation time on the size of oligosaccharides for the non-sulfated glycosaminoglycan hyaluronic acid [START_REF] Fennouri | Single molecule detection of glycosaminoglycan hyaluronic Acid oligosaccharides and depolymerization enzyme activity using a protein nanopore[END_REF] . This linear dependency is also the case for the glycosaminoglycan CS (discussed later) for which we also determined the same translocation time/size relationship (dp6 = 24 μs, dp8 = 40 μs, dp10 = 48 μs, dp12 = 63 μs, dp16 = 94 μs and dp20 = 109 μs). The dependence of translocation time on the size of oligosaccharides thus appears to be a general trend for GAGs. Scatter plots of the f HP dp4, g mixture of HP dp4 and HP dp6, h mixture of HP dp4, HP dp6 and HP dp10, i mixture of HP dp4, HP dp6, HP dp10 and HP dp16, and j mixture of HP dp4, HP dp6, HP dp10, HP dp16 and HP dp20. The relative density of events on the plots is highlighted by the markers color: the brighter the dot, the denser the events. Progressive appearing of the deeper and longer events is clearly seen by the shift of the clouds of events towards long translocation times. All data were recorded in 4 M LiCl, 25 mM HEPES buffer and 1.0 mM EDTA at pH 7.5, 20.0 °C, at the bias voltage of +60 mV and using 43 μM of each oligosaccharide. For mixture analysis, oligosaccharides were added one by one at the same concentration (10 μM) to the cis chamber and the residual current was measured. Source data for this figure are provided as a Source Data file.

The deviation from the linear dependency of the translocation time on the size of oligosaccharides for the smallest HP oligosaccharides (dp4 and dp6) is due to the cutoff frequency of the lowpass filter (f c = 50 kHz) that causes the broadening of the distribution (both in duration and current blockade) of the translocation events at short translocation times and a minimum threshold value of 20 µs (Figs. 2d and 3g) [START_REF] Payet | Temperature effect on ionic current and ssDNA transport through nanopores[END_REF][START_REF] Payet | Thermal unfolding of proteins probed at the single molecule level using nanopores[END_REF] . However, this does not preclude the observation of a clear relationship between translocation time and the size of oligosaccharides. This effect, well known in the nanopore field, is well described in the literature data [START_REF] Merstorf | DNA unzipping and protein unfolding using nanopores[END_REF] . Shortly, low pass filter of cutoff frequency f c is equivalent, in the time domain, to a convolution of the signal by a bell shape curve of width 1/ f c (i.e. 20 μs in our experiments). This results in a broadening of the event of 1/ f c . For shorter events, the distortion is thus more pronounced and leads to a shallower blocking of the current and thus a higher mean blocked current.

Nanopore experiments carried out using mixtures of HP oligosaccharides show that our experimental approach can identify the presence of different oligosaccharide species constituting the mixture despite a partial overlap. Scatter plots of the successive introduction of oligosaccharides HP dp4 to dp 20 into the cis chamber are shown in Fig. 2f-j. Note that the scatter plot of the mixture cannot be considered as the simple addition of the individual components because this would require considering the entry rate to be equivalent for each component. The scatter plots upon successive additions of longer HP showed the gradual shift of the event population towards the longer translocation times, and the occurrence of deeper events in the current traces. This observation demonstrates the convenience of the AeL nanopore to highlight the polydispersity of the HP oligosaccharides mixtures.

The size discrimination capability of the AeL nanopore was also investigated at the polysaccharide level by analyzing full size heparin (16 kDa, corresponding to dp55), and low molecular weight heparin preparations (11 kDa and 5 kDa, i.e. dp38 and dp18, respectively). As expected from the above results with HP dp16 and dp20, these polysaccharides almost completely occupy the pore, thus leading to similar normalized current blockades for the three heparins (Fig. 3a-c). However, they can be distinguished through distinct translocation times (Fig. 3e). The calculated event duration for the 16 kDa HP (1717 ± 200 µs) is almost four times larger than that of the 5 kDa HP (406 ± 20 µs). This confirms the linear dependence of the translocation time with chain length as shown in Fig. 3g, suggesting that, in principle, there is no size limit for polysaccharide analysis based on the translocation time criterion. Afterwards, the nanopore analysis technique was applied to enoxaparin (Fig. 3d), a low-molecular weight heparin (4.5 kDa) used in clinical applications as an anticoagulant medication for the treatment of deep vein thrombosis and pulmonary embolism as well as acute coronary syndrome and heart attacks [START_REF] Hofmann | Clinical application of enoxaparin[END_REF] . Given the close average molecular weights of enoxaparin and the aforementioned 5 kDa HP, their translocation behaviors were compared. Both enoxaparin and the 5 kDa preparation are low molecular weight heparins, thus sharing the same structural organization. Their superimposed I b histograms (Fig. 3f) reveal that enoxaparin has a fairly wide distribution of oligosaccharide sizes compared to 5 kDa HP in agreement with their size exclusion chromatography results (Supplementary Fig. 3). This result illustrates the sensitivity of the AeL pore to the identity of analyte and its utility for quality control analysis of clinical heparin.

By comparing the scatter plot of enoxaparin with that of the mixture of HP oligosaccharides, it can be qualitatively concluded that enoxaparin contains almost all the oligosaccharides from dp4 to dp20 in agreement with the literature data [START_REF] Ouyang | Systematic analysis of enoxaparins from different sources with online one-and two-dimensional chromatography[END_REF][START_REF] Liu | Salt-free fractionation of complex isomeric mixtures of glycosaminoglycan oligosaccharides compatible with ESI-MS and microarray analysis[END_REF] (Supplementary Fig. 4). Note that because of the wide distribution of the oligosaccharide population, it is difficult to perform a quantitative analysis.

Degree of sulfation

In addition to size heterogeneity, the diversity of sulfation patterns is also an analytic barrier. To investigate the effect of sulfation on the translocation behavior of the GAG oligosaccharides, heparin oligosaccharides were first compared with their unsulfated counterparts obtained from heparosan (HN). HN consists of [-4-N-acetylglucosamine-α1,4glucuronic acid-β1-] n disaccharide units, which can thus be considered as a fully desulfated N-acetylated HP with identical glycosidic linkages to HP (the uronic unit being GlcA in HN and GlcA/IdoA in HP) (Figs. 1 and4a).

A striking difference was observed between HP and HN oligosaccharides on the scatter plots, which show two quite distinct event populations for dp6 (Fig. 4b), dp10 (Fig. 4d) and dp20 (Fig. 4f). This discrimination is still observed for HP and HN oligosaccharides in mixture for each size class (Fig. 4c,e,g). Some scattering of translocation times of HP compared to HN can be noted, which can be explained by some deviation of the main structure of the HP trisulfated disaccharide unit (resulting from some variation in the degree and pattern of sulfation). HN is a regular unsulfated GAG and thus does not have this drawback. The resolution of the HP and HN oligosaccharides into two distinct populations results from different blocking currents: HP oligosaccharides led to greater signal extinction, suggesting the importance of sulfate groups in the total volume occupied by the molecule in the pore lumen. It should be noted that other structural parameters could also be involved (such as the hydration state of the pore and/or the analyte in the pore, hydrophobic interactions, hydrogen bonding, ion binding to the analyte, etc.), in particular due here to the sulfate groups that can generate ionic interactions with the pore. However, if the latter also made a significant contribution to translocation, we would also see its effect on the translocation time. But the observation of almost comparable T t values for HP and HN oligosaccharides shows that this interaction effect plays little, and that the dominant effect is that of the occupied volume.

Translocation times of HP and HN oligosaccharides shown in Fig. 4 are as follows: HP dp6: 23 ± 20 µs, HN dp6: 50 ± 15 µs, HP dp10: 86 ± 10 µs, HN dp10: 71 ± 10 µs, HP dp20: 391 ± 17 µs and HN dp20: 297 ± 20 µs. The higher anionic charge of the HP oligosaccharides due to the sulfate groups can promote their faster translocation as observed for the small oligosaccharide dp6. However, the larger monomer size in HP oligosaccharides should enhance the steric and electrostatic interactions within the pore. The impact of these interactions on transit time could be greater than that of the anionic charge as observed for dp20 oligosaccharides. Indeed HP dp20 have a longer translocation time than HN dp20. The two effects of anionic charge and steric hindrance are almost counterbalanced in the case of HP dp10 and HN dp10 leading to almost similar translocation times but remain discernible on the basis of their I b /I 0 values.

With the mixture of HP and HN oligosaccharides (Fig. 4e), a more intense peak is observed for HP than for HN due to the higher translocation frequency of the HP oligosaccharides than that of the HN oligosaccharides (at the difference of Fig. 4d in which the analysis of HP and HN dp10 was performed individually, and then their scatter plots were determined for a similar number of recorded events and were not rescaled by event frequencies). Despite higher concentrations of HN oligosaccharides to compensate for the higher translocation frequency of HP oligosaccharides, the effect of this higher frequency could not be fully mitigated.

To probe the regioselective influence of the sulfate groups on the translocation properties, selectively desulfated heparin dp10 oligosaccharides, namely 2-O-desulfated HP dp10, 6-O-desulfated HP dp10, N-desulfated HP dp10 and N-desulfated re N-acetylated HP dp10 (Supplementary Fig. 5), were analyzed. All these regioselectively desulfated HP decasaccharides showed distinct translocation behavior from that of HP dp10 (Supplementary Fig. 6). They all exhibited higher normalized blockade current values (shallower events), confirming the role of the sulfate groups in confinement properties via the occupied space. In addition, the translocation times of these selectively desulfated heparins have shifted to shorter times even though the partial lack of sulfate groups resulted in a decrease of the charge state of the molecule. Interestingly, the translocation time of the N-desulfated re N-acetylated HP dp10 most closely approximates that of HP dp10, although it remains shorter. Despite their similar volumes and the higher anionic charge of HP dp10, the translocation of HP dp10 is slower. These results indicate that the translocation behavior of HP dp10 through AeL nanopore is more influenced by the space occupied by the sulfate groups rather than by the anionic charge. Note the greater impact of 6-O-and N-desulfation, consistent with their exocyclic position on the sugar ring compared to the 2-O-sulfate group.

To examine whether the ability of the nanopore detection system to differentiate different levels of sulfation can be exploited to monitor biological reactions modifying the sulfate content, the enzymatic regioselective 6-O-desulfation of HP dp 10 and HP dp20 catalyzed by the sulfatase HSulf-2 was investigated (Supplementary Notes 2 and 3, and Supplementary Figs. 7 and8). The same shift of the translocation times to shorter times after enzymatic desulfation were observed in both cases (with a larger shift for HP dp20 compared to HP dp10), in accordance with the above observations using commercially purchased regioselectively desulfated heparins.

Osidic bond

To assess the effect of the osidic bond on the translocation behavior of GAGs, GAG oligosaccharides composed of similar building blocks but linked together by dissimilar osidic linkages were analyzed. For this purpose, dp6, dp8 and dp10 oligosaccharides were prepared from heparosan (HN) and hyaluronic acid (HA) composed of [-4-N-acetylglucosamine-α1,4glucuronic acid-β1-] n and [-3-N-acetylglucosamine-β1,4glucuronic acid-β1-] n disaccharide units, respectively (Fig. 5a).

A drastic difference in translocation time was observed between HA and HN oligosaccharides, with HA oligosaccharides showing longer dwell times (Fig. 5c,e,g). Calculated translocation times are for HA oligosaccharides: HA dp6: 35 ± 15 µs, HA dp8: 110 ± 15 µs and HA dp10: 237 ± 15 µs, and for HN oligosaccharides: HN dp6: 50 ± 15 µs, HN dp8: 46 ± 10 µs and HN dp10: 71 ± 10 µs. The differences in T t between HA and HN in each size class increase with oligosaccharide size, and HA oligosaccharides show a much steeper rate of increase in T t than HN oligosaccharides. The large difference observed between the dp10 oligosaccharides suggests that HA adopts conformations inducing more interactions within the AeL nanopore. In addition, much more dispersed event clouds are observed for oligosaccharides compared to HN oligosaccharides (Fig. 5b,d,f). This observation can be related to data from previous studies reporting the coexistence of various HA conformations depending on its environment, as well as interconversion between different HA conformations [START_REF] Hargittai | Molecular structure of hyaluronan: an introduction[END_REF][START_REF] Atkins | Hyaluronates: relation between molecular conformations[END_REF][START_REF] Winter | Hyaluronic acid: structure of a fully extended 3-fold helical sodium salt and comparison with the less extended 4-fold helical forms[END_REF][START_REF] Arnott | Hyaluronic acid double helix[END_REF] . In order to ascertain this hypothesis, and to make sure that the wider signal distribution observed for HA oligosaccharides is not a simple artefact of an impure sample, HA and HN dp6, dp8, and dp10 oligosaccharides were analyzed and compared with size exclusion chromatography system using two columns in series (Supplementary Fig. 9). Almost similar chromatographic separation is observed between the different sizes of oligosaccharides in each class, whereas narrow event distributions for HN oligosaccharides and broad distributions for HA oligosaccharides were observed in the nanopore data. This shows that the broad distribution of translocation events for HA oligosaccharides does not originate from impurities, and is, in fact, due to the coexistence of various conformations. Moreover, in each size class, no distinction between HN and HA oligosaccharides can be seen by chromatography. However, using the nanopore technique, we see a huge distinction in terms of translocation times and the width of the event distributions, demonstrating the strength of this technique. A high concentration of LiCl was used in our study, and further investigation is needed to determine the influence of the concentration and nature of salt on HA translocation pattern. Under our conditions, discrimination of HA and HN oligosaccharides of two different sizes in a mixture is feasible as observed for the mixture of HA dp6 and HN dp10 (Supplementary Fig. 10). However, due to the dispersion of the event population for HA oligosaccharides, it is difficult at this stage to distinguish them from HN oligosaccharides of various lengths in a mixture. Overall, these results show that differences in α/β anomery and 1,4/1,3 osidic bonds can be thus detected by nanopore sensing.

Epimerization

Determining the epimerization of uronic acid residues in GAG chains is one of the most challenging tasks in GAGs analysis, with the knowledge that this subtle difference in the hexuronic acid stereochemistry results in distinct biological properties. The ability of the AeL nanopore to distinguish between glucuronic acid (GlcA)/iduronic acid (IdoA) was investigated here by analyzing the translocation of sulfated oligosaccharides from dermatan sulfate (DS) containing IdoA and chondroitin sulfate (CS) containing GlcA. DS consists of [-3-Nacetylgalactosamine 4S-β1,4iduronic acid-β1-] n disaccharide units (Fig. 6a), and CS consist of [-3-N-acetylgalactosamine 6 S or 4S-β1,4glucuronic acid-β1-] n disaccharide units (Fig. 6b). The N-acetylgalactosamine residues are 4-O (CS-A) or 6-O (CS-C) sulfated in CS oligosaccharides, and 4-O-sulfated in DS oligosaccharides (Fig. 6a,b).

Slightly deeper current blockades, getting more significant with increasing the oligosaccharide size, were observed for DS oligosaccharides suggesting a larger spatial volume of DS compared to CS (Fig. 6d,g,j,m). Regarding translocation times, while DS dp8 presents a slightly shorter T t than CS dp8 (Fig. 6e), comparable dwell times are measured for the DS and CS dp12 and dp16 oligosaccharides shown in (Fig. 6h,k), and a longer T t is observed for the DS dp20 than for CS dp20 (Fig. 6n), suggesting a configuration of the DS dp20 molecule that induces more interactions within the pore. IdoA residues can adopt three different conformations, namely 1 C 4 (chair), 2 S 0 (skew boat) and [START_REF] Pepi | Developments in mass spectrometry for glycosaminoglycan analysis: a review[END_REF] C 1 (chair) with the possibility of inter-conversion, which allows a great flexibility of the DS chains, while CS chains are less flexible since GlcA exists only in the 4 C 1 (chair) conformation. We can assume that the longer DS oligosaccharide feeds a greater diversity of configurations, which leads to a higher probability of interaction within the pore resulting in longer translocation times. Translocation events appear more dispersed for CS oligosaccharides than for DS oligosaccharides, which may be due to the fact that the chondroitin sulfate oligosaccharides used in our experiments are mixtures of CS-A and CS-C. From these observations, it appears that the nanopore detection technique is able to distinguish two classes of GAGs differentiated by a structural feature as fine as epimerization.

Building block

Hyaluronic acid and chondroitin sulfate are two main families of GAGs with the same alternating glycosidic bonds β1-4/β1-3 between the units, but different types of building blocks. While HA has N-acetylglucosamine unit in its building block, CS has N-acetylgalactosamine 6 S or 4 S (Supplementary Fig. 11). From the translocation event plots and I b histograms of dp6, dp8, and dp10 oligosaccharides from HA and CS (Supplementary Fig. 11), we observe that CS oligosaccharides exhibit deeper translocation events than those from HA, most likely due to the larger spatial volume occupied by sulfated building blocks in CS. CS and HA oligosaccharides are also differentiated by their translocation time, with HA oligosaccharides moving much more slowly due to their lower anionic charge. As the size of the oligosaccharides increases, the difference between the translocation times of HA and CS becomes greater. Therefore, although the HA and CS oligosaccharides share the same osidic linkage pattern, they exhibit distinct nanopore signatures, which may be explained by the sensing capabilities of the AeL nanopore with respect to the isomerization and functional groups of the acetylhexosamine unit.

Discussion

To our knowledge, this is the first study to explore the ability of a biological nanopore to detect different sequences of GAGs oligo-and polysaccharides. the nanopore detection method is already widely applied for nucleic acid sequencing and more recently for peptide discrimination, it had not yet been explored for the very high structural complexity of GAGs sequences. Compared to nucleic acids and proteins, GAGs show an unparalleled diversity of sequences with different combinations of sulfation, epimers of the uronic acid unit and isomers of the N-acetyl hexosamine unit, and osidic linkages. Our study shows that the AeL nanopore, thanks to its confinement properties, is able to detect different GAGs sequences on the basis of each of these structural determinants. In particular, characteristic fingerprints could be obtained for various GAGs based on the sulfation and epimerization of the osidic units (Fig. 7). These structural elements distributed along the sequence of GAGs represent an informational content guiding the functional properties of GAGs, and the results obtained here open the way to their deciphering at the single molecule level. Unlike solid nanopores, biological nanopores have a diameter of the same scale as many single molecules and molecular building blocks, so that sequential reading of the monomeric composition and detection of fine modifications of the linear oligosaccharide molecule can be considered. This desired molecular resolution is not yet achieved in this study since the disaccharide units are not detectable due to their too fast translocation speed. However, this goal should be achievable through the ability of biological nanopores to be tailored with angstrom precision using protein engineering techniques, as previously reported for the successful detection and discrimination of single amino acids [START_REF] Boersma | Continuous stochastic detection of amino acid enantiomers with a protein nanopore[END_REF] . In addition, a recent work reported the ability of the AeL nanopore to directly detect a single amino acid molecule without any modification or labeling [START_REF] Yuan | The analysis of single cysteine molecules with an aerolysin nanopore[END_REF] . Thus, it can be anticipated that monosaccharide detection should be achievable by regulating the characteristic interaction between the monosaccharide unit and the designed AeL nanopore.

In addition to determining the different structural elements of oligosaccharides, nanopore analysis also highlights the impact of these elements on structural organization. Thus, a difference as subtle as that of the osidic bond between the constituent units, as with HA and HN, leads to distinct signatures, indicating a very different occupation of the space in the nanopore, and therefore reflecting very distinct conformational organizations of the confined molecules. Finally, the detection method proves to be useful to detect fine structural modifications generated by enzymatic action, as illustrated here by the regioselective 6-O-desulfation catalyzed by the sulfatase HSulf. This opens the way to fine analysis at the single molecule level of the regioselectivity of enzymes acting on GAGs. The single molecule level of detection, which requires only a minute amount of analyte, and label-free detection are very valuable features for GAGs monitoring in the biological and medical diagnosis context.

In this study, GAGs differing in one main structural attribute (e.g. sulfation) were compared. However, GAGs often show a spread of more than one structural attribute throughout their chain. In addition, sulfation and epimerisation of GAGs generally vary along individual GAG chains. Therefore, this study, which is an essential step on the way to the identification of GAGs, also highlights the need for further studies and progress to achieve the comprehensive identification of individual GAG molecules with their heterogeneities. We are considering several avenues to accomplish this goal of sequencing individual molecules. These could involve, among others, better control of the translocation rate using molecular motors as already exists for DNA sequencing, the use of GAG-specific enzymes combined with nanopore analysis, and improved data analysis capabilities through comprehensive signal processing of the recorded electrical traces and in-deep data mining. It should be noted that the same scientific barriers also had to be overcome for DNA sequencing and, more recently, for protein sequencing, bearing in mind that nucleic acids can be amplified to purity, whereas the pool of GAG molecules obtained from natural sources is never uniform. This difference poses an important additional challenge in GAG analysis. Advances in recent years in the nanopore analysis of these two classes of biological macromolecules are based on translocation control allowing the sequential reading of the molecules during its translocation and on fingerprinting obtained by enzymatic digestion, followed by the identification the fragments. For example, depolymerization of heparan sulfate with the sulfated sequence-specific heparinase I releases the unsulfated domains, and treatment with unsulfated sequence-specific heparinase III releases the sulfated domains. Nanopore fingerprinting of these two types of enzyme products will allow the determination of the organization of the HS domains as a function of tissue or pathophysiological status. The use of regioselective sulfatases, such as the 6-O-sulfatase tested in this study will be useful in deciphering the sulfation patterns. Whether one or the other method is used, it requires an improvement of the signal resolution. We are already working on other event parameters that could be added to the event duration and normalized blocked current to go beyond the level of statistical resolution achieved in this study and reach single-molecule scale identification. The use of machine learning on a database of nanopore signatures of known molecules will help in the structural identification within this complex parameter space.

Methods

Materials

Heparin, regioselectively modified heparin, chondroitin sulfate, dermatan sulfate and hyaluronic acid oligosaccharides as well as heparin 11 kDa were purchased from Iduron (Manchester, UK). These commercial oligosaccharides were prepared by the provider following well-established depolymerization methods using the specific enzymes heparinases, chondroitinases, and hyaluronidase, and purified by high-resolution gel filtration. Each size class of oligosaccharide is substantially homogeneous in molecular size as demonstrated by size exclusion chromatography analysis; however, as indicated by the provider, it may contain structures that can vary a little bit in content and pattern of sulfation, that is consistent between various dpn. Preparation methods of the different oligosaccharides studied, and their main structural features are summarized in Supplementary Table 1. Mass spectra of the regioselectively modified heparin oligosaccharides are presented in Supplementary Fig. 12 and Supplementary Data file 1. Heparin 5 kDa was obtained from Neoparin Inc., and heparin 16 kDa was from Celsus Laboratories Inc. (Cincinnati, OH, USA). The low molecular weight heparin enoxaparin Lovenox® was from Sanofi-Aventis (Bridgewater, NJ). Heparosan and derived oligosaccharides were produced by the multi-enzymatic complex synthase of heparosan and endo-lyase expression in metabolically engineered E. coli K5 [START_REF] Barreteau | Production of intracellular heparosan and derived oligosaccharides by lyase expression in metabolically engineered E. coli K-12[END_REF] . Briefly, recombinant E coli DH1 expressing kfiABCD and elma genes encoding for the multi-enzymatic complex synthase of heparosan and endo-lyase of E coli K5 was grown in 2 L bioreactor. The medium was composed of glycerol (17.5 g L -1 ), NH 4 H 2 PO 4 (7 g L -1 ), KH 2 PO 4 (7 g L -1 ), MgSO 4 •7H 2 O (1 g), thiamine•HCl (4.5 mg L -1 ), trace mineral solution (7.5 mL L -1 ), citric acid (0.5 g L -1 ), KOH (2 g L -1 ). The trace mineral stock solution contained: nitrilotriacetate (70 mM), ferric citrate (7.5 g.L -1 ), MnCl 2 •4H 2 O (1.3 g L -1 ), CoCl 2 •6H 2 O (0.21 g L -1 ), CuCl 2 •2H 2 O (0.13 g L -1 ), H 3 BO 3 (0.25 g L -1 ), ZnSO 4 •7H 2 O (1.2 g L -1 ), Na 2 MoO 4 •2H 2 O (0.15 g L -1 ). The pH was kept at 6.8 by ammoniac addition and temperature was kept at 33 °C until total consumption of glycerol. Then, temperature was dropped at 28 °C and a glycerol feeding at 5 mL h -1 was started and 0.2 mM IPTG was added allowing the recombinant gene expression. After 48 h, the culture was stopped and HN oligosaccharides were recovered from the cell pellet after centrifugation and heating at 100 °C for 40 min. They were purified by anion-exchange chromatography on Dowex 1 × 4-400 on formiate form upon gradient elution with 0-1 M ammonium formiate. A second step of purification by size exclusion chromatography on Superdex was performed if required. 6-O-desulfated HP dp10 and dp20 was formed by enzymatic desulfation catalyzed by the sulfatase HSulf-2 at 37 °C in 50 mM Tris buffer, 10 mM MgCl 2 , pH 7.5, containing HP oligosaccharides in a 100:1 (v/v) oligosaccharides/sulfatase ratio [START_REF] Seffouh | HSulf sulfatases catalyze processive and oriented 6-O-desulfation of heparan sulfate that differentially regulates fibroblast growth factor activity[END_REF] . TPCK-Trypsin (from bovine pancreas) was obtained from Thermo Fisher Scientific (Rockford, IL, USA), and lithium chloride, EDTA (ethylenediaminetetraacetic acid) and HEPES (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid), hexane and hexadecane from Sigma-Aldrich (Saint-Quentin Fallavier, France). Ultra-pure water (18.2 MΩ) was obtained from a Milli-Q purification system (Millipore).

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Fig. 2 |

 2 Fig. 2 | Single-channel detection of heparin (HP) oligosaccharides in the dpn range between tetrasaccharide dp4 and icosasaccharide dp20 (dpn, n = 4-20) analyzed individually and in mixture by successive introduction of HP oligosaccharides into the cis compartment. a Schematics of the experimental setup with aerolysin inserted in a lipid bilayer (not to scale). The ionic current is recorded while applying a constant voltage. b Illustration of typical single nanopore current recording in presence of HP dp10. The current is blocked by the presence of the molecule inside the channel (translocation). For each translocation event the blocked current I b , the open pore current I 0 and the duration of event T t are calculated. c Scatter plot for HP dp10 with the associated current histogram, translocation time histogram with logarithmic binning and the corresponding integrated probability (red curve and right axis). The black dot represents the first event represented on b. The distortion of the event cloud at short timescale is due to the signal distortion from the low-pass filtering of the signal. d Scatter plots obtained for different sizes of HP oligosaccharides when they were analyzed individually. Each dot represents the translocation of a single molecule. At least 2000 events were recorded for each molecule type. e Integrated probability of translocation for different sizes of HP oligosaccharides.Scatter plots of the f HP dp4, g mixture of HP dp4 and HP dp6, h mixture of HP dp4, HP dp6 and HP dp10, i mixture of HP dp4, HP dp6, HP dp10 and HP dp16, and j mixture of HP dp4, HP dp6, HP dp10, HP dp16 and HP dp20. The relative density of events on the plots is highlighted by the markers color: the brighter the dot, the denser the events. Progressive appearing of the deeper and longer events is clearly seen by the shift of the clouds of events towards long translocation times. All data were recorded in 4 M LiCl, 25 mM HEPES buffer and 1.0 mM EDTA at pH 7.5, 20.0 °C, at the bias voltage of +60 mV and using 43 μM of each oligosaccharide. For mixture analysis, oligosaccharides were added one by one at the same concentration (10 μM) to the cis chamber and the residual current was measured. Source data for this figure are provided as a Source Data file.

Fig. 3 |

 3 Fig. 3 | Investigation of size discrimination of heparin polysaccharides using AeL nanopore. Scatter plots derived from HP polysaccharides with the average molecular weights of a 5 kDa, b 11 kDa and c 16 kDa; and d of enoxaparin with the average molecular weight of 4.5 kDa. e Superimposed translocation time histograms corresponding to a-d scatter plots. Distinct translocation times are observed for three different sizes of HP polysaccharides. Enoxaparin distribution is clearly not mono-exponential contrarly to other samples. The timescales resulting from mono-exponential fits are 406 ± 15 µs, 1279 ± 30 µs, 1717 ± 73 µs and 278 ± 24 µs for HP 5 kDa (dp18), HP 11 kDa (dp38), HP 16 kDa (dp55) and enoxaparin, respectively. f Superimposed I b histograms corresponding to a, d scatter plots. The superimposed I b histograms of enoxaparin and 5 kDa HP show that enoxaparin has a rather broad distribution of oligosaccharides compared to 5 kDa HP despite their close average molecular weights. g Translocation time versus polymerization degree from data of Figs. 2 and 3e. The straight line is a guide to the eye and the errors bars represent the statistical standard deviation resulting from the curve fitting procedure of the distribution. The mean translocation time would be about 40 µs per monomer. The deviation from the linearity for short oligo length is due to the signal distortion at short timescales. All data were recorded in 4 M LiCl, 25 mM HEPES buffer and 1.0 mM EDTA at pH 7.5, 20.0 °C, and at the bias voltage of +60 mV. Source data for this figure are provided as a Source Data file.

Fig. 4 |

 4 Fig. 4 | Discrimination of the non-sulfated heparosan and fully sulfated heparin oligosaccharides analyzed individually and in mixture. a Structures of the heparosan and heparin. Superimposed scatter plots of b HN dp6 and HP dp6, d HN dp10 and HP dp10, and f HN dp20 and HP dp20. Scatter plot of the mixture of c HN dp6 and HP dp6, e HN dp10 and HP dp10, and g HN dp20 and HP dp20. Discrimination between HP and HN oligosaccharides in each size class is observed. All data were recorded in 4 M LiCl, 25 mM HEPES buffer and 1.0 mM EDTA at pH 7.5, 20.0 °C, and at the bias voltage of +60 mV. The concentrations in the mixture for HN and HP dp6 were 50 μM and 10 μM, respectively; HN and HP dp10 were 50 μM and 25 μM, respectively; and. HN and HP dp20 were 10.5 μM and 3.5 μM, respectively. In order to avoid pore closure, lower concentrations were used for the dp20 chains. Source data for this figure are provided as a Source Data file.

Fig. 5 |

 5 Fig. 5 | Effect of the osidic bond on the translocation behavior of the GAGs. a Structures of the heparosan (HN) and hyaluronic acid (HA). Superimposed scatter plots of the b HA dp6 and HN dp6, d HA dp8 and HN dp8, and f HA dp10 and HN dp10. Superimposed T t histograms of the c HA dp6 and HN dp6, e HA dp8 and HN dp8, and g HA dp10 and HN dp10. A noticeable difference in translocation time is observed between HA and HN oligosaccharides. HA oligosaccharides show a much steeper ramp in increase of the T t as a function of size than HN oligosaccharides. The time distributions were fitted to a mono-exponential function leading to the timescales given on the corresponding graphs. All data were recorded in 4 M LiCl, 25 mM HEPES buffer and 1.0 mM EDTA at pH 7.5, 20.0 °C, and at the bias voltage of +60 mV. Source data for this figure are provided as a Source Data file.

Fig. 6 |

 6 Fig. 6 | Probing the effect of GlcA / IdoA epimerization on the translocation behavior of the GAGs. Structure of a dermatan sulfate (DS) and b chondroitin sulfate (CS) A and C. Superimposed scatter plots of the c DS dp8 and CS dp8 f DS dp12 and CS dp12, i DS dp16 and CS dp16, and l DS dp20 and CS dp20. Superimposed I b histograms of the d DS dp8 and CS dp8 g DS dp12 and CS dp12, j DS dp16 and CS dp16, and m DS dp20 and CS dp20. Superimposed T t histograms of the e DS dp8 and CS dp8, h DS dp12 and CS dp12, k DS dp16 and CS dp16, and n DS dp20 and CS dp20. Slightly deeper current blockades are observed for DS oligosaccharides suggesting a larger spatial volume of DS compared to CS oligosaccharides. While DS dp8 presents a slightly shorter T t compared to CS dp8, DS and CS dp12 and dp16 oligosaccharides show comparable dwell times, and a longer T t is observed for the DS dp20 compared to CS dp20. The time distributions were fitted to a monoexponential function leading to the timescales given on the corresponding graphs. All data were recorded in 4 M LiCl, 25 mM HEPES buffer and 1.0 mM EDTA at pH 7.5, 20.0 °C, and at the bias voltage of +60 mV. Source data for this figure are provided as a Source Data file.

Fig. 7 |

 7 Fig.7| Superimposed scatter plot of events observed with dp10 oligosaccharides of hyaluronic acid (HA), heparosan (HN), chondroitin sulfate (CS), heparin (HP). It shows differences between the various families of oligosaccharides observed in this study. They vary by their osidic bond (HA vs HN), building block (HA vs CS) or degree of sulfation (HP vs HN). The timescales of translocation and the blocked current are different for each species and the event cluster are distinguishable, establishing a fingerprint of these oligosaccharides. All the experiments were performed using similar experimental conditions: 4 M LiCl, 25 mM HEPES buffer and 1.0 mM EDTA at pH 7.5, 20.0 °C, and at the bias voltage of +60 mV. Concentrations of HN and HA dp10 were 100 μM, and HP and CS dp10 were 43 μM. Source data for this figure are provided as a Source Data file.

Nature Communications | (2022) 13:5113

Acknowledgements

PB acknowledges post-doctoral fellowship from the program CHARMMMAT ANR-11-LABX-0039-grant supported by the French National Research Agency. C.R. and M.Bilong acknowledge PhD fellowships from doctoral school SDSV (No. 577, Université Paris-Saclay). Authors thank Dr. R. Vivès for the generous gift of HSulf-2 enzyme, and Dr. T. Ensslen and J. Behrends from University of Freiburg (Germany) for providing us aerolysin nanopore.

Data availability

Data files supporting this study have been deposited in the Zenodo database under https://doi.org/10.5281/zenodo.6861919. Source data for Figs. 2 to 7 and all Supplementary Figures are provided with this manuscript. Source data are provided with this paper.

Code availability

All analysis and graphs plotting custom code and algorithms used in this study are available at https://doi.org/10.5281/zenodo.6861919.

Single-channel recording experiments

Nanopore experiments were performed using a horizontal lipid bilayer Teflon device [START_REF] Akeson | Microsecond time-scale discrimination among polycytidylic acid, polyadenylic acid, and polyuridylic acid as homopolymers or as segments within single RNA molecules[END_REF] . Briefly, the setup is made of two chambers (i.e., the cis and trans chambers) connected to each other by a sub-mm inner diameter capillary. The lipid bilayer (with a typical capacitance of 10 ± 1 pF) was formed by painting a film of 1,2-diphytanoyl-sn-glycero-3-phosphocholine (Avanti Polar Lipids, Alabaster, AL, USA) over a conical aperture of 20-30 μm in diameter separating the cis and trans chambers. To apply a fixed voltage and measure the ionic current, two Ag-AgCl electrodes (1.0 mm diameter, Aldrich, Milwaukee, WI, USA) were installed in the cis and trans chambers filled with 100 μL of buffer containing 4 M LiCl, 25 mM HEPES and 1.0 mM EDTA at pH 7.5. The whole Teflon cell was fixed in a temperature-controlled copper enclosure in order to control the buffer temperature. A Peltier device controlled by a temperature controller (Newport 3040, Irvine, CA, USA) in conjunction with a heating circulator (CORIO CD-BT5, Julabo USA Inc., Allentown, PA, USA) around the copper enclosure base was used to set the temperature. The entire setup was placed within a grounded faraday cage to electrically shield it from electromagnetic interference.

Recombinant wild-type pro-aerolysin was synthetized using the following procedure as previously reported [START_REF] Piguet | Identification of single amino acid differences in uniformly charged homopolymeric peptides with aerolysin nanopore[END_REF] . We transformed Escherichia coli BL21 strain with pET22b-proAL plasmid containing proaerolysin sequence. This plasmid allowed the induction of proaerolysin production with IPTG (1 mM final concentration) and the periplasmic localization of the recombinant protein. The periplasm was extracted with an osmotic shock and pro-aerolysin was furthermore purified by affinity chromatography using the C-terminal his-tag of the recombinant protein (His SpinTrap minicolumns, GE Healthcare Life Science). Pro-aerolysin binding to the Ni-Sepharose was made with 100 mM Tris-HCl pH 7.4 and 50 mM imidazole, and after three washing steps, elution was made with 100 mM Tris-HCl pH 7.4 and 500 mM imidazole. The recombinant pro-aerolysin purity was determined by SDS-polyacrylamide gel electrophoresis and Coomassie blue staining, to 99 ± 1% (w/w). Concentration was calculated by absorbance at 280 nm. The pro-aerolysin monomers were stored at 4 °C at a final concentration of 0.1 g/L.

Recombinant wild-type pro-aerolysin was activated by trypsin digestion (0.6 μM trypsin final concentration) during 15 min at room temperature as previously reported [START_REF] Piguet | Identification of single amino acid differences in uniformly charged homopolymeric peptides with aerolysin nanopore[END_REF] to eliminate the pro-peptide sequence. After the formation of the lipid bilayer, activated aerolysin was added to the cis-chamber at 0.25 μg mL -1 final concentration. The insertion of a single nanopore in the lipid bilayer was identified as a step-jump of the transmembrane ionic current. Single AeL nanopore generated an ionic current of 58 ± 3 pA under an applied voltage of +60 mV (electrode in the cis chamber was grounded) at a temperature of 20 ± 1 °C.

The current traces were amplified and measured using a patchclamp amplifier (MultiClamp 700B, Molecular Devices, San Jose, CA, USA) with a CV-7B headstage and the signals were low-pass filtered at 50 kHz using a 4-pole Low-Pass Butterworth filter (Krohn-Hite 3361, Brockton, MA, USA). The signal was digitized at 1 MHz/12 bits using a DAQ card (NI DAQ PCI 6259 M serie, National Instruments, Austin, TX, USA). All data acquisition and measurement automation were performed using an in-house developed National Instruments LabView. Statistical analysis was carried out using Igor Pro 8 software (Wave-Metrics, Lake Oswego, OR, USA). The graphs were made using Python 3 with the Matplolib and SciPy libraries. Detailed description of the data analysis is presented in the supporting information (Supplementary Note 4).

Statistics & reproducibility

The experiments were not randomized. No statistical method was used to predetermine sample size.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Author contributions

Competing interests

The authors declare no competing interests.

Additional information

Supplementary information

The online version contains supplementary material available at https://doi.org/10.1038/s41467-022-32800-4.

Correspondence and requests for materials should be addressed to Jérôme Mathé or Régis Daniel.

Peer review information Nature Communications thanks Ralf Richter, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Reprints and permission information is available at http://www.nature.com/reprints Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Marie Asma Ben-Othmen

Babacar Thioye

/4 L'expertise universitaire, l'exigence journalistique

UniLaSalle

Dans les grandes métropoles, l'usage des sols fait depuis longtemps l'objet de fortes tensions entre artificialisation et agriculture. Les usages artificialisés et très lucratifs du sol, centrés sur la rente foncière et le prix du foncier bâti et constructible, ne cessent de progresser, convertissant continuellement autour des villes des terres agricoles en sols constructibles.

Malheureusement, ces transformations de l'usage du sol se font souvent au détriment de la qualité de vie des citadins, occultant de fait ses principales fonctions.

Prélèvements dans le jardin de Repainville pour analyser la composition du sol urbain. UniLaSalle, Author provided Le sol urbain, un sol fertile ? À Rouen, des étudiants les mains dans la terre

Le Champ des possibles

Même si un pas est franchi sur l'acceptabilité par les citadins de ces îlots de verdure au regard de leurs différentes fonctions paysagères, sociales, thérapeutiques et éducatives, l'approche scientifique quant à elle, demeure un défi que plusieurs associations ont tenté de relever.

Dans les jardins de Repainville, à Rouen, l'une d'elles nommée le Champ des possibles, a bénéficié du soutien du campus de Rouen d'UniLaSalle pour mettre en oeuvre une telle approche, dans l'objectif de valider la qualité des sols et par conséquent la sécurité des fruits et légumes qu'ils produisent. https://theconversation.com/le-sol-urbain-un-sol-fertile-a-rouen-des-etudiants-les-mains-dans-la-terre-163037 4/4

Démarche expérimentale simplifiée

En outre, la teneur en matière organique est plus que satisfaisante compte tenu de toutes les variables physicochimiques mesurées. Enfin, la granulométrie indique un sol limono-argileux, une texture très favorable à la circulation d'eau, des nutriments et au développement de la vie biologique du sol.

Mycorhizes, Rhizobium et vers de terre

Les résultats des composants biologiques ont montré une forte colonisation mycorhizienne Ces données permettraient in fine de dégager des typologies de jardins et fermes urbaines en fonction des caractéristiques des sols à l'échelle de la ville de Rouen.

Ateliers d'analyses au laboratoire sur les propriétés physicochimiques du sol (à gauche) et les composants biologiques du sol en interaction avec les plantes (à droite). UniLaSalle

L

  'association cultive dans ses serres et dans son jardin de la moutarde, des fleurs comestibles, du persil, des tomates, des laitues, des fraises, des radis, des poireaux, des plantes aromatiques et arbres fruitiers (pommiers, framboisiers…). Plan d'expérience et stratégie d'échantillonnage dans les trois agrosystèmes du site de Repainville (jardin potager, serre et exploitation maraîchère. UniLaSalle Plan d'expérience et stratégie d'échantillonnage dans les trois agrosystèmes du site de Repainville (jardin potager, serre et exploitation maraîchère. UniLaSalle https://theconversation.com/le-sol-urbain-un-sol-fertile-a-rouen-des-etudiants-les-mains-dans-la-terre-163037 3/4 Elle ambitionne d'être un démonstrateur des alternatives écologiques de l'usage des sols urbains et un lieu de partage des vertus de l'agriculture urbaine. Elle propose notamment des ateliers cuisine nature afin de reconnecter les citadins avec la nature et l'agriculture locale. Dans le cadre du Master of science « agriculture urbaine et villes vertes » (MAUV), dix étudiants du campus de Rouen ont réalisé un protocole d'expérimentation en deux étapes, intitulé « sol-planteclimat dans les jardins de Repainville ».

(

  association entre une plante et un champignon favorisant l'absorption d'eau et de minéraux en particulier le phosphore) et rhizobienne (association entre une plante et une bactérie favorisant l'absorption de l'azote) des racines des plantes prélevées dans les trois agrosystèmes. Un résultat très encourageant, synonyme d'un sol vivant et en bonne santé. D'ailleurs, la présence d'une forte abondance de vers de terre (aérateurs et mélangeurs du sol) conforte ces observations. Discussion et perspectives Pas forcément si difficile de bousculer les mentalités, donc, mais plus complexe en revanche de lutter contre la monétarisation du sol urbain au-delà de sa seule vocation de terrain constructible et sa valeur foncière. Les vertus des sols urbains offrent une multitude de bénéfices aux citadins dont la valeur est non marchande. Il est ainsi important d'arbitrer les dynamiques de l'occupation des sols et des transformations de leur usage autour et au sein même des villes. Cette étude permet d'attirer l'attention sur la nécessité de concilier l'approche scientifique à celle paysagère dans d'autres jardins et fermes urbaines à l'échelle de la ville. Même si des analyses préalables ont en l'occurrence montré l'absence dans la terre de tout contaminant dommageable à la santé humaine, il est indispensable d'acquérir des données supplémentaires d'inventaire dans le cadre d'une évaluation environnementale ou sanitaire des activités des projets d'agricultures urbaines.

  Lors d'une campagne de prélèvement sur le site de Repainville, les étudiants ont dans un premier temps établi un plan d'expérience et une stratégie d'échantillonnage. Un travail mené à la suite d'une analyse de lecture de paysage (orientation, relief, vent dominant, flore et faune spontanées, aménagement des cultures, espaces bâtis…) et d'une approche sensible (odeur, bruit, ressenti…).

	Approche scientifique
	Dans un second temps, les échantillons ont été observés au laboratoire d'analyse de sol de l'école. Les
	étudiants ont mesuré d'une part, les propriétés physicochimiques du sol (pH, granulométrie par
	sédimentation, détermination de la matière organique et du carbonate de calcium et détermination de
	la capacité d'échange cationique : CEC), d'autre part, les composants biologiques du sol en interaction
	avec les plantes (colonisation endomycorhizienne des racines, symbiose rhizobienne et abondance des
	vers de terre).
	L'approche scientifique a permis de consolider l'approche sensible issue de la lecture du paysage. Les
	résultats des propriétés physicochimiques dans les trois agrosystèmes ont révélé un pH un peu élevé

Ils ont prélevé du sol et des racines de plantes sur trois agrosystèmes différents : le jardin (pleine terre, plein air, agriculture biologique), la serre (pleine terre, sous-abris, agriculture biologique) et l'exploitation maraîchère (pleine terre, plein air, agriculture raisonnée). Un prélèvement de vers de terre a également été réalisé pour étayer les observations.

Approches paysagère et sensible

Exposés au sud et situés aux pieds des falaises creusées par la Seine, les jardins de Repainville sont parcourus par un petit ruisseau. Ils sont abrités du vent et du soleil par des arbres générant un microclimat favorable à la culture et offrant un refuge à une biodiversité spécifique. C'est le royaume des Lamiaceae, Fabaceae, Brassicaceae et des oiseaux à proximité du centre-ville de Rouen ! Un lieu paisible et plaisant où la rumeur de la ville est somme toute perceptible. variant de 7,6 à 7,8, toutefois favorable à la culture maraîchère. Cette valeur pourrait être expliquée par la grande quantité de calcaire mesurée dans les prélèvements et mise en évidence par la très forte effervescence en présence d'acide. Les valeurs de CEC obtenues soulignent par ailleurs que le pH n'impacte pas négativement les échanges sol-plante.


domain: math info

text:
T Carlier

B T Helenbrook

H Beaugendre

M Colin

L Nouveau
email: leo.nouveau@insa-rennes.fr

Numerical Analysis of Stefan Problems for Embedded Computation of Moving Internal Boundaries

Classical finite element methods used to model problems with internal boundaries rely on body fitted computational grids. However, those methods encounter computational challenges when the boundaries are deformed or moved substantially. In this direction, embedded methods do not require the use of


Patrick Xia

Elias Stengel-Eskin

Tongfei Chen

Seth Ebner

Nils Holzenberger

Ryan Culkin

Pushpendre Rastogi

Xutai Ma

Benjamin Van Durme

NIST TAC SM-KBP 2018 System Description: JHU/UR Pipeline

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The SM-KBP task involved processing a set of independent documents in English, Russian, and Ukrainian. For each document we were required to identify named entities, coreferring expressions, and events and relations the entities appeared in. Under the guidelines for the task, evidence for events and relations may occur in different sentences of a document than the supporting mention(s) for an entity that is deemed to be a relational argument or event participant. The task was evaluated via queries consisting of offsets into a given document, representing oracle span annotations for some initial element salient to the query. Based on a query, various knowledge structures were to be returned, represented in the AIDA Interchange Format (AIF). Task 1a consisted of a corpus and queries; Task 1b consisted of a corpus, queries, and associated hypothesis objects that could viewed as partial knowledge base fragments, upon which the extraction system should condition during document processing.

The 2018 instance of SM-KBP was considered a pilot, with participation by TA1 performers of DARPA's AIDA program. Our team constructed a system meant to establish a baseline performance, with our primary goal being to ensure we gained an understanding of the task, and had a non-trivial baseline in future work.

Our system involved training ERE extraction components on annotated documents provided under the DARPA AIDA and DEFT programs. Coreference was handled by a pre-trained, 3rd party system. No attempt was made to perform extraction directly on non-English documents: these were translated into English, processed with English-based components, then aligned heuristically back to Ukrainian and Russian spans in order to handle source-document queries.

The hypothesis information under Task 1b was handled by extracting named entity strings from the hypothesis, then performing a limited sort of entity linking against those entity strings and the coreference chains extracted under Task 1a. For any potential match, we then aimed to increase the recall of the relations and events we output for those entities. In short: if something downstream had a hypothesis about a particular entity, and we thought a given document could be about that entity, then we increased the amount of information we output, specific to that entity.

The following gives details on the modules involved in this system.

Machine Translation

We restricted our extraction models to English, with a reliance on Machine Translation (MT) into English to support non-English documents. We assembled various bitexts for training and interacted with NIST to establish whether each was constrained or unconstrained.

Model

Translation was performed using the Python implementation of OpenNMT [START_REF] Klein | Open-NMT: Open-source toolkit for neural machine translation[END_REF]. A 4-layer bi-directional LSTM encoder-decoder model with attention was employed. The dimensions of the embedding layers and LSTM hidden units were 300 and 1024 respectively.

The following describes the resources used for each language.

Russian -English

Two corpora were used to build Russian -English MT systems. The first consists of the training and development set from the WMT'17 news translation challenge.1 It is an aggregate of the Common Crawl corpus, News Commentary v12, Yandex Corpus, Wiki Headlines, and UN Parallel Corpus V1.0, and totals 25M sentences. The UN parallel Corpus mentions Crimea in several places, and so may only be used in the unconstrained setting. The second corpus is the OpenSubtitles 2018 Russian-English corpus2 . This is parallel text containing 26M sentences extracted from movie subtitles [START_REF] Lison | Opensubtitles2016: Extracting large parallel corpora from movie and TV subtitles[END_REF]. It does not mention the Crimea conflict and may thus be used in the constrained setting.

Ukrainian -English

To build Ukrainian -English translation systems, we used the OpenSubtitles 2018 corpus3 , which contains 878k sentences. It was built in the same way as the Russian-English Open Subtitles corpus and can also be used in the constrained setting.

Language Identification

LDC provided initial, automatic Language Identification (LID) determinations, which on inspection we determined to be error-prone. We therefore performed an internal, sentence by sentence LID analysis via the python port of Google's languagedetection library4 . For each document, the LID system only detected Russian and Ukrainian sentences. A sentence will be categorized as English if it was not classified as Russian or Ukrainian by the LID system. We then translated sentences classified as Russian and Ukrainian with machine translation describe in previous sections.

Information Extraction

There are two phases in extracting information from text: (1) independently detect entities, relations, and events; then (2) link detected entities as potential arguments to predicted relations and events.

Entity Mention and Type Detection

To detect entity mentions, we implemented a neural biLSTM-CRF BIO tagger [START_REF] Lample | Neural architectures for named entity recognition[END_REF], with tokens embedded using 300-dimensional GloVe word embeddings. We trained on ERE data made available under the DEFT program as it contains annotations for not just entities but also entity type, relations, and events. It also is exhaustively annotated (no false negatives), unlike initial data provided under AIDA. In the cases of overlapping spans, we discard all but the longest span. At test time, the tagger is run at the sentence level independently for each sentence, effectively placing each token into at most one span.

Our BIO tags were augmented with entity type labels (according to the DEFT ontology), including filler types. This greatly increased the label space but also allows the model to jointly make decisions regarding entity detection and type prediction.

We experimented with several hyperparameters, such as the number of layers, dropout, and tag types (IO, BIO, BIOE), selecting our final configuration based on a development set from DEFT data. A single-layer BIOE tagger with 0.9 dropout, and l 2 regularization of 0.001 over all the parameters was found to be the most effective, achieving an accuracy of 83.6%, precision of 65.3%, recall of 54.6% and F1 score of 59.5%. While these results are low for state-of-the-art NER, we kept with this solution for the pilot in order to stick with data most similar to the AIDA domains, and with a label set that could be directly mapped (as the filler types are comparable). In addition, manual inspection of the predictions concluded that while the type may be predicted incorrectly, the mention spans were quite accurate. Furthermore, early errors made during entity typing can be corrected later based on document-level information (and coreference resolution). For these reasons, we decided to use this in-domain BIOE tagger for entity mention and type prediction.

Relation and Event Detection

Our information extraction systems run on English text, some of which may be noisily translated, but graph queries specify spans in source documents. Because we don't have alignments in our training data, we detect the presence of relations and events (REs) at the sentence level instead of at the token level. The span for a found RE is then just the entire sentence. Analysis of the AIDA data showed that most sentences have at most 1 RE mention, so at query time we can find the RE in question simply by finding which sentence a query refers to.

Sentences may contain many REs (rarely of the same type), so we detect each type with its own binary classifier indicating whether an RE of a given type is present in the sentence. Most RE types do not have many labeled examples in the English portion of the AIDA training corpus. We did not incorporate DEFT ERE data as training data for RE detection because the style (forum posts) is too different from that of the AIDA data (profes-sional newswire), even though the ontologies are very similar. Our system does not make predictions for types that have no labeled instances.

We address the data sparsity problem in three ways: 1) we use pre-trained GloVe word embeddings [START_REF] Pennington | Glove: Global vectors for word representation[END_REF] 5 , giving randomly initialized word embeddings to words appearing in the training data without pre-trained embeddings; 2) we use a simple encoder consisting of mean-pooling concatenated with max-pooling over the sentence's word embeddings, which is then fed through a fully connected layer and a ReLU activation; and 3) we equip each binary RE classifier with a tunable hyperparameter that adjusts the precision and recall of detecting its corresponding RE type. We have found adaptive thresholding to improve performance in related tasks [START_REF] Zhang | Fine-grained entity typing through increased discourse context and adaptive classification thresholds[END_REF], and in practice we adjust the thresholds to avoid the proliferation or lack of predicted types.

These three techniques focus on building tunable models with small amounts of parameters, owing to the lack of data to support larger models. For example, we do not update pre-trained word embeddings during training, which substantially reduces the number of trainable parameters in our models. The encoder we use has been found to be useful in unpublished work in sentence-level topic identification, which is similar to our sentence-level RE detection task. Preliminary experiments with multilabel RE classification resulted in most sentences receiving similar predictions and did not give fine control over model predictions (due to the loss function we used). Our decision to switch to constructing a separate classifier and a tunable precision-recall hyperparameter for each RE type allowed us to qualitatively check that appropriate predictions are made for each type. The hyperparameters can be adjusted at inference time without needing to retrain the model.

Argument Linking

Once we have predicted an event or relation is triggered in a given sentence, and we have separately detected mentions (with types), then we optionally link each mention to a relation or event as an argument, as a second round of sequence tagging. We use the same framework was used for entity mention and type detection; a biLSTM-CRF tagger. We trained a single model for Events and one for Relations, using an IO tagset (owing to label sparsity in training we forwent a 'B' tag, trusting that very few distinct arguments would be contiguous spans).

To inform the argument linking model about mention and type predictions made earlier in the pipeline, we incorporated those token-level predictions as one-hot features concatenated to the token embedding. We observed F1 scores for relational arguments of 34.8 (91.1% accuracy) and event arguments of 46.8 (93.7% accuracy). We prioritized recall over F1 score when choosing between similarly competitive models, since downstream precision can be improved post-linking (when producing a response). Using forced decoding of tags (by manually lowering the weight of the 'O' tag), we could artificially boost recall, but the tradeoff in precision under this method was observed in development to be too costly.

Given a hypothesis to condition on (Task 1b) we can use forced decoding selectively. By identifying which strings in the documents are similar to those in the hypotheses (described more in Section 4), we can force the tagger to decode non-O tags for those entities, thereby linking the hypothesized entities to an event or relation. Since this approach links entities that are hypothesized to be salient to the document, recall is improved. Since it does not link arbitrarily (which is the result of applying forced decoding uniformly), it does not result in a large drop in precision.

Name Similarity

For determining a match between a document mention and an entity string from a hypothesis, we employed a model for learning string similarity [START_REF] Neculoiu | Learning text similarity with siamese recurrent networks[END_REF]. Given two strings, the model is capable of outputting a score (cosine similarity) that indicates how similar these two input strings are. The model is a Siamese network where two identical, parameter-shared modules are stacked upon the two input strings, with each string considered as a character sequence. Each module comprises of 4 bidirectional LSTM layers (size 64) followed by a feedforward layer that results in a vector of size 128. A cosine similarity function lays on top to connect these two Siamese branches. The model was trained using a dataset based on Wiki-Names, arising from earlier research at JHU [START_REF] Andrews | Name phylogeny: A generative model of string variation[END_REF].

Knowledge Base Construction

Given sentence-level predictions of entities, relations, and events, we can construct a knowledge base for each document. This fundamentally relies on identifying coreferent entities, events, and relations in text. In this system, we only identify coreferent entities.

We used a state-of-the-art coreference resolution system [START_REF] Lee | End-to-end neural coreference resolution[END_REF] and ran it directly on the English text. We use a fuzzy overlap heuristic to resolve tokenization disputes between the coreference resolution system and the tokenization provided by LDC or the output translations.

The coreference resolution system produces clusters of spans, each of which we can identify with an entity. Due to memory constraints, large documents were broken into chunks of 5,000 tokens each and independently processed. In practice, this affected fewer than 5% of the documents. Furthermore, we could label each entity with a type determined by the majority predicted type among the mentions in its cluster. The confidence of the type is determined by the proportion of members of the cluster with that type.

As a result, typed document-level entities, not text spans, are the arguments of relations and events after this step.

Alignment

The use of state-of-the-art English entity, event, and relation extraction systems on translations of Russian and Ukrainian documents introduced two alignment problems. Firstly, discovered spans on the target side (English) needed to be aligned back to their source spans in order to reason about Russian and Ukrainian events and coreference chains. Secondly, queries given as character offsets in the source (Russian or Ukrainian) document needed to be aligned to a span discovered in the English translation in order to resolve the coreferences and relations. To address both problems, the document sentences and their respective translations were concatenated to the same bitext used to train the machine translation system and fast-align [START_REF] Dyer | A simple, fast, and effective reparameterization of ibm model 2[END_REF] was run over the resulting augmented bitext, allowing alignments to be recovered.

Confirming the findings of [START_REF] Koehn | Six challenges for neural machine translation[END_REF] and [START_REF] Ghader | What does attention in neural machine translation pay attention to?[END_REF], we found that hard alignments obtained via neural attention were often incorrect, leading us to instead employ HMM-based alignment algorithms from the statistical machine translation literature. The choice to concatenate the documents to the training corpus rather than train on the bitext and decode the documents separately was motivated by concerns of domain mismatch between the bitext and the provided documents. Taking the union of the alignments in each direction (e.g. source to target, target to source) yielded pointers from English word tokens to source word tokens. Following these pointers from English to the source language and taking the maximum contiguous sub-span of the resulting sequence of source word indexes allowed English spans to be matched to their corresponding source tokens. Aligning query character offsets to spans in the translation presented a slightly more challenging problem since the span of translated word tokens to which the query tokens were aligned to needed to be a member of the set of spans discovered by the mention detection system -if this were not the case, coreference and event resolution would be impossible. To enforce this constraint, the corresponding span was chosen by computing the character index overlap between the span obtained by following the alignment pointers from the source and each of the spans discovered by the mention detection system (using F1 score as a metric), at which point the discovered span with the highest overlap score was chosen.

Query Handling

Output Knowledge Bases (KBs) under the evaluation were to be represented in the AIDA Interchange Format (AIF). We supported this via the USC ISI converter6 which maps from TAC-KBP ColdStart format to AIF. We chose to use Cold-Start as an intermediate representation because of its human-readability and general simplicity, which facilitated a faster development process. 7In order to evaluate the KBs during the pilot, NIST released three types of queries: 1) Class Queries, 2) Graph Queries, and 3) Zerohop Queries in two formats: a) Simple XML, and b) SPARQL format, which participants had to execute themselves. Later in the evaluation NIST released a Docker container for applying the SPARQL queries directly on the AIF graphs. In the time available we were unable to make use of this service,8 and also owing to comments from other performers on the slow speed of Jena when executing the SPARQL queries, we manually parsed and executed the Simple XML version of the queries. This ad-hoc Python script used the original TAC-KBP files as input, rather than the derived AIF format.

Class Queries: For each class query, for each KB, we iterated over all mentions of the entities of that type and serialized them to an xml file.

Graph Queries: A graph query is a tuple of (graph, entrypoints). A graph is a collection of edges, where each edge is a triple (subject, predicate, object). An entrypoint describes a node, and a typed descriptor, and we only processed two types of typed descriptors (string descriptor, and text descriptor). The string and text descriptor are analogous to string based search vs mention based search respectively. For the pilot evaluation, we had only one entry point entity per graph query, though each entry point entity may have more than one descriptor. For each query, for each KB, we first searched for the entry-point entity by using either the string or the text descriptor of the entry point if that was applicable for the current KB. Then all edges where the resolved entry-point was either a subject or an object and for which we also had a predicate in the KB that matched the predicate in the query for that particular edge were marked as resolved edges and the edge with the highest confidence amongst those was chosen. This was we greedily built the returned graph starting from the entry-point as a resolved node, and then adding more nodes to the set of resolved nodes. Once the process could not be continued we stopped.

Zero-hop Queries: Zero-hop queries provide a mention of a filler or entity from a document (given as a character offset) and expect all references of that entity from the document as a result. In the simpler case of English documents, a query character offset is compared to the character offsets of all entities detected in the document, and the entity with the highest character overlap (as measured by F1 score between query and entity offsets) is chosen. All entities in the same coreference chain as the chosen entity are returned. Russian and Ukrainian documents present a slightly harder problem, as entity detection and coreference resolution for Russian and Ukrainian source documents was performed in their English translations, meaning that query offsets and their coreferent spans needed to be aligned from source to English and English to source, respectively. The alignment methods described in §6 yield pointers from Russian or Ukrainian words to their corresponding English translations. Using these alignment pointers, zero-hop responses for queries in Russian and Ukrainian were generated by computing the F1 overlap between the characters of the English words aligned to the source offset and each detected entity and subsequently following the pointers of all mention spans coreferent to the entity with maximum F1 back to the source.

http://data.statmt.org/wmt17/ translation-task/preprocessed/ru-en

http://opus.nlpl.eu/download.php?f= OpenSubtitles2018/en-ru.txt.zip

http://opus.nlpl.eu/download.php?f= OpenSubtitles2018/en-uk.txt.zip

https://github.com/Mimino666/langdetect

nlp.stanford.edu/data/glove.6B.zip

https://github.com/NextCenturyCorporation/ AIDA-Interchange-Format

While there is active development of utility libraries for AIF, these are Java-based, while our software stack was rooted in Python. The ColdStart-AIF converter was the path of least resistance for this initial pilot

task.8 Out of the box attempts were unsuccessful, and on inspection the Perl-based software appeared to rely on an undocumented file hierarchy and naming structure for the AIF files; we left further exploration of this service for after the pilot.