dataesr/scientific-paragraphs-categorization huggingface dataset:
The number of experimentally tested compounds is chosen depending on various criteria, but it is usually the top of the list which is pursued (e. g. top 1%, 0. 1%, etc. ). Therefore, it is not the overall performance of a scoring method on the whole database, such as ROC AUC, which is most relevant for VS, but rather the performance in the top of the list, i. e. how many active compounds are among the best scored compounds. In our assessment, we focused on EF 1% (fold change of active molecules percentage within the top 1% of ranking list over random distribution) as the most relevant estimate of screening performance and machine learning predictive power. We perform a stratified 5-fold cross validation (see materials and methods for more details), to avoid model overfitting which hampers the performance on data sets other than the training set  46.
It is important to note that, while all target-ligand complexes are present at some point in training and test sets, they are never in both simultaneously (see Materials and Methods, validation section). Finally, the mean value of the performance in 5 independent folds is calculated, which estimates how a model will perform on independent datasets. This prevents testing the SF on complexes used for training and reporting artificially boosted performance. The first experiment we conducted was to train the SFs on horizontally split data. This approach mimics experiments where docking is performed on targets for which there are already known active ligands and VS is done to find new ones. Therefore, training was done on 4/5 of ligands from all 102 DUD-E targets and the model was tested on the remaining 1/5 (Fig.
1B ). In this setup only a single, unique model (generic SF) is built for the whole DUD-E dataset. Such model can be directly compared to a classical SF as they are also single model functions, trained on a defined set of protein-ligand complexes and developed to work with diverse targets. Our results show a dramatic increase of EF 1% performance between the best classical compared to machine-learning SF trained on horizontally split dataset: around two-to even 15-times increases depending on the docking engine and SF (Fig. 2 ). It is worth noting that the classical SF do not perform similarly here, as the obtained EF 1% varied significantly both in value and standard deviation. RF-Score v3, which is one of the best performing machine-learning SF to predict binding affinities on PDBbind, yields EF 1% similar to best performing classical approaches despite being trained on X-ray crystal structures and thus not incorporating any negative data (i.
1 ). The results of these experiments are presented in Figs  2  and 3. On Fig. 3 , we demonstrate pairwise comparison of per-target and horizontal SFs. Every point is a cross-validated predictions for single DUD-E protein. To our surprise most of the per-target functions tend to perform only slightly better than the generic, unique function, trained on all available data. Almost 2/3 of targets (64 out of 95 -some targets failed to dock with default Vina settings, thus 95 not 102) had its EF 1% increased less than 10%, regardless of the docking program used. As before, this is tested with 5-fold cross-validation and an average of test splits is the final result. Contrary to common assumption there was little advantage in training machine learning scoring for most of the targets vs using a single generic approach (trained on the horizontal split dataset).
Nevertheless, RF-Score-VS v2 and v3 outperform all classical approaches in this scenario. Interestingly, the EF 1% value obtained with Dock3. 6 and its built-in native function (Fig. 2B , Dock 3. 6 Score) was unexpectedly high in comparison with other classical SFs. Dock3. 6 was the only SF that we did not run ourselves, as both its docked molecules and predicted scores were downloaded directly from DUD-E website. Therefore, we assume that there was some kind of tailored procedure for each target prior or after docking, which is a potential source of overfitting. In our procedure (as described in the materials and methods section), we have not done any work on the receptor nor ligand datasets. In addition, we have only used the default settings for all the SFs that we have tested, including the two other docking algorithms Dock 6.
6 and Autodock Vina. The results obtained from the vertical split experiments show that machine-learning SF (such as RF-Score-VS v3) trained on data from other targets is: 1) able to outperform the five tested classical SF without the need for any Each data point is a separate corresponds to the performance of both models on a particular DUD-E target. The darker is the colour of DUD-E target is, the more active ligands it has. Docking conformations were obtained from Autodock Vina. Dashed red line denotes equal performance, and dotted green line show 5-unit intervals. For most targets and contrary to common assumption, there is little advantage in training machine machine-learning SFs for pertarget vs using a more generic approach (in this case horizontal split), especially for targets with greater number of active molecules.
Such data set is hard to find as either most of the targets overlap between sets or they provide a small number of ligands per target. In contrast, the DEKOIS 2. 0 benchmark overcomes both obstacles: only 4 of the 81 DEKOIS targets are also in the DUD-E benchmark (see Methods section for further details). The results of early enrichment for 76 DEKOIS targets are summarized on Fig. 4. Autodock Vina, which in previous experiments was the best scoring function run in-house, achieved EF 1% = 3. 95 and RF-Score v3 scored EF 1% = 2. 94. These results are in line with what was obtained with DUD-E data. By contrast, RF-Score-VS v2 and v3 performances were EF 1% = 9. 84 and EF 1% = 7. 81, respectively, thus more than doubling the active compounds yield in the top 1%. Despite the achieved improvement, structure-based VS remains a challenging problem on a number of targets, where all three SFs fail to find any active within the top 1% of their ranked lists.
Therefore, it is clear that more work is needed to advance further in this problem. Top results analysis. Boxplots in Fig. 2  show summaries of the classification data. If a compound was in the top 1% of ranking list and it was active the enrichment factor (EF 1% ) value increased, if it was a decoy (presumed inactive) EF 1% decreased. These plots however do not show if these top 1% molecules are actually the most active ones. Thus, we can check whether machine-learning methodology predict binding affinity better than a classical SF. The scatter plots presented in Fig. 5  address these questions. We took the original 1% of the best in silico predictions for each DUD-E target (i. e. on average about 86 compounds per target) and assessed how their scores correlate with experimentally measured activity (derived from DUD-E database per-target).
Indeed, training data sets for structure-based VS are much larger than those used binding affinity prediction and require prior docking of each considered molecule. Future work in this area is expected to yield particular insight in terms of improving our ability to discriminate between actives and inactives across targets. Even larger amounts of data can be used following the described procedure. Many other machine learning techniques can be applied to structure-based VS. This is not only restricted to regression techniques, but also classifiers. For example, state-of-the-art multi-category classifiers  48. Another promising avenue for future research is feature selection, not only in terms of the performance improvement but also considering the stability of the predictors  49. All data (docked poses) and workflow scripts required to recreate the generation of descriptors and training of machine-learning models are released here to the relevant research communities, making our software reproducible and for others to build upon it (http://github.
com/oddt/rfscorevs). We also propose a standalone machine-learning based SFs RF-Score-VS v2 and v3, as a general purpose and target independent VS tools. To the best to our knowledge, RF-Score-VS is the best performing SF in terms of early enrichment EF 1% on DUD-E. RF-Score-VS can be downloaded from https://github. com/oddt/rfscorevs_binary (it is provided as standalone binary for Windows, Mac and Linux without any further dependency, with wide range of supported molecular formats). Alternatively RF-Score-VS may be used with ODDT toolkit environment as a drop-in replacement for any other SF in custom workflows and other software.
Induced or spontaneous dynamics of individual zippers To determine the axon adhesion force more directly, not relying on the measurement of axon tension, we attempted to unzipper selected zippers using a calibrated pulling force. These attempts were not successful, due to insufficient strength of the bond between the bead and the red blood cell. This resulted in the detachment of the red blood cell before any significant effect on the zipper. To overcome this limitation, we bypassed the red blood cell and bead and used the pipette to drag the axon directly. This allowed us to use forces sufficiently large to induce unzippering at the price of losing the knowledge of the force magnitude. The following source data is available for figure  6:  source data 1 show an example. By dragging one of the axons of a zipper, we increased the zipper angle beyond its equilibrium value, leading to unzippering accompanied by a decrease of angle (Figure  9A-D ).
The quantitative analysis was performed on video recordings in which the axonal network showed clear evolution lasting over 1 hr. Initial preprocessing and manual segmentation were performed using the distribution Fiji  (Schindelin et al. , 2012)  of the project ImageJ  (Schneider et al. , 2012). The field was cropped to restrict the region of interest and 6 to 10 images (frames) uniformly spaced in time were chosen from the course of the recording. The network of axons was then manually segmented by drawing individual selection lines over the image. In some cases, successive frames were consulted to decide whether a line is an axon to include or a transient side-process. The list of segmentation selections was exported to Matlab  (The Mathworks, Inc, 2015) , where a set of custom-made functions was used to: (i) convert the list of selection lines into a graph data structure, (ii) detect cordless loops in the graph, (iii) semi-automatically measure zipper angles or determine crossing points, (iv) calculate the network statistics (notably the zipper angle distribution, total network length, number of vertices, average area of cordless loops), and (v) determine correlations between these statistics.
The segmentation selections underlying the analysis shown in Regarding the data of dynamics of individual zippers, 17 events were measured by manually tracking the coordinates of the zipper vertex in consecutive frames. The measurement was accepted only if the vertex remained in static equilibrium 5 min before and after the transition. Zipper measurements were obtained from 14 explants originating from four mother animals. They were chosen from the networks of low density to minimize disturbance from the areas adjacent to the zipper.
Analysis of BFP data The analysis was based on the captured recordings, recorded at rate of 65 fps. The BFPTool software package  (S ˇmı ´t et al. , 2017)  was used to subdivide each recorded video into intervals suitable for automated analysis, and then to track the pipette and bead position with sub-pixel precision. For the algorithms used, please refer to  (S ˇmı ´t et al. , 2017). The distance of the centre of the bead and a fixed point on the pipette tip were used to represent the length of RBC. The length of unloaded RBC was determined from a frame where the bead first touches the approaching RBC. Having established the RBC stiffness (from geometry and pressure, using Equation  5 ), the applied force was calculated for every frame. The force was corrected by a projection to the direction normal to manipulated axon.
Then, the stable plateaux would be identified in the force time course, and for each, the average force F i applied by the probe (over the duration of the plateau i) and the angle of axon deflection d i would be determined. Such pairs of values, ð2 sin d i ; F i sin f i Þ, constituted our data points for each experiment (f i represents the angle between pipette axis and axon axis). Finally, linear interpolation of the acquired data points was performed to obtain the tensile force within the axon as the slope of the interpolating line. Non-zero intercept of the interpolation line was often present; this happens when the selected reference distance does not truly correspond to the unstrained size of the RBC. With the slope determined from several plateaux (we chose experiments having at least three), this offset does not influence the resulting calculated axonal tension.
Calculation of distributions of biophysical parameters in the axonal population Each BFP experiment resulted in a value of tension and its experimental uncertainty for the given axon. This pair of parameters was used to construct the corresponding normal distribution, representing the tension of each axon. These Gaussian distributions were added and their sum normalized, to approximate the distribution of tensions within the whole axonal population. Similarly, the set of the measured equilibrium zipper angles (described in Results) was transformed into a distribution, by convolving the dataset with a Gaussian kernel (using Matlab's kernel distribution functions). Two complementary approaches were used to estimate the value of axon-axon adhesion strength S: in the first, the measured distribution of tensions and the distribution of zipper angles are fully determined by each other, while in the second approach, the tensions and angles are treated as statistically independent variables.
Acknowledgments: We wish to thank  C. Malrieux  for her help in cell culture. This work was supported by  CNRS , the  Agence Nationale de la Recherche  ( ANR-14-OHRI-0001-01 ),  IdEx Bordeaux  ( ANR-10-IDEX-03-02 ), and the  France-BioImaging national infrastructure  ( ANR-10-INBS-04-01 ). A. G. G. acknowledges financial support from the  Fondation pour la Recherche Médicale  and the  Fonds Recherche du Québec-Nature et Technologies.
The samples dispersed in hexane were stored in glass vials in dark. The elemental analysis was done with a Bruker Mistral M1 μ-XRF spectrometer. The Ag to Cd ratio was determined from the relative intensities of the Kα 1 peaks with the use of a calibration curve (Supplementary data of  [22] ). UV-vis absorption measurements were carried out with a Varian Cary 50 spectrophotometer from 300 to 1100 nm. The fluorescence was measured with 405 nm laser excitation and detected by an Ocean Optics USB 4000 spectrometer calibrated by a 2600 K W-lamp (450-1100 nm). The energy QYs were determined in diluted dispersions relative to rhodamine 6G solution of the same absorbance at 405 nm. IR-photoluminescence spectra were obtained with a MDR-23 monochromator with Ge photodetector calibrated with a 2600 K W-lamp.
Data analysis Statistical analysis was performed with Statgraphics software. Equality of group variances was checked with a Barlett Test. The normality assumption was assessed by Kolmogrov Smirnov Test. Differences between treatments were evaluated using Analysis of Variance (ANOVA) and the HSD Tukey test used to separate averages at p < 0. 005. When the normal curve of distribution was not verified or the number of samples was reduced, the non-parametric Kruskal Wallis test was applied. Simple regression analysis was used to show the correlation between certain variables. Multivariate statistical analysis of infrared spectra was performed by principal component analysis using the Unscrambler software (CAMO, Germany). ). For both varieties, the highest bark and wood thickness were obtained under irrigation conditions (p < 0.
Acknowledgments The authors thank  Hervé Gegout  for histology and  Nadia Messaddeq  for the TEM observations. This work was funded by the  INSERM. Financial support from the  EU  thanks to the  ERC Consolidator Grant program  ( ERC-2013-CoG-614715 ) is gratefully acknowledged. CIBER-BBN is an initiative funded by the  VI National R&D&i Plan 2008-2011, Iniciativa Ingenio 2010, Consolider Program ,  CIBER Actions  and financed by the  Instituto de Salud Carlos III (Spain)  with assistance from the  European Regional Development Fund. The corresponding author confirms that all co-authors have reported no conflict of interests. S. K. B. , P. S. , M. A. and N. B. J. were responsible for project conception and direction. L. P. , Y. I. G. , A. L. , V. S. and A. F. performed experimental work and analyzed the data.
Here, structure solution was also successful using either I-SAD or I-SIRAS (Fig. 3 ), suggesting that large crystals are not necessary for the collection Protein    S1 ), are seen for NarQ, KR2, MACR, and A 2A AR-BRIL-DC, respectively. As can be seen, the highest-occupancy (as estimated from anomalous difference Fourier maps) sites are clustered close to the predicted membrane interfaces. In the same way as was previously observed in the I-SAD crystal structure solution of soluble proteins (9, 10), the iodide ions interact with a wide variety of positively charged amino acid residues, ordered water molecules, and hydrophobic side chains (Fig. 4  and fig. S3 ). To examine the potential universality of the I-SAD method in the de novo solution of MP crystal structures, we examined the availability at the membrane interface of five amino acid residues, namely, arginine, lysine, histidine, tryptophan, and tyrosine in 445 unique transmembrane protein structures deposited in the Protein Data Bank [PDB; www.
rcsb. org (20) ; the database of unique MP structures: http://blanco. biomol. uci. edu/mpstruc/]. According to our analysis (Table  4 ), 96% of the structures contain aromatic amino acids close to the membrane interface, and 100% of the structures contain positively charged amino acids in this region. This, coupled with the recent report of the crystal structure of the outer membrane b-barrel assembly machinery complex, solved by I-SAD phasing  (7) , suggests that I-SAD could be a universal technique for the solution of MP crystal structures.
X-ray crystallography X-ray diffraction data were collected on the European Synchrotron Radiation Facility (ESRF) beamlines ID23-1 (29) or ID23-2 (30) equipped with Dectris pixel array detectors. For I-SAD experiments, diffraction data from crystals soaked in NaI were collected at l = 1. 85 Å. The x-ray beam size at the sample position was varied from 10 to 50 mm in maximum dimension depending on the crystal size. Experimental parameters for optimal data collection were designed using the program BEST  (31). Diffraction images were processed with XDS  (32)  and XSCALE  (32) , which was also used to merge and scale different data sets (see Table  1  for details). Structure factor amplitudes, anomalous differences (DANO), and FreeR labels were then generated using the CCP4 programs POINTLESS and AIMLESS  (33).
The KR2 I-SAD serial data collection was carried out as described by Zander et al. (19) using x-rays of l = 1. 85 Å. Here, 171 partial data sets were successfully processed, and hierarchical cluster analysis  (34)  was then used to select 136 of these for merging/scaling to produce the final data set. Structure solution and refinement were then carried out as described below for data sets obtained in a "standard" fashion. For the serial crystallography I-SIRAS experiments, the native data set was collected in a standard way, and only 39 of the I-SAD partial data sets were merged to provide the derivative data set. For Br-SAD experiments, diffraction data from crystals of NarQ soaked in 0. 5 M NaBr were collected at l = 0. 872 Å, whereas for KR2 crystals soaked in 0. 5 M NaBr, diffraction data were collected at l = 0.
This being the case, structure refinement based on the model obtained from native crystals was carried out using REFMAC5 and Coot. Anomalous difference Fourier maps were then calculated with FFT (CCP4) using DANO and a calc + 90 o as coefficients (a calc = calculated phases from the final refined models) and were used to verify the number, position, and occupancy of bromide ions in each of the individual and the merged Br-SAD data sets.
Acknowledgments Funding: The work on protein expression, purification and crystallization was supported by the program " Chaires d'excellence" edition 2008 of ANR France ,  CEA(IBS) -HGF(FZJ)   STC 5. 1  specific agreement, and used the platforms of the  Grenoble Instruct Centre (ISBG ;  UMS 3518 CNRS-CEA-UJF-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB). The work on photoactive proteins was supported by  Ministry of Education and Science of the Russian Federation  ( RFMEFI58716X0026 ). The work on GPCR expression, purification, and crystallization was supported by the  Russian Science Foundation  (project no. 16-14-10273 ). I. M. is the recipient of an  ESRF PhD Studentship. Author contributions: A.
Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors. All diffraction data and refined models have been deposited in the PDB:  5JGP  (NarQ),  5JRF  ( KR2 ),  5JSI  (MACR), and  5JTB  ( A 2A AR-BRIL-DC ).
They have proven efficacy in several animal models of fibrosis and in the murine model of HOCl-induced SSc  (5). In addition, the therapeutic interest of allogeneic MSCs is being evaluated in SSc patients in phase I/II studies (NCT00962923 and NCT02213705). Noteworthy, allogeneic MSC-based therapy could appear safer than autologous approach in SSc. Indeed, SSc-MSCs were shown to display altered phenotypical and functional properties. They expressed higher levels of transforming growth factor (TGF)-βRII  (6) , α-smooth muscle actin, SM22α genes  (7) , and senescence markers  (8)  as well as enhanced pro-angiogenic activity  (9). Since resident MSCs obtained from SSc patients (SSc-MSCs) may present altered functions, use of allogeneic MSCs could be more appropriate in the clinics and has already shown some promising results  (10).
statistical analyses Statistical analysis was performed with GraphPad 6 Prism Software. Data were compared using the Mann-Whitney test for non-parametric values. A p value <0. 05 was considered significant. We first evaluated the proliferation rate of MSCs cultured for 10 days in medium containing 5% serum from healthy patients (SAB) or oxidized SAB that has been submitted to H2O2 (SABH2O2) or HOCl treatment to get 400 or 1,000 µmol/L of AOPP (SAB400 and SAB1000). In SAB, MSCs rapidly proliferated during the first 3 days and then weakly proliferated till day 10 (Figure  1A ). By contrast, MSCs cultured in oxidized SAB did not proliferate compared to day 0 and even died when cultured in SABH2O2. By comparison with MSCs cultured in SAB, growth of MSCs in oxidized SAB was significantly inhibited at whatever the time point, indicating that oxidized human serum inhibited MSC proliferation.
Work in the laboratory INSERM U1183 was supported by the INSERM Institute and the University of Montpellier. We are indebted to Association des Sclérodermiques de France (ASF) for funding. AM received a fellowship from French Health ministry and INSERM institute for this work. We acknowledge the Agence Nationale pour la Recherche for support of the national infrastructure: "ECELLFRANCE: Development of a national adult mesenchymal stem cell based therapy platform" (ANR-11-INSB-005).