dataesr/scientific-paragraphs-categorization huggingface dataset:
After the alignment of EM and light level data (Figure  6C  and D), pre-and post-synapses are identified in EM using the presence of synaptic vesicles and the PSD (Figure  6E F ). Using conjugate FMRP light level data in combination with hand-classified synapses, FMRP-containing synapses were identified (Figure  6E  and F ). The distance to cleft from the FMRP punctum center was measured and graphed on a histogram (Figure  6G  and H ). The histogram of FMRP distance to cleft in EM (Figure  6G  and H) clearly reproduces the wide-field, synapse alignment result (Figure  6A ). The EM histograms are narrower in distance because the EM volumes in the z-axis are significantly smaller than an actual synapse (210 nm), whereas this is not the case in the wide-field dataset (EM volumes are 2. 8 mm).
Discussion We demonstrated a highly multiplexed method (SubSynMAP) for the visualization and quantification of the nanometer-resolution distribution of proteins within synapse populations. SubSynMAP uses the statistical power of large datasets to reveal discrete variations in the sub-synaptic distribution of synaptic proteins. For example, we can differentiate presynaptic vesicular proteins (such as SYNPH, VGluT1) from presynaptic active zone proteins (such as BSN). The comparison of these distributions can provide meaningful insight into the potential function of these proteins. For example, the greater localization to the synaptic cleft region of GLAST as compared to GLT1 suggests that GLAST plays a more important role in the direct clearing of glutamate in the cleft, whereas GLT1 localization reflects its known function as a peri-synaptic regulator of metabotropic glutamate receptor signaling  (Bar-Peled et al.
Microscopy Wide-field imaging of ribbons were accomplished on a Zeiss Axio Imager. Z1 Upright Fluorescence Microscope with motorized stage and Axiocam HR Digital Camera as previously described  (Micheva and Smith, 2007). A position list was generated for each ribbon array of ultrathin sections using software modules for Axiovision. Single fields of view were imaged for each position in the position list using a Zeiss 63 Â/ 1. 4 NA Plan Apochromat objective.
Image registration and processing Image stacks from ATD were imported into FIJI and aligned using both rigid and affine transformations with the Register Virtual Stacks plugin. The aligned image stacks were further registered across image sessions using FIJI and TrackEM. The aligned and registered image stacks were imported into Matlab (Mathworks) and deconvolved using the native implementation of Richardson-Lucy deconvolution with empirical PSF through ten iterations  (Wang and Smith, 2012). Custom functions were written to automate and facilitate this work flow (all custom Matlab functions are included in the publication). Empirical PSF were measured using 110 nm beads mounted on slides in the actual imaging system  (Wang and Smith, 2012).
Center of mass calculations for protein puncta Matlab native function (regionprops) was used to calculate the centers of mass of puncta in the image volumes using 26 neighborhood 3D connected component analyses with an assumed background threshold that is 0. 1 of the total dynamic range, which is 6553. 5 for a 16-bit image, and is in line with previous background thresholds used for ATD analysis  (Wang and Smith, 2012). Custom functions in Matlab were implemented to facilitate the handling and processing of the data (all custom Matlab functions are included in the publication).
Resolution beyond 100 nm pixel resolution of the images The pixel resolution of each single image used to generate the image volume is 100 nm. The section thickness is ~100 nm thus creating nearly 100 nm isotropic voxels. The resolutions in the distribution histograms are better than the 100 nm voxel resolution. This improved resolution arises from the statistics that combine tens of thousands of centers of mass calculations, which introduces sub-pixel jitter into the dataset which, over the large population, creates a dithering effect that provides sub-100 nm localization seen in our probability distributions.
Discretization effects mediated by aliasing and ringing in 3D histograms Halos are present in every distribution generated by SubSynMAP. These are caused by the discretization of the protein structures through pixilation. While the 3D weighted centroid calculations do provide sub-pixel coordinates, the very nature of the calculation being made on a discrete dataset increases the probability of certain sub pixel distances. The reason why these halos are more apparent in some instances is because the background is higher for those proteins; and the reason for a higher background is caused by: (1) fewer pooled synapses, some synapses are rarer and thus their histograms involve smaller protein numbers; and (2) some proteins have more background, because all protein centers within a 1 mm radius sphere are incorporated into the histogram.
Synapse classification and rotations The classification method has been published previously  (Wang et al. , 2014; Allen et al. , 2012; Hiu et al. , 2016). In short, classification occurs in a 1 mm radius sphere centered on the origin (PSD95 for excitatory synapses and gephyrin for inhibitory synapses). For example, a VGluT1 synapse will have PSD95 at the postsynaptic termini and synapsin at the presynaptic termini. Moreover, VGluT1 will be closer to synapsin than to PSD95. Thus, the algorithm will require the third point (VGluT1) to be closer to synapsin by a specified factor than it is to PSD95 in order for it to classify the grouping as a synapse. In some instances, after this initial classification step, a synaptic axis still retains more than one third point (VGluT1 puncta). If that is the case, then the algorithm looks through the entire dataset and removes sharing of the third point in a manner that maximizes the number of possible synaptic axis with third points.
We rotate the synaptic axis to a position on the x-axis so that the coordinates of all the termini is x, 0, 0. We then use the rotation defined by this axis to rotate all associated third points for all channels in the dataset. For the distributional histograms, the dataset in which all of the protein centers across all imaged protein channels within a radius of 1 mm from the origin of the axis is used. This is done to ensure that the volume normalization is correct. The volume normalization assumes that there is some baseline probability that a punctum will co-localize with a synaptic axis, and that that probability increases as the volume increases, which is exactly what happens as you extend the distance from the origin to the termini of the axis. Thus, for the volume normalization to work properly, one must take all of the points scattered around the synaptic axis.
Acknowledgements We thank  Drs. Michael Tranfaglia ,  Louis Leung  and  Logan Grosenick  for helpful discussion and edits on the manuscript. We thank  Drs. Yi Zuo  and  Xinzhu Yu  for providing the FXS KO mice for the experiments. We thank  Drs. Jeffrey D Rothstein ,  Justin Fallon , and  Rob Willemsen  for providing antibodies. Finally, we are grateful to the reviewers and editors for writing an excellent synopsis of our workflow, which is included in the 'Methods' under the heading 'Synapse histogram workflow'. We also thank  Dr. Juan Cueva  from  Aratome, LLC. for providing the electron microscopy in this manuscript.
[13] The formation of the hapten-protein complex, able to be fully recognized by naïve T cells, is one of the mandatory conditions for skin allergy to occur. In chemico alternative tests, such as the Direct Peptide Reactivity Assay (DPRA), are based on this capacity of chemical sensitizers to react with amino acids. To develop the DPRA, Gerberick et al. evaluated 82 chemicals, including allergens of different potencies and non-allergenic chemicals, for their ability to react with glutathione and two synthetic peptides having cysteine (Cys) or lysine (Lys) as the reactive amino acid (N-Ac-Arg-Phe-Ala-Ala-X-Ala-Ala; Pep-Cys if X=Cys; Pep-Lys if X=Lys), and measured peptide depletion using UV detection. [14] Peptide reactivity data were compared with existing in vivo local lymph node assay (LLNA) data using recursive partitioning methodology.
NMR Experiments and Structure Assignment The reactions were followed by mono-dimensional  13  C NMR on a Bruker Avance 300 spectrometer at 75 MHz. Chemical shifts (δ) are reported in ppm in comparison to TMS, using the residual signal of acetonitrile ( 1 H, δ = 21. 94 ppm;  13  C, δ = 118. 26 ppm) as internal standard. 13 C peak integrations were calculated using the signal of tert-butanol (δ = 29. 23 ppm) or deuterated tert-butanol (δ = 68. 46 ppm) as internal standard with NMRTEC/NMR notebook software (version 2. 0, NMRtec ® , Illkirch-Graffenstaden, France). The structure of the products formed during the reactions was assigned by heteronuclear single-quantum correlation (HSQC) and heteronuclear multiple-bond correlation (HMBC) experiments, and by combining all information from the different  13  C-substituted positions.
Bi-dimensional 1 H-  13  C heteronuclear experiments were carried out on a Bruker Avance 400 spectrometer ( 1 H, 400 MHz;  13  C, 100 MHz). Chemical shifts (δ) were compared with those calculated using ACD/CNMR and ACD/HNMR Predictor software (version 6. 0, ACD/Labs, Toronto, Canada).
Introduction Many chemical biology initiatives in Europe and the USA aim to screen large compound collections with dedicated bioassays i. e. EU Lead Factory  (1) , EU-Openscreen  (2)  or BARD in the USA  (3). Such large initiatives generate large amounts of data that support academic and industrial research in the discovery of safer chemicals, with better efficacy. To make chemical biology information accessible to scientists, several repositories of bioactive small molecules have been developed: ChEMBL (4), PubChem (5), ChemSpider  (6)  and OpenPhacts  (7)  are the largest, more general databases available to the public. The National Institutes of Health's Molecular Libraries Program (MLP) funding developed the BioAssay Research Database (BARD), focusing on assay ontologies for PubChem bioassays  (3).
Data sources We updated all the chemical protein interactions data from the open source databases ChEMBL (version 19) (4), BindingDB  (17) , PDSP Ki database  (18) , DrugBank (version 4)  (19) , PharmGKB  (20) , IUPHAR-DB database  (21)  and STITCH (version 4)  (22). Clinical information from the Anatomical Therapeutic Chemical Classification System  (23)  developed by the World Health Organization, as well as side effect data from Sider 2 were also integrated  (24). From a biological perspective, we updated our internal human interactome platform to reach 14 421 genes interacting through 507 142 unique PPIs  (25). OMIM  (26) , the human disease network (  27 ) GeneCards  (28) , KEGG  (29) , Reactome  (30) , UniPathway  (31)  and Gene Ontology  (32)  databases were also downloaded, curated and included in our system.
Overall, the integrated data sources were increased by over 60% compared to the earlier version. As many different data types were aggregated in ChemProt, a 'zChemProt' value for each compoundbioactivity interactions was computed for visualization in the several heatmaps developed. Basically, for each of the 11 most prevalent data types (IC 50 , EC 50 , Potency, AC 50 , pIC 50 , Log K i , pK i , pEC 50 , K d , K i ), a zChemProt value was computed using the mean and standard deviation calculated from the distribution of the associated data types for each target in a similar way described in CARLSBAD database  (33). IC 50 , EC 50 , Potency, AC 50 , K d and K i were log transformed before computing the zChemProt values. Large values indicate strong chemical-protein interactions and are represented in orange.
Predictions methods Daylight-like 1024 bit fingerprints was computed with RDkit (www. rdkit. org) and the chemical similarity between two compounds was quantitatively assessed using the Tanimoto coefficient  (34). The Similarity Ensemble Approach (SEA)  (35)  has been re-compiled on the ChemProt server and updated according to the novel zChemProt data and integrated into ChemProt 3. 0. Only proteins with >10 chemicals were included for SEA prediction, using the same protocol as described in the previous version  (36). For sequence analyses, protein sequences were obtained from Uniprot  (37). Sequences comparisons were computed using BLASTP and estimated to be similar when their E value was lower than 10 À10 (38). All compounds were decomposed into ring scaffolds based on an internal implementation of the 'Scaffold Hunter' hierarchical classification algorithm  (39) (40)  with the addition of decomposition of non-ring molecule based on rules 7-10, as described by  Schuffenhauer et al.
(41). This hierarchical decomposition allows the generation of scaffold trees enabling an easy and interactive navigation of the chemical biology space in large datasets and the identification of potential new compound classes with desired bioactivity. For this release, QSAR models were trained for each protein with >20 chemicals (in total 850 proteins). A Naı ¨ve Bayes classifier was trained using 5-fold cross-validation for performance assessment. Features selection, five different computational fingerprints (Daylight and Morgan fingerprints) and three different cutoffs (Àlog 10 value: 4, 5 and 6) for classifying active and non-active compounds, were used to produce overall 15 classification models for each target. To predict new compound, each model was weighted by the cross-validated performance measure resulting in a prediction between 0 and 1 (where 1 is high predicted binding).
To avoid bias toward negative or positive data, each of the three datasets used for training were balanced by including as many negative compounds as positive including random compounds from the ChemProt database. The performance of the developed QSAR ensemble approach was tested on a dataset of hERG binders and showed an improved performance compared to a previous reported study  (42)  (Aroc ¼ 0. 827, Matthews Correlation Coefficient (MCC) ¼ 0. 488 using 5fold cross-validation). Furthermore, the method was benchmarked against the SEA implementation on a dataset consisting of 143 proteins with associated activity values from the ChemProt-3. 0 dataset. In a 5-fold cross-validation scheme on each of the 143 proteins, the QSAR models outperform SEA (P val ¼ 2. 2e À 16 , paired t-test of Spearman correlation coefficients for each protein predictions).
From the protein ID, the user has access to the proteins complex (represented as a protein's network from proteinprotein interaction data). The complex of proteins is then mapped to biological terms such as diseases, GO terms and pathways with a corrected P value to evaluate the significance of these associations. Finally, the user has the possibility to download the results in flat-file format to perform others analyses. Methodology: Daylight-like fingerprints, defined by 1024 fragments were computed using RDkit (www. rdkit. org). The QSAR models were trained using scikit-learn software (http://scikit-learn. org/stable/). The visual interface was implemented using HTML 5 and JavaScript. The webserver is limited for an input file of 50 molecules (in SMILES or sdf file) per query to limit the time necessary to get the output.
For the Caffeine example, 105 similar compounds (with a Tanimoto coefficient > 0. 85) were found, with bioactivities associated to 449 proteins (from weak in blue to strong in orange). The user is able to zoom in the heatmap and to narrow the information from the classification proteins tree to specific proteins (defined by uniprot ID). The user has also the possibility to navigate inside the heatmap. One option is to fill out missing values by choosing 'SEA' or 'QSAR' under prediction in the top of the page. By clicking on the compound structure, physicochemical features [such as the Lipinski rules (  44 )], number of proteins with bioactivities and the databases from which the information was gathered, are shown. Similarly, the user can click on a specific protein name and get more information on the function of the protein, diseases associated to this protein and predictions based on SEA and QSAR.
For example, under 'family A GPCR', caffeine is shown to be potent (35. 5 nM) on the rat muscarinic M1 acetylcholine receptor (P08482). It also shows a strong association with the dopamine D2 receptor (P14416) based on the STITCH system. By clicking on this protein, the user is presented with information on this dopamine receptor. Notably, disease associations are queried through the TCRD (Target Central Ressource Database Application: http://juniper. health. unm. edu/tcrd/) database and the genetic variation through the Ensembl database  (45). A complex disease network is also associated to this protein. Clicking on this link, diseases (such as schizophrenia and epilepsy) and GO terms (plasma membrane, ligand-gated ion channel activity, etc. ) are shown. Instead of looking for 'functional' protein annotation, it is further possible to select 'pathway' or 'diseases' for caffeine (also for the set of 105 similar compounds).
Conclusion Given that access to many chemogenomics databases is possible, linking them to biological resources and using a number of machine learning tools, scientists can now estimate the bioactivity profile of molecules across a large number of targets, pathways, diseases and other clinical outcomes using ligand-based, target-based and network-based models. Such multi-target, multi-layer strategies are becoming more and more accepted by the scientific community. Within ChemProt, it is possible to navigate the chemogenomics space and to link chemically induced target perturbations to diseases and other biological outcomes. Such tools might be of interest for drug discovery, drug safety and also chemical risk assessment. ChemProt 3. 0 supports predicting bioactivities on targets and off-targets for new compounds and can assist in the associations to phenotypes and side effects relationships.
Funding This work was supported by the  Innovative Medicines Initiative Joint Undertaking , under grant agreement No  115002  ( eTOX ) for  O. Taboureau  and No  115191  OPENPHACTS (S. K. , J. K. , T. I. O. ) the resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies in kind contribution.
This first generation of  TET (Cabré et al. , 2001)  was monolingually designed, not scalable, and they were not handling term variants, except for ACABIT  (Daille, 2001)  and FASTR  (Jacquemin, 2001). This last question has always been a pain in the neck for TET. The current generation of TET improves on various aspects. As an example, TermoStat 1  deals with several Romance languages, reaches to treat text up to 30 megabytes, and proposes a first structuring based on lexical inclusion. Term-Suite goes a step forward: it is multilingually designed, scalable, and handles term variants. It is able to perform term extraction from languages that behave differently from the linguistic point of view. Complex terms in languages such as German and Russian are mostly compounds, while in Roman languages they are MWT.
Release TermSuite is a Java (7+) program. It can be used in three ways: the Java API, the command line API, or the graphical user interface as shown on Figure  2. Its only third-party dependency is TreeTagger, which needs to be installed separately and referenced by TermSuite configuration. TermSuite is under licence Apache 2. 0. The source code and all its components and linguistic resources are released on Github 7. The latest released versions, currently 2. 1, are available on Maven Central 8. All links, documentation, resources, and guides about TermSuite are available on its official website:.
Cycles of bending and unbending also create residual stresses, which, in turn generate springback deformations when the tools are released  [5] -  [8] ). In this paper, we describe the formulations of the code SIMEX as an example of one-step sheet metal forming simulations. The first version of SIMEX was released in 1994, and since 2004 it is integrated in the solution package of ESI Group. Other commercial one-step simulation codes are marketed by ALTAIR, AUTOFORM, LSTC, etc. This paper describes the theoretical foundation and the implementation in SIMEX of an equivalent model for the cycles of bending and unbending undergone by the blank over the die radius. This development greatly improved the accuracy of SIMEX simulation.
In Sect. 2, we introduce briefly the geographical and climate characteristics of the study area and period. Section 3 describes the snowpack model Crocus, then the atmospheric forcing from NWP model AROME at kilometric resolution and the forcing from SAFRAN reanalysis, and, finally, the observations dataset and verification methods. Section 4 details the results following three main axes: (i) global scores and spatial distribution of snow depth (SD), (ii) daily snow depth variations and winter precipitation and (iii) comparison to snow water equivalent (SWE) scores and study of bulk snowpack density. These results are discussed in Sect. 5, with concluding remarks and outlooks.
Soil properties were obtained from the HSWD (Harmonized World Soil Database) 1 km resolution database for soil texture (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2012). Aspect and slope are not taken into account for incoming solar radiations, since the 2. 5 km resolution topography can hardly represent the local orography of observation stations. As observations are collected in open fields, the interactions with the vegetation and the parameterization of fractional snow cover are not activated within the SURFEX scheme. Windinduced snow transport is not simulated.
(2011). In particular, the precipitation phase is derived from the cloud microphysical scheme. The implementation of AROME as an operational system is made through 30 h forecasts at the 00:00, 06:00, 12:00 and 18:00 UTC nominal analysis times, over a domain covering France. We use here the hourly forecasts issued from the 00:00 UTC analysis time, from +6 to +29 h, extracted on a regular latitude / longitude 0. 025 • grid to build a continuous forcing from 1 August 2010 to 31 July 2014 over the domain of study. Some changes in the operational configuration of AROME occurred during the 4 years of simulations: the simulation domain was extended during summer 2012 with a modification of the topographic database. The topography from Global 30 Arc-Second Elevation dataset (GTOPO30) was used in a low-resolution version (5 km) before summer 2012 and at 30 arcsec (approximately 1 km) resolution afterwards, which led to a modification of the forcing files orography in the middle of our simulation period.
Evaluation dataset The observational dataset contains SD, SWE and precipitation measurements available in the Pyrenean SAFRAN massifs, both in France and Spain. The SD observations consist of daily manual measurements at ski resorts (at 06:00 UTC) and hourly automatic measurements by ultra-sonic sensors at high-altitude stations. Only the value at 06:00 UTC from the hourly record is used in this study. The SWE measurements come from automatic stations with cosmic ray snow gauges  (Gottardi et al. , 2013). Daily values are obtained through a 24 h median smoothing of hourly measurements. Both SD and SWE data are independent (i. e. not assimi- lated in SAFRAN-Crocus nor in AROME-Crocus). The 24 h cumulated precipitations measurements are manually collected every day at ski resorts with precipitation gauges (at 06:00 UTC), without any correction.