dataesr/scientific-paragraphs-categorization huggingface dataset:
A new query tool has been implemented to search for inhibitors within the database using standard molecular descriptors. A novel version of the 2P2I-inspector tool has been implemented to calculate a series of physical and chemical parameters of the protein interfaces. Several geometrical parameters including planarity, eccentricity and circularity have been added as well as customizable distance cutoffs. This tool has also been extended to protein-ligand interfaces. The 2P2I database thus represents a wealth of structural source of information for scientists interested in the properties of protein-protein interactions and the design of protein-protein interaction modulators.
PPI modulators can also bind away from the interface (allosteric inhibition) often involving structural and/or dynamic changes over the target  (4, 17). Although both types of inhibitors can be successful in the design of chemical probes or drugs, allosteric inhibition is more difficult to predict in silico and a number of compounds have been identified serendipitously  (4, 18). Therefore, we decided to concentrate only on orthosteric inhibition. To improve the discovery of new orthosteric PPI modulators, it is important to uncover the basic principles underlying the mechanisms of protein-protein and protein-ligand recognition and to characterize the properties of known PPI disruptors. To that extend, and in an effort to organize and analyze the fast growing amount of structural data available, we have developed 2P2Idb, a hand-curated structural database dedicated to orthosteric modulation of PPIs that was first released in 2010  (19, 20).
The 2P2I structural database holds information on protein-protein and protein-ligand complexes that have been structurally characterized and available in the RCSB Protein Data Bank  (21, 22). Two other resources focusing on the properties of PPI modulators are available: TIMBAL  (23, 24)  and iPPI-DB  (25). TIMBAL contains >8000 PPI modulators automatically retrieved from the ChEMBL database, whereas iPPI-DB contains 1650 non-peptide inhibitors across 13 families of PPIs. The chemical structures, the physicochemical and the pharmacological profiles of the inhibitors in iPPI-DB are manually extracted from the literature. There are two major differences between 2P2Idb and the other two databases, i/2P2Idb is a structural database dedicated to PPI modulators with structural information for both the protein-protein and protein-ligand complexes as well as for the small-molecule compounds and ii/2P2Idb focuses only on orthosteric inhibitors (directly interfering at the interface).
These differences account for the limited number of entries in 2P2Idb compared to the other two databases. Over the last 5 years, the database has grown constantly leading to a 5-fold increase in the number of small molecule compounds. Conclusions from the analysis of 2P2Idb have already proved useful to characterize PPI inhibitors and to guide the development of tools to filter large chemical libraries in order to build PPI-oriented chemical libraries  (8, 26, 27).
2P2Idb 2P2Idb is a relational database that was built through data mining from literature and by exhaustive search of the Protein Data Bank (http://www. rcsb. org/). The list of 27 protein-protein complexes was retrieved through searches in the PDB, literature or conferences abstracts. Homology models were built with Modeller package v9. 14  (28)  for protein-protein complexes without a 3D structure (bromodomains BRD3-1, BRD3-2, BRD4-2, BRDT-1 and KRAS) using a close homolog (identity ranging from 75% to 94%) as a template. The database is updated through an automatic process with several checkpoints. For each protein-protein complex, a list of PDB codes is retrieved from Uniprot (http://www. uni prot. org) and new protein-ligand complexes are detected and superimposed onto the equivalent protein-protein complex.
A pymol file is automatically generated and a manual examination is performed to ensure that small molecule compounds are binding at the protein-protein interface (covalently bound ligands are not included in the database). Once validated, PDB files are formatted and a series of inhouse PhP and Perl scripts are used to automatically retrieve the necessary data to fill the database (such as Pubmed Id, Chain and Ligand 3-Character code). Binding affinity data are retrieved form PDBbind v2015 (http://www. pdbbind-cn. org/) or BindingDB (http://www. bindingdb. org/) or BindingMOAD (http://www. bindingmoad. org/). Small molecule ligands are retrieved from the PDB as SDF files. ChemSpider Ids are automatically retrieved using a Perl script adapted from resources available on the ChemSpider website (http://www.
2P2I-inspector Interface properties are calculated using a combination of in-house shell and tcl scripts and procedure in the VMD package version 1. 9. 2 (http://www. ks. uiuc. edu/ Research/vmd/). Missing hydrogen atoms are added with Pymol (https://www. pymol. org/). Gap volumes, planarity, eccentricity and circularity are computed using SURFNET  (29).
2P2Idb update The 2P2I database focuses on orthosteric small molecule inhibitors of PPI, we therefore only select the cases for which both the protein-protein and protein-inhibitor complexes are present in the RCSB Protein Data Bank and for which the inhibitor is directly competing at the interface. The current release of the database contains 27 proteinprotein complexes, 274 protein-inhibitor complexes, 28 free proteins and 242 unique small molecule modulators (Table  1 ). The protein-protein complexes were subdivided into three classes corresponding to protein-peptide complexes (class 1), protein-protein complexes with globular proteins (class 2) and bromodomains-histone complexes (class BRD) based on the total number of segments at the interface and target properties. Characteristics of the three types of protein-protein interfaces were computed with our local server tool (http://2p2idb.
2P2Idb web interface A complete new web interface has been designed with new contents and new features (Figure  2 ). All 3D structures and related data for a given PPI family can now be accessed on the same web page. New tools and menus have been added to visualize the different 3D structures through a javascript JSmol applet. More information can be found about protein-protein complexes, protein-ligand complexes, free    proteins and small molecule orthosteric modulators. These new data include non-bonded contacts, interfacial hydrogen bonds and salt bridges, geometrical, physicochemical parameters of the protein-protein interfaces, binding parameters and standard molecular descriptors for the small molecule compounds. Links to relevant websites and databases are provided such as literature (PubMed, DOI), proteins information (UniProt), 3D structures (PDBsum, RCSB, PDBe), ligand properties (ChemSpider, LigandExpo, CREDO), protein-protein and protein-lig and binding affinities from the latest version of PDBbind  (30) , BindingMOAD  (31, 32)  or BindingDB (http://www.
Figure2. Overview of the 2P2Idb interface. (A) Protein-protein complexes divided into three subclasses can be selected using dropdown menus. (B) PDB structures corresponding to protein-protein complexes, free forms and protein-inhibitor complexes can be easily selected and visualized in a JSmol applet. All information included in the database including pre-calculated interface parameters, small compounds molecular descriptors and links to various websites of interest can be accessed. (C) Ligands in the 2P2I database can been searched by chemical properties. The range values of standard molecular descriptors can be defined to retrieve compounds from the database with matching properties.
The initial strategy to prescribe intensified therapy to patients with a poor-predicted outcome. Meanwhile, dose de-escalation could be envisaged for patients with a good-predicted outcome to reduce the frequency of debilitating side effects (difficulties in swallowing, breathing, eating…), induced by the standard treatment. In this field, the Human Papilloma Virus (HPV) infection -known to be predictive of good outcome -is more and more considered as a marker for dose de-escalation, with encouraging results considering reduced toxicity (NCT01530997). However, treatment intensification based on HPV stratification may not be an adequate treatment strategy, since intensitymodulated radiation therapy (IMRT) improves the survival irrespective of the HPV status  [5] , and biomarker for dose intensification are awaited.
MiR-422a is significantly downregulated in oropharynx tumors from patients who experienced early loco(regional) recurrence The level of expression of 384 miRNA was determined by RT-qPCR (TaqMan low density microarray) in 75 stage III-IV oropharynx tumors (36 from NR and 39 from R), and in 38 adjacent healthy tissues (N) (19 from NR and 19 from R). Overall, 13 miRNA were significantly deregulated in NR versus R (Wilcoxon test, p<0. 05) and were also predictive of relapse-free survival (RFS) (LogRank test, p<0. 05) (data not shown). We refined our initial analysis and searched for miRNA that could differentiate patients who exclusively experienced local (and not loco-regional) recurrence (Local Rec) from patients who did not recur (R). Among the 13 miRNA initially identified, only the downregulation of miR-422a was associated with an exclusively local recurrence (Figure  1A ).
MiR-422a modulates cell adhesion and proliferation but not radio-sensitivity To better characterize the function of miR-422a, we conducted in vitro experiments on three cell lines: SCC61 and SQ20B derived from human HNSCC, as well as HaCaT derived from normal epithelial cells (all the three expressing miR-422a (Supplementary Figure  S2 )). Cells were transiently transfected with modified oligonucleotides, either mimicking (miRmim) or inhibiting (miRinhi) the endogenous expression of miR-422a. An irrelevant construct was used as a control (miRCo). The efficiency of the transfections was assessed using a fluorescent miRNA, and repeatedly reached 98% (data not shown). We initially evaluated whether the downregulation of miR-422a was responsible for radio-resistance (as local recurrence is indicative of radio-resistance).
MiR-422a targets the oncogene CD73/NT5E Among the predicted targets of miR-422a by miR-map (http://mirmap. ezlab. org/app/) and miRecord (http://c1. accurascience. com/miRecords/), we searched for oncogenes known to modulate the adhesion and proliferation processes and tested their level of expression in our tumor samples. Among them, we focused on CD73. This ecto-5'-nucleotidase (also referred as NT5E) is a GPI-anchored cell surface protein, which plays enzymatic and non-enzymatic activities. Its oncogenic property partly relies on the modulation of cell adhesion and proliferation  [15]. Interestingly, we demonstrated an inverse correlation (p<0. 01) between the levels of expression of miR-422a and CD73 (Figure  4A ). Next, we confirmed this inverse correlation by analyzing an independent public dataset (Geoset GS33232), with expression data available for 44 HNSCC tumors and 25 normal tissues (from uvulo-palatopharyngoplasty) (Supplementary Figure  S4 ).
To do so, we extracted expression data from the TCGA database for 255 patients with stage III-IV tumors of different locations (oral cavity, oropharynx, larynx and pharynx). We observed a significant (p=0. 018) increase in the risk of recurrence in the "high CD73" subgroup (Figure  6B , All recur. ). The significance is maintained when only loco-regional recurrence is included (p=0. 020), but is lost when only considering metastasis (p=0. 154), as first event of recurrence. This strengthens the hypothesis that a high expression of CD73 favors loco-regional recurrence, but may be deleterious for the metastatic process. Using the same TCGA database, miR-422a expression level was available for 465 samples (of which 42 healthy tissues and 423 HNSCC tissues). However, only 5. 8% of the samples were positive for miR-422a expression (maybe due to a lower sensitivity of the technic used), though a lower proportion of positive samples was observed in the tumors (4.
7%) versus healthy tissues (16. 7%), what confirmed our result. Selecting the stage III-IV patients (N=205) and despite a very low number of miR-422a positive specimens (N=9), we drew the RFS curves and observed a clear tendency (obviously not significant) to a longer survival time without recurrence (Figure  6B ). Hence, a biotherapy targeting miR-422a/CD73 may be highly beneficial for patients displaying miR-422aLow/ CD73high tumors, to prevent loco-regional recurrence in HNSCC in advanced stages (all locations included). CD73S, the splice variant of CD73L, is expected to bear the same 3'UTR sequence, comprising the miR-422a binding sites. Since CD73S is presumed to play a major role during oncogenesis  [16] , we investigated its expression in tumors from the TCGA database and observed a linear correlation between the transcript levels of CD73S and CD73L (in agreement with a co-regulation of both transcripts by miR-422a) (Supplementary Figure  S8 ).
Moreover CD73S is predictive of RFS considering  A. RFS for all of the patients (All Recur) of our oropharynx cohort, or for patients without metastasis at the time of loco-regional recurrence (Locoregional) from the same cohort. B-C. RFS was extracted from the TCGA database, for patients suffering from head and neck tumors (not restricted to oropharynx), at stages III and IV (N=255). Kaplan Meier representation of RFS as a function of total CD73 (B) or CD73S (C) for all types of recurrences (on the Left) or restricted to loco-regional (Middle) or metastatic (Right) recurrences (as first event). LogRank tests were conducted to calculate the p-values. all types of recurrences (p=0. 004) or loco-regional recurrences (p=0. 010), but is not predictive of metastasis (p=0. 104) (Figure  6C ). To conclude, miR-422a modulates the expression of both CD73S and CD73L, and CD73S bears the same predictive value (with even better p-value) with regards to RFS in Head and Neck tumors as compared with total CD73.
Regarding the clinical aspects, CD73 overexpression is predictive of a poor prognosis in colorectal, gastric, gallbladder and triple negative breast cancers, as well as in chronic lymphocytic leukemia (for review see  [15] ). Overall, our results corroborate the pro-adhesive and pro-proliferative functions of CD73, along with its involvement in treatment resistance. However, in our study a high level of CD73 appeared to be unrelated to the metastatic capacity in our cohort and in the TCGA dataset. Consistently, the results published in the literature are conflictual about the pro-migratory and the pro-metastatic function of CD73. In meduloblastoma cell lines, the metastatic potential is inversely correlated with CD73 expression and activity  [35]. Pharmacological inhibition of CD73 reduces adhesion but increases the invasion/ migration capacities in vitro  [17, 36] , and treatment with adenosine (produced by CD73) inhibits cell migration and invasion  [37].
Indeed, the anti-CD73 monoclonal antibody MEDI9447 targets and binds to CD73, leading to its clustering and its internalization, abolishing its activity. This immunotherapy combined with anti-PD-L1 is currently under evaluation in phase I clinical trials, for adult solid tumor (NCT02503774). It is thus reasonable to think that HNSCC patients in advanced disease stages with a high intra-tumoral level of CD73 may benefit from MEDI9447 in a near future. However, these drugs should be carefully prescribed taking into account the above mentioned considerations and the immune function of CD73. Furthermore, such an administration should be combined with a treatment preventing metastasis. The targeting of miR-422a is also plausible, with innovative therapies targeting microRNA currently being evaluated in clinical trials for different diseases.
Data mining from TCGA and GEOset databases We queried The Cancer Genome Atlas (TCGA) data portal and the Global Environmental Outlook (GEO) data portal (http://www. ncbi. nlm. nih. gov/geo). To test for an inverse correlation in the expression levels of miR-422a and CD73, we used the GSE33232 series including 44 HNSCC and 25 healthy tissues from uvulopalato-pharyngoplasty. To assess relapse-free survival (RFS), we used the 'TCGA2STAT' R package  [47]  to download RNAseqV2 normalized read counts (RPKM), as well as miRNASeq normalized read counts (RPMM). Both expression and clinical data were available for 473 HNSCC tumors (from different locations: oral cavity, oropharynx, larynx, and pharynx). RFS for stage III-IV tumors (with or without metastasis at the time of diagnosis) was available for 255 patients; among them, 179 did not relapse, 40 relapsed loco-regionally, 31 developed metastasis and 4 developed another cancer, as a first relapse event.
Molecular biology Total RNA was extracted by using miRNeasy Mini Kit (Qiagen); quality was controlled by the small RNA kit for the Agilent Bioanalyzer (Bio-Rad). 0. 7µg of total RNA were reverse transcribed and analyzed using the TaqMan Low density (TLDA) technology (human microRNA panel V2. 0) as previously published  [48]. The expression data (delta 2 CT method) were normalized against the geometrical mean of three reference genes (Let. 7a, miR-26a, Let. 7e) chosen according to the GeNorm instructions (https://genorm. cmgg. be/). Custom RT-qPCR were conducted on miR-422a and the three reference genes using specific TaqMan® MicroRNA Assays and the same master mix, on a MxPro 3000 (Agilent). In situ hybridization (ISH) against miR-422a was carried out following the instructions of the miRCURY LNA microRNA ISH Optimization kit (Exiqon).
Statistical analyses Survival analyses were performed with the R survival package software (R software: Language and Environment for Statistical Computing, R Foundation for Statistical Computing, http://www. R-project. org). Cox proportional hazard models provided estimates of the hazard ratios (HRs). For each covariate, we have checked that no evidence of violation of the proportional hazards assumption was found by plotting the scaled Schoenfeld residuals generated by the cox. zph function). A Wilcoxon test was used for mean comparison, while linear correlations were conducted using the spearman test. Considering the relapse free survival analysis: LogRank and Kaplan Meier curve drawings, were done using the SPSS software (IBM, France).
ACKNOWLEDGMENTS The experiments were done thanks to the contribution of the  SFR BioSciences Gerland-Lyon-Sud  ( UMS3444/ US8 ) platforms of flow cytometry and genetic. The microscopy Metasystem platform was financed by  LYric INCa-DGOS-4664 and Contrat Plan-Etat-Region ETOILE. We thank  Alison Baesa  for her participation and  Céline Maelys  for her help in IHC experiments. We would like to thank  B. Manship  ( CRCL ) for her advice on the manuscript.
Shifts in ecosystem phenology play an important role in the definition of inter-annual variability of net ecosystem carbon uptake. A good estimate at the global scale of ecosystem phenology, mainly that of photosynthesis or gross primary productivity (GPP), may be provided by vegetation indices derived from MODIS satellite image data. However, the relationship between the start date of a growing (or greening) season (SGS) when derived from different vegetation indices (VI's), and the starting day of carbon uptake is not well elucidated. Additionally, the validation of existing phenology data with in-situ measurements is largely missing. We have investigated the possibility to use different VI's to predict the starting day of the growing season for 28 FLUXNET sites as well as MODIS data. This analysis included main plant functional types (PFT's).
Introduction Ecosystem phenology shifts play an important role in describing the inter-annual variability of NEE (Net Ecosystem Exchange) due to its impact on Gross Primary Productivity (GPP). A shift in the start date of a growing season modulates annual GPP  (Churkina et al. 2005; Keenan et al. 2014; Richardson et al. 2010). Multiple data sources -primarily carbon dioxide (CO 2 ) eddy covariance flux data (NEE) as well as satellite imagery estimated vegetation indices (VI's)originating from different databases are used to estimate the start day of a growing season  (Garrity et al. 2011). GPP and NEE seasonality is frequently defined as carbon-flux phenology. Both variables describe the seasonality of ecosystem gross photosynthesis. Photosynthetic phenology is represented by the starting day of GPP and NEE and more specifically when NEE becomes positive.
Important to mention is that in-situ NDVI measurements offer the possibility for data acquisition under overcast conditions. Only low altitude remote sensing systems like UAV's offer this capacity as well. Finally, the objective of this paper is to explore the potential of six different VI's calculated from in-situ radiation measurements, and obtained from MODIS RED and NIR reflectances. This enables the estimation of the start of the carbon uptake season (i. e. SGS NEE ). Additionally the approach should also enable the phenological monitoring at twenty-eight different FLUXNET sites encompassing eight different PFT's (or ecosystems). (iii) The availability of measured carbon mass fluxes (in particular NEE). The application of these boundary conditions, leads to a subset of 28 FLUXNET sites (Table  1 ), representing 72 site-years.
They have a minimum of two years of both high quality flux measurements and measured radiation data. The 28 sites have been selected to establish the basic dataset used for the different procedures and analysis of which the results and conclusions are reported in this paper. The selected sites cover main global PFT's among which: CRO-Cropland; DBF-Deciduous Broadleaf Forest; EBF-Evergreen Broadleaf Forest; ENF-Evergreen Needle-leaf Forest; GRA-Grassland; OSH-Open Shrubland; WSA-Woody Savanna. The PFT's are defined as in the International Geosphere-Biosphere Programme -IGBP  (Loveland and Belward 1997). [Table  1 ] Furthermore, nine additional sites have been selected from the FLUXNET "La Thuile" database, representing 20 site-years, (cited in Table  S1  in the Supplementary data; validation sites).
Carbon flux measurements Eddy covariance (EC) measurements of ecosystem CO 2 mass fluxes have been acquired from the FLUXNET database  (Baldocchi et al. 2001). EC data are collected by the site manager according to a standard procedure and provide to the FLUXNET database. Typically data are collected at high sampling frequencies (at least at 10 Hz) and subsequently converted into mass fluxes integrated over a thirty minute time interval. Here, we used gap-filled NEE data from FLUXENT "La Thuile" database (www. fluxdata. org, October 2010) where half hourly data are processed following the standardized methodology described in  Papale et al. (2006)  and  Reichstein et al. (2005). In particular, the NEE data are storage corrected, spike filtered, u*-filtered, and subsequently gap-filled. The datasets thus obtained typically correspond with a source area footprint of hundreds of meters in the vicinity of the EC tower, depending on tower and vegetation height  (Schmid 2002).
e. between 11h00 and 13h00 local solar time (LST)) for each of the 28 main sites. In-situ VI's (Table  2 ) are derived using  PAR and  NIR reflectances and calculated according to eq. 1 and eq. 2. The acquisition dates of MODIS 8-day composite NDVI data are used to obtain representative in-situ VI data. The quality assessment and control (QA/QC) of half-hourly radiometric measurements is performed applying various physical limit tests. Typically, GLR in has to be less than the corresponding extraterrestrial radiation (R ext ) at the same point in time (hence GLR in < R ext ). An analysis of the statistical variability of the data (quantified by the standard deviation, ) has been performed as well in the QA/QC procedure. As a matter of fact GLR in can only be larger than R ext for high latitude regions (hence, above the 65° Northern or 65° Southern latitudes) and only under the condition that convective clouds are present  (Yang et al.