dataesr/scientific-paragraphs-categorization huggingface dataset:
As the authors suspected a lack of statistical power due to the attrition of the numbers of available pathological samples from the IFCT-0002 cohort, the survival data was further analyzed following resections from early-stage lung cancer patients from the publicly available Cancer Tumor Genome Atlas (CTGA) cohort. This analysis revealed that a high mRNA expression was associated with worse OS rates of NSCLC patients (Figure  S3E ). Finally, YAP intensity was reported to be associated with objective responses (partial or complete) of early-stage NSCLC patients treated with preoperative chemotherapy in the IFCT-0002 trial (Table  1 ): Overall, 63 of the 356 (17. 7%) patients studied in this trial showed no response and did not express or expressed low (0-1 intensities) tumoral YAP levels (37.
Plasmids encoding wild-type RASSF1A (pcDNA3-RASSF1A) and control mimic (Addgene, Cambridge, MA, USA) have been described in the supplementary data section  [4]. The introduction of siRNA and plasmids was performed using Lipofectamine RNAiMax (Invitrogen, Carlsbad, CA, USA) in accordance with the manufacturer's instructions at 30% and 70% of cell confluence, respectively.
Statistical Analysis For the cells, the data were expressed as the means ± SEM of experiments, independently conducted three times. All statistical analyses were performed using GraphPad Prism 4, a GraphPad Software program (San Diego, CA, USA). The data were analyzed using a two-tailed Student's t-test for single comparison or one-way ANOVA followed by Dunnett's multiple comparison analysis. The differences were considered significant at the p < 0. 05 level. For YAP staining in samples from the Bio-IFCT0002, the characteristics of patients with (n = 362) and without (n = 130) YAP IHC were compared using chi-squared tests or Fisher's exact test for qualitative variables, with Student's t-tests applied for quantitative variables. The associations between the YAP expression or subcellular localization and clinical characteristics were evaluated using Chi-squared, Fisher's exact or Student's t-tests.
The data were analyzed using SPSS for Windows Evaluation Version 15. 0 (SPSS, Inc. , Chicago, IL, USA 2006), the multivariable fractional polynomials (MFP) package of R software (R package Version 1. 4. 0, original by Gareth Ambler and modified by Axel Benner, 2007) and SAS 9. 3 (SAS Institute Inc. , Cary, NC, USA).
Funding:  F. Dubois  and  M. Keller  were recipients of PhD. grants from the  Association des Insuffisants Respiratoires de Basse-Normandie (AIR)  and from  Fondation pour la Recherche Médicale (FRM). E Maille  was recipient of PhD. grants from both  AIR  and  Région Basse-Normandie. This work was supported by research grants from  Ligue contre le Cancer de Normandie  ( Comité du Calvados, Comité de l'Orne, Comité de l'Eure, Comité de la Manche ) (to G. Z. and G. L. ) and from the  Comité National des Maladies respiratoires (CMNR)  (to G. L. ).
IMPLEMENTATION In order to solve the space resection problematic, we developed a semi-automatic approach to help photogrammetric tools estimate the pose of historical images. Since it is a semi-automatic web-based tool, the user will help georeference the historical photograph by pointing at least 6 to 8 details commonly shared by the photograph and the 3D model, then the rest of the procedure is completely automatic and provides interactive feedback (Figure. 1). To implement this semi-automatic and interactive online georeferencing process, we relied on our locally developed technology MicMac, a free open-source photogrammetric software for 3D reconstruction  (Rupnik et al. , 2017)  that was exposed as a web-service. MicMac is accessible via command line and provides a large number of commands that can each perform different photogrammetric computations depending on the user's needs.
In our context, we mainly used the command Aspro. This tool allows to orientate a set of images with known internal calibration from existing 3D points and their corresponding image projection. After retrieving the pose estimated by the MicMac web-service as an orientation XML file that is parsed into a We-bGL camera, we are able to superimpose the historical image on the 3D model. At the first stage, the user interaction (creating point correspondences) and the visualization of the poses estimated by the web-service in the background were both achieved within a simple 3D WebGL application. This application was then incorporated into a platform called iTowns. iTowns is a web framework written in Javascript/WebGL for viewing and exploiting all types of 3D geographic data across the web  (Devaux and Brédif, 2016)  therefore it enables the immersive navigation in both recent and historical images.
TESTS AND RESULTS The input data used for testing the application was composed of 2 different datasets each providing a different result. The first dataset was a historical photograph accompanied by a 3D point cloud, while in the second dataset we used a DTM (Digital Terrain Model) as the 3D photogrammetric model. For the first dataset, we used a historical postcard of the Chambord Castle in France (Figure. 2a) that we succeeded in georeferencing using a set of 6 control points acquired by pointing certain details (such as intersections, center of objects, corners. ) on the postcard and their correspondents on the point cloud (Figure. 2b ). It is very important for the points to be well distributed and to appear on different levels which allowed us to precisely project the historical image on the photogrammetric model.
CONCLUSION If we compare the results obtained using the different datasets we can conclude that in the case of an accurate 3D reference model, we recover accurate 3D coordinates (X,Y,Z) therefore the space resection is well achieved, in other words we succeed in projecting the historical image on the photogrammetric model accurately. On the other hand, in the case of approximated input coordinates or coordinates that appear to be in the same plane that leads to inaccuracy in the initial parameters used to solve the space resection equations, therefore the orientation computed does not converge to an acceptable solution. In order to overcome these issues, we are considering different approaches. The first one is using a mathematical approach for specific cases such as when the control points are all located on the same plane in order to resolve the space resection.
Both 135C12 and 136B4 Abs prevented the binding of NB01, the humanized version of the mouse 135C12 Ab (Fig. 2 D ), indicating that these Abs bind overlapping epitopes on PD-1. These studies show that antagonistic blocking and nonblocking Abs of the PD-1-PDL-1 interaction can bind to cell-surface PD-1 simultaneously. To complete the molecular details of the binding epitope of the newly generated PDL-1 nonblocker Abs, the structure of the NB01a (135C12) Fab in complex with hPD-1 was solved at a resolution of 2. 2 Å (Table  S1 ). The NB01a Fab binds at the opposite side of the PDL-1-binding site (Fig. 2 E ) with interacting residues on PD-1 confirming our mapping studies performed with the 135C12 Ab. The overall structure of hPD-1 fits previously published structures  (Zak et al. , 2015). Superposition of the Cα atoms of hPD-1 in complex with the NB01a Fab and hPD-1 in complex with hPDL-1 (Protein Data Bank [PDB] accession no.
2 F ). The epitope mapping and structural studies both revealed that Ab binding to a specific region of PD-1 on the opposing face of the PDL-1-PDL-2 interaction site resulted in an antagonistic functional activity (Fig. 1  and Fig. 2, C  and E ). Interestingly, sequence alignment across five species revealed that despite a low 52. 3% identity with human PD-1, conserved regions throughout the sequence come together to form a highly conserved patch on PD-1 (Fig. S3 B ) that is partially overlapping with the binding epitopes of both 135C12 (NB01 hIgG4) and 136B4 Abs (P1 patch in Fig. 2 F ). Structural models of hPD-1-NB01a Fab with either pembrolizumab (PDB accession no. 5GGS) or nivolumab (PDB accession no. 5GGR) were also generated and clearly show that NB01a can bind hPD-1 simultaneously with either PDL-1 blocking Ab (Fig.
(E) hPD-1 and NB01a Fab (humanized version of the mouse 135C12 Ab) complexes were purified by size-exclusion chromatography and crystallized. Crystals diffracted to 2. 2 Å resolution, and the structure was solved by molecular replacement. The structure reveals that the binding site of NB01a is adjacent to residues in purple involved in the PD-1 interaction with either PDL-1 or PDL-2. The CC9 loop (residues 70-74) of hPDL-1 is disordered and indicated as a dashed line. Loops connecting β strands BC (57-63), C9D (84-92), and FG (127-133) were also disordered. Strands are named following the canonical designation. Cα superpositioning of the hPD-1 present in the NB01a Fab and hPDL-1 (PDB accession no. 4ZQK) complexes show that NB01a Fab and PDL-1 bind distinct nonoverlapping sites on PD-1. (F) Mapping of variable residues between human PD-1 and monkey, dog, horse, mouse, and rat PD-1 revealed an evolutionarily conserved patch (P1) on the opposite face of PD-1 from the PDL-1 or PDL-2 interaction site (yellow, orange, and light green colored residues).
The P1 patch overlaps with the binding epitopes for the 135C12/NB01 (orange residues) and 136B4 (light green residues) antagonistic Abs that are nonblocking of PD-1-PDL-1. Residues N49, N58, N74, and N116 that are predicted N-linked glycosylation sites are show in brown on the PD-1 model to be excluded from the P1 patch, 135C12/NB01, and 136B4 Ab binding epitopes. (G) Cα superpositioning of hPDL-1 coordinates of the NB01a complex with pembrolizumab (PDB accession no. 5GGS) and the nivolumab (PDB accession no. 5GGR) confirms that NB01a Fab binding to PD-1 does not interfere with the binding of either pembrolizumab or nivolumab anti-PD-1 Abs. hPDL-1 is shown as a ribbon diagram in E and F, with the hPDL-1 binding surface (PDB accession no. 4ZQK) colored in purple in G. Nonblocking anti-PD-1 Abs act predominantly through the CD28 costimulatory receptor to promote the AKT-NF-κB pathway Mechanism-of-action studies with anti-PD-1 Abs were then investigated using phosphoflow intracellular staining of proteins important to the T cell signaling cascade.
Percent detection of PD-1 positive cells was determined for each Ab concentration and binding affinities were determined with the GraphPad Prism7 software using a nonlinear curve-fitting analysis. Competitive binding between different anti-PD-1 Ab clones to Jurkat PD-1 cells was performed by adding saturating amounts (40 µg/ml) mouse IgG1 competitor Ab (137F2, 135C12, or 136B4) with cells incubated for 30 min followed by 1 µg/ml of the indicated human IgG4 anti-PD-1 Abs. The PE-labeled goat anti-human IgG secondary Ab was used to detect the levels of human anti-PD-1 Abs binding to cell-surface PD-1 in the presence of competitor. Maximal binding to the Jurkat PD-1 cells was equivalent for all human anti-PD-1 Abs, and the positive control in each graph is shown for pembrolizumab staining.
3 mg/ml. Data collection, processing, and refinement of the x-ray structure X-ray data were collected at the European Synchrotron Radiation Facility (ESRF), Grenoble, France, at the ID29 beamline (Table  S1 ). Data were collected at a wavelength of 0. 97625 Å with a crystal to detector distance of 398. 81 mm and a 0. 15°rotation of the crystal to complete a total rotation of 150°. The data were processed with XDS (Kabsch, 2010) using the resolution range 65. 49-2. 2 Å. The structure of the complex was solved by molecular replacement in Phaser MR  (McCoy et al. , 2007; Read and McCoy, 2011 ; Z-score 14. 9) using hPD-1 in complex with hPD-L1 (PDB accession no. 4ZQK) and a model built in Phenix using coordinates from Fab BL3-6 (PDB accession no. 4Q9Q;  Huang et al. , 2014) , which exhibits high sequence homology with Fab NB01a (90% and 73% with the heavy and light chains, respectively) as search models.
The model of the complex was refined in Phenix  (Adams et al. , 2010)  and Refmac5  (Murshudov et al. , 2011)  and adjusted manually in Coot  (Emsley and Cowtan, 2004)  to values of R factor = 0. 2120 and R free = 0. 2760. The software Contact, part of the CCP4 suite like Phaser MR and REFMAC5  (Winn et al. , 2011)  and PDBePISA  (Krissinel and Henrick, 2007) , were used to identify contacts in the binding interface. Structure representations were prepared using CCP4mg  (McNicholas et al. , 2011). The data for this study are available under PDB accession no. 6HIG.
Modeling of the hPD-1-136B4Fv interaction The model of 136B4 was built using Phenix software and the aligned sequences of 136B4 and the Fv from PDB file 1DZB  (Aÿ et al. , 2000) , which shares 63% identity with 136B4. In addition to the high sequence identity, this PDB file was selected because it comprises an Fv, which is expected to adopt a similar fold to 136B4. Subsequently, models for the light and heavy chains were searched on the PDB to model the CDRs. The heavy and light chains from PDB files 1MVU and 4M61  (Stanfield and Eilat, 2014)  share 79% and 96% of identity with those from 136B4, respectively, and the length of the CDR is comparable. The CDR segments were built into the model provided by Phenix software using Coot.
Magnetic beads were attracted to the side of each tube with a magnet and washed three times with cell lysis buffer and then once with lysis buffer without detergent. Immune complexes analyzed by Western blot had proteins separated on a NuPAGE 10% Bis-Tris gel (Invitrogen) and then transferred onto nitrocellulose (Bio-Rad). Immunoblotting was performed by staining with the primary Ab overnight at 4°C followed by washing steps and then incubation with the HRP-conjugated secondary Ab and detection with the Pierce ECL Western blotting substrate (Thermo Fisher Scientific) in accordance with the manufacturer's protocol. Blots were imaged with the Fusion FX Vilber Lurmat camera (Witec), and the density of individual bands was evaluated using ImageJ software. Jurkat PD-1 NFAT and NF-κB reporter assays The Jurkat PD-1 NFAT reporter assay was performed according the manufactures protocol with minor modifications (BPS Bioscience).
Crystals diffracted to 2. 2 Å resolution, and the structure was solved by molecular replacement. The structure reveals that the binding site of NB01a is adjacent to residues in purple involved in the PD-1 interaction with either PDL-1 or PDL-2. The CC9 loop (residues 70-74) of hPDL-1 is disordered and indicated as a dashed line. Loops connecting β strands BC (57-63), C9D (84-92), and FG (127-133) were also disordered. Strands are named following the canonical designation. Cα superpositioning of the hPD-1 present in the NB01a Fab and hPDL-1 (PDB accession no. 4ZQK) complexes show that NB01a Fab and PDL-1 bind distinct nonoverlapping sites on PD-1. (F) Mapping of variable residues between human PD-1 and monkey, dog, horse, mouse, and rat PD-1 revealed an evolutionarily conserved patch (P1) on the opposite face of PD-1 from the PDL-1 or PDL-2 interaction site (yellow, orange, and light green colored residues).
The P1 patch overlaps with the binding epitopes for the 135C12/NB01 (orange residues) and 136B4 (light green residues) antagonistic Abs that are nonblocking of PD-1-PDL-1. Residues N49, N58, N74, and N116 that are predicted N-linked glycosylation sites are show in brown on the PD-1 model to be excluded from the P1 patch, 135C12/NB01, and 136B4 Ab binding epitopes. (G) Cα superpositioning of hPDL-1 coordinates of the NB01a complex with pembrolizumab (PDB accession no. 5GGS) and the nivolumab (PDB accession no. 5GGR) confirms that NB01a Fab binding to PD-1 does not interfere with the binding of either pembrolizumab or nivolumab anti-PD-1 Abs. hPDL-1 is shown as a ribbon diagram in E and F, with the hPDL-1 binding surface (PDB accession no. 4ZQK) colored in purple in G.
Acknowledgments We are grateful to  N. Grandchamp ,  P. Pochon ,  X. Bron ,  M. Graff ,  A. Crétignier ,  R. Mamin , and  C. André  for technical assistance. We are also grateful to  D. Hacker  at the  Ecole Polytechnique Fédérale de Lausanne  for the production and purification of the nonblocking anti-PD-1 Abs used in this study. W. Weissenhorn  acknowledges support from the  Institut Universitaire de France , the platforms of the  Grenoble Instruct-ERIC Center (Integrated Structural Biology Grenoble ;  UMS 3518 CNRS-CEA-UJF-EMBL ) supported by the  French Infrastructure for Integrated Structural Biology Initiative  ( ANR-10-INSB-05-02 ) and  Grenoble Alliance for Integrated Structural Cell Biology  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology , the  European Synchrotron Research Facility and European Molecular Biology Laboratory Joint Structural Biology Group  for access and support at the  ESRF beam lines , and  J.
Patients We undertook a large, observational, cross-sectional study that involved women referred to our gynecologic surgery department between January 2004 and September 2016 for a benign gynecologic condition and prospectively enrolled in our local database. Women with ongoing cancer, pregnancy, infectious disease, or who refused to provide their consent for participation in this study were not enrolled in the database. The local ethics committee (approval number 05-2006 provided by the ''Comite ́Protection des Personnes of Paris-Cochin'') approved the study protocol. Nonpregnant women between 18 and 42 years of age without a prior history of surgery for endometriosis and surgically managed for a benign ovarian cyst and for whom the preoperative serum AMH level was available were retained for the analysis.
Statistical Analysis All the statistical data were compiled in a computerized database. Continuous data are presented as mean and standard deviation. Student's t-tests were performed when appropriate. Student's t-test or the Mann-Whitney U test was used for quantitative variables, and Pearson's chi square or Fisher's exact test for qualitative variables, as appropriate. When more than two groups were compared, we used the Kruskal-Wallis test. When group medians were statistically significantly different by the Kruskal-Wallis test (P<. 05), pairwise comparisons were performed with Dunn's multiple comparison test. Correlation coefficients were calculated using linear correlations. The log of the serum AMH concentration and the log of cyst volume (using the formula S ¼ 4/3pR 3 ) were calculated.
5 cm to 21 cm and the combined volumes ranged from 0. 06 cm 3 to 6,282 cm 3. The model evaluates all of the included variables to identify those most strongly and concurrently related as a final output: age, BMI, oral contraceptive (OC) use, infertility, and bilateral cysts. To determine the factors that contribute to increased serum AMH levels, a backward multiple linear regression was performed with serum AMH levels serving as the dependent variable. The statistical analyses were performed using STATA and GraphPad software for Macintosh (Stata/IC 11. 0 for Mac, StataCorp; GraphPad).
European populations display low genetic differentiation as the result of long-term blending of their ancient founding ancestries. However, it is unclear how the combination of ancient ancestries related to early foragers, Neolithic farmers, and Bronze Age nomadic pastoralists can explain the distribution of genetic variation across Europe. Populations in natural crossroads like the Italian peninsula are expected to recapitulate the continental diversity, but have been systematically understudied. Here, we characterize the ancestry profiles of Italian populations using a genome-wide dataset representative of modern and ancient samples from across Italy, Europe, and the rest of the world. Italian genomes capture several ancient signatures, including a non-steppe contribution derived ultimately from the Caucasus.
To characterize the genetic variability of modern-day European populations and their relationship to early European foragers, Neolithic farmers, and Bronze Age nomadic pastoralists, we investigated the population structure of present-day Italians and other Europeans in terms of their ancestry composition as the result of migration and admixture, both ancient and historical. To do this, we assembled and analyzed a comprehensive genome-wide single nucleotide polymorphism (SNP) dataset composed of 1616 individuals from all 20 Italian administrative regions and more than 140 worldwide reference populations to give a total of 5192 modern-day samples (fig. S1, A  and B , and data file S1), to which we added genomic data available for ancient individuals (data file S1).
Distinctive genetic structure in Italy We initially investigated patterns of genetic differentiation in Italy and surrounding regions by exploring the information embedded in the SNP-based haplotypes of modern samples [full modern dataset (FMD) including 218,725 SNPs]. The phased genome-wide dataset was analyzed using the ChromoPainter (CP) and fineSTRUCTURE (fS) pipeline (see the Supplementary Materials)  (10, 11)  to generate a tree of groups of individuals with similar "copying vectors" (clusters; Fig. 1A ). The fraction of pairs of individuals placed in the same cluster across multiple runs was, on average, 0. 95 for Italian clusters and 0. 96 across the whole set of clusters (see Materials and Methods and the Supplementary Materials). Non-European clusters were pooled into larger groups in subsequent analyses (see Materials and Methods and the Supplementary Materials).
Italian clusters separated into three main groups: Sardinia, Northern (North/Central-North Italy), and Southern Italy (South/ Central-South Italy and Sicily); the first two were close to populations originally from Western Europe, while the last was closer to Middle Eastern groups (Fig. 1, A  and B ; figs. S1D and S2, A to C; and data file S1). These observations were confirmed using a subset of the dataset genotyped for a larger number of SNPs [high-density dataset (HDD) including 591,217 SNPs; see Materials and Methods and the Supplementary Materials; fig. S1D and data file S1]. To highlight the geographic distribution of the identified clusters along the Italian peninsula, we reconstructed the cluster composition of the various administrative regions of Italy by using the best sampling origin information available for each individual in our dataset (Fig.
We evaluated the impact of geography in shaping the genomic variability of Italy by testing the correlation between geographic and genetic coordinates, applying a Procrustes analysis (Fig. 1B ; Materials and Methods). A significant correlation was observed in our dataset, in agreement with reports for Italy and Europe  (3, 13)  (correlation in Procrustes rotation, 0. 77 and 0. 78, P < 0. 05 including or excluding Sardinia, respectively). Intracluster variation within Southern and Northern Italian clusters was comparable (data file S2). Sardinian clusters were characterized by a high proportion of genome copied by individuals from the same cluster (self-copying), in agreement with previous indications of drift in Sardinian groups  (12, 13). Southern Italian samples showed higher among-cluster F ST values than the Northern Italian ones, but lower TVD (total variation distance) values (data file S2; Materials and Methods)  (1, 2).
Multiple ancient ancestries in Italian clusters To further characterize the observed genetic structure, we investigated the ancestry composition of modern clusters. We tested different combinations of ancient putative sources using a "mixture fit" approach [non-negative least square (NNLS) algorithm]  (11, 20). We applied this approach to ancient samples using the "unlinked" mode implemented in CP, similar to other routinely performed analyses based on genotype data, such as qpAdm and ADMIXTURE. In addition, data from modern individuals (from the FMD dataset) were harnessed as donor populations (Materials and Methods and Supplementary Materials). Following Lazaridis et al. (8) , we performed two separate CP/NNLS analyses, "ultimate" and "proximate," referring to the least and the most recent putative sources, respectively (Fig.