dataesr/scientific-paragraphs-categorization huggingface dataset:
To theoretically explain the semiconducting behavior of AGNRs, Son et al  20  have introduced an edge deformation (ED) effect in the edges of armchair ribbons into the 1NN TB model (we note this model as 1NN + ED) and an additional term was introduced to increase the hoping energy between edge atoms up to about 12%. The added effect indeed corresponds to the underlying physics of the bandgap opening in group 3p + 2 although it still can not replicate accurately the width of the bandgap and the slope of energy bands. In 2008, Gunlycke and White  22  have improved the 1NN + ED model by introducing an additional term as third nearest neighbor hoping parameter t 3 and thus constructed a 3NN + ED model. This model can accurately reproduce the bandgap of AGNRs in most cases but still has discrepancies with the DFT data in the high-energy region of the conduction band.
The results are displayed in Fig. 4  where the DOSs were also reported for further comparison. As clearly manifested from the band structure and the DOS panels, TB calculations (dashed red lines) obtained from the set proposed in this work show an excellent agreement with DFT results for energies ranging from -3 eV to 3 eV. Around the region of the bandgap, the inset of the DOSs indicates the high accuracy of TB data as both the bandgap and the DOSs are identical to the DFT results. This new set of parameters seems to be well optimized compared to the existing TB ones proposed by Reich  12  and Kundu  16  fitted for 2D graphene and Son,  20  Gunlycke 22 fitted for ribbons. To further validate this new set of parameters, we compare other results of band structure obtained from DFT, 3NN TB with Gunlycke's set and 3NN TB model with the set of this work.
First, results for other groups of ribbons, i. e. M = 6 (group 3p) and M = 7 (group 3p + 1), are shown to support the robustness of the new set. In Figs. 5(a) and 5(b), the results obtained from our set of parameters are obviously better than the ones of Gunlycke's set and, overall, they fit very accurately with DFT outcomes although both Gunlycke's set and the new set give bandgaps slightly smaller than the one of DFT in the case of M = 6. Though the new set was constructed from a fit in a narrow structure with M = 5, Figs. 5(c ) and 5(d) displaying the results obtained for M = 11 and M = 19, respectively, show that this set still has strong relevance for larger ribbons. Band structures resulting from our model (red lines) still fit very precisely DFT data (black circles) even in the high energy regions.
These results reinforce the relevance of the new set of TB parameters proposed in the present work. Although the set proposed in this work is devoted to GNRs, we have also checked the merit of the set for 2D graphene sheets. The DFT calculation for 2D graphene was performed using the same method as for ribbons (LDA). Then the DFT data were compared with the results obtained from different TB models. The outcome is presented in Fig. 7(a). At first glance, we see that the bands obtained with the Reich's set are more accurate overall than the others are, which is consistent with the fact that this set was optimized for 2D layers. However, by zooming in on the energy bands around the Dirac point (K point), as shown in Fig. 7 (b), we see that the TB results obtained from the set proposed in this work fit accurately the DFT data and are comparable with Reich's results.
, 2016). However, the remarkable parallel between the transcriptome and methylome suggests that the main phenotypes in PDAC are epigenetically rather than genetically established. Clinical and histopathological characterizations revealed that patients of the basal subtype were more often non-resectable, had a marginally lower median survival, and presented a less differentiated tumor  (Figures 2C and S2L) , altogether indicating a more aggressive phenotype. Conversely, classical tumors were more frequently resectable, presented a higher level of differentiation, and were often associated with fibrosis and inflammation. In order to compare these PDX-based subtypes with currently available human PDAC classifications, methylome and gene expression classifiers were constructed from three different public datasets  (Collisson et al.
, 2011) ,  (Moffitt et al. , 2015) , ICGC methylation  (Nones et al. , 2014)  and RNA sequencing (RNA-seq)  (Bailey et al. , 2016). All published PDAC classification systems were in agreement with the proposed multiomics classification, thereby illustrating the reliability of PDX to model PDAC diversity. Altogether, our analysis on cancer cells using either PDX or human PDAC classifications reveals the presence of two clinically relevant types of PDAC. Unsupervised analysis of the stromal transcriptomes reconstructed from the murine RNA sequences also revealed two types of stroma with a strong agreement with tumor subtypes (Figure  2E ). In order to evaluate this remarkable echo in the diversity of the tumor cells and of their stroma, the tumor and stroma PDAC PDX subtypes were predicted in four public datasets of human primary PDAC.
To do so, we derived a tumor and a stromal gene signature predictive of PDAC subtypes based on tumor and stromal expression, respectively. A minor overlap was found between the genes of these signatures, a majority of which encoded cell communication proteins or extra-cellular components (e. g. , semaphorins, cytokines; Table  S2 ). The stromal and tumor subtype prediction made by these gene signatures significantly matched in all four primary human tumor datasets, which confirms the mirrored tumor-stromal classification observed in xenografts (Figure  S3E ). Interestingly, two recent studies on bulk primary human pancreatic tumors proposed a classification that was suggested to be linked to the stromal composition. The first, by Moffitt et al. , described an activated stroma that was linked to a poor outcome  (Moffitt et al.
In addition to the description of basal tumor cell pathways, the differential analysis of basal stromal pathways was also carried out. The genes involved in focal adhesion and axon guidance pathways, which imply complex interactions with the surrounding tissues, were overexpressed by the tumor and stromal compartments. Along with the enrichment of basal PDX in fibroblasts (Figure  2G ), this result suggests a basal-specific communication between tumor cells and adjacent fibroblasts, which is in agreement with recent observations in PDAC of the role of cancer-associated fibroblasts in axon guidance signaling  (Secq et al. , 2015). In order to validate the transcriptional activation of these pathways in human primary PDAC, an identical pathway analysis was performed on three independent datasets (Figure  3A ).
Our work revealed the potential sustaining role of the stroma as subtype specific. Indeed, the analysis of the recruited stroma diversity revealed the unidentified subtype specificity of the stroma through the discovery of convergent tumor/stromal classifications, which was verified in four different datasets of primary tumors. This is, to some extent, dissimilar to the activated stroma previously described  (Moffitt et al. , 2015)  as subtype independent. Overall, our results show that the stroma, originating from surrounding host cells, is closely associated with the phenotype of the tumor indicating that the composition and function of the tumor microenvironment may be trained by tumor cells. A major advantage of the PDX model is that it is a perpetual source of living material conserving original biological characteristics.
EXPERIMENTAL PROCEDURES PDXs Three expert clinical centers collaborated on this project after receiving ethics review board approval. Patients were included in this project under the Paoli-Calmettes Institute clinical trial number 2011-A01439-32. Consent forms of informed patients were collected and registered in a central database. The tumor tissues used for xenograft generation were deemed excess to that required for the patient's diagnosis. PDAC tissue from surgical samples was fragmented, mixed with 100 µL Matrigel, and implanted with a trocar (10G; Innovative Research of America, Sarasota, FL) in the subcutaneous right upper flank of an anesthetized and disinfected male NMRI-nude mouse. Samples obtained from endoscopic ultrasound-guided fine needle aspiration (EUSFNA) were mixed with 100 µL of Matrigel (BD Biosciences, Franklin Lakes, NJ) and injected in the upper right flank of a male nude mouse (Swiss Nude Mouse Crl:NU(lco)-Foxn1nu; Charles River Laboratories, Wilmington, MA) for the first implantation.
Genome-wide Profiles and Data Availability Genotype data from whole-exome sequencing have been deposited at the European Genome-phenome Archive (EGA; http://www. ebi. ac. uk/ega/), which is hosted by the EBI, under accession number EMBL-EBI: EGAS00001001928. Other datasets are available on ArrayExpress (http://www. ebi. ac. uk/arrayexpress): SNP data are available under accession number EMBL-EBI: E-MTAB-5006, Methylation data are available under accession number EMBL-EBI: E-MTAB-5008, miRNA sequencing data are available under accession number EMBL-EBI: E-MTAB-5018, and mRNA sequencing data are available under accession number EMBL-EBI: E-MTAB-5039. Sequencing datasets were processed using SMAP (see Supplemental Experimental Procedures) to separate mouse and human reads. In addition to raw data, all processed data produced by and used in this study are available through our institutional web portal (http://cit.
Public Dataset Comparison ICGC methylation chips, RNA-seq, and microarray gene expression datasets were downloaded from the ICGC data portal (dcc. icgc. org, release 20). TCGA data were downloaded through the Broad Institute TCGA Genome Data Analysis Center (GDAC) firehose tool (gdac. broadinstitute. org, 20160411 data snapshot). Other datasets were downloaded from the provided GEO entries GSE71729  (Barr et al. , 2012 (Barr et al. , , 2010;; Moffitt et al. , 2015)  and GSE17891  (Collisson et al. , 2011; Martínez-Arranz et al. , 2015; Nones et al. , 2014). All non-cancer samples were removed from each dataset. Expression datasets were then centered gene-wise. Centroid classifiers were built for each dataset defining a classification using a Pearson-based approach described in previous work  (Bailey et al.
Acknowledgments This work is part of the national program  Cartes d'Identité des Tumeurs (CIT)  funded and developed by the  Ligue Nationale Contre le Cancer. This work was supported by  INCa ,  Canceropôle PACA ,  Inserm  and by  DGOS  through a  SIRIC grant INCa-Inserm-DGOS 1068. We thank  Emilie Nouguerede ,  Jihane Pakradouni ,  Karim Sari , and  Regis Vitestelle  for their technical assistance and  Aurélie Kamoun  for fruitful discussions.
Two anti-VEGF agents are currently approved by the US Food and Drug Administration for the treatment of macular edema due to CRVO: ranibizumab and aflibercept. Bevacizumab is additionally used for this indication on an off-label basis. Aflibercept is a fusion protein of key domains from human VEGF receptors 1 and 2 with the constant region (Fc) of human immunoglobulin G that binds to multiple VEGF-A isoforms with a high affinity. 7, 8 ] [11] [12] [13] [14]  Results of the integrated analyses from COPERNICUS and GALILEO have not been published. Integrating data from both studies increases the number of patients included in the treated population, thus increasing the generalizability of the results to the global population and minimizing the risk of any outlier effect that might be seen in the individual studies.
Integrating the data also strengthens the ability to detect any safety signals and characterizes the safety profile of intravitreal aflibercept. Here, we report on the efficacy and safety of intravitreal aflibercept at 52 weeks in patients with macular edema secondary to CRVO in an integrated analysis of data from the COPERNICUS and GALILEO studies. Sham-treated patients in the COPERNICUS study were eligible to receive intravitreal aflibercept starting at week 24, whereas patients in GALILEO continued sham treatment through week 52. As treatment schedules and the duration of the two studies differed beyond week 52, integrated data are shown here only through the week 52 visits.
Patients and methods Design COPERNICUS and GALILEO were parallel randomized, double-masked Phase III studies comparing intravitreal aflibercept with sham for the treatment of macular edema secondary to CRVO. The details of these studies have been published previously;  [9] [10] [11] [12] [13] [14]  key aspects of study design and conduct are described here. COPERNICUS was conducted at 61 sites in the United States, Canada, Colombia, India, and Israel and GALILEO at 63 sites in Asia and Europe. The duration of COPERNICUS was 100 weeks and GALILEO was 76 weeks. The studies were conducted in compliance with ethical guidelines from the Declaration of Helsinki and International Conference on Harmonisation and are registered on ClinicalTrials. gov as NCT00943072 and NCT01012973, respectively. Institutional Review Board/Ethics Committee approval was obtained at each site before the start of the studies (Supplementary materials).
statistics All safety and efficacy variables were analyzed descriptively with appropriate statistical methods: categorical variables by frequency tables (absolute and relative frequencies) and continuous variables by sample statistics (ie, mean, standard deviation). Patients included in the evaluations are those from the full analysis set, which includes all randomized subjects who received any study medication and had at least one baseline and one post-baseline assessment. The full analysis set was analyzed as randomized. For missing efficacy data after start of treatment, the last observation carried forward (LOCF) approach was applied. Safety analysis included all patients who received any study treatment.
Results When data from the two studies were integrated, baseline demographic and disease characteristics were similar across the two treatment groups (Table  2 ). Mean age at baseline was 62. 9 years in the intravitreal aflibercept group and 65. 7 years in the sham group. Mean baseline BCVA was 52. 0 letters in the intravitreal aflibercept group and 49. 9 letters in the sham group. The majority of patients in both treatment groups had perfused retinas (classified as ,10 disk areas of retinal capillary non-perfusion on fluorescein angiography) at baseline (76. 5% of patients in the intravitreal aflibercept group and 73. 8% in the sham group). There were fewer patients with perfused retinas at baseline in COPERNICUS than GALILEO (67. 9% versus 83. 6%). From baseline to week 24, the mean (standard deviation) number of injections received in the intravitreal aflibercept group was 5.
Discussion The aim of this article is to report the efficacy and safety of intravitreal aflibercept in patients with macular edema due to CRVO in an integrated analysis of data from the COPERNICUS and GALILEO studies. The integrated data  of the COPERNICUS and GALILEO studies confirm the findings of the individual studies. Both the COPERNICUS and GALILEO studies documented the beneficial treatment effect of intravitreal aflibercept compared with sham injections; however, there were differences between the studies that should be considered. The natural history (sham-treated) groups demonstrate that the patients enrolled in the two studies were slightly different in some aspects, likely due in part to the different countries and geographic regions where they were conducted. Specifically, visual gains among patients originally randomized to sham treatment were generally lower in COPERNICUS than those in GALILEO, both before and after initiation of treatment with intravitreal aflibercept at week 24.
From week 24 to week 52, visual acuity gains were maintained in the intravitreal aflibercept group. The visual outcomes at week 52 in the sham group of COPERNICUS that crossed over to receive intravitreal aflibercept at week 24 did not reach the level of those attained by patients who received intervention with intravitreal aflibercept at the start of the study, despite a marked improvement in anatomy. Furthermore, patients in the GALILEO study who did not cross over to treatment with intravitreal aflibercept until week 52 also did not show any meaningful visual gains. These data suggest that a delay in treatment initiation with intravitreal aflibercept may result in less optimal visual outcomes. Integrated morphologic results at week 24 followed the same pattern as the visual results. At week 24, the improvements in CRT from baseline were greater in the intravitreal aflibercept group than in the sham group (-453.
Conclusion Based on the integrated data from the COPERNICUS and GALILEO studies, intravitreal aflibercept is a well-tolerated and effective treatment for macular edema secondary to CRVO. The integrated data show that macular edema secondary to CRVO can be managed successfully with initial monthly intravitreal aflibercept injections, consistent with results shown in the respective trials. 10, 12 Delay in treatment initiation with intravitreal aflibercept in the COPERNICUS study resulted in limited gains in visual acuity by week 52. The integrated results of these studies reinforce the robustness of the treatment effect of intravitreal aflibercept in retinal vein occlusions to maximize visual and morphologic benefits.
Acknowledgments The authors wish to thank the COPERNICUS and GALILEO study investigators. Medical writing assistance was provided by  Corey Eagan, MPH , of PAREXEL and was funded by  Bayer. COPERNICUS and GALILEO were sponsored by  Bayer  and  Regeneron Pharmaceuticals, Inc. Amelie Pielen has received research funding from  Novartis ,  Bayer ,  Roche ,  Genentech , and  Théa Pharm  and has been a lecturer and consultant for  Novartis, Allergan , and  Bayer Pharmaceuticals. W Lloyd Clark  is a consultant for  Genentech ,  Ohr Pharmaceuticals ,  Regeneron Pharmaceuticals, Inc. ,  Roche , and  Santen ; offers research support for  Allergan ,  Genentech ,  Regeneron Pharmaceuticals, Inc. ,  Roche , and  Santen ; and has equity in VERSYL,  Inc. David S Boyer  is a consultant for  Aerpio, Alcon, Alimera, Allegro, Allergan, Bausch & Lomb, Bayer ,  CoDa Therapeutics ,  Foresight Biotherapeutics ,  Genentech ,  GenSight Biologics ,  GrayBug Vision ,  GlaxoSmithKline, Neurotech, Notal Vision, Novartis, Ophthotech, Ohr, Optos, Regeneron Pharmaceuticals, RetroSense ,  Regulus Therapeutics ,  Sun Pharmaceuticals ,  StemCells , and ThromboGenics; is a lecturer for Alcon and Allergan; and holds minor equity in Allegro,  Neutotech, Ohr , and  Ophthotech.
The global initiative for fibrosis treatment (GIFT) Participants in the seminar unanimously agreed that an organisation specifically tailored to foster top-notch, cooperative, international IPF research was needed to promote the discovery, development and implementation of more effective and precise (i. e. personalised) treatment options for IPF. To achieve this, it was proposed to connect existing IPF networks across the globe and build upon databases from previous FIGURE 3 Iterative working phases of systems biomedicine. Systems biology is a process that becomes highly honed as new information is aggregated, filtered, tested, and allocated as essential data for further cycling through the four steps. Several lines of investigation may result. Reproduced from  [44]  with permission. ©Permanyer 2017.
Acknowledgements The authors thank all participants in the Barcelona ERS Research seminar (listed below) for their contribution to the discussion. We are also indebted to the ERS technical organisers for the event coordination. Participants in the ERS research seminar (December  15-16 ,  2016 ,  Barcelona, Spain) :  Aburto Barrenechea M, Asije G, Bibaki E, Bingle C, Bueno M, Bullich Ramon M, Campo Ezquibela A, Campo I, Carleo A, Cruz T, Daccord C, Duitman JW, Eagan T, Eickelberg O, Froidure A, Garcia-Moyano M, Garcia-Nuñez M, Gazdhar A, Homolka J, Hoyer N, Kool M, Korfei M, Koutsopoulos A, Kwakkel-van Erp H, Lofdahl A, Lopez-Rodriguez E, Margaritopoulos G, Lafuente JM, Montes-Worboys A, Morais A, Moreno A, Perona R, Petrek M, Pitozzi V, Planas Cerezales L, Purokivi M, Rivera P, Ruwisch J, Sala E, Schliep R, Shaker SB, Simillion C, Sterclova M, Tomos I, Trachalaki A, Trevisani M, Tsitoura E, Vasaková M, Vasarmidi E, Vicens-Zygmunt V, Wijsenbeek M, Woldhuis R, Wuyts W.
High Energy Physics and the LHC (Large Hadron Collider) provide an extreme example of this new scientific practice centred on data: a technical device built by engineersthe collider -is associated with instruments designed by researchers -the detectorsto study the particles emitted during the billions of collisions produced. Something like 15 petabytes of data are produced every year. Such a volume of data requires both colossal technical processing resources and a data management infrastructure capable of providing maximum availability to research teams. But, before they can be made available, the data must be prepared. For example, it is easy to understand that the major earth and ocean observation programmes, which are usually conducted cooperatively as international operations, require processes for preparing the data before any data mining can be undertaken.
Each of the stages of TDM is therefore essential, from pre-processing and formatting of data through to the conclusions. Let us take an example to better understand these techniques. INRA is currently conducting a study with the help of text mining in order to identify new species of fish suitable for captive breeding, i. e. species that could be adapted for aquaculture. Considerable amounts of data relating to fish (breeding, feeding, living environment) are analysed using text-mining technologies. Many of these data come from the ISTEX corpus. The goal is to identify the standard features of aquaculture fish in order to better understand the phenomenon of domestication, as well as to identify those species of fish most similar to aquaculture species (fundamental research). The species identified could then be used in domestication experiments (applied research).
More innovation A large proportion of this new knowledge can be converted into business innovation. For example, in the field of healthcare, an analysis using PubMed 36 came up with new hypotheses for research. A group of researchers extracted data from a corpus of medical articles. They looked for links between "drug-disease" and "drug-phenotype". 37 While browsing through all these articles, they identified genes that could be responsible for diseases because of the toxicity of certain medicinal drugs. This enrichment of the data could enable researchers and doctors to rapidly diagnose new links between a "drug" and a "disease". From an economic point of view, this discovery could enable great progress in medical research, with limited labour costs. The economic benefits resulting from the use of TDM therefore arise essentially from the role it can play as a source of incremental innovation.
But this development of algorithms for data analysis has to be backed up by less visible but equally important functions: infrastructures for calculation and for the management and sharing of data. The scientific approach has traditionally been based on deductive reasoning, starting from theories serving as premises to be tested. Hypotheses are based on theoretical knowledge, while the observations collected (data) are used to confirm these hypotheses (or refute them if they challenge the theory). A large part of Western education is based on this approach, as soon as children reach the "age of reason". But this pre-eminence of theory over experimentation has been challenged over the last few years, notably with the arrival of Big Data and the development of TDM.