dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Driver Genes Among Advanced AMD-Associated Pathways Because pathway structure and terminology vary across databases, we determined which genes were significantly contributing to the overall pathway signals detected by PARIS. We compared the significant genes in significant pathways from KEGG, Reactome, and GO (Fig. 1 ; Table  2 ) and identified eight such genes. Upon removing variants from our analyses that fell within the 34 known AMD susceptibility loci as defined in Supplementary Table  S5  in the IAMDGC GWAS,  5  we found that two genes (PPARA and PLCG2) remained statistical driver genes across associated pathways from KEGG, Reactome, and GO. To identify evidence of PPI for the proteins encoded by the eight statistical driver genes in our analyses (C2, C3, LIPC, MICA, NOTCH4, PPARA, PLCG2, and RAD51B), we queried the STRING database.
Each of these proteins have multiple binding partners identified through functional studies or in silico predictions (Fig. 2 ). When considering no more than 50 interaction partners for each of the eight proteins, we found three distinct clusters of PPIs (Fig. 2 ). One cluster connects MICA, PLCG2, LIPC, C2, C3, and other immune-related proteins (Fig. 2A ); another connects NOTCH4, PPARA, and other signaling proteins (Fig. 2B ); and the third contains RAD51B and other DNA repair proteins (Fig. 2C ). Using the MEME software suite, we identified sequence motifs with known TF binding sites near the eight statistical driver gene seqeuences from the UCSC Genome Table  Browser. 17 Five motifs were present for most of the statistical driver genes and contain binding sites for TFs (Table  3.
). Only one sequence motif ([GCA][AC][CT]AG[AT]G[CA][TGA]A[A G][AT][CA]T[CA][CG][GA]T[CG][TG][CA]A[AG]AAA[ATG][A G]AAA[AT][CA][AC]A[AC]A[AC][AT][AT]A ) was near all eight statistical driver genes and contained binding sites for 12 TFs. We further restricted our definition of statistical pathway driver gene to include genes that also strongly contributed to AMD-associated pathways from NetPath. This enabled us to further support PLCG2 as a candidate gene for advanced AMD (Fig. 3 ). This gene encodes a phosphodiesterase that is involved in phosphatidylinositol signaling and several other immune, metabolic, and signaling pathways curated in KEGG, Reactome, GO, and NetPath (Fig. 3 ). We interrogated potential interaction partners for the PLCG2 protein by constructing a PPI network for PLCG2 using the STRING database (Fig.
DISCUSSION Using knowledge-driven pathway analysis on GWAS data, we uncovered pathways that were enriched in variation potentially associated with AMD in individuals of European descent. Our study is, to our knowledge, the first to perform such analyses on the largest available advanced AMD case-control association dataset. We found several signaling, immune, metabolic, and disease-related pathways from the KEGG, Reactome, GO, and NetPath databases that are associated with advanced AMD. Our sensitivity analysis demonstrated that several of the pathways from KEGG, Reactome, and GO (Supplementary Tables  S1-S3 ) remained associated with advanced AMD following the exclusion of the 34 AMD The cross-database comparison of significant genes from significantly associated pathways. susceptibility loci described earlier.
5 This suggests that modest effects aggregating in these pathways may contribute to the missing heritability of AMD. Although the Wnt pathway from NetPath was no longer significant in our sensitivity analysis, the Wnt signaling pathway from GO remained associated with AMD. This results from the difference in the pathway definitions. These pathways are nearly identical in size (n ¼ 45 and 41 genes for NetPath and GO, respectively); however, only two genes overlap between them (PLCG2 and FZD4). Furthermore, the Wnt signaling pathway in KEGG (n ¼ 140 genes) and the signaling by Wnt pathway in Reactome (n ¼ 294 genes) only achieved pathway-level P values of 0. 032 and 0. 037 in our analyses, respectively. These pathway definition differences further justify our use of multiple curated databases in our analyses to uncover AMD-associated pathways and genes driving their statistical significance.
Due to varying nomenclature for pathways across databases and as a way of internal validation, we focused on eight statistical driver genes (C2, C3, LIPC, MICA, NOTCH4, PPARA, PLCG2, and RAD51B) that were consistently significant across GO, Reactome, and KEGG pathways. PPARA and PLCG2 were not previously identified as a part of the 34 IAMDGC loci associated with AMD risk. The strongest single-marker P values observed in PLCG2 and PPARA were 2. 05 3 10 À4 and 3. 10 3 10 À5 , respectively, and do not meet the classical GWAS significance levels. In our sensitivity analysis, PPARA and PLCG2 remained statistical driver genes in pathways from KEGG, Reactome, and GO, suggesting that pathway analysis can identify novel AMD genes. Additionally, the aggregation of nominally significant independent variants in PLCG2 suggests that the gene-wide significance of PLCG2 is greater than that of the individual variants and emphasizes the power of pathway analysis for identifying gene-wide signals rather than singlevariant associations.
DNA motif analysis identified five sequence motifs adjacent to the eight statistical driver genes in their promoter regions. These motifs represent sites of known TF binding and suggest that the expression of these genes may be controlled by similar mechanisms. One motif ( [GCA][AC][CT]AG[AT]G[CA][TGA] A[AG][AT][CA]T[CA][CG][GA]T[CG][TG][CA]A[AG]AAA[ATG] [AG]AAA[AT][CA][AC]A[AC]A[AC][AT][AT]A) was adjacent to the start positions of all eight statistical driver genes and contains known binding sites of several TFs (Table  3 ). Functional studies are required to confirm these in silico findings and elucidate the transcriptional mechanisms of these statistical driver genes in the context of AMD. One gene, PLCG2, was central to multiple pathways in all four databases and remained significant after our sensitivity analysis.
G[CG][TG]TG[AT]ACC[CAT][AG]G[GT][AG]GG[CT][GT][GT][AT][GA] [CG]TT For each motif, we identified TFs associated with the motif sequence using Tomtom. The P value represents the strength of the match between the sequence motif identified adjacent to the statistical driver genes and the curated sequences of the TF binding motifs in the HOCOMOCO database. [GC]C[AT]G[TA]GAGCC[GT]AGA[TA]C[GA][CG][GT][CT] C[AT][CG] KLF5 0. 0095 C2 LIPC MICA NOTCH4 PPARA RAD51B KLF12 0. 011 THA11 0. 012 ZN563 0. 013 IRF2 0. 013 NFIA 0. 017 ZN449 0. 019 ELF2 0. 020 ZBTB6 0. 021 RARG 0. 024 [CT][TA]G[GT]C[TC]AA[CA][AG][CT][AG][GC][TA][GC]AAACCC[CA] [GC][TA][CA][TA]C[TC]A[CT][TC][AC]AA[AG]ATA[CT][AT][AG] [AC]AAA[AT]TA[GT][TCG] PIT1 0. 0052 C2 LIPC MICA NOTCH4 PPARA RAD51B SOX5 0. 0093 AIRE 0. 010 CEBPE 0. 011 [GA][CG]CTG[CT][AT][GA][TA]CC[CA]AGCT[AGC][CT][TA][CGA][GT] [GT][GT][AT][GC]G[CTC][TG][GA]AG[GT]CAG[GA][AT]G[AC][ ] [24] [25] [26]  The PLCG2 protein interacts with several members (HCK, LYN, PIK3R1, and SYK) of the microglia pathogen phagocytosis pathway in humans.
Functional studies are required to determine causality for these genes and pathways in patients with AMD. Knowledge-driven pathway analyses are subject to the quality and coverage of the knowledge in a given database. We attempted to circumvent this limitation by utilizing multiple databases in our analyses and integrating our results. The GWAS data used in this study were generated from individuals of European descent. Consequently, these findings may not be applicable to non-European populations. The IAMDGC GWAS dataset is considered the largest available dataset for advanced AMD cases and controls in the world. We are unaware of any comparable datasets available for replication.
Acknowledgments The authors thank the study participants for their contribution to this study. This work made use of the  High Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University. Supported by Grants  1X01HG006934-01 ,  R01 EY022310 , the  National Institutes of Health National Center for Advancing Translational Science (NCATS)  Grant  KL2TR000440  (JNCB), and the  VSTP Training  Grant  T32EY007157-18  (MG). Disclosure:  A. R. Waksmunski , None;  M. Grunin , None;  T. G. Kinzy , None;  R. P. Igo Jr , None;  J. L. Haines , None;  J. N. Cooke Bailey , None.
Methods Optical setup. A schematic of the optical setup used is presented in Fig. 1a. We used a Nikon Eclipse Ti inverted microscope with a Nikon Perfect Focus System. The excitation was performed thanks to five different lasers: 637 nm (Obis 637LX, 140 mW, Coherent), 561 nm (Genesis MX 561 STM, 500 mW), 532 nm (Verdi G5, 5 W, Coherent), 488 nm (Genesis MX 488 STM, 500 mW, Coherent), and 405 nm (Obis 405LX, 100 mW, Coherent). The corresponding 390/482/532/640 or 390/ 482/561/640 multiband filters (LF405/488/532/635-A-000 and LF405/488/561/635-A-000, Semrock) were used. The fluorescence was collected through a Nikon APO TIRF ×100 1. 49 NA oil immersion objective lens, sent in the DAISY module and recorded on two halves of a 512 × 512-pixel EMCCD camera (iXon3, Andor). The camera was placed at the focal plane of the module of magnification 1.
We then started the data acquisition with a 50-ms (for AF647) or 100-ms (for AF555) exposure time and 150 EMCCD gain. The total number of acquired frames was typically between 15,000 and 30,000 per acquisition. For sequential dSTORM and DNA-PAINT acquisitions, the dSTORM acquisition was first performed as described above. Then, we removed the dSTORM buffer and added a 0. 5 nM dilution of DNA-PAINT imagers in imaging buffer (I1-560, Ultivue-2 kit, Ultivue). To achieve single molecule regime, the sample was lit with an irradiance of ~4 kW. cm -2 and we then started the data acquisition with a 100-ms exposure time and 150 EMCCD gain. The total number of acquired frames was around 50,000. Performance measurements on fluorescent beads were done at low illumination powers (0. 15 kW. cm -2 for 20-nm diameter dark red beads and 0.
025 kW. cm -2 for tetraspeck beads and 40-nm diameter dark red beads). The beads were immersed in PBS + 5% glucose and the exposure times and EMCCD gain were 50 ms and 150 ms, respectively. Except for the long-term axial drift tracking experiment, 500 frames were recorded for each performance characterization acquisition. The acquisition was performed using the Nemo software (Abbelight). Localization software. Each 512 × 512-pixel frame was pre-processed by removing the pixel per pixel temporal median of the previous 10 frames in order to get rid of the slowly varying background without altering the number of photons in the PSFs. The filtered frames were then split in two parts corresponding to the UAF and EPI paths of the DAISY module, respectively. On the 512 × 256-pixel sub-frames, the PSFs were detected using a second order wavelet filtering associated with an intensity threshold (typically 1.
All this processing was performed using a home-written Python code. Astigmatism calibration. Although in the literature, the calibration of axial detection methods is often performed by using fluorescent beads deposited on a coverslip and defocusing the objective, this method is biased since it does not take into account the effect of the spherical aberration, which affects both the position of the focal plane (the so-called focal shift) and the shapes of the PSFs. While the former can be compensated using a calculated correction factor depending on several experimental parameters, there is no simple way to get to correct the latter to our knowledge. Thus, we chose to perform the calibration of the astigmatic detection using a sample of known geometry in the nominal acquisition conditions, i.
Acknowledgements We thank  Ultivue  for consumable gifts and Abbelight for software and buffers gifts. We acknowledge the contribution of the  Centre de Photonique BioMédicale  to cell culture and labeling. We also acknowledge the help of  Marion Bardou  with cell culture. We thank  Rym Boudjemaa  for her contribution to the bacteria labeling project. Finally, we thank  Caroline Schou  and  Yann Kergutuil  for their help regarding software analysis. This work was supported by the  AXA research fund , the  ANR  ( LABEX WIFI ,  ANR-10-LABX-24 ), the  DIM CNANO Île-de-France , the  IRS Bioprobe , the  Mission interdisciplinarité of the CNRS, and LaserLab-Europe EUH2020654148. P. J. acknowledges a master funding from  GDR ImaBio , and PhD funding from  IDEX   Paris Saclay  ( ANR-11-IDEX-0003-02 ).
For instance, for N = 9 and N = 10, the convergence was too slow for N sim = 10N. So we have used N sim = 15N, which has presented good results. PEREIRA DA SILVA et al Fast Virt. Exac. Qua. Gat. Gen. Random matrices a, b in R 11×20 with entries in [-1, 1] were generated for once and for all, to be used in all numerical experiments. Then a 1 , b 1 are respectively the submatrices a(1 : m, 1 : M) and b(1 : m, 1 : M) (in MATLAB notation). The values of M are given in table 2. The chosen amplitudes for the seed (24) are A m = 2/M, and a = A m a 1 , b = a m b 1 and T = πT f. The policy of choosing X goal in each step that was considered is the one of (  13 ), which has presented better results than the one described in Algorithm C. The input saturation function that was used is the smooth version that is described in the paper.
The CPU model used by  Leung et al. [2017]  was an Intel Core i7-6700K CPU 4. 00 GHz and a GPU NVIDIA Tesla K40c and GRAPE was implemented in the TensorFlow library developed by Google's machine intelligence research group  Abadi et al. [2016]. Their software cannot run in our Windows platform, and so this comparison must take into account the difference of CPU's. The CPU benchmarks are 4612 for our CPU and 11109 for their CPU (2. 4 times faster than ours). PEREIRA DA SILVA et al Fast Virt. Exac. Qua. Gat. Gen. The software implementation of RIGA may be improved a lot, by optimizing the software and compiling the mfile. Even so, the results of the benchmark indicates that performance of the RIGA in our CPU is comparable, and even better in many cases, with ones of  Leung et al. [2017]  in their CPU.
For instance, for N = 6 and N sim = 10N, the RIGA has attained the desired fidelity of the benchmark in step = 103. For the same N = 6 and N sim = 20N, the desired fidelity was obtained in step = 77. In the numerical implementation of RIGA, the inputs are assumed to be piecewise constant in each interval [sδ, (s + 1)δ) where δ = T f /N sim and s = 1, 2,. , N sim. Note that N sim must be compatible with the technology for generating the inputs in real time. A MATLAB software was developed for implementing the RIGA. This software can be optimized for obtaining a CAD for Quantum Gate Generation that could be faster than GRAPE. The complexity of each step of the RIGA corresponds essentially to the simulation of the closed loop system in the interval [0, T f ]. One may include input saturations in this Algorithm, which may be useful in practical applications.
Istex is a database of twenty million full text scientific papers bought by the French Government for the use of academic libraries. Papers are usually searched for by the title, authors, keywords or possibly the abstract. To authorize new types of queries of Istex, we implemented a system of named entity recognition on all papers and we offer users the possibility to run searches on these entities. After the presentation of the French Istex project, we detail in this paper the named entity recognition with CasEN, a cascade of graphs, implemented on the Unitex Software. CasEN exists in French, but not in English. The first challenge was to build a new cascade in a short time. The results of its evaluation showed a good Precision measure, even if the Recall was not very good. The Precision was very important for this project to ensure it did not return unwanted papers by a query.
Motivation This paper describes the implementation of a mining tool which uses the named entities for the open access resource Istex, a database of twenty million scientific papers (https://www. istex. fr/). The main objective of the Istex Investment for the Future project (ANR-10-IDEX-0004-02) is to provide the whole of the French higher education and research community with online access to retrospective collections of scientific literature in all disciplines by driving a mass-scale national policy of document acquisition including journal archives, databases, text corpora and so on. The full text versions of these papers were bought by the French Government for the use of academic libraries. Papers are usually searched for using the title, authors, keywords or perhaps the abstract. To authorize new types of queries on Istex, we implemented a system of named entity recognition on all papers and we offer users the possibility to search using these entities.
We wrote an annotation guide to precise what entities we wanted to annotate (Section 3. 1), we organized the link between the document and the list of contained named entities with the use of a standoff file (Section 3. 2). To run Unitex without fatal errors and without too much time consuming, we used a dockerized application (Section 3. 3). Finally we describe the Istex system of queries to named entity enrichment (Section 3. 4), the possibility to directly query the database (Section 3. 4. 1) or to build a complete TEI file (Section 3. 4. 2). We finally conclude in (Section 4) with some perspectives.
The Beginning of the Istex Project The Istex Project was launched in 2012 and has documented resources acquired from around twenty publishers in the main scientific fields available to the French higher education and research community: Nearly 9000 scientific and technical journals representing a total of over 21 million articles, several thousand books, encyclopedias and dictionaries and some databases. This acquisitions program ended on 1st January 2019 when the Ministry of Higher Education and Research's "Investments for the Future" program and its funding of around 60 million euros by the French National Research Agency both came to an end. In the future, other resources could be added to this corpus if a new acquisitions campaign is launched.
These kinds of processing enrich the initial documents with complementary information. Several explorations for the Text and Data Mining use of Istex are implemented. An example is the current work on the alignment of Unitex named entities <placeName> and <geogName> with the GeoNames database which allows you to locate the place recognized as a named entity. By using the identifier retrieved via GeoNames, you can access to additional information from Wikidata and DataBNF repositories (This alignment is available on https://placename-entity. data. istex. fr, https: //geogname-entity. data. istex. fr and also on the SPARQL part of the website).
Unitex allows the use of graph cascade with the CasSys program inspired from  [16]. Cascades  [17]  are used in many NLP applications, as chunking  [18] , syntactic analysis  [19] , morphological analysis  [20]  and so on. The graph shown in Figure  1  is the first in our cascade. The principle of a cascade is simple: Each graph of the cascade parses the output text of the preceding graph. The graph order is difficult to decide but it is very important for a good parsing. For instance, you have to recognize a building name like Charles de Gaulle airport after you recognized Charles de Gaulle as a person's name. Another example is shown in Figure  2 : to encapsulate URL, you have to use the XML tags ext-link, if these exist in the paper (1) or you have to describe the URL  (2). The distribution of Unitex -and its resources -is free under the terms of the Lesser General Public License (LGPL).
NER with Unitex For more than a decade, the Lifat has been developing cascade NER rules for French, CasEN, which is a free and open source, under a LGPL licence (http://tln. lifat. univ-tours. fr/Tln_CasEN. html). After adjudication, CasEN probably was the first software in the Etape campaign (http://www. afcpparole. org/spip. php?rubrique132). So the first point in the Istex project was to try CasEN on the section of the Istex collection written in French. We worked on the improvement of CasEN for scientific texts in French, and in parallel built a new cascade for scientific texts in English. The French version of CasEN is based on very different corpora (newspapers, querying, books, scientific papers. ) but the English version is only based on the Istex collection, i. e. , scientific papers.
Results The second part of the evaluation consists of calculating the classical measures of Recall and Precision, and also the Slot Error Rate (SER)  [21]. Table  1  explains the calculated data. The founded results on the English corpus of 49 Istex documents manually annotated for the test (see Section 2. 4. 1) are detailed in Table  2. Finally, Table  3  presents the result measures: The Recall is weak, because the English cascade was not finished at the end of the project (for the evaluation time). As we said in Section 2. 3, we developed CasEN before the Istex project, but only for French. The evaluation described here concerns the English version of CasEN. We are always working this cascade. In this project, the most important point is the precision, because one doesn't want to find a text with an error of tags when one queries the database (Section 3.
NER Implementation at Inist This section speak about our second challenge: The cascade implementation at Inist for the Istex project. We wrote an annotation guide to precise what entities we wanted to annotate (Section 3. 1), we organized the link between the document and the list of contained named entities with the use of a standoff file (Section 3. 2). To run Unitex without fatal errors and without too much time consuming, we used a dockerized application (Section 3. 3). Finally we describe the Istex system of queries to the named entity enrichment (Section 3. 4), the possibility to directly query the database (Section 3. 4. 1) or to build a complete TEI file (Section 3. 4. 2).
Unitex Implementation at Inist The Istex-RD team decided to delegate the processing of Unitex/CasEN on the Istex corpus to the IT Operation team of Inist (Iprod). The first goal was to evaluate the CasEN accuracy on a small bilingual (FR, EN) corpus, then to strengthen the process by scaling up to more than 2 millions of documents, and finally to apply it to the Istex corpus. Iprod was associated early in this project, whereas the software and linguistic components involved were still in development mode because the Lifat was working on the CasEN cascades for French and English. In parallel, in order to process a 20 million document corpus, the Ergonotics start-up developed an add-on for Unitex to enhance Unitex overall performance, as well as a custom Unitex build script taking this add-on into account.
The Unitex software itself was regularly improved using the feedback resulting from the first results of processing Unitex/CasEN on Istex documents (https://github. com/UnitexGramLab/ -the fact that one of the Ergonotics founders is an active committer of Unitex was a major point in this continuous improvement process ). To implement this Unitex development workflow at Inist, Iprod had to build and test frequently the processing chain, integrating the above-mentioned artefacts which were delivered at a different pace by each partner. It was also a requirement for Iprod to be able to run the processing chain using different versions of the tool chain (gcc/g++) used to build the Unitex software. After some iterations on increasing workloads, we concluded that the optimizations resulting from using a tool chain instead of another where not significant enough compared to the time required by the linguistic part of the process.