dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors would like to thank  EPSRC  ( EP/P004229/1 ,  Dynamically Adaptive  and  Resilient Water Supply Networks  for a Sustainable Future),  EPSRC CDT in Sustainable Civil Engineering  and  Suez  for supporting the research presented in this paper, as well as the reviewers for their suggestions and assistance in writing this manuscript. The authors would also like to acknowledge the contribution of  Filippo Pecci  for the implementation of the forest-core decomposition algorithm.
C. Algebraic calculations Solving Eq. (  5 ) is computationally complex. The major bottleneck is the polynomial algebra (to be precise-calculating the greatest common divisor needed to reduce the fractions of polynomials to their canonical form). The code was implemented in C with the FLINT library  [11]  for polynomial algebra. To group automorphically equivalent configurations, it also relies on the subgraph-isomorphism algorithm VF2  [5]  as implemented in the igraph C library  [6]. Finding subgraph isomorphisms-although a classical, computationally hard problem-is in practice relatively quick and this enables us to discover and exploit all symmetries rather than a priori focusing on symmetrical graphs (cf. Ref. [19] ). Our code is available at github. com/pholme/sis_ exact/. (a) 1 / (2 β + 1 ) 1 / (β + 1 ) 1/(β+2) 1/(β+1) 1 / ( β + 2 ) 1 / 3 1 / 3 β / (β + 2 ) β / (β + 1 ) β / ( β + 1 ) β/(2β+1).
To rank polynomials as β → ∞, one goes through the coefficients in the opposite direction-one polynomial is considered larger than the other if the highest order coefficient where they differ is larger. As the full equations of the solution for x s (β) take much disk space, we only save the rankings. With the ranking of the solutions at hand, we proceed to calculate τ (using the NumPy library of Python). In Fig. 5(a) , we see τ as a function of the prevalence ω averaged over all connected graphs of given number of vertices N = 3,. , 8. τ is strictly decreasing from +1 to -1. The decrease (in particular for larger N) is faster in the beginning and end than in the middle. Since the curve gets consistently less steep with N for intermediate prevalence values, it seems possible that the curve would flatten out with a growing N.
5 min, followed by a change to 50% A and 50% B for 2. 5 min and back to the initial condition at 8 min. ESI(+)-MS and tandem ESI(+)-MS/MS were obtained under the following conditions: capillary, 3. 5 KV; cone, 30 V; source and desolvation temperature, 150 and 300 °C, respectively. For ESI(+)MS/MS, the energy for the collisioninduced dissociations (CID) was 15 eV. Data were acquired in the 50-300 m/z range. Ions were identified by the comparison of their m/z, retention time, and ESI(+)MS/MS dissociation patterns with pure standards.
RNA extraction Total RNA from C. arabica L. cv. IAPAR59 roots was extracted using an adapted CTAB method for plants  (Chang et al. 1993; Tong et al. 2012). RNA integrity was examined by 1% agarose gel electrophoresis and RNA preparations were treated with DNase (RNase-free; Invitrogen, Carlsbad, CA, USA). Absence of DNA contamination was confirmed by PCR using GAPDH primers and approximately 100 ng of RNA (data not shown). All samples were purified using the PureLink™ Micro-to-Midi Total RNA Purification System (Invitrogen, Carlsbad, CA, USA) to have an A260/A280 > 2. 0. RNA concentration and absorbance ratios were determined using a NanoDrop™ 1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). High-throughput sequencing: mRNA and sRNA libraries mRNA was isolated from 20 μg of total RNA using DynaBeads mRNA purification kit (Life Technologies, Carlsbad, CA, USA) according to the manufacturer's instructions.
In total, nine sRNA libraries (three for each time point) were sequenced. Sequencing data containing data from mRNA-seq and sRNA-seq libraries have been deposited at the European Nucleotide Archive (ENA) (https://www. ebi. ac. uk/ena) under accession number ERP017352. mRNA-seq: data processing, assembly, annotation, and differential expression analyses Fastq-mcf version 1. 04. 662 (Andrews 2010 ) was used to process and trim reads from the 454 platform. We applied a quality filter to trim reads with mean Phred quality scores < 25, reads covered by more than 90% of homopolymers and low complexity sequences, reads with at least 5 Ns or 5 poly-A/Ts and adapters at the ends, and removing reads with minimum length of 40 after trimming. De novo transcriptome assembly was initially performed using three assemblers: Newbler, CLC Genome Suite, and Trinity (data not shown).
Reads from individual libraries were mapped to final transcripts using default RSEM parameters using the script un_RSEM_align_n_estimate. pl, followed by joining RSEM-estimated abundance values for each sample using merge_R-SEM_frag_counts_single_table. pl. Lastly, differential expression analysis was performed using run_DE_analysis. pl as implemented in the Bioconductor package EdgeR in the R statistical environment (Robinson and Oshlack 2010; R Development Core Team 2011). Differentially expressed genes (available at https://doi. org/ 10. 7910/DVN/OIN1UW) were selected using two criteria: fold change > 2 and < -2 and p value < 0. 05. For annotation, Blast2GO  (Conesa and Gotz 2008)  was first used to obtain Gene Ontology (GO) attribution of the assembled transcripts. Next, the GO functional classifications of unigenes were performed with Web Gene Ontology Annotation Plot (WEGO)  (Ye et al.
Transposable elements identification All assembled contigs were compared to the RepBase  (Jurka et al. 2005 ) plant nucleotide database (version 21. 08; http://www. girinst. org/repbase/) and to reference transposable elements (TEs) from the C. canephora genome  (Denoeud et al. 2014; Guyot et al. 2014 ) using tBLASTx. Contigs were considered related to TEs when there was a minimum alignment of 100 bp, a score > 150, 75% of identity, and a 1e-05 e value in tBLASTx. Intersections between the two databases were highlighted using InteractiVenn  (Heberle et al. 2015).
Identification of non-coding mRNA transcripts The coding potential of assembled contigs was estimated using coding potential calculator (CPC) to discriminate coding and non-coding RNAs  (Kong et al. 2007 ). Contigs were considered as non-coding sequences if they had a CPC score < 0 in both senses and returned Bno hit^when submitted to tBLASTx analysis against Uniref90 protein database. sRNA-seq: data analysis, miRNA prediction, and target prediction Basic analyses were performed based on previous studies that aimed to identify microRNAs in plants  (Guzman et al. 2012; Katiyar et al. 2015; da Silva et al. 2016). All procedures for miRNA prediction used the UEA sRNA workbench V3. 2-Plant version (http://srna-tools. cmp. uea. ac. uk/)  (Stocks et al. 2012). Low-quality reads (FASTq values < 13), low complexity sequences (sequences with < 3 distinct nucleotides), and 5′ and 3′ adapter sequences were all trimmed using the Bsequence file pre-processing tool.
^Plant t/rRNAs from Rfam (excepted miRNAs), Arabidopsis tRNAs from BThe Genomic tRNA Database,^and plant t/rRNA sequences from BEMBL^release 95 were removed using the Bfilter pipelinef rom UEA sRNA workbench. Sequences shorter than 18 nt and larger than 25 nt were also excluded from further analysis. For the identification of miRNAs, all unique reads obtained after filtering were submitted to the miRCat pipeline from UEA sRNA toolkit-Plant version, and default criteria were used to predict specific and conserved miRNAs. Reads were first mapped against the C. canephora genome  (Denoeud et al. 2014). The remaining reads were re-mapped against the de novo assembled transcriptome. Potential precursors were identified following the criteria for plant microRNAs annotation  (Meyers et al. 2008)  to recover the potential miRNA candidates.
Clean reads fully matching other RNAs, including repeat RNA, rRNA, snRNA, snoRNA, and tRNA, were excluded from the analysis. For newly discovered miRNAs, we included only those with their miRNA's co-existing in at least one library. To further validate the existence of miRNA precursors, we aligned miRNAs against the C. canephora genome  (Denoeud et al. 2014)  and C. arabica transcriptome datasets  (Mondego et al. 2011).
Primers specific for each gene were designed using Primer Express (Tables  S1  and S2 ). Thermal conditions were 95 °C for 10 min, followed by 40 cycles at 95 °C for 30 s, and 60 °C for 60 s. All reactions were performed in triplicate for each of the three biological replicates, following the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines  (Bustin et al. 2009). Dissociation curves were constructed to determine the presence of nonspecific products and primers were used only in the case of a single peak, with amplicons checked in an agarose gel. The mean amplification efficiency of each primer pair was checked using the LinRegPCR program  (Ramakers et al. 2003). Data were analyzed using the (1 + E) -ΔΔct method  (Livak and Schmittgen 2001; de Carvalho et al.
Statistical analysis Statistical tests were performed using SISVAR software  (Ferreira 2011 ) and means were compared by the Tukey's test at 5% significance. The Duncan, Dunnett's, and Student's t tests were used for amino acid analysis by UPLC-MS in SAS software (SAS Institute 2004). Data for differentially expressed gene candidates, genes regulated by N starvation, and miRNAs were analyzed by analysis of variance (ANOVA) and the means were compared by the Student's t test (p < 0. 05) using ASSISTAT software  (Silva and Azevedo 2013).
2c ). Other minor amino acids are not shown. These changes in amino acid levels likely reflect the metabolic changes of coffee roots to adjust N supply in response to N limitation. The changes in TAA and protein levels described above indicate that N starvation in coffee roots leads to a temporal adaptation that relies on inducing HATS activity and amino acid transport to cope with N-limited conditions. In total, 809,517 raw reads were generated from roots of control plants and plants grown for 1 and 10 days without N supply (Table  1 ). To perform the de novo assembly, we used 774,529 high-quality reads, generating 34,654 contigs. Our dataset (34,654 contigs, Table  1 ) is comparable to most coffee transcriptomic resources available: previous studies reported 35,113 contigs for C. arabica  (Mondego et al.
2011 ) and 36,935 contigs for Coffea eugenioides  (Yuyama et al. 2016). Contigs ranged from 200 to 8514 bp, with a mean contig length of 966 bp, an average open reading frame (ORF) length of 569 bp, a N50 of 1314 bp, and a contig GC content of 43. 4% (Tables  1  and 2 ). Most contigs had BlastX hits in the nr database with an e value cut-off of 1 e -5 (Fig. 3a ). Almost 85% of all assembled genes have one or two isoforms (Fig. 3b ). We used BlastX tool to compare, annotate, and study the conservation of our transcriptome contigs from coffee roots to other plant species genome sequences. A total of 25,026 contigs had correspondents in sequenced plant genomes. The top three species with first hits to Arabica coffee sequences were Vitis vinifera (23. 1%, 5774 contigs), Theobroma cacao (16. 3%, 4080 contigs), and Manihot esculenta (11.
8%, 2947 contigs) (Fig. 3c ). Only 1. 1% of the total contigs had their first hit in A. thaliana. We mapped contigs of C. arabica roots against Gene Ontology using BlastX analysis. We were able to classify 20,963 contigs in GO terms and 22,501 transcripts had a protein domain. A summary of the contigs annotated according to the GO terms classification is shown in Fig. 3d.
Transcriptional analysis of N transporter genes To investigate the transcriptional patterns of N transporter genes by RT-qPCR, we selected three ammonium transporters (AMTs) and three nitrate transporters (NRTs) based on a recent study by our research group AT5G12010 Cc06_g11710 0 × 1 ▼and 0 × 10 ▲ comp11089_seq8 Expansin AT2G03090 Cc11_g15080 0 × 1 ▲ comp5715_seq2 Glycosyl transferase, family 3 AT1G70570 Cc04_g11160 0 × 1 ▼ and 0 × 10 ▲ comp10285_seq3 Plant peroxidase AT5G05340 Cc01_g15110 0 × 1 ▲ and 0 × 10 ▲ comp10246_seq1 Cytochrome P450 AT4G31940 Cc10g_09170 0 × 1 ▲ and 0 × 10 ▼ comp8633_seq2 Protein kinase-like domain AT2G40980 Cc06_g22710 0 × 1 ▼ and 0 × 10 ▲ (Table  4 ; dos  Santos et al. 2017). Most N transporters analyzed showed higher transcript abundance under long-term N starvation when compared the Nsufficient condition (day 0) (Fig.
Transposable elements and non-coding transcripts We identified 862 contigs in our analysis that contained TE fragments (Table  S4 ). Most hits (806) were related to C. canephora transposable elements, whereas 35 contigs were related to TEs uniquely based on RepBase reference TEs. In addition, 21 contigs were identified by both approaches (Fig. S3 ). Most annotated contigs (86%, 744 contigs) were identified as belonging to class I elements (retrotransposons), which is I agreement with most analyses of transcriptionally active TEs in plants  (Vicient 2010; Santos et al. 2015). In addition, 6192 transcripts had a CPC score < 0 and no hits in the Uniref database (Online Resource 1) and can be considered putative long ncRNAs. Identification of miRNAs in N-deficient coffee roots by small RNA library sequencing Nine small RNA libraries were prepared from roots maintained in N-sufficient and N-free nutrient solution.
Acknowledgments This study received grants from  Brazilian Coffee Research Consortium, Fundação Araucária ,  CAPES  ( Ciência sem Fronteiras   PVE 084/2013 ) and  CNPq. TBS, JDMS, JCS, VYB, and  STI  acknowledge  CAPES ,  CNPq , and  FAPESP  for the student fellowships. DSD, LGEV, and LFPP are CNPq research productivity fellows. Author contribution statement TBS, LGEV, and LFPP conceived and designed the research, performed transcriptional experiments, wrote and revised the final manuscript; ARM assisted in designing the experiment; JEL performed experiments using 15 N and wrote the manuscript; STI RT-qPCR statistical analysis, revised the final manuscript; JDMS and VYB RT-qPCR and statistical analyses; SGHS performed GS and NR analyses; JBM and PM performed the amino acid analysis; MYNJ, JCS, ORJ, APRL, and ARP bioinformatic analysis; ILMJA and  UCO  library construction and mRNAseq sequencing; RG and DSD transposable element analysis, coordinated the research, and revised the final manuscript.
CFHQSIR survey The CFHQSIR is a CFHT Large Program carried out at the 3. 6 m CFH telescope with the Wide field IR camera (WIRCam) from 2011 to 2013. It aims to extend the highly successful 5. 8 < z < 6. 5 CFHQS survey  (Willott et al. 2005 (Willott et al. , 2007 (Willott et al. , 2009 (Willott et al. , 2010b ) to higher redshifts. The CFHQSIR covers ∼130 deg 2 over the four CFHTLS Wide fields up to a 5σ limiting magnitude of Y lim ≈ 22. 4 for point sources and is characterised by an average image quality of 0. 7. The data were obtained in two epoch observations separated by at least 20 days in order to discard transient sources and slow-moving objects. We refer to  Pipien et al. (2018)  for further details about the CFHQSIR data. Assuming the quasar luminosity function derived by  Willott et al.
Initial candidate selection Using the SExtractor software  (Bertin & Arnouts 1996) , we produced catalogues of objects detected in the Y-band images. We first ran SExtractor in dual-image mode for source detection in Y-band images and photometry in all bands (u, g, r, i, z, and Y). We set the detection threshold to 1. 5σ above the background and required at least four connected pixels for a measurement. This double analysis of our NIR and optical data reveals a total of ∼6 million sources detected in Y band. We then applied some automatic cuts including geometric and photometric criteria in order to keep the reddest point-like sources and remove the faintest objects in Y band. We set the following criteria: no detections in the u, g, r, and i filters, (z -Y) > 1. 0, Y ≤ 23. 0 (M 1450 -24. 0), and a signal-to-noise ratio (S /N) Y 4.
J-band follow-up observations were carried out at CFHT using the WIRCam camera, but also the SOFI (son of Isaac) and LIRIS (Long-slit Intermediate Resolution Infrared Spectrograph) cameras mounted on the 3. 6 m New Technology Telescope (ESO NTT) and the 4. 2 m William Herschel Telescope (WHT), respectively. The detectors used by SOFI and LIRIS are HgCdTe 1024 × 1024 Hawaii arrays with pixel scales of 0. 288. and 0. 251, respectively. LIRIS data were reduced using the Image Reduction and Analysis Facility (IRAF 3  ), and SOFI images were reduced with the SOFI pipeline 4. The photometric calibration for both datasets was performed following a method similar to that presented in  Pipien et al. (2018). We astrometrically calibrated the LIRIS images with the SCAMP 5  software and used the on-line service Astrometry.
net for SOFI images. We combined our follow-up observations with existing data when available, coming from the Canada France Brown Dwarfs Survey (CFBDSIR, J band;  Delorme et al. 2008) , the Visible and Infrared Survey Telescope for Astronomy (VISTA-VHS, J-, H-, and K s -bands;  McMahon et al. 2013) , and the Vimos Public Extragalactic Redshift Survey (VIPERS-Multi-Lambda Survey, K s band;  Moutard et al. 2016). This allowed us to apply the colour cuts defined in Eq. (  1 ) and to confirm the reality of these objects. We identified a total of 36 high-z quasar candidates that are represented in the (z -Y) versus (Y -J) diagram of Fig. 3. Magnitudes were measured in 2. 5 apertures, and aperture corrections were applied to give the total magnitudes. Non-detected objects in the z and/or J band (arrow points in Fig.
3. 2. 1 and 3. 2. 1). Given a photometric dataset, we need to define the probability that a source detected in the CFHQSIR survey is a high-z quasar rather than a low-mass star. The answer is given by the conditional posterior probability, which can be estimated with the Bayes theorem by comparing parametric models for high-z quasars and low-mass stars. For a detected source with photometric measurements d, this probability is defined by P q = Pr(q|d) = W q (d) W q (d) + W s (d) , (2) where the weighted evidence terms are given by W q/s (d) = ρ q/s ( p) Pr(det | p, q/s) Pr(d | p, q/s) dp. (3) Here the subscripts q and s denote the high-z quasars and low-mass stars, respectively. The vector p corresponds to the parameters used to model the quasar and stars populations. The A127, page 5 of 13 quantity ρ q/s ( p) represents the surface density of quasars or lowmass stars as a function of their intrinsic properties p.
(2012). Given that such measurements cannot be converted into traditional logarithmic magnitudes  (Pogson 1856) , we decided to follow the approach developed by  Mortlock et al. (2012)  by working in terms of flux units. In the following, for each population, stars or quasars, we therefore considered the observational dataset given by d = F z obs , F Y obs , F J obs , where F z obs , F Y obs , and F J obs are the observed flux of the candidates in the z, Y, and J bands, respectively. In the case of non-detections (in z or J), a flux is measured at the exact position where the object has been detected in Y band, using the SExtractor software in double-image mode.
Discussion and conclusions We have presented a complete method for selecting and classifying z ∼ 7 quasar candidates using optical and NIR photometric data as part of the CFHQSIR survey. After visual inspection and removal of artefacts, we selected 228 candidates with red z -Y colours. The sample was eventually culled to 36 sources by considering candidates with Y -J colours consistent with being high-z quasars after NIR follow-up in J band. We extended and refined the Bayesian formalism developed by  Mortlock et al. (2012)  in order to improve our selection of high-z quasar candidates. This robust statistical approach allowed us to classify our candidates in the best possible way according to their probability of being a high-redshift quasar. Applying a Bayesian method is indeed very efficient to identify the most promising candidates in any photometrically selected sample since it allows combining the prior knowledge of the brown dwarf and quasar populations and the filter and noise properties of the observational data.
Acknowledgements. We thank the anonymous referee for valuable comments and suggestions. Based on observations obtained with  MegaPrime/MegaCam , a joint project of  CFHT  and  CEA/IRFU , at the  Canada-France-Hawaii Telescope (CFHT) , which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Science de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii. This work is based in part on data products produced at Terapix available at the  Canadian Astronomy Data Centre  as part of the  Canada-France-Hawaii Telescope Legacy Survey , a collaborative project of  NRC  and CNRS. Based on observations made with  ESO Telescopes at La Silla Paranal Observatory  under programmes  ID 095. A-0349 ,  097. A-0650, 098.
A-0524 ,  099. A-0358  and  0100. A-0061. The WHT is operated on the island of  La Palma  by the  Isaac Newton Group of Telescopes in the Spanish Observatorio del Roque de los Muchachos  of the  Instituto de Astrofísica de Canarias. The LIRIS photometry was obtained as part of  OPT/2015B/26  and  OPT/20184/007. This research has benefitted from the  SpeX Prism Library and SpeX Prism Library Analysis Toolkit , maintained by  Adam Burgasser  at http://www. browndwarfs. org/spexprism. This research makes use of the VIPERS-MLS database, operated at  CeSAM/LAM, Marseille, France. This work is based in part on observations obtained with WIRCam, a joint project of  CFHT, Taiwan, Korea, Canada and France. The CFHT is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Science de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii.
This article reports on the results of the survey with respect to research services. Highlights include respondents wanting regular access to online conference and academic articles (89%), a range of research services including online library resources, video conferencing, collaboration tools, online data access and storage, online library resources and inter-university login (>87%), access to remote computing facilities (80%) and high performance computing facilities (77%). A desire to share data with others online (74%) was also identified. Respondents also indicated that they would like to access research services through a range of device types-Laptops (96%), Fixed PC (86%) and Mobile Devices (81%). Poor network connectivity was consistently identified as being a major barrier to research in the region.
Literature review The literature review was conducted by using Google Scholar and SCOPUS as the primary data sources for identifying relevant articles. The references cited in these articles were used to identify further sources. NREN service-related reports from the European Commission, the US Office of Advanced Cyberinfrastructure, the National Science Foundation (NSF) and Research Councils UK were also consulted as were sources from RRENs GE ´ANT (Europe), RedClara (Latin America), ASREN (North Africa), WACREN (West and Central Africa) and the Ubuntunet Alliance (South and East Africa), the European Grid Initiative (EGI. eu) and other recent e-Infrastructure projects. The purpose of this review is to provide further context to our study. A NREN is a dedicated Internet infrastructure and service provider supporting the research and educational communities within a country  (Dyer, 2009).