dataesr/scientific-paragraphs-categorization huggingface dataset:
The Localization slot further specifies the plant part, for example to define in which organ a given cell type was located or when the subject is a subset of the original plant part term. The Developmental stage of the plant part can be recorded with the corresponding ontology term. GO terms can be entered in the Process slot. Finally, the Factuality slot qualifies certain statements with ad hoc labels such as 'negation' and 'speculation', to respectively mark negative statements or capture recorded statements that are suggested by the experimental data but not fully supported by additional evidence. 3. Additional information types. The details of plant growth conditions, special treatments or stress circumstances ("Environment") were entered in the Growth condition slot only if they were specifically stated in the Results section of the paper.
Monitoring relation consistency Literature curation is prone to occasional errors and inconsistencies, especially when carried out by multiple contributors. To deliver a coherent data set, the annotation effort was designed with rigorous guidelines and community curators were trained during hands-on sessions backed up by documentation explaining the details of the annotation scheme and customized functions within Leaf Knowtator (see Supplemental Files, including an annotation manual and training documents). In addition, the quality of the records was monitored throughout the project with scripts designed to detect different types of errors that were subsequently corrected. Fully automated scripts first validated the completeness of the relation annotations, i. e. whether all required slots had been filled.
Next, the consistency of ontology terms was automatically verified. Finally, orphan annotations or seemingly undefined ontology terms were also reported. Details of the quality control scripts are provided in Section 2. The curators examined the resulting logs and the relations were adjusted when necessary. In rare cases, it was impossible to enter data in all required slots because textual descriptions were ambiguous or incomplete. While such annotations violated the initial guidelines, their information value often justified their inclusion in the final version of our database. As expected, the relations produced initially by the sole reference annotator -and main developer of Leaf Knowtator -were highly consistent and complete. Based on 174 curated articles, the quality control script reported on average only 3.
The KnownLeaf database XML files resulting from the curation of each annotated article were converted, flattened and parsed into database tables. The resulting KnownLeaf relational database consists of three tables: knowtator contains the annotated relations and has database foreign keys to knowtator papers with bibliographic information and to knowtator agi with AGI code references. The final database contains 9947 relations in the knowtator table with a total of 12,534 references to AGI codes, corresponding to 883 unique genes (3. 2% of the TAIR10 protein coding genes) with on average 14. 2 references per gene. Table  3  shows a breakdown of AGI references by annotated relation type in the completed data set. Phenotype statements form the largest category and refer to 381 distinct Arabidopsis genes, on average 14.
7 statements per gene across the text corpus, highlighting the dense phenotype knowledge extracted from the literature. Statements on Gene expression are fewer but refer to more genes because the transcription of multiple genes was often described in a single mutant background or plant part. A wide range of queries can be performed within the KnownLeaf database. For example, the annotated relations can be looked up for a given Arabidopsis gene or protein; genes can be searched that are linked to a particular plant part or to specific phenotype alterations (see example SQL within https://www. agronomics. ethz. ch/ knowtator/code repository. zip). To provide molecular context to our records, publicly available Arabidopsis resources were merged into the database, including transcriptional networks (AtRegNet from AGRIS)  [25, 26] , Arabidopsis protein interactome (AI-1)  [27]  gene co-expression measures (ATTED-II)  [28] , and TAIR gene and protein annotations  [24].
Graphical representation of leaf phenotype records in a molecular context As an alternative to structured command line queries, an interactive graph was created with the Cytoscape tool  [29] , in which Knowtator relations are part of a larger network of objects. The data residing in KnownLeaf were joined and simplified in several ways to produce a graph with information pertinent to interpret and further expand these relations. Co-expression edges were only represented below a threshold value (ATTED-II co-expression mutual rank score < 25) to reduce the noise in the co-expression dataset. Next, the network was seeded with a list of AGI codes including (i) all 883 genes curated via Knowtator (Table  3 ) and (ii) 111 genes coding for proteins whose levels vary significantly across leaf development as determined by iTRAQ (according to more stringent cut-off criteria than previously reported in Baerenfaller, et al.
[22] ; global fold change > 2. 8, p-value < 0. 05) (Table  S5 ). This combined set counted 977 non-redundant AGIs. Finally, these initial gene nodes were enriched with neighboring gene nodes through the connectivity defined by the public molecular resources incorporated within the KnownLeaf database (above). The corresponding network, known as LeafNet, contains 19,055 nodes connected by a total of 39,649 edges, combining gene/protein-phenotype relationships with molecular information (Fig. 3 ). In LeafNet, the information about molecular functions, collected from the primary literature via Knowtator, complements that from the public resources. For example, 123 non-redundant protein-protein interactions were annotated involving 121 proteins. Of these, 41 (33%) overlap with those found in the AI-1 interactome, in line with the intersection commonly found between literature-curated datasets and high-throughput yeasttwo hybrid datasets  [7].
The dimensions of our dataset are comparable to recent general-purpose annotation efforts  [43] , but it is unique in size and scope in the plant domain. The specific slot-value structure defined for each relation category was also designed to facilitate future text mining efforts. By providing formal semantics and ontology terms, computer reasoning can be enhanced and well-structured annotations produced in a more time-efficient manner. For instance, the combination of manual curation, as described here, with partially automated extraction of textual information  [44] [45] [46]  would dramatically speed up literature curation projects. We view these opportunities as interesting follow-up work to this study. The text corpus at the basis of this work is not exhaustive and can be expanded in several ways.
Considering all primary research articles in which detailed leaf phenotypes can be linked to specific alleles of identified Arabidopsis genes (i. e. AGI codes), we estimate that the 283 papers we curated represent a third to a quarter of the relevant published literature. While the current dataset demonstrates the usefulness of high quality manual annotation, it would be even more valuable if it encompassed all targeted research results. Our system provides solid grounds to build up the resource by drawing in a larger community. The Leaf Knowtator interface can be adopted by any willing researcher, with documentation and training examples available to guide the first steps. Downstream software is available to monitor the consistency of the recorded relations, to transfer them into the KnownLeaf relational database, and to represent them graphically in an increased Cytoscape version of the LeafNet network.
Alternatively, Leaf Knowtator relations can be merged with large inventories of Arabidopsis mutations that are precious for their exhaustive coverage but that provide little detail about associated phenotypes  [47, 48]. Leaf Knowtator relations can also be imported into other Arabidopsis databases and online web tools designed to query large-scale datasets, for example TAIR  [24] , BAR  [49] , Genevestigator  [11] , VirtualPlant  [50]  or CORNET  [51]. However, it is worthwhile emphasizing that such integrative systems do not yet include advanced functions to probe -beyond free text search or display -the connections between mutations in specific genes and corresponding phenotypes, stressing the usefulness of structured phenotype data enriched with ontology terms as presented here. Through LeafNet, the integration of curated and reference knowledge resources showed that genes of interest can be placed into an informative molecular and phenotypic network landscape.
Acknowledgments We thank  Annick Bleys  for help with the manuscript. We thank  Dr Georgios Gkoutos  for advise on the potential for machine reasoning with the curated ontological relations. This work was supported by the  Integrated Project AGRON-OMICS , in the  Sixth Framework Programme  of the  European Commission  ( LSHG-CT-2006-037704 ). S. W. was supported by TiMet -Linking the Clock to Metabolism, a  Collaborative Project (Grant Agreement 245143 ) funded by the  European Commission FP7 , in response to call FP7-KBBE-2009-3. S. V. L. thanks the  Research Foundation Flanders (FWO)  for funding her research. A. D. J. v. D. was supported by the transPLANT project (funded by the  European Commission  within its 7th  Framework Programme  under the thematic area 'Infrastructures', contract number  283496 ).
Resilience Parameters. In the context of smart metering, data gathering should be firstly successful, secondly efficient, and finally fairly distributed among all network nodes. The following five performance parameters were selected for the resilience metric evaluation: (1) Average Delivery Ratio (ADR). It is defined as the fraction of the number of received packets by a data collector and the number of sent packets by all sources. It represents the main goal of a WSN in terms of data delivery. (2) Energy Efficiency (EE). It is defined as the efficiency of the overall energy expenditure for all routing generated and forwarded (CONTROL and DATA) packets by all network nodes; this is the protocol overhead in terms of power consumption. (3) Delay Efficiency (DE). It is defined as the efficiency of average time delay (including queuing, retransmissions, and propagation delays) required for a packet to go from the source to the data collector; this is a function of the average path length (hop count).
Study populations The study included 55,540 breast cancer cases and 51,168 controls from 50 studies participating in the BCAC. Details of the studies, sample selection, and genotypes are described elsewhere  (1). The dataset included 39 studies from European-ancestry populations  (48, 155  cases and 43,612 controls), nine from Asian populations (6,269 cases and 6,624 controls) and two from populations of African ancestry (1,116 cases and 932 controls).
9. In total, 490 SNPs were successfully genotyped and passed quality control. We imputed genotypes for the remaining SNPs using the program IMPUTE2  (24)  and the March 2012 release of the 1000 Genomes Project as a reference. Those imputed SNPs with common SNPs (MAF > 0. 02) and imputation r 2 > 0. 3 were included in the current analysis.
Functional annotation We annotated 29 candidate causative variants for potential functional significance using chromHMM annotation across nine ENCODE(26) cell lines: HMEC, GM12878, H1-hESC, K562, HepG2, HSMM, HUVEC, NHEK, and NHLF  (27). For each variant, we investigated whether it is mapped to functional regions (i. e. promoter and enhancer) through chromatin states annotation from the UCSC Genome Browser (28). The epigenetic landscape of histone markers H3K4Me1, H3K4Me3, and H3K27Ac was also examined through layered histone tracks on seven ENCODE cell lines including GM12878, H1-hESC, K562, HSMM, HUVEC, NHEK, and NHLF from the UCSC Genome Browser. DNase I hypersensitive and TF ChIP-Seq datasets were investigated in all available ENCODE cell lines, including breast normal cell line, HMEC, and breast cancer cell lines, T-47D and MCF-7.
Two publicly available tools, RegulomeDB  (29)  and HaploReg v2  (30) , were also used to evaluate those likely functional variants  (9, 31). In addition, we also investigated whether each variant is overlapped with regulatory elements of enhancers and TSS from two previous studies including Hnisz et al  (32)  and Andersson et al (FANTOM5 project)  (33). Chromatin Interaction Analysis by Paired End Tag (ChIA-PET) (mediated by RNA polymerase 2) data from MCF7 cell were downloaded from GEO (GSE39495) and the ggbio R package was used to represent the interactions between cell enhancers (containing a strongly associated variant) and a predicted gene promoter.
TCGA data resource and eQTL analysis We downloaded RNA-Seq V2 data (level 3) of 1,006 breast cancer tumor tissues from the TCGA data portal  (34). DNA methylation data measured by the Illumina HumanMethylation450 BeadChip were also retrieved from TCGA level 3 data. We also downloaded level 3 SNP data genotyped using the Affymetrix SNP 6. 0 array. Copy Number Alteration (CNA) data for genes PPA2, ARHGEF38, INTS12, GSTCD and TET2 at 4q24 for TCGA samples were collected from the CbioPortal  (35). We analyzed a total of 645 breast tumor tissues in Caucasian population including matched CNV, genotype and expression data. We performed eQTL analysis in TCGA tumor tissues described above. We applied several steps to reduce the batch or other technical effects on gene expressions following the approach described by Pickrell et al  (36).
Functional Annotation We used a likelihood ratio > 1:100 relative to the best associated SNP in each signal to select candidate variants for functional annotation in order to identify potentially causative variants in this region (Supplementary Table  1 ). In total, 29 SNPs were identified including 24 for signal 1 and 5 for signal 2. Of these, 17 SNPs in signal 1 were strongly correlated with the original index SNP rs9790517, and the remainder were more weakly correlated. All SNPs were evaluated using DNase-Seq and ChIP-Seq data from the ENCODE project. The most promising evidence for functionality was found for SNPs rs62331150 and rs73838678, both in LD with rs9790517 (r 2 = 0. 98 and r 2 = 0. 09, respectively) in signal 1. The annotation from chromatin states  (27)  revealed that rs62331150 resides an active promoter region, and rs73838678 in a strong enhancer region, on several ENCODE cell lines including HMEC (Human Mammary Epithelial Cell) but not for other SNPs in either signal 1 or 2 (Figure  2A ).
Gene expression analyses We used both The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) data to examine the association of the putative functional SNP rs62331150 and rs73838678 with expression of TET2 and several other neighboring genes, including PPA2, ARHGEF38, INTS12, and GSTCD, in breast cancer tissues. No significant correlations with any genes were observed for variant rs73838678. Variant rs62331150 was weakly correlated with TET2 expression in both datasets (p = 0. 039 and p=0. 025 respectively for TCGA and METABRIC), the reference allele G being associated with increased expression relative to the risk allele T (Figure  3 ). The result was consistent with the observation from our functional annotation that SP1 may preferentially bind to the reference G allele, leading to a significant increase in TET2 transcription activation.
No correlation between rs62331150 and the expression of any other gene in the region was found in either dataset. Overall, our findings supported a hypothesis that TET2 is the target gene for the signal 1 association, and that the association with breast cancer risk may be mediated through regulation of TET2 gene expression. The result is also in line with previous findings that TET2 functions as a tumor suppressor and its high expression level may reduce breast cancer risk  (44, 45).
07% to 0. 15% among women of European descent. Functional analyses revealed one potentially functional variant, rs62331150. The risk allele is associated with lower expression of TET2, consistent with previous findings that this gene acts as a tumor suppressor. A) The index SNP rs9790517 is plotted in diamond purple. The LD (r 2 ) for the index SNP with each SNP was computed based on European ancestry subjects included in the 1000 Genome Mar 2012 EUR. P values were from the single-marker analysis based on logistic regression models after adjusted for age, study sites and the first six principal components plus one additional principal component for the LMBC in analyses of data from European descendants. The plot was generated using LocusZoom (50). The detailed color scheme for each ENCODE cell line described in the UCSC genome browser).
Acknowledgment We thank all the individuals who took part in these studies and all the researchers, study staff, clinicians and other healthcare providers, technicians and administrative staff who have enabled this work to be carried out. In particular, we thank:  Andrew Berchuck  (OCAC),  Rosalind A. Eeles ,  Ali Amin Al Olama ,  Zsofia Kote-Jarai ,  Sara Benlloch  (PRACTICAL),  Antonis Antoniou ,  Lesley McGuffog ,  Ken Offit  (CIMBA),  Andrew Lee , and  Ed Dicks , The HEBCS was supported by the  Helsinki University Central Hospital Research Fund ,  Academy of Finland  ( 266528 ), the  Finnish Cancer Society, The Nordic Cancer Union  and the  Sigrid Juselius Foundation. The HERPACC was supported by a  Grant-in-Aid for Scientific Research on Priority Areas  from the  Ministry of Education, Science, Sports, Culture and Technology of Japan , by a  Grant-in-Aid for the Third Term Comprehensive 10-Year Strategy for Cancer Control  from  Ministry Health, Labour and Welfare of Japan , by a research grant from  Takeda Science Foundation , by  Health and Labour Sciences Research Grants for Research on Applying Health Technology from Ministry Health, Labour and Welfare of Japan  and by  National Cancer Center Research and Development Fund.
The HMBCS was supported by the  Rudolf Bartling Foundation. Financial support for KARBAC was provided through the regional agreement on medical training and clinical research (ALF) between  Stockholm County Council and Karolinska Institutet , the  Stockholm Cancer Foundation  and the  Swedish Cancer Society. The KBCP was financially supported by the special Government Funding (EVO) of  Kuopio University Hospital  grants,  Cancer Fund of North Savo , the  Finnish Cancer Organizations , the  Academy of Finland  and by the strategic funding of the  University of Eastern Finland. kConFab is supported by grants from the  National Breast Cancer Foundation , the  NHMRC , the  Queensland Cancer Fund , the  Cancer Councils of New South Wales, Victoria, Tasmania and South Australia  and the  Cancer Foundation of Western Australia.
The  kConFab Clinical Follow Up Study  was funded by the  NHMRC  ( 145684 ,  288704 ,  454508 ). Financial support for the AOCS was provided by the  United States Army Medical Research and Materiel Command  ( DAMD17-01-1-0729 ), the  Cancer Council of Tasmania and Cancer Foundation of Western Australia  and the  NHMRC  (199600). G. C. T. and P. W. are supported by the  NHMRC. LAABC is supported by grants ( 1RB-0287 ,  3PB-0102 ,  5PB-0018  and  10PB-0098 ) from the  California Breast Cancer Research Program. Incident breast cancer cases were collected by the  USC Cancer Surveillance Program (CSP)  which is supported under subcontract by the  California Department of Health. The  CSP  is also part of the  National Cancer Institute's Division of Cancer Prevention  and  Control Surveillance ,  Epidemiology, and End Results Program , under contract number  N01CN25403.
and Margaret T. Grohne Family Foundation  and the  Ting Tsung and Wei Fong Chao Foundation. MCCS cohort recruitment was funded by  VicHealth  and  Cancer Council Victoria. The MCCS was further supported by Australian  NHMRC  grants  209057 ,  251553  and  504711  and by infrastructure provided by  Cancer Council Victoria. The MEC was supported by  NIH  grants  CA63464 ,  CA54281 ,  CA098758  and  CA132839. The work of MTLGEBCS was supported by the  Quebec Breast Cancer Foundation , the  Canadian Institutes of Health Research for the "CIHR Team in Familial Risks of Breast Cancer" program  -grant #  CRN-87521  and the  Ministry of Economic Development, Innovation and Export Trade  -grant #  PSR-SIIRI-701. MYBRCA is funded by research grants from the  Malaysian Ministry of Science, Technology and Innovation (MOSTI) ,  Malaysian Ministry of Higher Education  ( UM.
OBCS was supported by the  Academy of Finland  (grant number  250083 ,  122715  and  Center of Excellence  grant number  251314 ), the  Finnish Cancer Foundation , the  Sigrid Juselius Foundation , the  University of Oulu , the  University of Oulu Support Foundation  and the special Governmental EVO funds for Oulu University Hospital -based research activities. The ORIGO study was supported by the  Dutch Cancer Society  ( RUL 1997-1505 ) and the  Biobanking and Biomolecular Resources Research Infrastructure  ( BBMRI-NL CP16 ). The PBCS was funded by  Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. pKARMA is a combination of the KARMA and LIBRO-1 studies. KARMA was supported by  Märit and Hans Rausings Initiative Against Breast Cancer.
KARMA and LIBRO-1 were supported the  Cancer Risk Prediction Center  (CRisP; www. crispcenter. org),a Linnaeus Centre (Contract  ID 70867902 ) financed by the  Swedish Research Council. The RBCS was funded by the  Dutch Cancer Society  ( DDHK 2004-3124 ,  DDHK 2009-4318 ). SASBAC was supported by funding from the  Agency for Science, Technology and Research of Singapore (A*STAR) , the  US National Institute of Health (NIH)  and the  Susan G. Komen Breast Cancer Foundation. KC was financed by the  Swedish Cancer Society  ( 5128-B07-01PAF ). The SBCGS was supported primarily by  NIH  grants  R01CA64277 ,  R01CA148667 , and  R37CA70867. Biological sample preparation was conducted the  Survey and Biospecimen Shared Resource , which is supported by  P30 CA68485. The SBCS was supported by  Yorkshire Cancer Research   S305PA ,  S299  and  S295 , and the  Sheffield Experimental Cancer Medicine Centre.
Funding for the SCCS was provided by  NIH  grant  R01 CA092447. The Arkansas Central Cancer Registry is fully funded by a grant from  National Program of Cancer Registries, Centers for Disease Control and Prevention (CDC). Data on SCCS cancer cases from Mississippi were collected by the  Mississippi Cancer Registry which participates in the National Program of Cancer Registries (NPCR)  of the  Centers for Disease Control and Prevention (CDC). The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the CDC or the  Mississippi Cancer Registry. SEARCH is funded by a programme grant from  Cancer Research UK  ( C490/A10124 ) and supported by the  UK National Institute for Health Research Biomedical Research Centre  at the  University of Cambridge.