dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, GMM scores were calculated for each gene and each co-expression cluster and represent the Pearson correlation coefficient between their respective expression profiles. The vector of all gene-wise GMM scores per module is considered as the characteristic GEP of that module. The preservation of the IBC co-expression clusters in the series of nIBC preclinical models was investigated using connectivity and density statistics. Finally, network-based prioritization of the co-expression clusters was performed using the R package igraph (v. 1. 2. 5). Therefore, based on the expression levels of the co-expression modules in the cell lines, the correlation structure amongst the co-expression modules was determined and dichotomized relative to 0 (i. e. , positive and negative correlation coefficients transformed into 1 and 0 respectively).
Then, individual cell lines were linked to the co-expression modules by dichotomizing the cell line-specific expression values of the co-expression modules (i. e. , positive and negative expression values transformed into 1 and 0 respectively). The resulting binary adjacency matrix, representing both modules and cell lines, was analyzed using a minimal spanning tree algorithm to determine the minimal set of edges that connect all components. The result was visualized using the R package ggnetwork (v. 0. 5. 8).
Systems biology To translate expression profiles (i. e. , vectors of log 2 -transformed fold changes or GMM scores) into biological themes, GSEA was performed for the hallmark gene sets of the molecular signatures database (https://www. gsea-msigdb. org/gsea/msigdb). Overrepresentation analysis (ORA) of DEGs between IBC and nIBC cell lines in each of the co-expression modules was performed using the hypergeometric test. Both analyses were performed using the BioConductor package fgsea (v. 1. 14. 0). To assess MYC transcriptional activity in IBC and nIBC tissue samples based on published MYC activation signatures  [32] [33] [34]  , GEPs were subjected to gene set variation analysis using the BioConductor package GSVA. Finally, the analysis of the PMN was performed based on genes reported by Schaub et al.
35. To identify regulators of IBC biology and potentially effective target/drug combinations for treatment based on the WGCNA results, the GEP of each co-expression module based on 300 marker genes with the highest or lowest GMM scores (i. e. , 150 each) was analyzed against 1. 3 million L1000 profiles present in the CMAP data set (https://clue. io/cmap). These L1000 profiles catalog the transcriptional responses of human cells to a variety of chemical or genetic (i. e. , both knockdown and overexpression) perturbations. The resulting connectivity scores (CSs) reflect the level of agreement between the analyzed GEPs and the L1000 profiles and range between -100 and 100 reflecting incongruent or congruent profiles respectively. Then, for each co-expression module, regulators are defined as genes with a CS of at least 75 upon overexpression and at most -75 upon knockdown and drug/target combinations are defined based on a CS smaller than -75 for the drug and greater than 75 upon overexpression of the drug target.
Acknowledgements The authors acknowledge discussions with  T. Brauner  and  S. Tchoumakov. The work has been supported by the  ANR   DIRAC3D  project ( ANR-17-CE30-0023 ) and exchange programme  PHC ORCHID  ( 47044XE ). A. A. acknowledges funding from the  Swiss National Science Foundation  through Project No. PP00P2-170544. M. N. acknowledges the support of  CeNIKS  project co-financed by the  Croatian Government  and the  EU  through the  European Regional Development Fund -Competitiveness and Cohesion Operational Program  (Grant No. KK. 01. 1. 1. 02. 0013 ). R. S. acknowledges financial support provided by the  Ministry of Science and Technology in Taiwan  under project numbers  MOST-110-2112-M-001-065-MY3  as well as  Academia Sinica  for the budget of AS-iMate-109-13. D. K. M. acknowledges partial support from  NSF  Grant No.
DMR-1914451 , and the  Research Corporation for Science Advancement  through a  Cottrell SEED award. This work was also supported by  CNRS  through  IRP  " TeraMIR" and by ANR Colector  ( ANR-19-CE30-0032 ). The authors also acknowledge the support of the LNCMI-CNRS in Grenoble, a member of the  European Magnetic Field Laboratory (EMFL).
We performed MR within each data source and then meta-analysed the results across the two datasets using a random-effects model with the R package metafor (RRID:SCR_003450;  Viechtbauer, 2010) , where the data was available in both. For one published GWAS (the GECCO consortium), we only had information for FA and UFA variants. To provide further MR evidence, we used a third source of disease data -disease status in the UK Biobank (Supplementary file 1ciii). We ran the same models but did not meta-analyse with published GWAS and FinnGen because most of the body fat percentage, FA, and UFA variants were identified in the UK Biobank. We obtained heterogeneity Q statistics for each IVW MR and MR-Egger, and I 2 statistics for each MR-Egger analysis using the MendelianRandomization R package  (Yavorska and Burgess, 2017).
Acknowledgements This research has been conducted using the  UK Biobank Resource under Application Number '9072 ' and '9055'. We acknowledge the use of the  University of Exeter High-Performance Computing (HPC)  facility in carrying out this work. We acknowledge use of high-performance computing funded by an  MRC Clinical Research   Infrastructure award (MRC Grant :  MR/M008924/1 ). ASTERISK: We are very grateful to  Dr. Bruno Buecher  without whom this project would not have existed. We also thank all those who agreed to participate in this study, including the patients and the healthy control persons, as well as all the physicians, technicians and students. CCFR: The Colon CFR graciously thanks the generous contributions of their study participants, dedication of study staff, and the financial support from the  U.
whi. org/ researchers/ Documents% 20% 20Write% 20a% 20Paper/ WHI% 20Investigator% 20Short% 20List. pdf. HY is funded by  Diabetes UK RD Lawrence fellowship  (grant:  17/0005594 ). SM and TMF are funded by the  MRC  ( MR/T002239/1 ). CJB is supported by the  World Cancer Research Fund (WCRF UK) , as part of the  World Cancer Research Fund International  grant programme (IIG_2019_2009). JAB works in a unit funded by the  UK MRC  ( MC_UU_00011/1 ) and the  University of Bristol. EEV is supported by  Diabetes UK  ( 17/0005587 ) and the  World Cancer Research Fund (WCRF UK) , as part of the  World Cancer Research Fund International  grant programme (IIG_2019_2009) and works within the  CRUK Integrative Cancer Epidemiology Programme  ( C18281/A29019 ).
Genetics and Epidemiology of Colorectal Cancer  Consortium (GECCO) :  National Cancer Institute, National Institutes of Health, U. S. Department of Health and Human Services  ( U01 CA164930 ,  U01 CA137088 ,  R01 CA059045 ,  R21 CA191312 ,  R01201407 ). Genotyping/Sequencing services were provided by the Center for Inherited Disease Research (CIDR) contract number  HHSN268201700006I  and  HHSN268201200008I. This research was funded in part through the  NIH/NCI Cancer Center Support  Grant  P30 CA015704. Scientific Computing Infrastructure at Fred Hutch  funded by  ORIP  grant  S10OD028685. ASTERISK: a  Hospital Clinical Research Program  ( PHRC-BRD09/C ) from the  University Hospital Center of Nantes (CHU de Nantes ) and supported by the  Regional Council of Pays de la Loire , the  Groupement des Entreprises Françaises dans la Lutte contre le Cancer (GEFLUC) , the  Association Anne de Bretagne Génétique  and the  Ligue Régionale Contre le Cancer (LRCC).
PLCO:  Intramural Research Program  of the  Division of Cancer Epidemiology and Genetics  and supported by contracts from the  Division of Cancer Prevention, National Cancer Institute ,  NIH ,  DHHS. Funding was provided by  National Institutes of Health (NIH) ,  Genes, Environment and Health Initiative (GEI)   Z01 CP 010200 ,  NIH   U01 HG004446 , and  NIH   GEI U01 HG 004438. SEARCH: The  University of Cambridge  has received salary support in respect of PDPP from the  NHS in the East of England through the Clinical Academic Reserve. Cancer Research UK  ( C490/A16561 ); the  UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge. SELECT: Research reported in this publication was supported in part by the  National Cancer Institute of the National Institutes of Health  under Award Numbers  U10 CA37429  ( CD Blanke ), and  UM1 CA182883  ( CM Tangen/IM Thompson ).
The collection and availability of cancer registry data is also supported by the  Cooperative Agreement   NU58DP006333 , funded by the  Centers for Disease Control and Prevention. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the  Centers for Disease Control and Prevention  or the  Department of Health and Human Services. ColoCare: This work was supported by the  National Institutes of Health  (grant numbers  R01 CA189184  Author ORCIDs Susan Martin http://orcid. org/0000-0001.
The  CCFR Set-3 (Affymetrix Axiom CORECT Set  array) was supported by  NIH  award  U19 CA148107  and  R01 CA81488  (to SBG). The CCFR Set-4 (Illumina OncoArray  600K  SNP array) was supported by  NIH  award  U19 CA148107  (to SBG) and by the  Center for Inherited Disease Research (CIDR) , which is funded by the  NIH  to the  Johns Hopkins University , contract number  HHSN268201200008I. Additional funding for the OFCCR/ARCTIC was through award  GL201-043  from the  Ontario Research Fund  (to BWZ), award 112746 from the  Canadian Institutes of Health Research  (to TJH), through a  Cancer Risk Evaluation (CaRE) Program  grant from the  Canadian Cancer Society  (to SG), and through generous support from the  Ontario Ministry of Research and Innovation. The SFCCR Illumina HumanCytoSNP array was supported in part through  NCI/NIH  awards  U01 CA074794  (to JDP) and / U24 CA074794  and  R01 CA076366  (to PAN).
This study was conducted with Institutional Review Board approval. CRCGEN:  Colorectal Cancer Genetics & Genomics, Spanish study  was supported by  Instituto de Salud Carlos III , co-funded by  FEDER  funds -a way to build Europe -(grants  PI14-613  and  PI09-1286 ),  Agency for Management of University and Research Grants (AGAUR) of the Catalan Government  (grant  2017SGR723 ), and  Junta de Castilla y León  (grant  LE22A10-2 ). Sample collection of this work was supported by the  Xarxa de Bancs de Tumors de Catalunya  sponsored by  Pla Director d'Oncología de Catalunya (XBTC) ,  Plataforma Biobancos   PT13/0010/0013  and ICOBIOBANC, sponsored by the  Catalan Institute of Oncology. Czech Republic CCS: This work was supported by the  Czech Science Foundation  ( 20-03997  S) and by the  Grant Agency of the Ministry of Health of the Czech Republic  (grants  NV18/03/00199  and  NU21-07-00247 ).
The  EPIC-Norfolk study  (https:// doi. org/ 10. 22025/ 2019. 10. 105. 00004 ) has received funding from the  Medical Research Council  ( MR/N003284/1  and  MC-UU_12015/1 ) and  Cancer Research UK  ( C864/ A14136 ). The genetics work in the EPIC-Norfolk study was funded by the  Medical Research Council  ( MC_PC_13048 ). Metabolite measurements in the EPIC-Norfolk study were supported by the  MRC Cambridge Initiative in Metabolic Science  ( MR/L00002/1 ) and the  Innovative Medicines Initiative Joint Undertaking  under EMIF grant agreement no. 115372. EPICOLON: This work was supported by grants from  Fondo de Investigación Sanitaria/FEDER  ( PI08/0024 ,  PI08/1276 ,  PS09/02368 ,  PI11/00219 ,  PI11/00681 ,  PI14/00173 ,  PI14/00230 ,  PI17/00509 ,  17/00878 ,  PI20/00113 ,  PI20/00226 ,  Acción Transversal de Cáncer) ,  Xunta de Galicia  ( PGIDIT07PX-IB9101209PR ),  Ministerio de Economia y Competitividad  ( SAF07-64873 ,  SAF 2010-19273 ,  SAF2014-54453R ),  Fundación Científica de la Asociación Española  contra el Cáncer ( GCB13131592CAST ),  Beca Grupo de Trabajo "Oncología" AEG  ( Asociación Española de Gastroenterología ),  Fundación Privada Olga Torres ,  FP7   CHIBCHA  Consortium,  Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR, Generalitat de Catalunya ,  2014SGR135 ,  2014SGR255 ,  2017SGR21 ,  2017SGR653 ),  Catalan Research article Epidemiology and Global Health | Genetics  and  Genomics Martin  et al.
eLife 2022;11:e72452. DOI: https://doi. org/10. 7554/eLife. 7245224 of 32  Tumour Bank Network (Pla Director d'Oncologia ,  Generalitat de Catalunya) ,  PERIS  ( SLT002/16/00398 ,  Generalitat de Catalunya) ,  CERCA Programme  ( Generalitat de Catalunya ) and  COST Actions BM1206  and  CA17118. CIBERehd is funded by the  Instituto de Salud Carlos III. ESTHER/VERDI. This work was supported by grants from the  Baden-Württemberg Ministry of Science, Research and Arts  and the  German Cancer Aid. Harvard cohorts (HPFS, NHS, PHS ): HPFS is supported by the  National Institutes of Health  ( P01 CA055075 ,  UM1 CA167552 ,  U01 CA167552 ,  R01 CA137178 ,  R01 CA151993 ,  R35 CA197735 ,  K07 CA190673 , and  P50 CA127003 ), NHS by the  National Institutes of Health  ( R01 CA137178 ,  P01 CA087969 ,  UM1 CA186107 ,  R01 CA151993 ,  R35 CA197735 ,  K07CA190673 , and  P50 CA127003 ) and PHS by the  National Institutes of Health  ( R01 CA042182 ).
Hawaii Adenoma Study :  NCI  grants  R01 CA72520. HCES-CRC: the  Hwasun Cancer Epidemiology Study-Colon and Rectum Cancer (HCES-CRC ; grants from  Chonnam National University Hwasun Hospital ,  HCRI15011-1 ). Kentucky: This work was supported by the following grant support: Clinical Investigator Award from  Damon Runyon Cancer Research Foundation  ( CI-8 );  NCI   R01CA136726. LCCS: The  Leeds Colorectal Cancer Study  was funded by the  Food Standards Agency and Cancer Research UK   Programme Award  ( C588/A19167 ). Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by  VicHealth  and  Cancer Council Victoria. The MCCS was further augmented by  Australian National Health and Medical Research Council  grants  209057 ,  396414 , and  1074383  and by infrastructure provided by  Cancer Council Victoria.
Cases and their vital status were ascertained through the  Victorian Cancer Registry  and the  Australian Institute of Health and Welfare , including the  National Death Index  and the Australian Cancer Database. Multiethnic Cohort (MEC) Study:  National Institutes of Health  ( R37 CA54281 ,  P01 CA033619 ,  R01 CA063464 , and  U01 CA164973 ). MECC: This work was supported by the  National Institutes of Health, U. S. Department of Health and Human Services  ( R01 CA81488  to SBG and GR). MSKCC: The work at Sloan Kettering in New York was supported by the  Robert and Kate Niehaus Center for Inherited Cancer Genomics  and the  Romeo Milio Foundation. Moffitt: This work was supported by funding from the  National Institutes of Health  (grant numbers  R01 CA189184 ,  P30 CA076292 ),  Florida Department of Health Bankhead-Coley  Grant  09BN-13 , and the  University of South Florida Oehler Foundation.
Funding was provided to Michael O. Woods by the  Canadian Cancer Society Research Institute. NSHDS: Swedish Research Council ;  Swedish Cancer Society ;  Cutting-Edge Research Grant  and other grants from  Region Västerbotten; Knut and Alice Wallenberg Foundation ;  Lion's Cancer Research Foundation at Umeå University ; the  Cancer Research Foundation in Northern Sweden ; and the  Faculty of Medicine, Umeå University, Umeå, Sweden. OSUMC: OCCPI funding was provided by  Pelotonia  and HNPCC funding was provided by the  NCI  ( CA16058  and  CA67941 ).
Data availability GWAS data from the outcome diseases studied is available from links published in the original studies (Supplementary File 1ci). FinnGen data is available at: https://finngen. gitbook. io/documentation/, and the list of disease outcomes used is in Supplementary File 1cii. Individual-level UK Biobank data cannot be provided, but it is available by application to the UK Biobank: https://www. ukbiobank. ac. uk, and a list of the traits used is in Supplementary File 1ciii. Code used to conduct this analysis will be made available on GitHub after removing any sensitive information (https://github. com/susiemartin/ uncoupling-bmi, copy archived at swh:1:rev:f3472762ad6cb7f313656f684e07c14b8735efe5).
BMC Research Notes *Correspondence: zh. guangqi@gmail. com mechanical functions  [11] [12] [13]. NSC are mainly stored in the live radial and axial parenchyma cells, therefore the size of the parenchyma fraction drives the capacity for NSC storage in trees  [14] [15] [16]. In an attempt to disentangle how NSC and xylem traits are linked to tree physiological processes and ecological strategies, we collated data on stem and root NSC and xylem cell patterns, as well as leaf traits, from 90 tree species in temperate, Mediterranean and tropical climates. Our data will allow researchers to explore the direct relationships between leaf, stem and root NSC contents with patterns in xylem cell composition. To our knowledge, this dataset represents the largest freely available collection of data for NSC xylem and leaf traits measured simultaneously in adult trees from diverse climates.
Measurement of leaf and xylem functional traits A total of 270 leaf samples, 390 stem samples and 180 root samples were used to determine NSC, carbon (C) and nitrogen (N) measurements. For NSC content, a colorimetric method  [20]  was performed on all leaf samples, and a subsample of stems (n = 113) and roots (n = 140). C and N content were measured in a sub-sample of leaves (n = 110), stems (n = 113) and all root samples, using an elemental analyzer (CHN model EA 1108, Milan, Italy). Then, near infra-red spectroscopy (NIRS)  [21]  was performed on all samples and calibration models developed using data obtained from the analytical methods to predict soluble sugars, starch, C and N in the three organs (Table  1 , file 1). Cross-sections of 15-20 µm thick for stem (n = 270) and root (n = 180) samples were cut with a sliding microtome and stained with a mixture of safranin and alcian blue.
Limitations Root NSC data were only collected for 60 species (n = 20 in each climate type). It was not possible to collect NSC in samples from tropical trees over two different seasons within a 12-month time period because of travel restrictions linked to the COVID pandemic. However, there is significantly less seasonal variability in NSC in tropical trees  [10] , compared to Mediterranean and temperate species, therefore we do not consider this limitation as detrimental to the quality of data.
Acknowledgements We are grateful to  N. Boutahar  ( CIRAD ) and  J. Cazal  ( INRAE ) for their help with field and laboratory work. We thank  G. Derroire  and  A. Dourdain  for their aid with logistical arrangements and field authorizations in French Guyana. The collecting of genetic resources in French Guyana was authorized by the  French Government  ( APA No. 1371201 ). We kindly thank to managers of state-owned land for their collaboration and to  B. Dupin  ( Eco-Altitude ) for permission to sample trees on private property at  Luz-Saint-Sauveur , France. The authors would like to thank  SILVATECH  (https:// doi. org/ 10. 15454/1. 55724 00113 62785 4E12 ) from UMR 1434 SILVA, 1136 IAM, 1138 BEF and 4370 EA LERMAB from the research center  INRAE Grand-Est Nancy  for its contribution to NSC analyses.
We used SWS products derived from Sentinel-1 ascending orbit (relative orbit 161) and descending orbit (relative orbit 139). The Fractional Snow Cover (FSC) product is derived from Sentinel-2 optical satellite data following the methodology described in  [12, 15]. The FSC product has a resolution of 20 m and provides the fraction of the surface covered by snow as well as the delimitation of areas covered by clouds for which there is no estimate of snow cover. From the snow cover fraction value, we derive the binary information: snow presence if the fraction > 0%, no snow otherwise. A detailed description of the products is provided on the Copernicus Land Monitoring Service web site https://land. copernicus. eu/user-corner/technical-library/hrsi-snow-pum, accessed on 1 January 2022. As stated earlier, the study period is from the fall of 2016 to the summer of 2021.
The graphs also compare favourably with some snowpack seasonal patterns as seen by the Crocus model even though differences do exist between the different data. Comparisons with the Crocus model suggest two avenues of exploitation of SWS products, namely (1) their use to evaluate the outputs of snow models and (2) their potential application as inputs to feed models through data assimilation methods. Studies are underway to systematically compare the snow liquid water content simulations of the Crocus model with the SWS products and to evaluate the points of agreement and disagreement between these databases at the scale of the French Alps. This would allow, among other effects, to better identify potential model biases during melting periods and to implement an adequate evaluation system for the snow model.
SRF3 is a regulator of iron homeostasis genes and root growth under low iron levels Genome wide association studies (GWAS) for root growth rate under low iron levels revealed multiple significantly associated singlenucleotide polymorphisms (SNPs; Supplementary Fig. 1a, b  and Supplementary Data 1). The association peak containing most significant SNPs was observed on chromosome 4 near the genes AT4G03390 (STRUBBELIG-RECEPTOR FAMILY 3, SRF3) and AT4G03400 (DWARF IN LIGHT 2, DFL2) for root growth between days 4-5 (Fig. 1a  and Supplementary Data 1). To identify potential causal genes at this locus, we obtained Col-0 T-DNA mutant lines for these genes (Supplementary Fig. 1c, d ). We then measured the root growth of these lines grown on iron sufficient and low iron medium. To provide more clarity, we calculated for each genotype the root growth response (the ratio of three days of root growth in low iron and sufficient iron conditions).
Finally, to investigate the extent of SRF3-dependent coordination of bacterial immune responses and iron homeostasis, we performed a RNAseq analysis after 2 h of exposure to low iron levels or flg22 in srf3 mutants and WT roots. Strikingly, in WT, 90% of the differentially expressed genes (DEGs) in low iron overlapped with DEGs upon flg22 treatment and were regulated in the same manner (e. g. , upregulated DEGs in low iron were also upregulated upon flg22). Importantly, these DEGs were not associated with a general stress response since only few of them were overlapping with those in cold, heat, drought, salt and mannitol datasets  32, 33  (Fig. 6f  and Supplementary Fig. 10b , Data 4 and 5). To further confirm that low iron levels trigger immunity genes and to validate the RNAseq, we conducted qPCR for two early markers of flg22-triggered immunity, FRK1 and MYB51 which showed a transient activation of the two genes within 4 h (Supplementary Fig.
10c )  34. To determine how much of this common transcriptional program is coordinated by SRF3, we analyzed the srf3 transcriptome datasets. DEGs in flg22 and low iron in srf3 mutants only overlapped by 24% demonstrating that SRF3 coordinates a large part of the common transcriptional program that is triggered in response to early response to low iron and flg22 (Fig. 6f ). Overall, our work establishes SRF3 as a major coordinator of bacterial immune response and iron deficiency signaling pathways, which relies on callose synthase activity.