dataesr/scientific-paragraphs-categorization huggingface dataset:
The KARMA study was supported by  Märit and Hans Rausings Initiative Against Breast Cancer. 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 , and by the strategic funding of the  University of Eastern Finland. kConFab is supported by a grant from the  National Breast Cancer Foundation , and previously by the  National Health and Medical Research Council (NHMRC) , the  Queensland Cancer Fund , the  Cancer Councils of New South Wales, Victoria , Tasmania and  South Australia , and the  Cancer Foundation of Western Australia. Financial support for the AOCS was provided by the  United States Army Medical Research and Materiel Command  [ DAMD17-01-1-0729 ],  Cancer Council Victoria, Queensland Cancer Fund, Cancer Council New South Wales, Cancer Council South Australia , The  Cancer Foundation of Western Australia, Cancer Council Tasmania  and the  National Health and Medical Research Council of Australia  (NHMRC; 400413, 400281, 199600).
LMBC is supported by the 'Stichting tegen Kanker'. D. L. is supported by the  FWO. The MABCS study is funded by the  Research Centre for Genetic Engineering and Biotechnology "Georgi D. Efremov", MASA. The MARIE study was supported by the  Deutsche Krebshilfe e. V. [ 70-2892-BR I, 106332 ,  108253 ,  108419 ,  110826 ,  110828 ], the  Hamburg Cancer Society , the  German Cancer Research Center (DKFZ)  and the  Federal Ministry of Education and Research (BMBF) Germany  [ 01KH0402 ]. MBCSG is supported by grants from the  Italian Association for Cancer Research (AIRC)  and by funds from the  Italian  citizens who allocated the 5/1000 share of their tax payment in support of the  Fondazione IRCCS Istituto Nazionale Tumori , according to Italian laws ( INT-Institutional strategic  projects "5×1000").
The MCBCS was supported by the  NIH  grants  CA192393 ,  CA116167 , CA176785 an  NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer  [ CA116201 ], and the  Breast Cancer Research Foundation  and a generous gift from the  David F. and Margaret T. Grohne Family Foundation. The 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.
The content of this manuscript does not necessarily reflect the views or policies of the  National Cancer Institute  or any of the collaborating centres in the  Breast Cancer Family Registry (BCFR)  or the  Colon Cancer Family Registry (CCFR) , nor does mention of trade names, commercial products, or organisations imply endorsement by the  USA Government  or the BCFR or CCFR. The  Carolina Breast Cancer Study (NCBCS)  was funded by  Komen Foundation , the  National Cancer Institute  ( P50 CA058223 ,  U54 CA156733 ,  U01 CA179715 ), and the  North Carolina University Cancer Research Fund. The NHS was supported by  NIH  grants  P01 CA87969 ,  UM1 CA186107  and  U19 CA148065. The  NHS2  was supported by  NIH  grants  UM1 CA176726  and  U19 CA148065. The OBCS was supported by research grants from the  Finnish Cancer Foundation , 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. Genotyping for PLCO was supported by the  Intramural Research Program  of the  National Institutes of Health, NCI, Division of Cancer Epidemiology and Genetics. The PLCO is supported by the  Intramural Research Program of the Division of Cancer Epidemiology and Genetics  and supported by contracts from the  Division of Cancer Prevention, National Cancer Institute, National Institutes of Health. The POSH study is funded by  Cancer Research UK  (grants  C1275/ A11699 ,  C1275/C22524 ,  C1275/A19187 ,  C1275/A15956  and  Breast Cancer Campaign   2010PR62 ,  2013PR044.
PROCAS is funded from  NIHR  grant  PGfAR   0707-10031. The RBCS was funded by the  Dutch Cancer Society  ( DDHK 2004-3124 ,  DDHK 2009-4318 ). The SASBAC study 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. The SBCS was supported by  Sheffield Experimental Cancer Medicine Centre and Breast Cancer Now Tissue Bank. SEARCH is funded by  Cancer Research UK  [ C490/A10124 ,  C490/A16561 ] and supported by the  UK National Institute for Health Research Biomedical Research Centre  at the  University of Cambridge. The  University of Cambridge  has received salary support for PDPP from the  NHS in the East of England through the Clinical Academic Reserve.
The  Two Sister Study  ( 2SISTER ) was supported by the  Intramural Research Program  of the  NIH, National Institute of Environmental Health Sciences  ( Z01-ES044005  and  Z01-ES102245 ), and, also by a grant from  Susan G. Komen for the Cure , grant  FAS0703856. SKKDKFZS is supported by the  DKFZ. The SMC is funded by the  Swedish Cancer Foundation  and the  Swedish Research Council  ( VR 2017-00644 ) grant for the  Swedish Infrastructure for Medical Population-based Life-course Environmental Research (SIMPL+++ER). The SZBCS was supported by Grant  PBZ_KBN_122/P05/2004  and the program of the  Minister of Science and Higher Education  under the name " Regional Initiative of Excellence " in 2019-2022 project number  002/RID/2018/19  amount of financing  12 000 000 PLN. The TNBCC was supported by: a  Specialized Program of Research Excellence (SPORE) in Breast Cancer  ( CA116201 ), a grant from the  Breast Cancer Research Foundation , a generous gift from the  David F.
and Margaret T. Grohne Family Foundation. The UCIBCS component of this research was supported by the  NIH  [ CA58860 ,  CA92044 ] and the  Lon V Smith Foundation  [LVS39420]. The UKBGS is funded by  Breast Cancer Now  and the  Institute of Cancer Research (ICR) ,  London. The UKOPS study was funded by  The Eve Appeal (The Oak Foundation ) and supported by the  National Institute for Health Research University College London Hospitals Biomedical Research Centre  Mendelian randomisation study of smoking exposure in relation to breast. HA. Park et al.
Methods We undertook a systematic review of published journal articles that correlated measures derived from white matter tractography with cognition or clinical symptoms, following PRISMA guidelines  (Liberati et al. , 2009). The resources obtained from this study and created for this data are made available as supplementary material: https://github. com/StephForkel/PhenotypesReview. git Data Sources: A title/abstract search in MEDLINE and Scopus (which includes most of the EMBASE database, https://www. elsevier. com/solutions/embase-biomedical-research) was conducted. The search term 'tractography' returned a total of 5,303 in PubMed and 7,204 results in Scopus. We hence restricted our search (conducted on February 25 th , 2020) to the following strings: (predictor OR "correlat*" OR regression OR "assoc*") AND (tractography).
Additional filters were applied to include only human adult studies published in English as final stage peer-reviewed articles in scientific journals. The search returned 1333 results on Pubmed and 2380 results on Scopus yielding a combined total of 3,713 records. There were no internal duplicates within each database, but 1224 external duplicates were excluded between the databases. After removing duplicates from these lists a total of 2489 results were screened. Data screening and eligibility: Figure  1  summarises the following workflow. During the screening, we applied further exclusion criteria leading to the exclusion of paediatric studies, non-human studies, pure methodological papers without behavioural correlates, correlations between tractography and physiological rather than behavioural measures, non-brain studies (e.
Data synthesis and analyses: In the present synthesis of this dataset, we summarised degeneration, neurosurgical and common neurological symptoms (e. g. stroke) as a neurological group. Similarly, the psychiatric group included neurodevelopmental and psychiatric studies. We also synthesized clinical symptoms, behaviour and/or cognitive domains using the following terms: We limited the cognitive domains taxonomy to the terms that are currently widely accepted, including attention, executive functions, language, memory, and reward. Similarly, terms defining behavioural domains included addiction, auditory, visual and motor behaviour, sleep, mood, social measures (e. g. theory of mind). In order to assess the sensitivity of tracts to clinical measures we also included a "symptoms" dimension corresponding to neurological and psychiatric severity measures.
Ingenuity Pathway Causal Network Analysis Identified RPTOR as a Master Regulator of Methylation Changes in RPE We performed moderated t-tests using limma and conducted IPA CNA on the top 301 most significantly differentially methylated CpG probes in blood, based on a p-value threshold of p ≤ 10 -3 between AMD and Normal groups. Following filtering of CpG probes in intergenic areas and assigning multiple CpG probes mapping to a single gene as one single gene, the IPA CNA in blood revealed 212 genes for analysis. Supplementary Materials: Additional File S3, demonstrates the full results of IPA CNA for the top most differentially methylated CpG probes between AMD and Normal samples in blood. We also assessed the top most variable probes from our published genome wide DNA methylation dataset obtained from RPE  [27].
Discussion This is the first study to our knowledge formally evaluating whether EAA is associated with AMD and important risk factor covariates including smoking status. We sought to address whether EAA is observed in the RPE  [27] , as it is a primary site of AMD pathogenesis, and in whole blood, as the epigenetic clocks have been widely applied and validated in blood-derived gDNA. In the RPE, we observed a marked negative EAA across all groups with no significant differences in EAA between AMD and Normal samples using all three clocks. This result cannot be characterised as true negative age acceleration because of poor performance of the epigenetic clocks in RPE  [33]. The consistent poor correlation of predicted DNAm age with chronological age observed in the RPE markedly improved when analysing whole blood-derived gDNA data, explained by the datasets used to train each respective epigenetic clock  [3, 4, 8].
The data provided welcomes the generation of multivariate models to further delineate the complexity of ageing in RPE or address the current calibration issues with existing clocks. Reasonable performance of each respective epigenetic clock in whole blood, however, strengthens the observation of no association of EAA with AMD in blood, though this remains open to further investigation in the RPE, which can be addressed using a bespoke RPE epigenetic clock with greater predictive accuracy. The highly accurate skeletal muscle  [35]  and breast tissue  [36]  epigenetic clocks have set precedents to this approach. These clocks share little overlap in CpG probe selection with Horvath's multi-tissue clock suggesting that bespoke clocks capture separate tissue specific epigenetic ageing processes  [36].
Therefore, construction of a tissue-specific RPE clock is necessary for future studies to capture the specific epigenetic ageing processes in the RPE. A strength of this study was having information regarding the smoking status of AMD patients from which blood samples were taken, allowing stratified analyses to investigate whether smoking was associated with EAA in AMD. Smoking, the greatest modifiable risk factor for AMD, substantially increases AMD relative risk (RR) with RR of disease varying from 2. 7 to 6. 6 in current smokers in unrelated mixed-gender cohorts  [19] [20] [21] [22]. In accordance with previous studies examining EAA using Horvath's multi-tissue clock on DNAm data from lung tissue of smokers  [30] , we observed a significant positive EAA in all three epigenetic clocks tested.
However, previous poor performance in older datasets for the Horvath multi-tissue and Hannum clocks may explain why the Skin & Blood clock performed best out of all three clocks tested in blood-derived gDNA. While the Skin and Blood clock has also shown a degree of error in elderly samples due to age confounding  [37] , it consistently outperforms predictive capacity in blood samples when compared to Horvath's multi tissue clock and Hannum's clock, a finding confirmed in this study  [7, 37]. Therefore, our observation that smokers with AMD exhibit accelerated epigenetic ageing compared to Non-Smokers with AMD is strengthened by identifying this effect in the clock with the greatest predictive accuracy for the demographic age and tissue type represented in our dataset. To support our analyses of epigenetic ageing in AMD, we performed DML analysis in AMD whole blood-derived gDNA.
Pre-Processing and Normalisation EPIC-array data was analysed using functionality within the minfi package  [50]. Each sample was subjected to various quality control measures. Cell type proportions were corrected using the estimateCellCounts2 function in R. Briefly, "CD8T", "CD4T", "NK", "Bcell", "Mono", and "Neu" were deconvoluted from our mixed whole blood samples  [51]. Furthermore, samples were checked for global hybridisation quality based on an average probe detection p-value threshold of p ≥ 0. 05. Samples not meeting these criteria were removed from the analysis. Individual CpG probes exceeding a detection p-value of p ≥ 0. 01, indicating a failed position, were removed from the analysis. Probes located on chromosomes X & Y and probes within two base pairs of a single nucleotide polymorphism (SNP) with a minor allele frequency ≥0.
Epigenetic Clock Analyses DNAm age was calculated on DNAm data obtained from whole blood-derived gDNA of Normal and AMD patients from the Southampton Cohort, analysed using the EPICarray, and DNAm data from ocular tissue (human RPE) of Normal and AMD patients from our previously described cohort of individuals of European descent, analysed using 450K-array data (accessed from ArrayExpress: E-MTAB-7183) [date accessed: 24 May 2020]. [27]. DNAm age estimations were performed using Horvath's multi-tissue [3], Hannum's  [4] , and the Skin & Blood epigenetic  [8]  clocks using a publicly available online calculator (available at: http://dnamage. genetics. ucla. edu/, accessed on 9 September 2021). For outputs of all epigenetic clock data in whole blood and RPE-derived gDNA, please see Supplementary Materials: Additional File S1.
We assessed the performance of each respective clock on DNAm data derived from RPE gDNA (n = 44) using 450K-array data from our previously described cohort  [27]. We also assessed the performance of these three clocks using whole blood-derived (n = 30) gDNA methylation data acquired by EPIC-array from the Southampton Cohort. Performance was assessed using linear regression of estimated DNAm age with chronological age of patient-derived samples from each respective tissue type. EAA was calculated as the difference between estimated DNAm age and chronological age (DNAm age-chronological age). Assessment for normal distribution of data was performed using a Shapiro-Wilks Test for Normality prior to conducting comparisons using statistical tests. Normality tests were passed following α > 0. 05.
Compound Identification MS/MS was carried out on compounds 1 and 2 using a Finnigan LTQ MS (Thermo-Electron Corporation) using Tune Plus software version 1. 0, as previously described  (Mevers et al. , 2011). Spectral files were converted to mzXML files (publicly accessible at http://gnps. ucsd. edu under MassIVE accession no. MSV000080055) and analyzed using previously described algorithms  (Liu et al. , 2009; Ng et al. , 2009; Mohimani et al. , 2011). Compounds 1 and 2 were analyzed by 1 H and 13 C NMR, recorded in methylene chloride-d 2 (CD 2 Cl 2 , Cambridge Isotope Laboratories, Inc. , Andover, MA, United States) (Supplementary Figures  2 3 4 5 ). Veraguamide M (1): amorphous solid; [α] 23 d -49. 5 (c 0. 5, CHCl 3 ); 1 H NMR (400 MHz, CD 2 Cl 2 ) δ 6. 25 (d, J = 8. 6 Hz, 1H), 4. 88 (d, J = 7.
ACKNOWLEDGMENTS We would like to thank the  Ministerio de Ambiente de Panamá (MiAmbiente)  formerly known as the  Autoridad Nacional del Ambiente (ANAM)  for providing access to the collection sites and the biological samples. We would also like to thank  F. Vargas  and  B. Teke  for carrying out ESIMS/MS acquisition ( University of California, San Diego ),  A. Hermosillo  ( Universidad de Guadalajara ) for participating in the initial exploratory scuba trip to Coiba to locate sea hare habitats,  A. Ibáñez  ( STRI ) for field assistance,  R. Noble  for advice on statistical analyses,  Jean Pigozzi  of the  Liquid Jungle Lab located on Isla de Canales de la Tierra , and  Naos Marine  and  Molecular Laboratories at STRI for laboratory and aquarium space ,  C. F. Gurgel  ( Smithsonian Marine Station, Fort Pierce ) for identifying the algae samples,  R.
FUNDING Financial support was received through the  Natural Sciences and Engineering Research Council of Canada -Canada Graduate Scholarships -Master's (NSERC CGS M) ,  Levinson  and  STRI/McGill NEO  fellowships (to KEC). Funding for this research was also provided by the  Fogarty International Center International Cooperative Biodiversity Group (ICBG) program based in Panama  ( U01 TW006634 ,  PI William H. Gerwick).
Phylogenetic tree building Phylogenetic trees were constructed by combining phylogenetic and taxonomic data from the literature, with the assumption that the identical branch lengths between genera and species were 1 and 0. 5, respectively, as distances were not available for all species (also, we wanted to be able to compare results between taxa). The phylogenetic tree of spiders (appendix A, figure  4 ) was adapted from that of Wheeler et al. [52]  by adding missing genera using Arnedo et al. [53] , Frick et al. [54] , and Wang et al. [55]  for Linyphiidae & Millidge  [56] , Agnarsson  [57] , Maddison  [58] , Azevedo et al. [59] , Piacentini & Ramírez  [60]  and Scharff et al. [61]  for other families. The phylogenetic tree of carabids (appendix B, figure  5 ) was adapted from that of Martínez-Navarro, Galián, & Serrano  [62] , Sasakawa & Kubota  [63] , Ober & Maddison  [64] , Ruiz Jordal & Serrano  [65]  and López-López & Vogler  [66].
Statistical analysis TD was estimated by measuring the species richness of samples per trap (both sites together), and computed using the BAT package  [68]  following the methods described by Lopez et al. [69]  for Table  1. Environmental variables (mean ± s. d. , n = 12) for each salt-marsh zone and for each site. (Successive letters indicate significant differences by ANOVA test followed by Tukey post-hoc tests or Kruskal-Wallis or Mann-Whitney or Welch tests, where appropriate. Bonferroni correction was used for post-hoc tests if necessary. Plant species are given only when they occur in more than 75% of the surveys carried out in the area and had a Braun-Blanquet coverage > 1. 5. HM, high marsh; MM, middle marsh; LM, low marsh. ) obtaining corrected jackknife estimators. Final TD was calculated by averaging the corrected jackknife estimates in order to account for sampling variability, and checked with the accuracy function (scaled mean squared error <5% in each case).
In the same manner, PD and FD were estimated using the Petchey & Gaston  [4, 5]  estimator for FD and Faith  [70]  for PD, following the methods described by Cardoso et al. [71]  using the BAT package. Final PD and FD were also calculated by averaging the corrected jackknife estimates. Distance matrix for phylogenetic distances was calculated using Gower distance from the FD package  [72]. The correlation between PD and FD was estimated in a Bayesian framework with a Student's t-distribution (which reduces sensitivity to outliers) using the brms package  [73]. We used 2000 iterations on four chains. Model convergence was checked by visually inspecting diagnostic plots. To select environmental variables affecting PD and FD (for later use in structural equation models (SEM)), models were built within a Bayesian framework using brms  [73]  with two chains and default priors.
We used the piecewise SEM package  [76]  as it allows us to use mixed models in association with the nlme package  [77]. Our initial model included the following links: (i) PD is affected by TD and selected environmental variables; (ii) FD is affected by TD, selected environmental variables, and PD; (iii) TD is affected by selected environmental variables; and (iv) there is a correlated error between PD and FD (table 2). Site was used as a random factor in every link model using nlme  [77]. After the specification of the initial model, we re-defined our model excluding non-significant links ( p < 0. 05) using a stepwise approach until ΔAICc < 2 between two subsequent models. Finally, we assessed model fit using Fisher's C statistic. All statistical analyses were performed using R STUDIO software (v.
Acknowledgements. We would like to thank the  Editage company  for English editing, one anonymous referee for providing relevant comments and suggestions, and the nature reserve staff of  Möeze-Oléron  and  Lilleau des Niges  for field assistance:  Fréderic Robin ,  Jean-Christophe Lemesle ,  Philippe Delaporte ,  Vincent Lelong ,  Julien Gernigon , and  Lucas Deplaine.
Shotgun Metagenomics and Genome-Resolved Metagenomics Metagenomic sequencing for fecal DNA samples was performed via Illumina HiSeq at the Marine Biological Laboratory and yielded paired-end reads of 2 Â 150 nucleotides. Noisy sequences were denoised with iu-filter-quality-minoche  9  using default parameters. 10 The remaining reads were co-assembled using MEGAHIT,  11  version 1. 0. 3, with a minimum scaffold length of 2. 5 kilo base pairs, and scaffold header names were simplified using anvi'o,  12  version 6. 1 (available from https:// merenlab. org/software/anvio). Scaffolds were subsequently binned as previously described (see Supplementary Methods). 12, 13 Bins with <10% redundancy and with either >2 mega base pairs in length or >70% of estimated completion were defined as metagenome-assembled genomes (MAGs), manually curated using the same interactive interface.
Statistical Analysis The Mann-Whitney U test was performed with GraphPad Prism (GraphPad Software) to compare 16s ribosomal RNA (rRNA) gene copy number, Shannon diversity index, T-cell populations, body weights, histologic scores, fecal lipocalin-2 (LCN-2) levels, and cytokine levels between the no treatment (NT) and CPZ groups. For 16S rRNA gene amplicon sequencing analysis, permutational analysis of variance tests were computed with QIIME2  15  to assess bacterial composition differences between treatment groups or for unweighted and weighted UniFrac distance data. Statistical significance was achieved at P <. 05. MAG detection rates between the CPZcolitis group vs the CPZ-no colitis group at 11 weeks of age were compared using the Student t test, and P values were adjusted for multiple tests using the Benjamini-Hochberg method.