dataesr/scientific-paragraphs-categorization huggingface dataset:
Prevalent CRC status is susceptible to survival bias. While they differ in the design and assessment of CRC, the results from the two datasets are consistent in finding no association of HFE genotype with CRC or age at CRC diagnosis. Lack of reliable data on HH diagnosis is an additional limitation, as we could not assess risk of CRC in penetrant HH. Given the incomplete penetrance of HH, a true association that relies on elevated iron stores as a mechanism might be very small and, thus, difficult to detect, even in this large sample size; however, the CIs here rule out a large increased risk of CRC. Our analyses are limited to individuals of European ancestry and may not be generalizable to other ancestry groups. Strengths of the current study include the large sample sizes and the use of two very different ascertainment systems: a consortium of existing CRC case-control studies and nested case-control studies and the large population-based 23andMe dataset, where enrollment is likely not correlated with CRC status or HH risk.
In summary, we do not find any evidence of an HFE C282Y/C282Y genotype association with CRC in two large and complementary datasets, the first ascertained by CRC case-control status and the second ascertained independently of either CRC or HH diagnoses status. Further, we see no association of HH risk genotypes on CRC age at diagnosis. These results indicate no increased CRC risk in individuals with HH genotypes and suggest that persons with HH risk genotypes can follow population screening recommendations for CRC. 32, 36, 37 ta and code availability Genotype data for GECCO and CORECT have been deposited in the database of Genotypes and Phenotypes (dbGaP) under accession number phs001078. v1. p1, phs001415. v1. p1, and phs001315. v1. p1. The UK Biobank resource is available upon application to the Access Management Team of UK Biobank Limited.
Acknowledgments This work was supported by  National Human Genome Research Institute  (NHGRI;  U01HG008657 ). Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO)  funding includes that from the  National Cancer Institute (NCI)   U01 CA137088 ,  R01 CA059045 ,  R01201407 , and  P30 CA015704. GECCO genotyping services were provided by the Center for Inherited Disease Research (CIDR;  X01-HG008596  and  X-01-HG007585 ). CIDR is fully funded through a federal contract from the  National Institutes of Health  to The Johns Hopkins University, contract  HHSN268201200008I. We would like to thank all  CRC  studies' and 23andMe research participants and employees for making this work possible. Additional colon cancer consortia acknowledgments are in supplemental acknowledgments. Where authors are identified as personnel of the  International Agency for Research on Cancer/World Health Organization , the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the  International Agency for Research on Cancer/World Health Organization.
Data analysis We conducted descriptive analysis using frequencies and percentages for binary and categorical data and means with standard deviations and medians with interquartile ranges for continuous data. We analyzed data using Stata 15 (Stata: College Station, Texas). We assessed factors associated with the outcomes (risk for delay or low development related to peers) using logistic regressions (for binary endpoints) and accounting for clustering at the sampling level using Stata's "cluster" function. Factors in univariable analysis significant at an alpha of 0. 1 were entered into a full multivariable model and reduced to a final model using backward stepwise regression with an alpha of 0. 05 for retention. Wald tests were used to compare reduced to full models. We assessed factors in the final models for interaction.
The primary planned analysis was to be conducted with the SPM software (as described in the Quantification and Statistical Analyses section) using second-level group analyses to enable within-group repeated measures comparisons using flexible factorial random effects models to test the prediction that response would decrease to sweet, but not sour, salty and savory taste. However, data collection was cut short when the fMRI scanner was serviced and upgraded, rendering our acquisition protocol incompatible. The adolescent study (experiment 2), approved in an addendum under the parent R01, was projected to be 15 subjects per group, as this was a pilot study. Experiment 3 was a control experiment designed to test if consuming maltodextrin alone (rather than in combination with sucralose) alters insulin response.
Sample size was projected to be 15 to match the sample sizes in experiment 1. Experiment 1: 55 adult participants were recruited, 6 dropped out during the experiment, blood sampling during at least one full OGTT failed for 4 participants, and 6 participants later revealed in a timeline follow back measurement that they were regular users of NNS. Perceptual, blood, and brain data of these subjects were discarded before data analysis. Data analysis was performed on data from 39 adult participants (13 per group; 21 women; mean age 27. 79 ± 3. 96; mean BMI 23. 72 ± 3. 13). Experiment 1 was registered on 10-24-2014 as Clinical Trial Registration Number NCT02335021 associated with the first submission of R01DC006706 (06-21-2013). The outcome variables included sweet taste perception, brain response to sweet taste and food intake.
This experiment was revised on the funded resubmission (10/22/2014), which included sweet taste perception, food intake, neural response to sweet taste as well as GLP-1, insulin and glucose response to glucose ingestion (OGTT) as the main outcome variables. The GLP-1 data was analyzed but results suggested that the samples were contaminated because values were not physiologically possible. The clinical trial registry was not updated to reflect these changes, but the study design is available upon request as the submitted R01. Data were collected from 1-9-2015 to 4-24-2019. Experiment 2: 17 adolescents were recruited, 2 refused blood sampling, 3 dropped out, and 1 group assignment was lost in a software crash. Participants, aged 13-17 years were recruited similarly to the adult study, with the same inclusion and exclusion criteria, apart from the age range.
The outcome variables were sweet taste perception, brain response to sweet taste and fasting insulin and glucose. The study was approved by the Yale Human Investigations Committee and all participants provided written informed assent at the start of their first lab visit together with their parents who provided parental consent. Before the Yale Human Investigations Committee advised study termination, Data analysis was performed on data from 11 adolescents (8 women; mean age 15. 95 ± 1. 37; mean BMI 22. 13 ± 3. 63). Data collection occurred from 2-20-2017 to 7-6-2017. Experiment 3: 16 young adult participants were recruited between 3-5-2018 and 1-3-2019 for a control maltodextrin experiment, 1 participant dropped out. Experiment 3 included all procedures of Experiment 1 with exception of the fMRI measurements/training.
The pumps, which infuse liquids at rates of 6-15 mL/min, are controlled by programs written using Matlab 7. 11 (MathWorks Inc. , Sherborn, MA) and Cogent2000 v1. 25 (Wellcome Department of Cognitive neurology, London, UK). Each pump holds a 60 mL syringe connected to a 25-foot length of Tygon beverage tubing (Saint-Gobain Performance Plastics, Akron, OH) with an inside diameter of 3/32". All tubing terminates into a specially designed Teflon, fMRI-compatible gustatory manifold (constructed in the Pierce Laboratory Electronics and Machine Shop), which is anchored to the MRI headcoil and interfaces with the subject. This set-up is depicted in a close-up (Figure  S1A ), with the subject in the mock scanner (Figure  S1B ). The gustometer mouthpiece or "manifold" was designed to deliver up to 11 taste solutions and one tasteless rinse.
13). We used this form of imputation as it uses the interrelation across measurement time points for insulin and glucose curves, respectively. Subsequently, incremental area under the curve (iAUC) values for plasma insulin and glucose were calculated for the first 30 minutes of the OGTT using the auc-function in the MESS-package (version 0. 5. 2). We then calculated absolute and relative iAUC difference scores (%). To test for group differences in insulin levels, ΔInsulin iAUC 0-30m was entered in a linear model as dependent variable while group ID was entered as independent variable to test for group differences. In a similar model, we tested for differences in glucose levels by entering ΔGlucose iAUC 0-30m as a dependent variable while group ID constituted the independent variable. For completeness, we also investigated the results for the complete OGTT AUC using an identical statistical analysis procedure.
31, P=0. 82; Δinsulin iAUC 0-120m : F(3,30)=0. 31, P=0. 83) For Experiment 3, glucose samples were analyzed by Yale Center for Clinical Investigation Core Laboratory Services (Glucose, plasma(ace)). Plasma insulin (sensitivity: 0. 1817 mU/L (1. 09 pmol/L)) was measured using insulin ELISA Jumbo kits (ALPCO, Salem, NH). One glucose sample was missing and imputed using the procedure reported for Experiment 1. fMRI scans-fMRI data were analysed using SPM12 (v6906, Wellcome Trust Centre for Neuroimaging, http://www. fil. ion. ucl. ac. uk/spm) running in Matlab 2016b (The MathWorks Inc. , Natick, MA). The first 2 dummy images from each functional run were removed. Subsequently, functional images from both visits were realigned, co-registered to the T1weighted anatomical image acquired during the first visit, normalized to MNI space, and smoothed with a 6 mm FWHM kernel.
FWE correction for mass univariate analyses was performed on cluster level. TLFB Questionnaire-In addition to measuring LCS consumption using the TLFB as control exclusion criterion, we tested whether our experimental manipulation affected sugar sweetened beverage (SSB) consumption outside of the experiment. To test for (a) any group differences in SSB consumption, (b) group x time (pre vs post exposure) differences in SSB consumption, and (c) any associations between ΔInsulin iAUC 0-30m and SSB consumption, we performed two linear mixed models (LMMs) using package LME4 (version 1. 1-7)  (Pinheiro and Bates, 2000). Subsequent statistical tests on the LMMs were performed using the Satterthwaite's approximation for the degrees of freedom, provided in the package ImerTest (version 2. 0-11, http://cran.
Parent recruitment Based on our previous studies  [27, [29] [30] [31] , we anticipated that 20-30 parents (n=10-15 in each setting) would be needed to reach data and thematic saturation, the point where additional data does not lead to any new major themes during analysis and the researcher notes high levels of 'information redundancy' during data collection  [32] [33] [34]. Parents of children with experience of tube feeding in NNU and/or PICU in the last 3 years were recruited through four routes: (1) social media or website advertising [including Twitter (URL: www. twitter. com; Twitter, Inc. , San Francisco, CA, USA), Bliss (URL: www. bliss. org. uk), Sepsis Trust (URL: https://sepsistrust. org), hospital charities and Mumsnet (URL: www. mumsnet. com)], (2) targeted emails to study team contacts and a database from our previous research  [29] , (3) an advert in a national newspaper and (4) word of mouth (e.
Practitioner focus groups and interviews At the start of the focus group or interview, the researcher (ED, LR) checked that participants had read the PIS. Eleven closed questions were administered in focus groups using a voting system (Turning Technologies, Youngstown, OH, USA) and administered verbally during interviews. This allowed for the collection of staff demographic information, to insure data collection from all staff on key questions and facilitate discussion. As part of an iterative approach, we added or amended questions in the parent and practitioner topic guides as interviewing and analysis progressed (see S3, practitioner topic guide)  [35]. Audio-recorded verbal or written consent was sought before interviews or focus groups began. Interviews stopped when saturation point was reached.
Data analysis LR and ED led the qualitative analysis with assistance from KW (PhD, sociologist). Analysis used a thematic analysis approach  [36] [37] [38]  to examine patterns within the data related to views on trial design and acceptability (see S4, Table  1 ). Analysis was broadly interpretive, iterative and used a combination of inductive and deductive approaches to coding  [39, 40]. NVivo 10 software (QSR International Pty Ltd. , Melbourne, Australia) was used to assist in the organisation and coding of data. Quantitative data from closed questions were entered into SPSS Version 20. 0 (IBM Corp. , Armonk, NY, US). Descriptive statistics are presented with frequencies and percentages. Synthesis of qualitative and quantitative data drew on the constant comparative method  [41, 42]. This involved ED and LR looking across quantitative and qualitative themes and quantitative output for themes/data output related to trial acceptability.
Leticia and its populace are also at risk for directional spread from the border city of Tabatinga, Brazil, which has had about 1,629 COVID-19 cases and 78 deaths to date (Fig 1A  and 1B ; Table  1 )  [12]. Here again, resources to fight this pandemic are severely limited. There are only 2 hospitals in Leticia city, totaling 70 beds. Only 1 of the 2 has an ICU, with 23 beds, all currently occupied  [15]. [29]  in cases per 100,000 people is shown in a spectrum of white (zero incidence) to dark red (more than 200 cases per 100,000). (B) Lethality  [29]  rates per 1,000 infections is shown in a spectrum of white (zero deaths) to dark blue (104 to 125 deaths). The Indigenous territories are indicated in light green. [30]  The map was created with the QGIS software (https://www. qgis. org/es/site/).
Acknowledgments We would like to thank the support of the  Vector Borne Disease Control Network-Venezuela (VeConVen) ;  Professor Jaime Miranda  from  Universidad Peruana Cayetano Heredia (Peru) ;  Napoleon Malpica from Grupo de Trabajo Socioambiental de la Amazonia Wataniba (Venezuela) ; The Regional Organization of Indigenous Peoples from Amazonas (ORPIA; Venezuela);  Audrey Andrieu ,  Manon Guidarelli ,  Tiphanie Succo  and  Julie Prudhomme  from  Sante Ṕublique France ,  French Guiana Regional Unit  and  Alice Sanna  from the  Regional Health Agency (French Guiana ).
Funding was provided by the  University of Glasgow, Scottish Funding Council  and the  Global Challenges Research Fund (GCRF)  and  GCRF Research Network   EP/T003782/1. CZ is funded by a  Welcome Trust International Training Fellowship. MAOM and MTQV (Wataniba) are funded by  Swift Foundation and The Full Circle Foundation, Special Grants  to support Amazon Indigenous Peoples on the context of the COVID-19 emergency. JCN is funded by Grant  P011617_2 DII-UISEK , Ecoepidemiology in silico of Emerging Diseases. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Parent recruitment Based on our previous studies  [27, [29] [30] [31]  we anticipated that 20-30 parents (n=10-15 in each setting) would be needed to reach data and thematic saturation, the point where additional data does not lead to any new major themes during analysis and the researcher notes high levels of 'information redundancy' during data collection  [32] [33] [34]. Parents of children with experience of tube feeding in NNU and/or PICU in the last three years were recruited through four routes: 1) social media or website advertising [including: Twitter (URL: www. twitter. com; Twitter, Inc. , San Francisco, CA, USA), Bliss (URL: www. bliss. org. uk), Sepsis Trust (URL: https://sepsistrust. org), hospital charities, and Mumsnet (URL: www. mumsnet. com)] , 2) targeted emails to study team contacts and a database from our previous research  [29] , 3) an advert in a national newspaper and 4) word of mouth (e.
Practitioner focus groups and interviews At the start of the focus group or interview the researcher (ED, LR) checked that participants had read the PIS. Eleven closed questions were administered in focus groups using a voting system (Turning Technologies, Youngstown, OH, USA) and administered verbally during interviews. This allowed for the collection of staff demographic information, to insure data collection from all staff on key questions and facilitate discussion. As part of an iterative approach we added or amended questions in the parent and practitioner topic guides as interviewing and analysis progressed (see S3, practitioner topic guide)  [35]. Audio-recorded verbal or written consent was sought before interviews or focus groups began. Interviews stopped when saturation point was reached.
Data analysis LR and ED led the qualitative analysis with assistance from KW (PhD, sociologist). Analysis used a thematic analysis approach  [36] [37] [38]  to examine patterns within the data related to views on trial design and acceptability (see S4, table  1 ). Analysis was broadly interpretive, iterative and used a combination of inductive and deductive approaches to coding  [39, 40] ,. NVivo 10 software (QSR International Pty Ltd. , Melbourne, Australia) was used to assist in the organisation and coding of data. Quantitative data from closed questions were entered into SPSS Version 20. 0 (IBM Corp. , Armonk, NY, US). Descriptive statistics are presented with frequencies and percentages. Synthesis of qualitative and quantitative data drew on the constant comparative method  [41, 42]. This involved ED and LR looking across quantitative and qualitative themes and quantitative output for themes/data output related to trial acceptability.
Before each injection, columns were equilibrated by running 15 min with 18 mM NaOH. The sample was eluted isocratically with 18 mM NaOH for 25 min, followed by a linear gradient between 0 to 1 M sodium acetate in 200 mM NaOH for 20 min to elute acidic monosaccharides. The columns were then washed with 200 mM NaOH for 15 min by keeping the eluent flow constant at 1 mL/min and thermostated at 25 • C. Results were analyzed with Dionex Chromeleon 6. 80 software (Dionex Corporation, Sunnyvale, CA, USA) using the standards monosaccharides (l-Rha, l-Ara, d-Gal, d-Glc, d-Man, d-Xyl, d-Fru, d-GalA and d-GlcA).
Statistical Analysis For each experiment, data were systematized and analysed using Microsoft Excel statistical package and expressed as mean values ± SD. Three-way ANOVA was used to analyse the statistical significance of individual factors (with different number of levels) and their interactions on dependant variables. Tukey's HSD (Honestly Significant Difference) tests for post-hoc analysis were used in each of the ANOVA tests to conclude whether all the mean differences of the dependent variables were significant. Statistical analyses and modelling were performed using IBM SPSS Statistics Version 25 software. The calculations are given in the Supplementary Materials (Tables  S1-S3 ).
major leaves had a content of 36. 0% and 73. 4% GalA, and 26. 2% and 8. 45% Glc, using an extraction temperature of 50 • C and 100 • C respectively. They particularly showed that the Rha content did not depend on temperature and was estimated at around 2%. More, after further fractionation of the crude polysaccharides, Samuelsen et al. [24]  separated two acidic fractions defined as rhamnogalacturonan and acidic arabinogalactan type II, and a neutral fraction composed of high amounts of glucose and mannose. A fraction of 1,4-linked xylose units was also identified  [24]. ( a) Mw: Mass-average molecular mass was measured by SEC-MALLS-DRI. Each assay was conducted in duplicate, and data are expressed as means (±SEM). ( b) Mn: number-average molecular mass was measured by SEC-MALLS-DRI. Each assay was conducted in duplicate, and data are expressed as means (±SEM).
Physicochemical Characterization Structural properties of the obtained material were verified employing X-ray powder diffraction (XRPD) measurements in a 2θ range of 15°-100° with X'Pert Pro PANalytical X-ray diffractometer (Cu Kα1: λ = 1. 54060 Å, U = 40 kV, I = 30 mA). The structural refinement was performed by the Maud 2. 93 software and using a better approximation as well as indexing of the Crystallographic Information File (CIF). Primary particle size and morphology were investigated using a high-resolution transmission electron microscope (HRTEM) Philips CM20 SuperTwin operating in 200 kV with a resolution 0. 25 nm. Samples were prepared by dispersion of powders in methanol and the suspension was deposited on a copper microscope grid covered with perforated carbon. Whereas, the typical hydrodynamic size of the nanoparticles was determined by Dynamic Light Scattering (DLS) method with a Zetasizer Nano ZS apparatus from Malvern Instruments operating under He-Ne 633 nm laser and equipped with the Dispersion Technology Software.
Statistics All experiments were performed at least in three replicates. Statistical analysis was performed using the oneway ANOVA with Tukey's test in GraphPad Prism Software (La Jolla, CA, USA). Differences with the probability of P < 0. 05 were considered significant. Statistical significance is indicated as an asterisk (*) when comparing the between corresponding MF-and MF+ groups, and a number sign (#) when comparing to CTRL MF-.
Fe 3 O 4 and MF Increase the Formation of Mineralized Matrix On the last day of the differentiation process, cells were visualized using SEM and EDX mapping (Figure  9A ) which allowed assessing the amount of Ca, P, and Fe. Furthermore, data were quantified and presented as Ca and P atomic weight percentages (Figure  9B ). The ratio of Ca to P was significantly enhanced after the exposition of cells to MF and Fe3O4 (Figure  9C ). To visualize calcium deposits, cells were stained with Alizarin Red and representative photographs are shown in Figure  9D. To quantify the results, after incubation with cells, the dye was collected and spectrophotometrically measured. Obtained data indicated an increased calcium accumulation in cells treated simultaneously with Fe3O4 and MF (Figure  9E ).
Fe 3 O 4 and MF Modulate Osteopontin and Integrins Expression Considering that the OPN plays a major role in the regulation of osteogenic differentiation, we investigated its levels in cells after the 18th day of differentiation. Immunofluorescence staining indicated that OPN levels are increased after exposure (Figure  11A ). Our data showed that 66 kDa OPN isoform is increased in CTRL MF+ and Fe 3 O 4 I MF+ groups (Figure  11B ). A similar phenomenon was observed for 32 kDa isoform (Figure  11C ). Representative Western blot bands are shown in Figure  11D. Interestingly, OPN expression was enhanced in the control group treated only with MF and in cells treated simultaneously with iron oxide nanoparticles and MF (Figure  11E ). Next, RT-qPCR analysis was employed to measure mRNA levels of selected integrins are they were shown to be modulated by OPN.
Acknowledgments The research was funded by  National Science Centre Poland  grant no:  2017/26/M/NZ5/01184. 4B12 cell line was kindly provided by  Dr Shigeru Amano -Division of Microbiology and Immunology, Department of Oral Biology and Tissue Engineering, Meikai University School of Dentistry, Keyakidai, Sakado City , Saitama  350-0283 , Japan. All animal experiments were performed in accordance with the  Guidelines of the Animal Center of the Meikai University School of Dentistry. Paulina Sobierajska received financial resources within the confines of financing the ETIUDA doctoral scholarship from the  National Science Centre in Poland  (no. UMO-2018/28/ T/ST5/00326 ).