dataesr/scientific-paragraphs-categorization huggingface dataset:
The limitations of the present study are summarized in the following: (a) data for both populations were collected in a cross-sectional manner, limiting the potential for generalized cause and effect conclusions to be drawn; (b) use of the PCA for the dietary patterns' extraction, including subjective choices regarding the amount of food groups that are included in the analysis, as well as the number of components to be drawn; (c) comparisons between the two populations' dietary habits might be affected by the different socio-economic conditions existing in the two countries during the mid-1990s for the STANISLAS and late 2000s for the TEENAGE study. This prolonged gap between the two baseline data collections might manifest itself in the Greek teenagers' dietary habits, which could have potentially been affected by social changes and changes in food availability and accessibility, mediated by the growing social and technological advancements taking place throughout the 15-year gap.
Acknowledgments: The TEENAGE study was financed by  European Social Fund-ESF  of the  European Union  and the National Strategic Reference Framework (NSRF)- Research Funding Program: Heracleitus II through its Operational Program "Education and Lifelong Learning ". The  STANIS-LAS Family Study  was recruited in the Centre for Preventive Medicine and also supported by  INSERM ,  CNAMTS ,  CNRS, Nancy University, Region Lorraine, Communaute Urbaine du Grand Nancy  and also received funds from  Abbott ,  Bayer Pharma ,  Beckman Coulter ,  Biomerieux ,  Merck ,  La Previade ,  Roche Diagnostics, Randox, Daiichi, Dade-Behring, Kellogg's, Johnson and Johnson (Ortho-Clinical Diagnostics ) and  Sebia. The present study was funded by " La Région Grand Est, France-GUTENBERG chair 2018 , grant number  18CP-1413 ".
SUnSET Assay Rates of protein synthesis were evaluated using the surface sensing of translation (SUnSET) method as previously described  [49]. A549 cells were seeded onto 6-well plates (2. 50 × 10 5 cells per well) and treated with Pemetrexed and/or deprived of glucose for 72 h at 37 • C, 5% CO 2. Fifteen minutes before being harvested and processed to prepare whole cell extracts in RIPA buffer, cells were incubated with 5 µg/mL of puromycin (Sigma P9620), directly added into the medium. The amount of puromycin incorporated into nascent peptides was then evaluated by Western blot analysis on 15 µg of proteins using anti-puromycin antibody purchased from Millipore (MABE343) and quantified with the ImageJ software (National Institutes of Health, Bethesda, MD, USA).
Funding: This work was supported by the  Cancéropôle Lyon Auvergne Rhône-Alpes  ( R19152CC ,  R19075CC ),  Region Auvergne Rhone-Alpes  ( 19 010898 01-41024 ),  Fondation ARC pour Recherche sur le Cancer  ( R16173CC ),  Ligue Nationale contre le Cancer  ( R17167CC ,  R19007CC ),  PLASCAN (BioMHet, MiStiM'Plast ,  17IA66ANR-PLASCAN-MEHLEN ),  CNRS Prematuration program  ( NA-7-07-20 ) and  Marie Sklodowska-Curie  fellowship (grant agreement n • 839398). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Selection of the study population Daily practice clinical and biological data were extracted from the prospective cohort of kidney transplant recipients (KTRs) [DIVAT cohort from Nantes University Hospital (Donn ees Informatis ees et VAlid ees en Transplantation) (www. divat. fr, French Research Ministry: RC12_0452, last agreement No. 13 334, No. CNIL for the cohort: 891735)]. First, a total of 5984 KTRs with medical follow-up in Nantes University Hospital were screened. Adult ( 18-year-old) KTRs alive between January 2008 and December 2018 with stable graft function at 3 months post-transplant were selected (n = 1682). Stable graft function at 3 months post-transplant was defined as creatinine < 150 mmol/L and proteinuria < 1 g/24 h under standard immunosuppression (calcineurin inhibitors (CNIs) or mTOR inhibitors and antimetabolite +/-corticosteroids).
We established a baseline period at 3 months post-transplant to obtain the steady state of BCEo. Indeed, high-dose of systemic corticosteroids (intra-venous or oral), immunosuppressive drugs (CNI, antimetabolites, mTOR inhibitors) and induction therapy are given within the first 3 months after transplantation and the impact of immunosuppressive drugs (CNI and oral corticoids mostly) combination on BCEo is yet not well established. Among those 1682 KTRs, patients with pretransplant DSA or who had undergone simultaneous kidney-pancreas transplantation were excluded. Patients with post-transplant haematologic disorders, active parasitic infection and HIV infection were also excluded since those factors are known to increase BCEo (n = 1393)  [19]. At last, Patients with a lymphocyte count greater than 20 G/L or patients who did not have eosinophil count data in the baseline period (3 months post-transplant +/-10 days) and during the follow-up period (2 BCEo at least) were excluded leading to a total of 1013 KTRs enrolled in the study.
HLA and DSA antibody detection were based on highresolution bead-based assay (One Lambda Ò , Thermo Fisher Scientific, California, USA). Routine blood eosinophil counts after 3 months post-transplant and all subsequent counts using automated flow cytometry (morphological gating on eosinophils) were noted. All eosinophil counts in this study refer to blood eosinophil counts. Classical and available risk factors likely to influence blood eosinophil count changes were extracted from the database. HIV-positive patients, patients diagnosed with post-transplant lymphoproliferative disorders (PTLDs), active parasitic infections or acute allergic process which could cause an increase in BCEo  [19] , were excluded. The appearance of kidney allograft rejection (humoral or cellular rejection including borderline rejection) and the appearance of de novo DSA with time-dependent variation in BCEo were used to define analysis outcomes.
Statistical analysis The analysis was performed in 2 steps. Step 1 was an explanatory variable reduction process based on clinical ground and statistical methods. The principal explanatory variable of interest was multiple eosinophil counts during follow-up. All other predictors of the survival outcome in the raw dataset were selected by 2 independent nephrologists. Variables with small variance and/or redundant variables (checked by using the variance inflation factor or exploratory multidimensional data analysis) were removed. Ten predictors were included in the multivariable regression model: ECD, recipient features (age, sex, multiple BCEo, and the use of corticosteroids and/or CNIs as maintenance therapy at each BCEo), previous immunization status (presence of rejection episodes, HLA antibody or DSAdn antibody before the 3-month post-transplant baseline eosinophil count) and the rank of transplantation.
Step 2 involved a multivariable regression model to investigate the etiologic relationship between the dynamic eosinophil count during follow-up and kidney allograft rejection and DSAdn appearance. The outcomes were survival data with multiple changes updated during the patient follow-up. We used a time-dependent covariate cause-specific Cox regression model (competitive risk model for aetiologic purpose)  [23, 24]. Three explicative variables were considered time-dependent covariates: BCEo and the use of corticosteroids and/or CNIs as maintenance therapy at each BCEo measurement to overcome the uncertainties of the association of these two immunosuppressive drugs on BCEo. To obtain the cause-specific hazard ratio, we studied kidney allograft rejection appearance (DSAdn, death and graft failure defined as return to dialysis or pre-emptive retransplantation were censored) based on the time between the baseline BCEo and the occurrence of HPR.
This a priori threshold was confirmed fisrt by inspecting Martingale residuals according to continuous blood count eosinophils, then by plotting the spline function of blood eosinophil according to hazarad ratio using cause-specific multivariable Cox model (Figs. S1A  and B ) and at last by performing time-dependent multivariable analysis of immunological event onset (rejection and/or DSA) in KTRs during follow-up according to BCEo as categorical variable (0-0. 1; 0. 1-0. 2; 0. 2-0. 3; > 0. 3) (Table  S1 ). The statistical significance (alpha risk) was set at 0. 05. All analyses were performed using R software version 4. 0. 3 using Thernau et al package (https://github. com/therneau/survival).
Higher eosinophil count during follow-up was associated with a high risk of a rejection episode Patients with a higher eosinophil count ( 0. 3 Giga/L) at a given follow-up time after 3 months had a 3-fold higher risk of experiencing a rejection episode during the follow-up period than patients with a lower eosinophil count (< 0. 3 Giga/L) at the same time point, independent of other confounding factors (HR 2. 87, 95% CI 1. 38À5. 98, p = 0. 004) in patients without pre-transplant DSA and with CNI-based immunosuppression. No association was observed with BCEo 0. 3 G/L when stratifying by HPR subsets (ABMR, TCMR, and borderline rejection), probably due to a lack of power since only 15 patients experienced a rejection episode 3 months post-transplant and had a BCEo 0. 3 G/L (data not shown). The use of CNIs as maintenance therapy at eosinophil measurements significantly reduced the risk of a rejection episode by 76% (HR 0.
Conclusion Our data thus revealed that a BCEo 0. 3 G/L threshold could be an interesting and routine biological marker for monitoring immunological outcomes along with other routine parameter such as DSAdn in kidney transplant recipients without pre-transplant DSAs and with CNI-based immunosuppression at steady state (i. e. , 3 months post-transplant). In clinical practice after eliminating common causes of an increase in BCEo (PTLD, acute allergic process, parasitic or viral infections)  [19] , BCEo 0. 3 G/L could lead to monitor more regularly biological parameters associated with rejection such as DSAdn (IgG anti-HLA class I or II with MFI >2000) and/or proteinuria > 1 g/24 h and/or hematuria (> 10 red blood cells/mL) and/or an increase of 25% in serum creatinine compared to baseline. Though, multicentric studies challenging BCEo > 0.
Acknowledgments We thank the patients and their families, whose trust, support, and cooperation were essential for the collection of the data used in this study. Luc Colas is under a Cifre contract with GlaxoSmithKline and financially supported by  Institut de Recherche en Sant e Respiratoire des Pays de la Loire  (IRSRPL). This work was performed in the context of the  IHU-Cesti project  ( ANR-10-IBHU-005 ), the  DHU Oncogreffe , the  ANR  project  PRELUD  ( ANR-18-CE17-0019 ), the  ANR  project  BIKET  ( ANR-17-CE17-0008 ) and the  ANR  project  KTD-innov  ( ANR-17-RHUS-0010 ) thanks to French government financial support managed by the  National Research Agency. The  IHU-Cesti  project was also supported by  Nantes M etropole and R egion Pays de la Loire. The laboratory received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreement No.
With this in mind, we evaluated the incidence of PEG35 on the energy breakdown prevention during cold storage by measuring ATP content in preserved livers. Data reported in Figure  4  revealed higher ATP levels in PEG-containing solutions and evidenced the PEG35-dependent energy breakdown prevention during cold preservation. With this in mind, we evaluated the incidence of PEG35 on the energy breakdown prevention during cold storage by measuring ATP content in preserved livers. Data reported in Figure  4  revealed higher ATP levels in PEG-containing solutions and evidenced the PEG35-dependent energy breakdown prevention during cold preservation. With this in mind, we evaluated the incidence of PEG35 on the energy breakdown prevention during cold storage by measuring ATP content in preserved livers.
Statistics Data are expressed as mean ± standard error and were compared statistically by variance analysis, followed by the Student-Newman-Keuls test using GraphPad Prism version 8. 1. 0 for Windows (GraphPad Prism software, San Diego, CA, USA, 2018) and one-way ANOVA. A level of p < 0. 05 was considered significant. Significant differences between groups are represented with different letters in the graphs. A group labeled with a letter has significant statistical differences compared to a group labeled with a consecutive letter with a p < 0. 05.
Setup We designed and built a custom hardware similar to the one designed by  Noy et al. (2011)  for recording dyadic motions in the mirror game (Figure  1 ). Players moved handles along parallel tracks 0. 6 m long. Handle positions were measured by an optical encoder with a spatial resolution of 0. 054 mm and were sampled at 100 Hz. Data were recorded on a computer using National Instruments LABVIEW 13. 0 software.
Data Reduction Two socio-motor variables were extracted from the mirror-game: socio-motor improvisation and socio-motor synchronization. Position signals were pre-processed with MathWorks MATLAB Software. Position signals were interpolated with a shapepreserving Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) to correct small variations in the sampling rate  (Kahaner et al. , 1989)  and filtered with a zero-phase forward and reverse digital second-order low-pass (10 Hz cut-off) Butterworth filter. Position time-series were then used to numerically estimate their corresponding velocity time-series using a fourth-order finite difference scheme  (Słowiński et al. , 2016). A detailed sample of time series is displayed in Appendix Figure  1. Time-series performed by participants were mostly non-stationary, multi-modal and multi-scale due to the nature of the task.
Blood samples were collected in K2EDTA vacutainers and PBMCs were separated from plasma using Sepmate isolation tubes (STEMCELL Technologies) and plasma was used in the downstream HAT assay. Figure  6 : Capillary samples were collected from members of institute staff with informed consent (New Delhi, India). The study is a part of the COVID-19 project "IPA/2020/000077". The project has been approved by the Institutional Human Ethics Committee; Ref. no. -IHEC#128/20. Cloning, expression, and purification of VHH(IH4)-RBD. The amino acid sequence for IH4 was obtained from US Patent No US 9,879,090 B2, kindly corrected by Olivier Bertrand. The codon-optimised gene encoding IH4-RBD sequence (Fig. 1B  and supplementary for the cDNA sequence) was synthesized by Integrated DNA Technologies. The gene was cloned into the AbVec plasmid (Genbank FJ475055) using the restriction sites AgeI and HindIII (the vector supplied the signal sequence).
This expression plasmid for IH4-RBD is available on request. Protein was expressed in Expi293F TM cells using the manufacturer's protocol (Thermo Fisher). Protein supernatant was harvested on day 5/6 after transfection, spun, and 0. 22 µm filtered. The protein was affinity-purified using a His-Trap HP column (Cytiva). Binding buffer consisted of 20 mM sodium phosphate, 150 mM NaCl, and 20 mM imidazole at pH 7. 4 and the elution buffer of 500 mM imidazole in 1x binding buffer. Protein was concentrated using 15 mL Vivaspin 30 kDa MWCO filter and then buffer exchanged to PBS using a 10 mL 7 kDa Zeba spin column (Thermo Fisher). For large-scale production, the protein was synthesized by Absolute Antibody Ltd, Oxford, using the same plasmid construct in HEK293 cells. Monoclonal antibodies. The variable heavy and light genes of the monoclonal antibodies CR3022  26  , VHH72-Fc 31 , C121  21  , H11-H4-Fc  29  , and S309  24  were synthesized by Integrated DNA Technologies IDT and cloned into antibody expression vectors (GenBank FJ475055 and FJ475056).
Pre pandemic negative controls: these samples were collected from healthy adults in the Oxfordshire region of the UK between 2014 and 2016, ethics approval: Oxfordshire Clinical Research Ethics Committee 08/H0606/107+5. Positive sample set: these were convalescent plasma donors recruited by NHS Blood and Transplant (NHSBT), ethics approval (NHSBT; RECOVERY [Cambridge East REC (ref: 20/EE/0101)] and REMAP-CAP [EudraCT 2015-002340-14] studies). The serum from a convalescent patient used for FACS analysis was left-over clinical material obtained from the Virology Laboratory of the Toulouse Hospital, where, by default, all patients gave informed consent for such materials to be used for research purposes.
Acknowledgements A. T. and P. R. are funded by the  Medical Research Council  ( MR/P021336/1 ),  Townsend-Jeantet Charitable Trust  (charity number  1011770 ), and the  Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Science (CIFMS), China  (grant no. 2018-I2M-2-002 ). T. K. T is funded by the  EPA Cephalosporin Early Career Research Fellowship and Townsend-Jeantet Charitable Trust  (charity number  1011770 ). N. G. is funded by the  Science Engineering Research Board, Department of Science and Technology, India. P. C. M. is funded by the  Wellcome Trust  (grant ref  110110Z/15/Z ). D. J. R. , D. C. , T. S. and S. H. are supported by  NIHR Oxford Biomedical Research Centre. G. R. S. was supported by the  National Institute for Health Research Biomedical Research Centre Funding Scheme , the  Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Science (CIFMS), China  (grant number:  2018-I2M-2-002 ) and was supported as a  Wellcome Trust Senior Investigator  (grant  095541/A/11/Z ).
LT is supported by a  Wellcome  fellowship ( 205228/Z/16/Z ). E. J. is employed by  INSERM , and the expenses for his work, which was performed at the IPBS, Toulouse, France, were funded by a private donation. The Spike Glycoprotein RBD from  SARS-Related Coronavirus 2 Wuhan-Hu-1  (for Fig. 6), Recombinant from HEK293 Cells, NR-52306 was produced under HHSN272201400008C and obtained through  BEI Resources ,  NIAID ,  NIH. The authors would like to thank the healthy volunteers who kindly donated their O-ve red cells for titration of samples,  Jacques Izopet  and  Laurence Abravanel (Toulouse)  for providing a set of positive and negative serum samples, and  Olivier Bertrand  for providing us with the corrected version of the IH4 amino acid sequence from the patent number  US 9 , 879 , 090 B2.
Data Extraction To assess the public's interest in infectious diseases during the initial days of the COVID-19 outbreak, we extracted Google Trends search data from January 20, 2020, onward and compared them to data from March 15, 2020, which was approximately 2 months after the COVID-19 outbreak began. Apart from in a few countries such as China and Japan, the Google search engine has an overwhelming dominance of market shares  [15]. Google provides data on search word volumes through Google Trends, which provides usable data for specific countries and time periods. In this study, our unit of data was extracted from data on total worldwide numbers. In addition, to account for trends by country, we selected the United States and the United Kingdom for analysis, as these countries are the most representative countries of the English-speaking world that use the Google search engine.
Moreover, we also studied Italy, which was the first European country to experience an explosion in the number of COVID-19 cases. The market share of Google in Italy in January 2020 was 98. 9%  [16]. Finally, we studied South Korea, which had the second highest COVID-19 spread rate in Asia and the fourth highest spread rate in the world at the time the Google Trends search was performed. On the date that the data were gathered, South Korea was ranked fourth in the world for COVID-19 case numbers, with 8000 cases. In South Korea, the domestic portal site Naver (Naver Corporation) has a much higher market share than Google. Therefore, additional data supplied by Naver Data Lab were used for cross-verification.
Data and Statistical Analysis Search volumes were calculated for each period (1 week), and the search volume of the period with the highest search volume was 100, which normalized every other search volume value from 0 to 100. The unit of extraction was 1 week. In order to account for the spread of COVID-19, we used the cumulative number of confirmed cases. The official announcements made by the WHO and announcements from each country's respective government were the most accurate indices for tracking the spread of SARS-CoV-2. The number of cumulative, confirmed cases was verified with data downloaded from Our World in Data  [18]  and additional data from COVID-19 situation reports issued by the WHO  [1]. We conducted an analysis of the correlation between the number cumulative, confirmed COVID-19 cases and search terms.
637; P<. 001); however, its correlation with the search volume for the term immune (R=0. 254; P=. 06) was not statistically significant. When analyzing data from Naver, the search volume for the term coronavirus increased from the middle of February onward, and the search volumes for the terms immune and vitamin increased slightly in the beginning of January and increased again in the second half of January and from February 16 onward. All three search terms reached their peak search volumes in the beginning of March, and thereafter, these search volumes began trending downward. When compared with data from Google Trends, the results were similar, and the increasing trend in search volumes for the terms immune and vitamin that appeared after the occurrence of confirmed cases was slightly clearer (Figure  5 , Table  5 ).
In contrast to LOAD, ADAD and DSAD have an increased initial accumulation in the subcortical regions, particularly in the striatum. Table  1  recapitulates the similarities and differences between the three forms of AD, highlighting the heterogeneities, particularly in ADAD and age associated comorbidities There remain gaps in our understanding the reasons for the differences in clinical presentation and the genotypephenotype relations of these conditions. With on-going large longitudinal clinical studies of DS, ADAD and LOAD (all funded by the National Institutes of Health), namely ABC-DS (Alzheimer's Biomarkers Consortium-DS), DIAN and ADNI, respectively, data from these studies are beginning to provide a fine-grained characterisation and understanding of AD. Concurrently, biochemical and cellular understanding is being made possible by studying known mutations of the genetic forms of the disease aided by the use of induced pluripotent stem cells, organoids and gene-editing techniques.
Foundation Trust; J. F. : the  Fondo de Investigaciones Sanitario (FIS) ,  Instituto de Salud Carlos III  ( PI14/01126 ,  PI17/01019  and  PI20/01473 ) and the  CIBERNED program (Program 1 ,  Alzheimer Disease  and SIGNAL study, www. signalstudy. es,accessed on 1 July 2021), partly jointly funded by  Fondo Europeo de Desarrollo Regional ,  Unión Europea ,  Una manera de hacer Europa. The work was also supported by the  National Institutes of Health  (NIA grants  1R01AG056850-01A1 ;  R21AG056974  and  R01AG061566 ),  Fundació La Marató  de  TV3  ( 20141210 ), grants from  Fundació Víctor Grífols i Lucas , and by the  Generalitat de Catalunya  ( SLT006/17/00119 ); A. S. :the  MRC  ( MR/S011277/1 ;  MR/S005145/1 ;  MR/R024901/1 ),  Lumind IDSC ,  The LeJeune Foundation  and the  European Commission  ( H2020   SC1 Gene overdosage and comorbidities during the early lifetime in Down Syndrome   GO-DS21-848077 ).