dataesr/scientific-paragraphs-categorization huggingface dataset:
Plant colonization for microscopy N. benthamiana leaves ~6 cm length were infiltrated with STEC (Sakai) at 10 7 c. f. u. ml -1 or Pba (1043) 10 6 c. f. u. ml -1 in 0. 5 × Murashige and Skoog (MS) medium (Sigma Aldrich) and maintained in an environmental cabinet with 16 h daylength and a continuous temperature of 21 °C. Samples were imaged 4-11 days post-infiltration for STEC (Sakai) and 1-4 days post-infiltration for Pba (1043). Broccoli seed was surface sterilized and germinated on purple capillary matting (Grofelt, UK) watered with bacteria at 10 3 c. f. u. ml -1 in 0. 5 × MS as described previously  [30]  and imaged 5-11 days Fold-change gene-expression levels for E. coli O157:H7 (Sakai) exposed to temperature shift (37 to 18 °C), or plant extracts (all at 18 °C), data extracted from previous microarray measurements  [16].
Confocal microscopy N. benthamiana leaves were infiltrated with sterile distilled water (SDW) and secured to microscope slides with doublesided adhesive tape prior to imaging of the abaxial surface as described previously  [32]. Broccoli cotyledons were mounted on slides under cover-slips, in SDW for STEC (Sakai) or dry for Pba colonized leaves (since addition of water affects distribution of Pba rendering images meaningless), and imaged via either surface. Images were collected at Nyquist resolution using a Nikon A1R confocal laser scanning microscope fitted with either an NIR Apo 40×0. 8W water dipping lens or a CFI Plan Fluor 10×0. 3 lens, and GaAsP detectors. Images represent false-coloured maximum intensity projections, unless otherwise stated, produced, linearly adjusted and re-sized using the splines option, where necessary for publication, using NIS-elements AR software.
Both constructs were expressed in planta in the non-native backgrounds, although morphological differences occurred in Pba cells presumably due to GFP toxicity (Fig. 3i-k ). Variation in expression at the single cell level was evident and in STEC: GFP levels from most cells was low with only a small number expressing a high level. A similar pattern occurred for STEC hosting araF Pba. GFP was only detected from the araBA STEC reporter in the STEC ΔaraC mutant background (Fig. 3l ), in keeping with quantitative data (Table  2 ). There was no detectable GFP from araF STEC or either of the Pba contructs araBA Pba or araF Pba in this background (Fig. S4d-f ), confirming their responsiveness to l-arabinose. Since Pba (1043) encodes PCWDE, the responsiveness of the araBA Pba reporter was quantified in vitro from incubation with a crude preparation of leaf cell-wall polysaccharides (spinach).
Funding information N. H. , R. W. J. , I. T. and L. C. were supported by a PhD studentship funded by the  James Hutton Institute  and the  University of Reading ; N. H. , J. M. , A. H. , B. M. , S. H. and K. W. were supported by the  Scottish Government Strategic funding  ( RD3. 1. 3 , RD2. 3. 3 );N. H. and Y. R. was supported by a  Leverhulme Trust  grant ( RPG-096 ).
Jin et al. [23] , for example, demonstrated the ability of ion mobility-mass spectrometry (IM-MS) to resolve the structural complexity of some isomeric glycans, which is not possible by LC/ MS alone. Indeed, ion mobility coupled with mass spectrometry has many advantages, the main one being its ability to characterize a large range of positional isomers, conformers but also stereoisomers  [24, 25]. In addition, IM-MS analyses proceed in a high throughput fashion (a couple of minutes). In some cases, ion mobility separation can be a way to facilitate interpretation of complex samples by revealing the presence of minor compounds from biological matrices  [26]. Moreover, ion mobility separation provides a new highly reproducible data feature to ascertain the compound identification: the collision cross section (CCS)  [27]  and in that respect, efforts have been made to provide CCS databases for the glycan analyses  [28, 29].
To avoid TIMS saturation, the ion charge control (ICC) was maintained at 2. 0 Mio. The experiments were acquired in the 100e1350 m/z range using variable mobility ranges (see below) with a transfer time of 70 ms and a pre pulse storage of 5 ms. All parameters were controlled through the oTof control software (Bruker Daltonics). TIMS CCS N2 values were determined from the reduced mobilities, K 0 , measured, through a calibration step. Thus, an external calibration for m/z values (in quadratic mode) and reduced mobilities (in linear mode) was carried out before each experiment series using the ESI-L low concentration tuning mix solution. The calibrant ions are detected in positive ionization at m/z 322. 0481 (C 6 H 19 N 3 O 6 P 3 þ ), m/z 622. 0289 (C 12 H 19 F 12 N 3 O 6 P 3 þ ), m/z 922. 0098 (C 18 H 19 F 24 N 3 O 6 P 3 þ ) and m/z 1221.
To perform a HMO global fingerprinting (the first IM-MS analysis type), a custom mode was applied and, the mobility range as well as the scan rate were manually optimized to cover the m/z range of 100e1350 (corresponding to CCS range of 100e450 Å 2 considering singly-charged ions). R ¼ K 0 DK 0 ¼ CCS DCCS (1) The generated data were manually treated using Data Analysis 5. 2 (Bruker Daltonics). All ion mobility spectra reported were extracted from a fixed m/z (mass to charge ratios) of the ionic specie considered. To evaluate the ion mobility separation efficiency, the difference in CCS values, DCCS% (in %, see Equation (  2 )), was considered as the best criterion for estimating the ion mobility separation capacity. Indeed, the commonly used resolving power was shown to underestimate the real separation capability of some ion mobility instrumentations (e.
Indeed, our results demonstrated the possibility for a given HMO structure to display multiple ion mobility peaks due to different HMO configurations and/or ion isomers. The availability of pure standards is therefore mandatory in order to unambiguously build a reference HMO mobility database. Finally, in addition to IM-MS analysis, tandem mass spectrometry (MS/MS) experiments were performed on the breastmilk sample, providing three data features for a high degree of compound identification (m/z, fragmentation pattern and TIMS CCS N2 ) in a single experiment (see Fig. 4 ). With our ion source parameters, no in-source fragmentation is detected under ESI conditions. Therefore, collision-induced dissociation (CID) experiments were systematically performed on both protonated and deprotonated precursor ions after their ion mobility separations.
Our results constitute the proof of concept study of the application of the direct IM-MS approach as a high throughput analytical platform for the characterization of HMOs in breastmilk. It could, for example, be used for the classification of secretor status without lengthy LC-MS analyses. Further investigations on other HMO standards are required to build an HMO database that includes ion mobility and fragmentation pattern data useful for identifying as many HMOs as possible in breastmilk.
Background: Topical calcineurin inhibitors are used off label in the treatment of vitiligo, and there is a lack of placebo-controlled, blinded studies to support their use. Objective: This study aimed to compare the efficacy of tacrolimus 0. 1% ointment with that of the vehicle for repigmentation in adult patients with facial vitiligo. Design: This study was a 24-week multicenter randomized parallel double-blind study with a 24-week posttreatment follow-up extension. Population: Participants included were adult patients with recent facial vitiligo target lesions (<2 years) without changes in pigmentation or size over the previous 3 months. Intervention: Patients received either tacrolimus 0. 1% ointment or vehicle twice daily. Main Outcomes and Measures: The primary outcome was a therapeutic success, defined as a change 75% in the repigmentation of the target lesion between baseline and week 24, measured by ImageJ software.
Secondary outcome measures were a variation of the physicians' global assessment scores and patients' satisfaction scores, safety data, and the rate of relapse at week 48. Results: A total of 42 patients were included. Therapeutic success was achieved in 65% of tacrolimus-treated patients versus 0% of vehicle-treated patients at week 24 (P < 0. 0001). Only 40% of relapse was observed at 48 weeks. Conclusions and Relevance: Twice-daily tacrolimus 0. 1% ointment showed superior efficacy to that of the vehicle through the 24 weeks of intervention and 24 weeks of follow-up in adult patients with facial vitiligo. Trial Registration: This study was registered at ClinicalTrials.
Patient characteristics In the context of recruitment difficulty related to the widespread off-label use of tacrolimus for the treatment of patients with facial vitiligo, the Data Safety and Monitoring Board, which met for an interim analysis after 40 inclusions, advised for an interruption of recruitment and dissemination of the study results with the scientific community. The Scientific Advisory Board endorsed this proposition, and the trial was therefore closed. In total, 44 adult patients with facial nonsegmental vitiligo were assessed for eligibility. Of these, 42 were randomized (20 in the tacrolimus group and 22 in the vehicle group). During the 24-week treatment period, six patients were lost to follow-up (three in each group), one patient in the vehicle group withdrew consent, and one patient in the tacrolimus group discontinued tacrolimus treatment because of pregnancy.
Participant flow and follow-up Study visits were scheduled at weeks 12, 24, and 48. At each study visit, UV light and normal light photographs were taken. From those photographs, target lesion size was assessed by the transparent sheet method using ImageJ software (National Institute of Health, Bethesda, MA) for digital surface measurement  (van Geel et al. , 2019). The DLQI score, VIPs score  (Salzes et al. , 2016) , and both patients' satisfaction and physicians' global assessment scores were recorded at baseline and week 24 (see Appendix). Additional followup phone calls were scheduled at weeks 6 and 18 to ensure patient compliance and to ensure that side effects were recorded. The main objective of the study was to investigate the efficacy of twice-daily tacrolimus ointment at the atopic dermatitis label dosage of 0.
1%, compared with that of the vehicle, for repigmentation after 24 weeks of treatment. The primary outcome was a therapeutic success, defined as a 75% change in repigmentation of the target lesion between baseline and week 24. Repigmentation was evaluated using scanned images of skin mapping technique (planimetry), with ConvaTec (Deeside, United Kingdom) transparencies of lesions under Wood's lamp examination at baseline and week 24, as recommended  (van Geel et al. , 2019). Scanned lesions were then measured using a freely available software for analysis and measurements of digital images, the ImageJ 1. 47d software (Image processing and analysis in Java [Oracle, Austin, TX] by the National Institute of Health; http://imagej. nih. gov/ij). All patients provided written consent to the publication of follow-up photographs.
Data availability statement The data that support the findings of this study are available from the corresponding author KE (khaled. ezzedine@aphp. fr), JS (julien. seneschal@chu-bordeaux. fr), or NA (Nicolas. andreu@chubordeaux. fr) on request. Datasets related to this article are available on request and are hosted by the University Hospital Center of Bordeaux. The data are not publicly available because they contain information that could compromise the privacy of research participants.
Individual WP Contents. Quantification of individual WP, α-LA, and β-LG, was conducted after precipitation at pH 4. 6 using the reversed-phase HPLC method adapted from  Resmini et al. (1989) , using a PLRP-S column (gel of polystyrene divinylbenzene, 300 Å, 8 µm, 150 × 3. 0 mm) connected to a HPLC System (Dionex UltiMate 3000, Thermo Fisher), equipped with a detector VWD3400RS (Thermo Fisher) operating at 214 nm for the quantification of eluted proteins. Chromatographs were analyzed by Chromeleon software (Version 7. 2 SR4, Thermo Fisher). The denaturation extent of individual WP (α-LA or β-LG) was calculated as described above. All measurements were carried out in duplicate. Total and Free Fat Contents. Total fat content was measured in IF powders by Gerber's acid-butyrometric method  (AFNOR, 1990).
5 to 18 mg/L. Chromatographs were analyzed by Chromeleon software (Version 7. 2 SR4). All measurements were carried out in triplicate after the hydrolysis step. N ε -Carboxymethyl-Lysine. Sample preparation for CML analysis was adapted from Niquet-Léridon and  Tessier (2011). Samples (of different weights, being equivalent to ~10 mg protein) were weighed into 2-mL polypropylene tubes and lyophilized. Their dry weight was then recorded, and the samples reduced by adding 500 µL of 0. 1 M NaBH 4. This reduction step (2. 5 h at 20°C) is considered necessary to block the Amadori product and avoid de novo formation of CML during subsequent acid hydrolysis. To each tube, 500 µL of 12 M HCl was then added and hydrolysis performed at 110°C for 21 h. Aliquots (200 µL) of hydrolysates were evaporated and resolubilized in 200 µL of 10 mM nonafluoropentanoic acid (NFPA).
Statistical Analysis Statistical analyses were conducted with R, version 3. 3. 1 (https: / / www. r -project. org). A nonparametric analysis for repeated measurements was conducted with treatment and production time (and their interaction) as factors, using the nparLD package  (Noguchi et al. , 2012). For a significant treatment effect (P < 0. 05), the nparcomp package  (Konietschke et al. , 2012)  was used. For a significant interaction effect, a linear mixed effect model taking into account the repeated measurements was performed and followed by the difflsmeans of the lmerTest package  (Kuznetsova et al. , 2017). Results are expressed as means ± standard deviation.
ACKNOWLEDGMENTS This research project was funded by the institute Carnot Qualiment. We acknowledge  A. Dolivet ,  F. Lambrouin ,  A. Leduc ,  J. -L. Thomas  ( STLO, INRAE, Institut Agro, Rennes, France ), and  G. Le Maout  ( Bionov SA, Rennes, France ) for their technical support. The authors have not stated any conflicts of interest.
All participants gave written informed consent and completed questionnaires on their diet, lifestyle, and medical history. The study was approved by the local ethics committees and by the Internal Review Board of the International Agency for Research on Cancer. Details of the study design, recruitment, and data collection have been previously published  [32] [33] [34]. Of the 521,324 participants enrolled, 451,390 were included in the analyses, with 46,627 recorded deaths between 1992 and 2014. We excluded participants with missing lifestyle or dietary information, those with an extreme ratio of energy intake to energy requirement (top and bottom 1%, as these values were considered physiologically implausible  [34] ), volunteers with null follow-up, those with prevalent disease at baseline (history of cancer, cardiovascular diseases [CVDs], and diabetes), and all participants from the EPIC-Greece cohort, due to administrative constraints (S1 Fig).
Baseline data collection An extensive and standardized phenotypic characterization was performed for each participant upon enrollment. Questionnaires were used to collect sociodemographic information, educational level (standardized for the whole cohort), personal and familial history of diseases, lifestyle (e. g. , smoking, alcohol use, and physical activity), and menstrual and reproductive history of women. Anthropometric measurements (e. g. , height, weight, waist, and hip circumferences) were performed in all centers (except France, Oxford, and Norway: self-reported data)  [35]. Height and weight were complemented with available self-reported values or imputed with center-, age-, and gender-specific average values when missing. Body mass index (BMI) was calculated as weight divided by height squared (kg/m 2 ).
Semiquantitative FFQs were used in Denmark, Norway, Naples, Umeå, and the UK, while an FFQ was combined with a 7-day record on hot meals in Malmo ¨  [33]. Post-harmonization of DQ data was conducted, following standardized procedures (e. g. , decomposing recipes into ingredients), to obtain a standardized food list for which the level of detail is comparable between countries. The EPIC food composition database comprises more than 11,000 food and beverage items reflecting the specificities of each country  [36].
Follow-up for vital status and cause of death Data on vital status and cause and date of death were obtained using record linkages with population-based cancer registries, boards of health, health insurance registries, pathology registries and mortality registries (Denmark, Italy, the Netherlands, Norway, Spain, Sweden, and the UK), or through active follow-up and next of kin (France and Germany). Germany identified deceased individuals from undelivered follow-up mailings and subsequent enquiries to municipality registries, regional health department, physicians, or hospitals. In France, information on deceased participants was obtained using the database of health insurance for school employees and national death index. The end of follow-up/closure dates of the study period varied between 2009 and 2014 depending on the countries.
Cause-specific mortality data were coded according to the 10th revision of the International Statistical Classification of Diseases, Injuries, and Causes of Death (ICD-10)  [47]. Causes of death assessed include heart disease [i. e. , coronary heart disease (CHD) (ICD-10 codes: I20 to I25) and CVD other than CHD (I00 to I99, excluding I20 to I25)], cancer (C00 to D48), diseases of the respiratory system (J00 to J99), and diseases of the digestive system (K00 to K93). Total mortality (main outcome) was defined as mortality from all causes, except external causes of death (S00 to T98 and V01 to Y98).
e. , to reflect carbohydrate quality  [51] , such as whole grains). Possible multicollinearity of (dietary) variables included in our models were assessed by "collin" and "vif" options in proc phreg, SAS (all condition indices <30 and variance inflation factors <3, respectively). When data on categorical covariates were missing, a "missing class" was introduced to the model. We performed several sensitivity analyses to test the robustness of our all-cause mortality results. First, we stratified our multivariable-adjusted models to estimate associations by sex and country (owing to the varying detail of food and drink items captured by DQs) separately. Furthermore, we removed Mediterranean diet score, red and processed meat, fiber, and total energy intake from the models to examine their role as mediators, rather than confounders.
To assess the potential for residual confounding, we carried out subgroup analyses according to major potential categorical effect modifiers: educational level, smoking status, marital status, and physical activity. Furthermore, we used a "complete cases" approach, excluding participants with missing/unknown data on covariates. Not all food and drink items received a specific FoodEx2 species code, but rather kept a generic NCLASS classification (e. g. , "other root vegetables," which counted as one species toward an individual's overall DSR). Therefore, we reran our models dropping these generic food and drink items. Analyses were also conducted including DSR without the lowest 5% and 10% of species intakes for each EPIC food group (see methodological reasoning above). In addition, we repeated our prospective analyses for species richness within each main EPIC food group adjusted for overall DSR (minus itself) to investigate whether one or more food groups were responsible for the observed associations  [52].
(rice), S. tuberosum (potatoes), Triticum spp. (wheat), and Zea mays (maize)  [64]. Our individual level self-reported dietary intake data from 9 diverse European populations, which includes consumer level waste and intra-household food distribution, suggest that animal species alone contributed over a quarter of total dietary energy, whereas the aforementioned staple crops also contributed a further 25% between 1992 and 2000 in the EPIC cohort. Our findings are alarming considering the growing realization that upstream agroforestry, aquatic, and other biosphere biodiversity loss, approximately 1 million species are now threatened with extinction  [5] , might have caused a further bottleneck of downstream consumer food choice  [15]  and thus have subsequent negative impacts on dietary (bio)diversity and food system sustainability  [65].
Therefore, individuals with lower DSR may have been underrepresented in this study, which may have weakened the observed inverse associations by inducing a smaller contrast between high and low DSR (or a potential food biodiversity threshold reached in the UK cohorts). Furthermore, in our models, we included all the participants with available dietary intake data, but with potential missing data on other covariates replaced with a "missing" class or imputation. Although this may have induced some bias, a "complete cases" model alone might have led to a selection bias toward more adherent participants in an already health-conscious population. Yet, our sensitivity analyses with complete cases provided similar results. In addition, this study used a single assessment of self-reported dietary intakes at baseline.
Conclusions In this cohort study, we observed that maternal diet that is of low quality and high inflammatory potential is associated with lower offspring birth size and higher risk of offspring being born SGA in this multicenter meta-analysis using harmonized IPD. Improving overall maternal dietary pattern based on predefined criteria may optimize fetal growth and avert substantial healthcare burden associated with adverse birth outcomes. institutions. Contacts for data requests for each cohort are listed in S2 Table. Funding: This work was supported by an award from the European Union's Horizon 2020 research and innovation programme under the ERA-Net Cofund of the Joint Programming Initiative Healthy Diet for Healthy Life (JPI-HDHL) (http://www. healthydietforhealthylife. eu) action number 696295 (Biomarkers for Nutrition and Health).
Co-funding was provided by Science Foundation Ireland, Ireland (Grant Number SFI/16/ERA-HDHL/3360) to CMP (used to support LWC's salary); the UK Biotechnology and Biological Sciences Research Council (ERA-HDHL Biomarkers: BBSRC: BB/ P028179/1 and BB/P028187/1) to CLR and NCH; National Centre for Research and Development (ERA-HDHL/01/ALPHABET/1/2017) to KP (used to partially support KP's and WH's salaries); the ZonMW The Netherlands (no 529051014; 2017) ALPHABET project (no 696295; 2017)) to LD; the French National Agency of Research (reference AnrR16227KK) to BH (used to support AMA's and JYB's salaries). Cohort-specific sources of funding are listed in S1 Text. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: JRH owns controlling interest in Connecting Health Innovations LLC (CHI), a company that has licensed the right to his invention of the dietary inflammatory index (DII) from the University of South Carolina in order to develop computer and smart phone applications for patient counselling and dietary intervention in clinical settings.