dataesr/scientific-paragraphs-categorization huggingface dataset:
Samples were mounted using ProLong TM Diamond Anti-Fade with DAPI (Life Technologies, Thermo Fisher). Z-stacks were acquired with a 63X objective on a Zeiss LSM710 confocal microscope and analyzed using ImageJ software. Extracellular H-2 L d was stained with 1,2 µg/ml of mouse anti L d (clone 30-5-7, Biotem) diluted in PBS-3 % NGS for 30 min on ice followed by two washes with cold PBS. Steps of intracellular staining were the same as described above except for the antibodies used. Cells were stained with a polyclonal rabbit anti-microtubule-associated protein 2 (MAP2, 1/1000, Merck) or with a polyclonal rabbit antiglial fibrillary acidic protein (GFAP, 1/500, Merck), followed by both AF488-coupled anti-rabbit IgG (1/500, Life Technologies, Thermo Fisher) and AF594-coupled anti-mouse IgG (1/500, Life Technologies, Thermo Fisher).
Floating sections were incubated overnight at 4°C with rabbit anti-Iba1 (1/1000, Wako) in TBS -3 % NGS -0,1 % Tween®20. Following several washes in TBS -0,1 % Tween®20 and TBS, sections were incubated for 2 h at RT with secondary antibody AF647-coupled antirabbit IgG (Thermo Fisher) diluted in TBS -3 % NGS -0,1 % Tween®20 (1/500) and protected from light. Samples were washed alternatively in TBS -0,1 % Tween®20 and TBS twice and incubated with 1 µg/ml of DAPI (Sigma-Aldrich) in TBS for 30 min at RT. Sections were mounted in Fluoromount medium (Electron Microscopy Sciences) after 2 washes in TBS. Images were acquired with 5 X and 20 X objectives on Apotome Zeiss microscope and analyzed using ImageJ software.
Acknowledgments F. L'Faqihi-Olive ,  V. Duplan-Eche ,  A. -L. Iscache ,  L. de la Fuente  for technical assistance at the  CPTP-Inserm U1043  flow cytometry facility ;  S. Allart ,  A. Canivet-Laffitte ,  D. Daviaud  for technical assistance at the CPTP-Inserm U1043 imaging facility ;  G. Tavernier  and  Y. Barreira  at  INSERM UMS006-CREFRE  for the generation of the  B6. L d Lox mice,  R. Balouzat  and the zootechnicians at INSERM UMS006-CREFRE mouse facility ; the  Blanchard and Robey teams  for help and discussions ; M. -F. Cesbron-Delauw  for the anti-GRA1 and anti-GRA2 antibodies,  H. Acha-Orbea  for the MutuDC,  D. Buzoni-Gatel  for the 76K parasites,  S. -K. Kim ,  J. Boyle  and  J. Boothroyd  for the GFP + Pru tachyzoites ;  T. Hansen  for the pL d 4 plasmid ;  G. Schutz  for the B6.
CamKIIα-iCre mice ;  I. Cebrian  and  D. Dunia  for critical reading of the manuscript. This work was supported by  'Institut National de la Santé et de la Recherche Médicale', Idex Toulouse 'Attractivity Chair' Program  (to ER and NB),  Human Frontier Science Program Organization  ( CDA00047/2011  to NB),  PIA   PARAFRAP Consortium  ( ANR-11-LABX0024  to NB),  PIA ANINFIMIP equipment  ( ANR-11-EQPX-0003  to NB),  Agence Nationale pour la Recherche  ( ANR-18-CE15-0015  to NB and ES), ' Fondation pour la Recherche Médicale ' to AS ( FDT20170436953 ).
5 L min -1 through the chamber, which was then passed through the analyser unit. Flowrates were chosen based on preliminary trials to ensure that O 2 concentration within the chamber remained at ~20. 5% and CO 2 concentration did not exceed 0. 5%. The O 2 and CO 2 concentrations within the chamber, main flow rate through the chamber, humidity of the gas sample, and barometric pressure were recorded every second onto a laptop computer using ExpeData software  (1. 4. 15, Sable Systems). Lag time between air leaving the respiration chamber and detection by the gas analysers was found to be 25 s for the oxygen analyser and 20 s for the CO 2 analyser. These lag times were taken into account when calculating oxygen consumption rates and CO 2 production rates. The time constants of the respiration chambers were calculated to be 7.
Respirometry data Respirometry data were analysed using ExpeData, correcting for gas-analyser drift and lag time of the respirometry system. Also, main flow rate was corrected to STP dry (STPD) using equation 8. 6 in  Lighton (2008). Similarly, we did not scrub water vapour before gas analysis but corrected for this dilution effect during data analysis using equation 15. 3 in  Lighton (2008). O2 and CO2 were calculated using equations 11. 7 and 11. 8 in  Lighton (2008) , respectively. From each respirometry trial, a 15-min data segment encompassing the lowest stable O2 values was selected to represent the resting metabolic.
Additionally, capture date was included as random effect, in a more conservative approach that accounts for potential correlation among individuals that were caught and processed together. Sex, respirometry chamber temperature during O2 measurements and, for adult geese, progress of wing moult (expressed by the length of the 9th primary, P9) were added as further explanatory variables. For further details see Supporting Information: Appendix 1. Juvenile growth and adult body mass dynamics were analysed from cross-sectional data from the same colonies and seasons, using simple linear ANCOVA models. For juvenile geese, length of wing, head, tarsus and body mass were combined in a principal component analysis (PCA) to obtain the first principal component (PC1) that was then related to gosling age and colony.
Similarly, in order to investigate whether genetic differences play a role, one should try and rear goslings from eggs of both populations under the same conditions. and cross symbols mark subjects that were also measured for O2 in this study. Growth rate was 36. 2% slower in the sedentary (NL) colony. See Supporting Information Table  S3  for details of the statistical model. line and open symbols), respectively. Data were collected during same seasons when O2 was measured, and cross symbols mark subjects that were also measured for O2 in this study. The slope is statistically significant different from zero for NL but not for RU, and slopes differ significantly from each other (Supporting Information: Table  S4 ). Figure Legends.
J Heat-map depicting similarities, as Pearson's correlation coefficient, between DEG in keratinocytes from WT or K14DNLef1 mice and IFE vs SG vs HF transcriptome analysis (left panel). Additional correlation with DNLef1 Chip-Seq data is depicted in the right hand heat-map. K GO enrichment analysis of DNLef1 direct target genes among DEG of WT and K14DNLef1 mice. regulators of the SG  (Chang et al, 2009; Niemann & Horsley, 2012). In parallel, DNLef1 directly downregulates genes such as Klf5, important for IFE identity  (Ge et al, 2017) , Igfbp3 that is expressed in SG  (Dahlhoff et al, 2016) , and Slco2a1. Loss-of-function mutations in Slco2a1 are associated with sebaceous hyperplasia  (Guo et al, 2017). We conclude that DNLef1 has activating and repressive functions that converge in promoting a SD/SG phenotype in K14DNLef1 mice.
Gata6 acts as a tumor suppressor by controlling the DNA mismatch repair response Muir-Torre syndrome is a rare genetic condition that predisposes patients to developing sebaceous skin tumors and visceral malignancies. This autosomal dominant variant of Lynch syndrome is caused by mutations in DNA mismatch repair (MMR) genes, resulting in microsatellite instability  (Eisen & Michael, 2009a,b; John & Schwartz, 2016). Since loss of Gata6 in cultured mouse keratinocytes leads to DNA damage, triggering apoptosis  (Wang et al, 2017) , we investigated whether the tumor suppressive function of Gata6 might be due to an effect on MMR gene transcription. Computational analysis of RNA-Seq data from Gata6-deficient keratinocytes  (Wang et al, 2017)    Using ChIP-Atlas, an extensive database of publicly available ChIP-Seq experiments  (Oki et al, 2018  and http://chip-atlas.
org), we analyzed Gata6 ChIP-Seq experiments performed in human ES cell-derived mesendodermal cells and in human colon, gastric and pancreatic adenocarcinoma cell lines. We identified eight genes in the MMR pathway for which a Gata6-binding peak was found within 10 kb from the transcription start in at least 2 different datasets: Msh3, Rfc2, Pold4, Ssbp1, Pold3, Exo1, Rfc1, and Mlh3 (Fig  EV4 ). It is possible that these were not found in Gata6-overexpressing primary mouse keratinocytes  (Donati et al, 2017)  because the ChIP-Seq analyses were performed too soon after lentiviral infection for Gata6 to induce MMR genes. Nevertheless, the MMR genes identified as Gata6 direct transcriptional targets using ChIP-Atlas closely match the MMR genes that were downregulated in Gata6 cKO keratinocytes and confirm that the MMR pathway is regulated by Gata6.
Mutation and downregulation of Gata6 are features of sebaceous carcinomas with a high mutational burden North et al (2018) have distinguished three subclasses of human SebC based on their mutational profile: the ocular and cutaneous pauci-mutational SebC (with a low prevalence of mutations); SebC with a MSI mutational signature (intermediate prevalence of mutations) and SebC with a UV mutational signature (highest somatic mutation burden). Within this dataset, we found GATA6 mutations in approximately 30% of SebC harboring a MSI or UV damage signature  (Fig 7A). GATA6 missense mutations were mostly deleterious (Fig  7B ). In addition, analysis of RNA-Seq data showed that Gata6 expression was significantly lower in UV-induced SebC than in other SebC  (Fig 7C). In agreement with our observations in mice (Fig 4B  and C ), UV-related SebC expressing a low level of Gata6 were less differentiated and more aggressive than the pauci-mutational and MSI-mutant tumors  (North et al, 2018).
Flow cytometry Epidermal cells were isolated, labeled with anti-CD34 (RAM34 clone, BD Biosciences 553731) and anti-CD49f (Itga6) (GoH3 clone, eBioscience 14-0495-81), and sorted by flow cytometry as previously described  (Jensen et al, 2010). Data were analyzed using FlowJo software. ChIP-Seq, microarray, and computational analysis ChIP-Seq was performed as previously described  (Mulder et al, 2012). Briefly, crosslinked material corresponding to $ 10 7 K14DNLef1 cultured primary keratinocytes (collected from 9. 5week-old mice) was incubated overnight with 10 lg of Lef1 (Cell Signaling) or Flag antibody (Sigma), and immune-precipitated DNA was sequenced. Base-calling, genome alignment, filtering against potential PCR duplications and peak calling were performed as previously described  (Donati et al, 2017).
RNAs from FACS-purified Cd34 + Itga6 + and Cd34 À Itga6 + epidermal cells collected from WT and K14DNLef1 epidermis (9. 5-weekold mice) were provided to the Paterson Institute Microarray Core Facility to perform gene expression profiling using the mouse Exon1. 0ST Affymetrix platform. Differential expression analysis was carried out on normalized data as described previously  (Donati et al, 2017). Gene Set Enrichment Analysis was performed to compare expression signature genes in SG vs IFE and HF (ranked Z-score values of differentially expressed genes (DEG);  Donati et al, 2017)  with respect to a gene subset composed of the nearest genes to the DNLef1 peaks from Chip-Seq data. Human Gata6 ChIP-Seq datasets were interrogated using the "Target Genes" module of ChIP-Atlas  (Oki et al, 2018  and http:// chip-atlas.
org). This module predicts genes directly regulated by a given protein, based on binding profiles of all public ChIP-Seq data for particular gene loci. KEGG pathway analysis of DEG in WT vs epidermal specific Gata6 knockout keratinocytes  (Wang et al, 2017)  and Gene Ontology analysis of DEG in WT vs K14DNLef1 keratinocytes were performed using DAVID software (https://david. ncifcrf. gov). Heatmaps were generated via Gene-E (http://www. broadinstitute. org). Network visualization was achieved using Cytoscape (http://www. cytoscape. org). Protein schematization was performed using IBS (http://ibs. biocuckoo. org). SNAP2  (Hecht et al, 2015)  and PredictSNP  (Bendl et al, 2014)  algorithms were used to predict the effect of Gata6 mutations.
Acknowledgements We are grateful to all members of the  Watt laboratory  for helpful discussions. We thank  Beate M Lichtenberger  for providing mouse skin samples and  Melis Kayikci  for support on computational analysis. We thank  Raymond J. Cho  for providing the data from North et al (2018). We thank  Rudolf Grosschedl  and  Ingrid Falk  for providing mutant mouse skin. We thank  DNA Sequencing & Services  ( MRC I PPU, School of Life Sciences, University of Dundee , Scotland, www. dnaseq. co. uk) for DNA fragment analysis. We are also grateful for funding from the  Department of Health via the National Institute for Health Research comprehensive Biomedical Research Centre award to Guy's & St Thomas' National Health Service Foundation Trust  in partnership with  King's College London and King's College Hospital NHS Foundation Trust.
Data availability All data that support the conclusions are available from the authors on request. Microarray and ChIP-Seq data that support the findings of this study have been deposited in the Gene Expression Omnibus (GEO) under accession codes GSE62608 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE62608), GSE118073 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE118073), and GSE118074 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE118074). Expanded View for this article is available online.
NanoZoomer Slide Scanner (Hamamatsu). Digital images were processed using NIS-Elements Advanced Research (Nikon), ImageJ (https://imagej. nih. gov/ij/), or NDP. view2 (Hamamatsu). Primary antibodies were used at the indicated dilutions: Fabp5 (1:100, R&D Systems AF1476); Krt15 (1:1,000, LHK-15 clone, Abcam ab80522); Lef1 (1:100-500, C12A5 clone, Cell Signaling 2230 and 8490); Gata6 (1:100-1,000, D61E4 clone, Cell Signaling 5851 and 26452); Krt14 (1:1,000, LL002 clone, Abcam ab7800 and 1:1,000, Covance SIG- 3476); Sox9 (1:100, R&D Systems AF3075 and 1:400, D8G8H clone, Cell Signaling 71273); Ki67 (1:50, Tec3 clone, Dako); Lrig1 (1:200, R&D Systems AF3688); Cd34 (1:100, RAM34 clone, BD Biosciences 553731); Tcf3/4 (1:100, Abcam ab12065); pankeratin (1:1,000, clone LP34, LSBio LS-C95318); Fasn (1:100, G-11, Santa Cruz sc- 48357); GFP (1:200, Abcam 6673 and 1:800, Thermo Fisher Scien- tific A-11122); Plet1 (1:200, LSBio LS-C149191); Atp6v1c2 (1:200, Sigma HPA034735); Mlh1 (1:100, Abcam ab92312); and Msh2 (1:100, Clone D24B5, Cell Signaling 2017).
Patients This observational study was conducted in the framework of a population-based image-screening trial for AAA in Danish men aged 65-74 years (VIVA, ClinicalTrials. gov NCT00662480)  [28]. The study protocol design has been published in detail elsewhere  [29]. Briefly, 50,156 male individuals were randomized to either receive an invitation for vascular screening or being a control subject between October 2008 and October 2010. A total of 18,749 individuals out of the 25,078 invited in the vascular screening arm agreed to participate  [28]. The study protocol was performed in compliance with the Declaration of Helsinki and approved by the Institutional Review Board from the Mid Region of Denmark (M20080028). All participants gave written informed consent prior enrolment. At study entry, an ankle-brachial index (ABI) measurement, an ultrasound scan of the aorta as well as a questionnaire regarding lifestyle parameters, medical and smoking status was performed by trained project-nurses in 3 mobile units at local hospitals in the mid region of Denmark.
Statistical Analyses Normal distribution of data was assessed using graphical methods and the Shapiro-Wilk test. Residuals were assessed in histograms and p-p plots. Levels of IgG anti-HDL were log-transformed to gain an acceptable normal distribution before being entered in parametric and multivariate tests. Continuous variables were summarized as median (interquartile range) or mean ± standard deviation (SD) depending on the distribution of the data. Categorical variables were expressed as n (%). Differences between AAA and controls in main clinical characteristics and laboratory analyses were assessed by Student's t test. Independent associations between AAA prevalence and IgG anti-HDL antibodies were assessed by logistic regression analyses with adjustments for active smoking, hypertension, use of statins, use of low dose aspirin, BMI, systolic blood pressure, and PAD at screening.
To generate these data, clinical studies need to be performed and the results published in scientific journals. Further, summaries or short information leaflets could form part of sales and marketing campaigns to build user awareness. That said, it is appreciated by the JPIAMR AMR-RDT working group that establishing and conducting these studies can be very expensive, especially for innovators in small and medium-sized enterprises. However, being trustworthy and open with consumers, healthcare authorities, investors, and patients would help avoid prominent crises, such as occurred with Theranos  [13]. For further guidance, PoCT innovators should consult regulatory frameworks such as the latest In Vitro Diagnostic Device Regulation (EU 2017/746) of the European Union which also lays the legal framework for the European database on medical devices (EUDAMED) which by 2020 will facilitate access to information on existing diagnostics (http://eurlex.
To be feasible, standardised data collection, databases, data exchange, privacy protocols, and cybersecurity issues will have to be implemented, especially in an age of connectivity via the Internet of Things. However, it should be noted that saving costs on data security in an age of PoCT diagnostic connectivity could potentially result in poorly protected devices with vulnerabilities that leave them open to hacking and malware (www. theregister. co. uk/2015/10/19/bods_brew_ ikettle_20_hack_plot_vulnerable_london_pots/). The complex 'mix and match' implementation package is shown in Fig. 1. Fig. 1  Complex 'mix-and-match' implementation package for the successful implementation of rapid infectious disease and antimicrobial resistance point-ofcare testing. One barrier to the successful uptake and sustainability of rapid infectious disease and antimicrobial resistance point-of-care testing (PoCT) is the need to take into account the longterm viability, sustainability, and durability of these diagnostics.
Program context The intervention was nested into Universal Access to TB Care (UATBC), a PPM programme between the Bihar state government and PPIA World Health Partners (WHP),  24  under an operational research framework. Since 2013, UATBC has engaged over 1500 of Patna's licensed private practices (medical doctors, laboratories, pharmacies and informal providers) in standardised TB management, via a state-sanctioned memorandum of understanding (MOU). TB tests and treatment are provided at no cost, akin to coverage in the public sector. New diagnoses are notified to the RNTCP; patients may transfer into the public programme at any point. Test, treatment and referral activities are facilitated through programme vouchers and a call centre, to support integration into the RNTCP's electronic data platform, eNikshay.
Incentives include the following: Rs 50 (US$0. 80) to pharmacy and informal providers for completed doctor referrals, Rs 50 (US$ 0. 75) to doctors and informal providers for completed radiographic and/or microbiological tests and Rs 200 (US$3) to doctors and informal providers for any referrals that result in TB notification. Doctor consultation fees (ranging Rs 200-500 or US$3-7. 50 per visit) are not covered but some providers offer free or discounted consultations to very poor patients. In 2016, 84% of 22 291 TB case notifications in Patna came from the private sector, including from UATBC. 25  Situation analysis UATBC programme data showed that 554 of the city's 972 mapped pharmacies were engaged in the PPIA programme in 2014-2015; 33% of pharmacies participated in TB drug dispensing activities but only 16% participated in screening and referral activities (WHPs 2014-15 programme data, unpublished).
data collection Quantitative methods Quantitative data were collected from December 2015 to June 2017 from all 804 UATBC pharmacy providers from the programme database: month-wise number of referrals per provider and number of TB cases detected as a result of these referrals; and for each pharmacy referral, the date of patient registration, CXR referral and completion, doctor referral and completion, microbiological diagnostic test referral and completion, CXR and diagnostic test outcomes, number of diagnosed patients initiating treatment, and date of treatment initiation. An assessment of whether the referral was made during the pilot intervention was made based on the date of providers' entry into the intervention period and date of patient registration. Patient data were de-identified.
Qualitative methods Qualitative data were collected from February 2016 to June 2017. Nine focus group discussions (FGDs)  26  were held with 83 intervention providers during quarterly meetings at a central non-study location where lunch was provided. In all, 22 providers were unable to attend a FGD, and participated in private interviews at their places of practice. 27 FGDs and interviews were conducted in Hindi and audio-recorded by a local qualitative interviewer. The following broad topics were discussed: experiences and challenges serving the community; specific experiences with TB service delivery; interactions with patients and other providers involved in TB care; and opinions and feedback about the intervention. Providers were familiar with one another from having participated in the UATBC programme; hence rapport was easily established.
Analysis Quantitative data on the diagnostic evaluation cascade are displayed within a flow diagram indicating the number The first two rates (1 and 2) were compared to assess the relative difference in the rate of registration of persons with TB symptoms during the non-/pre-intervention and intervention periods, and next two rates (3 and 4) were compared to assess the relative difference in TB cases diagnosed during the non-/pre-intervention and intervention periods. Crude rate ratios for comparing the rates of persons with TB symptoms and TB cases detected during intervention and control periods were estimated by the conditional maximum likelihood estimate method; 95% CI were estimated by the Mid-P Exact method. 28 Characteristics of patients diagnosed with TB during the intervention and control periods were evaluated by χ 2 independence test (for categorical variables) and Wilcoxon rank-sum test (for non-normally distributed continuous variables); a p value<0.
ethics The state-sanctioned UATBC programme granted permission to nest the pilot intervention into its ongoing routine service delivery programme. A supplementary MOU was created and signed by all pharmacy providers participating in the pilot. They also signed an informed consent form to participate in FGDs or interviews. Patients diagnosed under the pilot intervention were treated via the routine UATBC programme by the Bihar state government. Patient data were de-identified; patients were not required to be consented. Institutional ethics approval was also granted by the Research Ethics Office of the McGill University Health Centre (Ref 15-360).
42 While we did not undertake a cost-effectiveness analysis, our model may be a fiscally feasible strategy. The study had several limitations. We were unable to capture numbers of symptomatic patients who presented at pharmacies during the intervention and control periods; only patients who were registered and referred were recorded. It was difficult to gauge the intervention's impact on timing or volume of OTC drug sales, as these records were unavailable. Qualitative data suggest that only some providers may have replaced the offer of an OTC drug with a screening test. We were also limited in identifying objective characteristics of high-performing versus low-performing providers. Qualitative data suggest the intervention may be most relevant for pharmacies catering to lower-income clients.