dataesr/scientific-paragraphs-categorization huggingface dataset:
Images were analyzed using LAS AF Lite (Leica Microsystems) and Fiji (ImageJ). For 3-D-rendered stacks, Imaris (Bitplane) was used. HC/HT analyses were performed using an Operetta CLS TM (Perkin Elmer) and co-localization of mCherry/HLA-DR or GFP/Siglec-1 automatically quantified using the Harmony TM Software and RMS Spot Analyses (Perkin Elmer). For these automated analyses, first fluorescence intensities were measured, since if HIV particles are in the cytoplasm, the fluorescence intensities are significantly lower compared to packed virus in endosomes. Lower intensities can then be excluded from the automatic screening process. Then co-localization of virus particles with HLA-DR, which is in endosomal compartments only, was measured.
Multicolor FACS Analyses Differentiation and maturation of DCs exposed to Chlamydia or LPS and HIV-1 were analyzed by using anti-human CD11c-AlexaFluor488, HLA-DR-PerCP/Cy5. 5, DC-SIGN-PE (Biolegend), CD86-BV421, CD83-APC, CD169-PE (BD Biosciences) on a FACS Verse flow cytometer (BD Biosciences). Cell surface expression of receptors for HIV and HIV-C binding was determined by flow cytometry as described using antihuman CD11b-APC, CD4 PerCP/Cy5. 5 and DC-SIGN-PE (BioLegend). Data was analyzed using FACS DIVA software (BD Biosciences) and R.
Siglec-1 Does Not Play a Role With Respect to HIV-C Capture Since Siglec-1 (CD169) was described-at least in vitro -to exert a prominent role with respect to capture and transfer of HIV-1 in LPS-stimulated mDCs  (27) (28) (29) , we analyzed colocalization of this molecule with GFP-tagged HIV or HIV-C in differently stimulated DCs. For this, we performed high content screening of differentially stimulated and infected DCs and analyzed the co-localization of GFP-tagged virus with PE-labeled Siglec-1. We automatically analyzed two fields á 100 cells for their co-localization of HIV-1 and Siglec-1 using the Harmony TM software (Perkin-Elmer) and mean values of spots co-localizing within 100 cells are depicted in Figure  S3. These analyses revealed no significant differences but only slightly higher Siglec-1/HIV-C co-localization in Chlam-DCs compared to iDCs or HIV/Chlam-DCs and compared to background fluorescence of non-infected cells, which served as negative controls (Figure  S3 ).
ACKNOWLEDGMENTS We would like to thank our technician  Karolin Thurnes, Divison of Hygiene and Medical Microbiology , and  Prof. Oliver Keppler ,  Max-von-Pettenkofer Institute, Munich, Germany , for supplying the HIV plasmids. We would like to thank the  Austrian Science Fund  ( MCBO graduate program / W011010-21  and  P24598  to DW,  P25389  to WP) and the  Oesterreichische Nationalbank Anniversary Fund  (Project number:  17614  to WP) for supporting this work. Further, this publication was made possible with help from the  HIV Vaccine Trials Network Mucosal Immunology Group Program , an  NIH  funded program (HVTN LC Grant  UM1AI068618  to TJH and DW) and the  Tyrolean Science Fund  (to DW). AM is supported by  ANRS ,  Sidaction  and  ANR  fundings.
The proposed ontology for additive manufacturing For limits of space the following sections introduce only some modeling elements of the ontology; the reader can refer to the ontology file for an insightful overview of the ontology's structure. 9 Section 4. 1 presents the upper-level branch of the ontology, whereas the AM branch is introduced in Section 4. 2. The ontology is developed in OWL by means of Protégé 10. For the sake of readability, we use the syntax of Description Logics  [3]  throughout the paper. The languages SWRL and SPARQL are used for expressing rules and queries, respectively. 11.
In particular, by taking benefit of the OWL distinction between data properties and object properties, we distinguish between quantitative and non-quantitative quality-relations, namely, (i) relations linking instances of Object, Process, Material, or Type (see below) to numerical values (e. g. , being 30kg heavy or being 3mm height), and (ii) relations linking instances of these classes to non-numerical values (e. g. , being light, being flake-shaped, being small, etc. ). The class NonQuantitativeValueSpace is used to collect the latter values. E. g. , the data property hasThicknessInMillimetre can be used to model the numerical value of a product's layer thickness, whereas the object property hasShapeValue can be used to model, e. g. , the flake shape of metal powders. 16  Finally, the class Type is used when quantification over properties is needed within the expressivity of languages based on first-order logic like OWL  [9, 47].
23 inally, the ontology covers various parameters, mostly modeled as OWL data properties, to represent, e. g. , the maximum power value of lasers, the velocity of scanning processes, hatch distances, the building volume capabilities of machines, and so on. The reader can refer to the OWL file for an insightful overview of these relations. Figure  5  shows some of the classes introduced across Section 4. 2. Figure  5 : The AM ontology branch (partial view).
Cytotoxicity Assay Cytotoxicity was assessed 24 h after cell contact by flow cytometry. Targets cells were loaded with PKH26 dye (Sigma-Aldrich) according to the manufacturer's directives and seeded in 96 well round bottom plates. Effector cells were then added at different effector:target ratios and medium alone was added to control wells. Before acquisition, 7AAD was added to each well to discriminate dead cells. The cytotoxicity was calculated as: Cytotoxicity (%) = [1-live targets (sample)/live targets (control)] × 100%. mRNA Quantification RNA-seq expression studies were independently performed in two laboratories (Accession #GSE128696, #GSE129044). For the FI-NK vs. IL-21-NKAES/IL-15-NKAES comparisons, extraction of total RNA was done using the RNeasy mini kit (Qiagen) and Total RNA Purification Plus Kit (Norgen Biotek), respectively.
The quality of RNA was verified with 2100 Bioanalyzer (Agilent) prior to preparation of sequencing libraries with the TruSeq RNA Sample Prep v2 Kit. Quality of libraries was verified via Agilent 4200 Tapestation using a High Sensitivity D1000 ScreenTape Assay kit. For the IL-15 NKAES analysis, approximately 60-80 million paired-end 150 bp sequence reads per library were generated, whereas for the IL-21 NKAES analysis, 30 million single-end 101 bp sequence reads per library were generated, both using Illumina HiSeq4000 platform. Kallisto, an RNA quantification program based on pseudoalignment was used to obtain read count estimates per gene  (14). The differential gene expression analysis was done using DESeq2, edgeR, and limma R packages.
These data were confirmed by qPCR (not shown). Also, the expression of both BaEV receptors by NK cells may explain the higher transduction efficacy of BaEV-LVs as compared to RD114 which use only one of those receptors. CAR-expressing NK-cells represent one of the most relevant clinical applications of efficient NK-cell transduction. We first tested a single 3rd generation CAR construct recognizing CD22 (Figure  3A ). We obtained a transduction rate of 38. 3% ± 23. 8% (mean ± SD) of NKAES and a high sustained level of CARexpression (58. 4% ± 7. 8%; mean ± SD) after sorting and reexpansion (Figures  3B, C ). We demonstrated that CD22-CAR-NK-cells efficiently and specifically killed B-ALL RS4;11 target cells, which were resistant to untransduced NKAES (Figure  3D ). We could obtain 5 × 10 8 CAR-expressing cells from 5 × 10 5 transduced cells after an expansion of 14 days (not shown).
The simulations are conducted using a commercial EM simulation software, CST Microwave Studio, using a time-domain solver. The frequency-diverse antenna is built upon our previous work on computational polarimetric imaging  33. The antenna consists of a metasurface layer with sub-wavelength, circular shaped irises excited using a cavity-backed feeding architecture. The cavity-backed feeding has a single waveguide port (WR90) as depicted in Fig. 12. It should be noted that, because of the computational complexity of the problem at hand, the frequency-band of operation is chosen to be 9-11 GHz within the X-band frequency regime (8-12 GHz). For the full-wave simulations, we consider two scenarios; case 1: on-axis incident (θ = 0°, ϕ = 0°) and case 2: oblique incident (θ = -15°, ϕ = 0°). The retrieved DoA estimation patterns for case 1 and case 2 are shown in Fig.
First-strand cDNA was synthesized from 1 µg total RNA using the SuperScript III Reverse Transcriptase enzyme kit (Invitrogen, Carlsbad, CA, United States). Real-time RT-PCR was performed using the GoTaq qPCR Master kit (Promega, Madison, WI, United States) with a 1:20 dilution of cDNA. Primer sets were designed using the CRAFRU 8. 43 reference genome (Pseudomonas Genome DB 1  ) to detect the transcripts of genes pvdE (cyclic peptide transporter; WP_017684002. 1), pvdS (pyoverdine sidechain peptide synthetase IV; WP_019716667. 1), pvdO (chromophore maturation protein; WP_017684003. 1), lip_1 (annotated lipase; WP_025987999. 1), aprA (serine 3-dehydrogenase; WP_017682688. 1), fleQ (ATPase AAA; WP_003382141. 1), pilM (pilus assembly protein; WP_003378852. 1), algU (RNA polymerase sigma factor; WP_003378493.
1). Mean normalized expression (MNE) values were based on the constitutive housekeeping gene rpoD encoding RNA polymerase sigma factor WP_017683803. 1. All primer sets used for real-time RT-PCR were first tested by conventional PCR (Supplementary Table  S1 ). Real-time RT-PCR was carried out using the StepOnePlus Real-Time PCR System (Applied Biosystems, Foster City, CA, United States) under the following conditions: initial heating to 95 • C for 2 min followed by 40 cycles of 95 • C for 15 s, 55 • C for 30 s and 60 • C for 30 s, followed by melting curve analysis to check primer specificity. The comparative threshold cycle method ( Ct) was used for the analysis of transcript levels. Results are presented as MNE values relative to rpoD. Mean and standard error (SE) values were determined from three biological replicates.
Microarray and Data Analysis The Psa transcriptome was interrogated using a custom SurePrint G3 GE 8 × 60K chip designed in house and produced by Agilent Technologies (Santa Clara, CA, United States). CRAFRU 8. 43 gene features and annotations were retrieved from GenBank Assembly GCA_000233815. 2 (ASM23381v2). The input sequence collection representing the whole CRAFRU 8. 43 genome and encompassing the nucleotide sequences of 7919 coding regions (CDSs) was provided to the Agilent eArray web tool 2  to facilitate probe design (Colombo, Vandelle, Polverari, unpublished results; GEO platform ID: GPL27505) 3. The results of the hybridizations were analyzed using Agilent G4900DA SureScan Microarray Scanner System with the Agilent Scan Control software and the data were extrapolated using the Agilent Feature Extraction software (2010).
Plants were checked daily for 14 days to identify disease symptoms (necrotic spots) on the leaves emerging from the apex after infection. Symptom severity was scored on these apical expanding leaves (not directly sprayed) as follows: 0 spots = no symptoms; 1-10 spots = mild symptoms; > 10 spots = severe symptoms. Data were presented as the percentage of plants in each treatment group showing different scores at 7 and 14 days post-infection (dpi).
This may be a consequence of the modification of motility. To evaluate whether the inhibition of swimming was associated with the loss of bacterial viability, all bacterial suspensions were also spotted onto KB containing 1. 5% agar for a standard vitality assay. The presence of tea also affected bacterial growth, but only when both the liquid culture and soft agar plates were supplemented (+/+) or when tea was solely present in the soft agar at the highest concentration of 0. 8 mg/ml EGCG (Figures  4C, D ). As stated above, the presence of tea at the highest EGCG concentration of 0. 8 mg/ml in both the liquid cultures and agar completely inhibited Psa growth (regardless of the agar concentration). These data show that the high concentration of Gunpowder tea extract reduces Psa growth and motility, whereas the lowest concentration has a significant effect on swimming motility without inhibiting growth.
ACKNOWLEDGMENTS We are grateful to  Dr. Marco Scortichini  ( Italian Council for Agricultural Research and Economics, Unit for Fruit Crop Research, CREA-FRC, Caserta, Italy ), who provided the Psa strain CRAFRU 8. 43, and to  Dr. Teresa Colombo  ( Institute of Molecular Biology and Pathology (IBPM) National Research Council Rome, Italy ) for the design of custom Psa microarray.
DATA AVAILABILITY STATEMENT The custom design of microarray chip can be found in the GEO platform under the accession number GPL27505 (https: //www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GPL27505). The datasets of gene expression generated by microarray for this study have been deposited in NCBI GEO repository under the series record GSE137806 (https://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE137806).
This ANOVA was performed to test the effect of cropping system on 1) SON stocks in 1998 and 2014 and their variation; 2) N inputs, N exportations and N surplus from 1998 to 2016; 3) N leaching between 1998 and 2017; 4) N2O emissions between April 2014 and July 2017 and 5) GHG balance. The assumptions of ANOVA were checked by using the Shapiro-Wilk and Levene's tests. The existence of significant effects (p<0. 05) was followed by a post-hoc comparison test of means with the CLD from the emmeans package  (Lenth, 2018).
Acknowledgements This study was funded by the  French Ministry of Agriculture , the  Water Agency of Seine-Normandie Basin, INRA Versailles  within the  ENBIO  project and the  PIREN-Seine. "La Cage " experiment is coordinated by  INRA Versailles (France). We gratefully acknowledge  P. Saulas ,  D. Le Floch  and  C. Montagnier  for managing the experiment,  J. P. Pétraud ,  F. Mahu  and E. Venet for their technical assistance in soil samplings,  C. Dominiarczyk  and  A. Teixeira  for processing samples and  O. Delfosse  for nitrogen analyses.
SNP-heritability varies with minor allele frequency, linkage disequilibrium, and genotype certainty; we note that approaches to estimate heritability leveraging GWAS data are constantly evolving. We also note that estimate variability needs to be taken into account when comparing the SNP-heritability with the classical twin-heritability, in particular for cancers with small sample sizes such as head/neck cancer (SNP-heritability varied between 5-14% and twinheritability varied between 0-60%, although both point estimates were 9%). Further, our data were based on GWAS metaanalysis from multiple individual GWAS across European ancestry populations from Europe, Australia and the US. Intra-European ancestry differences are likely to be a source of bias. However, since we limited our analysis to SNPs with MAF > 1% and HapMap3 SNPs (which have proven to be well imputed across European ancestry populations), we believe that any population structure across cancers will have minimal effect on our results.
Methods Studies and quality control. We used summary statistics from six cancer GWASs based on a total of 597,534 participants of European ancestry. Cancer-specific sample sizes were: breast cancer: 122,977 cases/105,974 controls; colorectal cancer: 36,948/30,864; head/neck cancer (oral and oropharyngeal cancers): 5452/5984; lung cancer: 29,266/56,450; ovarian cancer: 22,406/40,941; prostate cancer: 79,166/ 61,106. These data were generated through the joint efforts of multiple consortia. Details on study characteristics and subjects contributed to each cancer-specific GWAS summary dataset have been described elsewhere  [43] [44] [45] [46] [47] [48] [49]. SNPs were imputed to the 1000 Genomes Project reference panel (1KGP) using a standardized protocol for all cancer types  18. We included autosomal SNPs with a minor allele frequency (MAF) larger than 1% and present in HapMap3 (N SNPs = ~1 million) because those SNPs are usually well imputed in most studies (note that excluding sex chromosomes could reduce the overall heritability estimates).
A brief overview of the quality control in each cancer dataset are presented in Supplementary Table  5. For some of the cancers, we further obtained summary statistics data on subtypes (ER + and ER-breast cancer; lung adenocarcinoma, and squamous cell carcinoma; serous invasive ovarian cancer and advanced stage prostate cancer, defined as metastatic disease or Gleason score ≥ 8 or PSA > 100 or prostate cancer death). Sample sizes and more details shown in Supplementary Table  1. We additionally assembled European ancestry GWAS summary statistics from 38 traits, which spanned a wide range of phenotypes including anthropometric (e. g. , height and body mass index (BMI)), psychiatric disorder (e. g. , depressive symptoms and schizophrenia), and autoimmune disease (e. g. , rheumatoid arthritis and celiac disease) (Supplementary Table  6 ).
Data availability The datasets generated during and/or analyzed during the current study are available from the authors on request. Breast cancer: summary results for all variants are available at http://bcac. ccge. medschl. cam. ac. uk/. Requests for further data should be made through the Data Access Coordination Committee (http:// bcac. ccge. medschl. cam. ac. uk/). Ovarian cancer: summary results are available from the Ovarian Cancer Association Consortium (OCAC) (http://ocac. ccge. medschl. cam. ac. uk/). Requests for further data can be made to the Data Access Coordination Committee (http://cimba. ccge. medschl. cam. ac. uk/). Prostate cancer: summary results are publicly available at the PRACTICAL website (http://practical. icr. ac. uk/blog/). Lung cancer: genotype data for lung cancer are available at the database of Genotypes and Phenotypes (dbGaP) under accession phs001273.
v1. p1. Readers interested in obtaining a copy of the original data can do so by completing the proposal request form at http://oncoarray. dartmouth. edu/. Head/neck cancer: genotype data for the oral and pharyngeal OncoArray study have been deposited at the database of Genotypes and Phenotypes (dbGaP) under accession phs001202. v1. p1. Colorectal cancer: genotype data have been deposited at the database of Genotypes and Phenotypes (dbGaP) under accession number phs001415. v1. p1 and phs001078. v1. p1.
; and Grant FEDER-Innterconecta. Ministerio de Economia y Competitividad, Xunta de Galicia, Spain. The BSUCH study was supported by the Dietmar-Hopp Foundation, the Helmholtz Society and the German Cancer Research Center (DKFZ). The CAMA study was funded by Consejo Nacional de Ciencia y Tecnología (CONACyT) (SALUD-2002-C01-7462). Sample collection and processing was funded in part by grants from the National Cancer Institute (NCI R01CA120120 and K24CA169004). CBCS is funded by the Canadian Cancer Society (grant # 313404) and the Canadian Institutes of Health Research. CCGP is supported by funding from the University of Crete. The CECILE study was supported by Fondation de France, Institut National du Cancer (INCa), Ligue Nationale contre le Cancer, Agence Nationale de Sécurité Sanitaire, de l'Alimentation, de l'Environnement et du Travail (ANSES), Agence Nationale de la Recherche (ANR).
Collection of cancer incidence data was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885. H. A. C eceives support from the Lon V Smith Foundation (LVS39420). The University of Westminster curates the DietCompLyf database funded by Against Breast Cancer Registered Charity CSP is also part of the National Cancer Institute's Division of Cancer Prevention and Control Surveillance, Epidemiology, and End Results Program, under contract number N01CN25403. L. M. B. C. is supported by the 'Stichting tegen Kanker'. D. L. is supported by the FWO. The MABCS study is funded by the Research Centre for Genetic Engineering and Biotechnology "Georgi D. Efremov" and supported by the German Academic Exchange Program, DAAD.
-the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, and Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. The DRIVE Consortium was funded by U19 CA148065. The Australian Breast Cancer Family Study (ABCFS) was supported by grant UM1 CA164920 from the National Cancer Institute (USA). The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR. TheABCFS was also supported by the National Health and Medical Research Council of Australia, the New South Wales Cancer Council, the Victorian Health Promotion Foundation (Australia), and the Victorian Breast Cancer Research Consortium.
J. L. H. is a National Health and Medical Research Council (NHMRC) Senior Principal Research Fellow. M. C. S. is a NHMRC Senior Research Fellow. The ABCS study was supported by the Dutch Cancer Society [grants NKI 2007-3839; 2009 4363]. The Australian Breast Cancer Tissue Bank (ABCTB) is generously supported by the National Health and Medical Research Council of Australia, The Cancer Institute NSW and the National Breast Cancer Foundation. The ACP study is funded by the Breast Cancer Research Trust, UK. The AHS study is supported by the intramural research program of the National Institutes of Health, the National Cancer Institute (grant number Z01-CP010119), and the National Institute of Environmental Health Sciences (grant number Z01-ES049030). The work of the BBCC was partly funded by ELAN-Fond of the University Hospital of Erlangen.
The BBCS is funded by Cancer Research UK and Breast Cancer Now and acknowledges NHS funding to the NIHR Biomedical Research Centre, and the National Cancer Research Network (NCRN). The BCEES was funded by the National Health and Medical Research Council, Australia and the Cancer Council Western Australia and acknowledges funding from the National Breast Cancer Foundation (JS). For the BCFR-NY, BCFR-PA, and BCFR-UT this work was supported by grant UM1 CA164920 from the National Cancer Institute. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government or the BCFR.
For BIGGS, ES is supported by NIHR Comprehensive Biomedical Research Centre, Guy's & St. Thomas' NHS Foundation Trust in partnership with King's College London, United Kingdom. IT is supported by the Oxford Biomedical Research Centre. BOCS is supported by funds from Cancer Research UK (C8620/A8372/A15106) and the Institute of Cancer Research (UK). BOCS acknowledges NHS funding to the Royal Marsden/Institute of Cancer Research NIHR Specialist Cancer Biomedical Research Centre. The BREast Oncology GAlician Network (BREOGAN) is funded by Acción Estratégica de Salud del Instituto de Salud Carlos III FIS PI12/02125/Cofinanciado FEDER; Acción Estratégica de Salud del Instituto de Salud Carlos III FIS Intrasalud (PI13/01136); Programa Grupos Emergentes, Cancer Genetics Unit, Instituto de Investigacion Biomedica Galicia Sur.