dataesr/scientific-paragraphs-categorization huggingface dataset:
After 1. 5 h of exposure, the medium was removed, and cells were washed three times with acid and basic PBS at 4 °C to remove receptor-bound free folate and not internalized liposomes. The cells were then fixed with 4% paraformaldehyde for 30 min. The cells were further washed three times with PBS. After drying, the coverslips were put on slides coated with 2 μL of Vectashield mounting medium (VECTOR) and sealed. Slides were examined using an inverted Zeiss confocal laser scanning microscope (CLSM, Olympus Fluoview FV1000). Volume rendering and 3D models were created with the software Imaris7. 0 (Bitplane, Zurich, Switzerland). RNA Isolation, cDNA Synthesis and Quantitative Reverse Transcription Polymerase Chain Reaction (RT-PCR). Total RNA was extracted using the SV Total RNA Isolation System (Promega) according to the manufacturer's instructions and quantified with a NanoDrop ND-1000 spectrophotometer (Nanodrop Technologies, Wilmington, DE).
Reverse transcription (RT) was performed using the iScript cDNA synthesis kit (BioRad) with a unique blended oligo(dT) and random hexamer primers, according to the manufacturer's protocols. Quantification of gene expression was performed with a CFX96 Real Time PCR Detection System from BioRad using reagents and protocols from the TaqMan Gene Expression Assay Protocol (Applied Biosystems). Each sample was normalized against the expression of mitochondrially encoded ATP synthase (MT-ATP6) as a stable internal control. The assay IDs of the TaqMan Gene Expression Assays were Hs02596862_g1 (MT-ATP6), Hs00153133_m1 (COX-2), HS00174103_m1 (IL-8), and Hs01042796_m1 (MMP7). Statistical Analysis. Statistical analyses were performed with GraphPad Prism software (version 5. 0). Unless otherwise stated,.
Therefore, this peptide was selected for further the studies. To confirm the deep insertion of SP-DS3 in the bilayer, we used the photoactivatable phosphatidylcholine analogue [ 125 I]TID PC/16, which has been used to identify and characterize regions within membrane proteins that interact with lipids. 19 [ 125 I]TID PC/16 positions itself in the phospholipid milieu and, upon photolysis, reacts with the peptide if present in the vicinity. 20, 21 During the reaction between the photoactivatable group and the peptide, the aryl group is transferred to the peptide and quantified via 125 I, demonstrating that SP-DS3 was incorporated in the phospholipid milieu. To further assess the behavior of SP-DS3 in the lipid bilayer, a molecular modeling study was performed using GROMACS software. The resultant model indicated that FA is located at the membrane surface, whereas SP-DS3 is deeply inserted into the lipid bilayer with the tryptophan residue in contact with the hydrocarbon region (Figure  1D ).
29, 30 2] [33]  Since (q) PCR is a very sensitive technique, it was considered the best option to monitor this highly regulated pathway. We observed that celecoxib-loaded liposomes decreased the expression of COX-2 (Figure  3A ), which is consistent with the fluorescence data showing that these liposomes can be internalized to some extent by Caco-2 cells. This reduction was much more pronounced in liposomes integrating SP-DS3, which showed similar levels to those obtained with celecoxib alone, indicating the effective release of the cargo from the targeted liposomes in vitro. Similar results were obtained with SP-DS3 liposomes encapsulating CORM-2 34 assessed in Caco-2 cells stimulated with pro-inflammatory cytokines (Cytomix); mRNA levels of interleukin-8 (IL-8) and metalloproteinase-7 (MMP-7), which are crucial for cancer cell proliferation and invasion, were quantified.
The use of liposomes equipped with the novel bifunctional SP-DS3 peptide linker reported here for FAmediated delivery opens new opportunities for the treatment of human diseases, including chronic inflammatory diseases and cancer. ■ ASSOCIATED CONTENT * S Supporting Information Data of SP-DS3 characterization by analytical high-performance liquid chromatography and mass spectrometry are provided as Supporting Information. Furthermore, physicochemical characterization, cell viability, hemolytic properties and specificity of liposomal formulations, as well the biological effect of liposomes encorporating CORM-2 are included. The Supporting Information is available free of charge on the ACS Publications website at DOI: 10. 1021/acs. biomac. 5b00823. (PDF).
■ ACKNOWLEDGMENTS Eugeńia Nogueira  ( SFRH/BD/81269/2011 ) and  Ana Loureiro  ( SFRH/BD/81479/2011 ) hold scholarships from Fundacaõ para a Ciencia e a Tecnologia (FCT). Goncalo J. L. Bernardes  is a  Royal Society University Research Fellow  at the  Department of Chemistry, University of Cambridge  and an  Investigador FCT  at the  Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa. This study was funded by the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement  NMP4-LA-2009-228827  NANOFOL. The authors thank the  FCT Strategic Project  of  UID/BIO/04469/2013  unit, the project  RECI/BBB-EBI/0179/2012  ( FCOMP-01-0124-FEDER-027462 ) and the Project " BioHealth -Biotechnology and Bioengineering approaches to improve health quality ",  Ref.
Molecular typing Possible genomic relatedness of strains was analyzed by RAPD using genomic DNA as previously described. 20 Multilocus sequence typing (MLST) was performed on the K. pneumoniae isolates using seven conserved housekeeping genes (gapA, infB, mdh, pgi, phoE, rpoB and tonB). 21 A detailed protocol of the MLST procedure, including allelic type and ST assignment methods, is available in MLST databases from the Pasteur Institute, Paris, France, at the website http://www. pasteur. fr/ recherche/genopole/PF8/mlst/Kpneumoniae. html.
Genetic organization of bla ampC genes For the analysis of genetic arrangement of the resistance genes, overlapping PCR amplification of internal regions of the transposon-like element that carried bla CMY-4 was performed based on known sequences. Genetic structures surrounding the bla DHA-1 gene were studied by PCR mapping, cloning and sequencing method using a large variety of primers based on the previously reported structures. 8 he nucleotide sequence and the deduced protein sequence were analyzed using the Basic Local Alignment Search Tool (BLAST) through the Internet (http://www. ncbi. nlm. nih. gov/BLAST/). Multiple sequence alignment of deduced peptide sequences was carried out with the Vector NTI program (Invitrogen).
Characterization of the genetic contexts of bla AmpC genes Analysis of the genetic structure of the bla CMY-4 gene in our collection showed that it was located on a transposon-like DNA element consisting of a specific ISEcp1/ ISEcp1-bla CMY-4blc-sugE structure. This structure was similar to that found in plasmid pCC416 (GenBank AJ875405). A region typical of a complex sul1-type integron, from the int gene to CR1 was amplified using PCR mapping and then sequenced. By cloning the region encompassing ampC and ampR, a recombinant plasmid (p413C) with an insert that conferred inducible resistance to ceftazidime was selected. The insert was found to contain bla DHA-1 and the regulatory gene ampR, which was downstream of bla DHA-1. This insert shared also part of pRBDHA's backbone carrying a complex integron (GenBank AJ971343).
Bacterial Strain Providencia rettgeri H1736 was isolated from the rectal swab of 74-year-old man in May 2011 in Israel. He presented with uncontrolled diabetes, hypertension, and an indwelling urinary catheter  (Lachish et al. 2012). Genome Sequencing and Assembly of P. rettgeri H1736 Genome sequencing of P. rettgeri H1736 was done with both a 454-Titanium instrument (454 Life Sciences, Branford, CT) and SOLiD (Applied Biosystems, Foster City, CA) and assembled as previously described  (Olaitan et al. 2014). Genome annotation was performed using the MicroScope platform available on www. genoscope. cns. fr (last accessed April 8, 2015). Analysis of AR Genes, Mobilomes, Regions of Genomic Plasticity, and Virulence-Associated Genes AR-encoding genes were analyzed using ARG-ANNOT database  (Gupta et al.
Genome Features The draft genome of P. rettgeri H1736 has a genome size of 4,609,352 bp (excluding the plasmidic sequence), 40. 22% GC content with 4,525 predicted coding sequences (CDSs). We predicted the bacterium harbored at least five different plasmids. The whole-genome sequence of P. rettgeri H1736 has been deposited in the EMBL database (accession numbers: CVLT01000001-CVLT01000099).
Resistome The P. rettgeri H1736 genome harbored a total of 27 AR genes; 19 (70. 37%) on the chromosome and 8 (29. 63%) on plasmids (table  1 ). Six efflux pump systems known to be involved in drug resistance were also identified (table  1 ). The plasmid-mediated quinolone-resistance gene qnrD was located on a small nontransmissible plasmid of 2,683 bp. The 48. 5-kb region containing bla NDM-1 gene showed 100% identity to the same region of bla NDM-1 harboring plasmids which includes pPrY2001 from P. rettgeri (GenBank accession number: KF295828. 1).
Poisson distributions were fit for count data, and binomial distributions were fit for binary data. Data were scaled when necessary to improve model performance. To account for phenotypic variation arising from environmental differences between sampled locations, each full model included a composite abiotic environmental covariate determined by a principal component analysis (PCA) of altitude, latitude, and 19 bioclimatic variables of each sampled seed collection location taken from the WorldClim database of current climatic conditions (hereafter, the "experimental PCA"; Table  S2 ;  Hijmans et al. 2005). The principal component that explained the most variance among collection locations (PC1) was used in all trait models as the environment term. Models were also run using a second composite environmental variable (PC2) or only latitude in place of the environment term, but this did not substantially alter results (not shown).
To test how invasive populations of C. diffusa perform relative to native populations, we ran range differentiation models, where origin (native or invasive), environment (PC1 from the experimental PCA), and their interaction, as well as treatment (water addition or limitation), were included as fixed effects in all full models. Population (uniquely named) and maternal lines nested within population were used as random effects in all full models (e. g. , Trait ~Origin 9 Environment + Treatment + (1 | Population/Maternal line)). When a range differentiation model had a significant origin-by-environment term, slopes of regression lines from model estimates are reported. Additionally, to test for resource allocation trade-offs, we assessed differences in morphological and life-history traits between treatments using models that explicitly tested for a trade-off between performance in benign conditions and tolerance to water limitation (Data S1).
Occurrence data and principal component analysis To determine whether differing relationships between phenotype and environment between origins observed in our dataset are reflected in a difference between the realized climatic niches of the species ranges at a spatial scale larger than our sampling area, we investigated evidence of a climatic niche expansion in the invaded range of C. diffusa. Five hundred and ninety-two geo-referenced occurrence locations for C. diffusa from North America, Europe, and western Asia were retrieved from the Global Biodiversity Information Facility, using the R package "rgbif"  (Chamberlain et al. 2014; GBIF 2014). This was combined with 70 seed collection locations from previous sampling efforts  (Turner et al. 2014). For each occurrence record, corresponding climate data were retrieved from the WorldClim database as above (Table  S2 ;  Hijmans et al.
2005). This dataset was then used in a PCA of the climate, altitude, and latitude of all occurrence locations (hereafter, the "occurrence PCA"). The magnitude and statistical significance of the niche shift between the occurrence centroids in the invaded and native ranges in the PCA graph were assessed using a between-class analysis with the R package "ade4" yielding a between-class inertia percentage  (Broennimann et al. 2007; Dray et al. 2007). This ratio was further tested with a Monte Carlo randomization test (999 iterations;  Dray et al. 2007 ). In addition, 99% confidence ellipses describing the cluster for each range using the bivariate t-distribution are presented. Because the GBIF data used here may not be error-free, we reran this analysis using only populations within two standard deviations away from the mean of PC1 and PC2 to verify results.
Phenotypic differentiation In the common garden dataset, of the 13 traits assessed for range differentiation, nine exhibited significant phenotypic differentiation between the native and invaded ranges of C. diffusa (leaf number, leaf width, root crown diameter, rosette area, shoot mass, bolting probability, bolt date, wilt date, and yellowing date; Table  1 , Table  S3 ; for correlation among traits, see Figure  S2 ). In each case, origin (native versus invasive) had an effect on trait values, often via an interaction, but sometimes not. Origin was significant for leaf number and was marginally significant for leaf width and rosette diameter. Random effects were common, and at least one (most commonly population; Table  1 ) was significant in every model that differentiated the two ranges, except bolting probability.
Evidence of niche expansion Principal component analysis of the climatic data of all C. diffusa occurrences (376 invasive and 286 native occurrences) in the occurrence dataset defined the realized environmental space by two significant axes of variation. The first two components obtained by the occurrence PCA of abiotic environmental variables explained 32% and 27% of variance among occurrences, respectively (Fig. 4 , Figure  S4 ). Axis 1 was correlated most strongly with precipitation during driest month and quarter/mean diurnal temperature range (BIO14, BIO17, BIO2; Table  S2 ). Axis 2 was correlated most strongly with precipitation during the coldest and wettest quarters, and mean temperature during the coldest quarter (BIO19, BIO16, BIO11). Niche centroids in this dataset differ slightly but significantly between ranges (between group inertia: 6.
Acknowledgments We thank  C. Devaux ,  A. Mignot ,  I. Olivieri ,  N. Espuno , and  S. Hello  for plant care and data collection help;  T. Mathieu  and  D. Deguedre  for technical at  CEFE, Plateforme des Terrains d'exp eriences du Labex CeMEB ;  A. Guggisberg ,  R. Hufbauer ,  A. Blair , and  A. Shipunov  for European seed collections; and  M. King  and  A. Stephens  for North American seed collections. This material is based upon work supported by the  National Science Foundation , Grant No. 0541673. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. Additional funding was provided by  NSF   Graduate Research Fellowship  to  K. G. Turner ,  Natural Sciences and Engineering Research Council of Canada  Grant No.
Funding Information This material is based upon work supported by the  National Science Foundation , Grant No. 0541673. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. Additional funding provided by:  NSF   Graduate Research Fellowship  to KGT,  Natural Sciences and Engineering Research Council of Canada  grant No. 353026  to LHR, and  University of Montpellier   SIBAGHE visiting scholar grant  to KGT.
Data Accessibility Data available from the Dryad Digital Repository: http:// dx. doi. org/10. 5061/dryad. doi:10. 5061/dryad. 60p5d. Code for analysis and figures are available on GitHub: https:// github. com/kgturner/FranceCG/. ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
Acquisition used data-dependent selection of the top 20 ions with dynamic exclusion, followed by collision-induced dissociation or higher-energy collisional dissociation and analysis of fragment ions in the Orbitrap. Data were analyzed using MaxQuant version 1. 3. 0. 5  (25)  with a 1% false discovery rate for proteins and peptides and allowing as variable modifications methionine oxidation; acetylation of protein N termini; and mono-, di-, and trimethylation of lysine. Candidate methylation sites were verified by manual inspection. Proteome-wide Labeling and Analysis of SETMAR Substrates-SILAC labeled HT-1080 nuclear extracts were prepared by hypotonic lysis and high salt extraction of nuclei  (10). Lysates were dialyzed into KMT reaction buffer (50 mM Tris (pH 8. 0), 20 mM KCl, 5 mM MgCl 2 , and 10% glycerol) using Slide-A-Lyzer MINI dialysis devices with 3.
* This work was supported, in whole or in part, by  National Institute of Health Grants   R01 GM079641  (to O. G. ). This article contains supplemental Table S1. 1 Both authors contributed equally to this work. 2Supported in part by  Postdoctoral  Fellowship  123711-PF-13-093-01-TBE  from the  American Cancer Society Illinois Division. Supported by a  National Science Foundation Graduate Research Fellowship  under Grant  DGE-1147470  and by the  Hubert Shaw and Sandra Lui Stanford Graduate Fellowship. 4 Supported by funds from the  W. M. Keck Foundation.
Study Design We performed MR analyses using single nucleotide polymorphisms (SNPs) with known associations with potentially modifiable AD risk factors. We used summary statistics from the International Genomics of Alzheimer's Project (IGAP)  [12] , the largest genome-wide meta-analysis of AD reported to date, and individual genotype data from a large subset of IGAP to estimate the unconfounded association between each risk factor and AD risk. S1 Fig illustrates the study design.
Alzheimer Disease Genetic Data IGAP is a large two-stage study based upon genome-wide association studies (GWASs) of AD in individuals of European ancestry  [12]. In stage 1, IGAP used genotyped and imputed data on 7,055,881 SNPs to meta-analyze four previously published GWAS datasets consisting of 17,008 AD cases and 37,154 controls (full details in S1 Text). Further details on the original genetic discovery analyses, including information regarding recruitment and diagnostic assessment as well as analytical approaches to adjust for population structure, are provided in S1 Text or described in detail elsewhere  [12]. We extracted individual SNP associations with AD from IGAP's stage 1 results. Three SNPs (rs850303 for SBP; rs3177928 for total and LDLcholesterol; rs645040 for triglycerides) were not available (S1 Table ), so were excluded from analyses.
SNPs included in each risk score, their associations estimated by the relevant consortium, and their association with AD (IGAP  [12] ). Proxies are marked by an asterix in the leftmost column. The effect allele frequency was based on data from the EPIC-InterAct study  [26]. Double asterisks indicate that frequency information was not available in the EPIC-InterAct study, and were obtained from European 1000 Genomes samples. (XLSX) S1 Text. Supplemental methods, supplemental results, and acknowledgments. (DOCX).
The  MRC and Mercer's Institute for Research on Ageing  supported the  Trinity College group. The South West Dementia Brain Bank acknowledges support from  Bristol Research into Alzheimer's and Care of the Elderly. The  Charles Wolfson Charitable Trust  supported the OPTIMA group. Washington University was funded by  NIH  grants,  Barnes Jewish Foundation  and the  Charles and Joanne Knight Alzheimer's Research Initiative. Patient recruitment for the  MRC Prion Unit/UCL Department of Neurodegenerative Disease collection  was supported by the  UCLH/UCL Biomedical Centre  and  NIHR Queen Square Dementia Biomedical Research Unit. LASER-AD was funded by  Lundbeck SA. The Bonn group was supported by the  German Federal Ministry of Education and Research (BMBF) ,  Competence Network Dementia and Competence Network Degenerative Dementia , and by the  Alfried Krupp von Bohlen und Halbach-Stiftung.
The  GERAD Consortium  also used samples ascertained by the  NIMH AD Genetics Initiative. The KORA F4 studies were financed by  Helmholtz Zentrum München ;  German Research Center for Environmental Health ; BMBF;  German National Genome Research Network  and the  Munich Center of Health Sciences. The Heinz Nixdorf Recall cohort was funded by the  Heinz Nixdorf Foundation  (Dr. jur. G. Schmidt, Chairman) and  BMBF. Coriell Cell Repositories is supported by  NINDS  and the  Intramural Research Program  of the  National Institute on Aging. We acknowledge use of genotype data from the 1958 Birth Cohort collection, funded by the  MRC  and the  Wellcome Trust  which was genotyped by the  Wellcome Trust Case Control Consortium  and the  Type-1 Diabetes Genetics Consortium , sponsored by the  National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Allergy and Infectious Diseases, National Human Genome Research Institute, National Institute of Child Health and Human Development and Juvenile Diabetes Research Foundation International.
to further Alzheimer's Disease research, qualified investigators can submit proposals to conduct analyses using results from the  Genetic and Environmental The Kathleen Price Bryan Brain Bank at Duke University Medical Center  is funded by  NINDS  grant #  NS39764 ,  NIMH MH60451  and by  Glaxo Smith Kline. Genotyping  of the TGEN2 cohort was supported by  Kronos Science. The TGen series was also funded by  NIA  grant  AG041232  to AJM and MJH, The  Banner Alzheimer's Foundation ,  The Johnnie B. Byrd Sr. Alzheimer's Institute , the  Medical Research Council , and the state of  Arizona  and also includes samples from the following sites:  Newcastle Brain Tissue Resource  (funding via the  Medical Research Council , local NHS trusts and  Newcastle University) ,  MRC London Brain Bank for Neurodegenerative Diseases  (funding via the  Medical Research Council) , South West Dementia Brain Bank  (funding via numerous sources including the  Higher Education Funding Council for England (HEFCE) ,  Alzheimer's Research Trust (ART) ,  BRACE  as well as  North Bristol NHS Trust Research and Innovation Department and DeNDRoN) ,  The Netherlands Brain Bank  (funding via numerous sources including Stichting  MS Research, Brain Net Europe, Hersenstichting Nederland Breinbrekend Werk, International Parkinson Fonds, Internationale Stiching Alzheimer Onderzoek ),  Institut de Neuropatologia, Servei Anatomia Patologica, Universitat de Barcelona.
HCC is the most frequent primary form of cancer affecting the liver, an organ that plays a critical role in many metabolic pathways  (9). HCC is a disease with multifactorial origins embracing lifestyle and dietary exposures whose intersection may reveal metabolomic signals  (10)  relevant to cancer onset. The system of relationships between metabolomic profiles and lifestyle factors in relation to HCC was evaluated by means of mediation analysis. The methodological challenges characterising the analysis of large and complex metabolomic datasets are described and discussed.
Metabolite identification The assignment of NMR signals observed in the 1 H one-dimensional fingerprints to metabolites has been achieved by the analysis of additional 2D NMR experiments 1 H- 13 C HSQC and 1 H-1 H TOCSY obtained on a subset of representative samples (one control and one case). The measured chemical shifts were compared to reference shifts of pure compounds using HMDB  (18) , MMCD  (19)  and ChenomX (ChenomX NMR suite, ChenomxInc, Edmonton, Canada) databases.