dataesr/scientific-paragraphs-categorization huggingface dataset:
In the context of this biological rationale, our results expand on the interplay between c-Met and PD-L1 as it pertains to human mRCC. While our data require larger, prospective validation, this understanding appears to support the emerging data of c-Met and PD-L1 as rational targets for combination therapeutic trials, such as the ongoing Phase I CaboNivo study (NCT02496208)  [28]. With the promise of current and ongoing investigations into c-Met inhibition and PD-1/PD-L1 treatment in RCC, the need for appropriate tissue sampling to facilitate effective biomarker analysis will be important for patient selection and rational clinical trial design. In a descriptive analysis, we observed that patients in our cohort who were treated with VEGF-TT and had high c-Met expression had numerically worse clinical outcomes, in terms of reduced TTF and OS, compared to those patients treated with VEGF-TT who had low c-Met expression.
Azo dyes represent the by far most important class of textile dyes. Their biotransformation by various skin bacteria may release aromatic amines (AAs) which might be dermally absorbed to a major extent. Certain AAs are well known to have genotoxic and/or carcinogenic properties. Correspondingly, azo dyes releasing one of the 22 known carcinogenic AAs are banned from clothing textiles in the European Union. In the present study, we investigated the mutagenicity of 397 non-regulated AAs potentially released from the 470 known textile azo dyes. We identified 36 mutagenic AAs via publicly available databases. After predicting their mutagenicity potential using the method by Bentzien, we accordingly allocated them into different priority groups. Ames tests on 18 AAs of high priority showed that 4 substances (22%) (CASRN 84-67-3, 615-47-4, 3282-99-3, 15791-87-4)  are mutagenic in the strain TA98 and/or TA100 with and/or without rat S9 mix.
Overall, combining the information from the Ames tests and the publicly available data, we identified 40 mutagenic AAs being potential cleavage products of approximately 180 different parent azo dyes comprising 38% of the azo dyes in our database. The outcome of this study indicates that mutagenic AAs in textile azo dyes are of much higher concern than previously expected, which entails implications on the product design and possibly on the regulation of azo dyes in the future.
From the 896 azo dyes with known chemical structure in our textile dyes database  (FriedliPartner, 2009b; Brüschweiler et al. , 2014) , 426 azo dyes (48%) are potential parent compounds of one or more of the 22 regulated AAs, while the other 470 azo dyes (52%) are exclusively metabolized to non-regulated 1 AAs. Biotransformation of these 470 azo dyes can release 397 different AAs from which  Brüschweiler et al. (2014)  could compile a priority list with 15 non-regulated AAs suspected mostly to be genotoxic and/or in some cases carcinogenic based on available literature. Following reductive cleavage according to EN 14362-1 (DIN, 2012) performed on 153 samples of clothing textiles that were purchased from clothing retail outlets in Switzerland, four potentially genotoxic and/or carcinogenic substances, 2,2'-dimethylbenzidine, 4-aminophenol, 4-ethoxyaniline and aniline, were detected.
Dataset This study refers to an inventory of textile dyes using available data sources from dye producers, industrial associations, textile labels, seals of quality, official authorities and scientific institutions  (FriedliPartner, 2009a (FriedliPartner, , 2009b)). The database contains 470 azo dyes that can be cleaved into 397 unique, non-regulated 1 AAs  (Brüschweiler et al. , 2014;  this study).
Sources for mutagenicity data The available experimental mutagenicity data were taken from the mutagenicity dataset by  Kazius et al. (2005) , the Chemical Carcinogenesis Research Information System (CCRIS), and the Carcinogenicity Potency Database (CPDB). The CCRIS database contains chemical records with mutagenicity, carcinogenicity, tumor promotion, and tumor inhibition test results. It was developed by the National Cancer Institute (NCI). Data are derived from studies cited in primary journals, current awareness tools, NCI reports, and other sources. Test results have been reviewed by experts in carcinogenesis and mutagenesis. Further important data sources were the informations on chemicals by ECHA and  Jung et al. (1992).
Kazius et al. (2005)  compiled a database with Ames results for 4337 substances (Organon Ames dataset). Bentzien et al. (2010)  selected a subset of primary AAs out of the Kazius database that (i) contain no electric charge in the formula, (ii) have molecular weight below 500 Da, (iii) have no more than one stereocenter, (iv) have less than 10 rotatable bonds, (v) have only one AA functionality, (vi) do not contain aromatic nitro groups as they could exhibit Ames toxicity because of their nitro-moiety. This subset was used to validate our own implementation. Primary AAs CASRNs were downloaded from the supporting information in  Bentzien et al. (2010). The complete Kazius database was retrieved as an SD file from www. cheminformatics. org and primary AAs were extracted based on the CASRN.
In silico preparation of the chemical structures Cleavage products were downloaded as SMILES string from  Brüschweiler et al. (2014)  supplementary database and converted to an SD file. Structures were salt-stripped and neutralized, except those with a fixed formal charge (e. g. quaternary ammonium). In particular, carboxylic acids and basic nitrogen were drawn in their neutral forms. The most abundant protomer was selected on the basis of the primary AA structure. No reassessment of the major protomer form was performed on the nitrenium ions. The 3D geometry of structures was optimized using our own implementation of the MMFF94s force field  (Halgren, 1996a (Halgren, , 1996b (Halgren, , 1996c (Halgren, , 1996d (Halgren, , 1999a (Halgren, , 1999b;; Halgren and Nachbar, 1996)  and the lowest energy conformation of the primary AA was used in further quantum mechanics calculations.
Prioritization Cleavage products were allocated to one of the following priorities: priority 1 (P1) are potential mutagens to test in highest priority, priority 2 (P2) are other potential mutagens to test, priority 3 (P3) are substances for which Ames test results could be found in a database, or substances for which the prediction was borderline, priority 4 (P4) are predicted to be non-mutagens, and priority P5 are substances for which quantum mechanics calculation failed. Cleavage products were clustered according to the substituents found on the aryl ring in order to select substances representative of the structural diversity. Substances in priority group 1 (P1) were selected from structures for which DDE ArNHþ < À15 kcal/mol, which do not contain sulfonic acid, sulfonamide, sulfonic ester, or 2-aminophenols.
Validation dataset Energies of the substances in the Bentzien dataset were calculated as described above. The truth table and statistics are reported in Tables  1  and 2. The sensitivity (93%) is identical to the one reported by Bentzien. The specificity calculated in our own validation test is slightly higher than the one reported by Bentzien (72% vs 62%). Overall, the accuracy of our implementation is comparable to that reported by Bentzien.
Cleavage products The energies of cleavage products were calculated as described above. 397 cleavage products were processed, and energies could be calculated for 366 substances (Supplementary Table  1 ). The 31 substances for which energy was not calculated were either large substances (MW > 500 Da) for which the calculation took too long or metal complexes. Among the 366 predicted substances, 179 substances (48. 9%) had DDE ArNHþ smaller than À15 kcal/mol, 68 substances (18. 6%) had DDE ArNHþ between À15 kcal/mol and 0 kcal/ mol, 57 substances (15. 6%) had DDE ArNHþ between 0 and þ 15 kcal/ mol and 62 substances (16. 9%) had DDE ArNHþ greater than þ15 kcal/ mol. We used the prediction method with the ± 15 kcal/mol grey zone because it was the most accurate on the validation dataset. Aiming at the prioritization of the substances for subsequent testing it was more important to increase accuracy than to assign a prediction to the 125 (34.
Compilation of mutagenic AAs A complemental query for indications of mutagenicity in our database resulted in 33 additional AAs showing positive Ames test results next to the four AAs for which we obtained positive Ames test results in this study. These 33 additional AAs are part of the Kazius Mutagenicity Dataset  (Kazius et al. , 2005)  basically referring to the CCRIS database (Table  4 ). Supplementary Table  2  depicts positive test results with information of the strains, test conditions and references. Additionally, three further substances with point of concern were found in our previous publication  (Brüschweiler et al. , 2014). Two of these three substances are also listed as Salmonella assay positive in the CPDB (Fig. 1 ). Overall, we identified a total of 40 different potential cleavage products of azo dyes being mutagenic substances, as indicated in different sources (Table  4 ).
Seventeen substances, i. e. 43% of all positive AAs, were found to contain at least one nitro group. Four out of these 17 substances have two nitro groups. Six out of 40 positive substances have alkylgroups on the aromatic rings. Eleven out of 40 positive substances have diamino groups, one has a triamino group. Six out of 40 positive substances have a halogen (Br or Cl) on the aromatic ring. Only one positive substance has a sulfonate group (o-aminobenzenesulfonic acid). The identified 40 non-regulated mutagenic AAs are potential cleavage products of approximately 180 parent azo dyes (Supplementary Table  3 ). They represent 38% of all azo dyes in our database. This illustrates that that mutagenicity of AAs being potential cleavage products of azo dyes is of much higher concern than originally expected.
From the 18 substances assigned to priority group P1 or P2, only four substances (22%) were found to be mutagenic in the Ames screening test (Table  3 ). As a conclusion, our prioritization system based on the Bentzien method can be regarded as a very conservative method for a preliminary screening to select substance candidates for testing. It can only be validated empirically whether a substance is positive in the Ames test or not. Since AAs are an important class of intermediates in drug synthesis and are metabolites of various drugs, there are great ongoing efforts by pharmaceutical companies to enlarge the AA mutagenicity database, share the data and develop more predictive models  (Ahlberg et al. , 2016; Elder et al. , 2015). Pharmaceutical companies set up a consortium dedicated to the investigation of genotoxicity of aromatic amines that performs Ames tests with drug relevant AAs.
However, coverage of AAs as cleavage products from azo dyes in textile industry was found to be very small in the CIGAA database with only 11 matches (A. -L. Werner, Lhasa Limited, Leeds UK, personal communication). Ahlberg et al. (2016)  have compiled a set of activating and deactivating functional groups. A well known deactivating group is sulfonate independent on its position to the amino-group. Sulfonated AAs, in contrast with some of their unsulfonated analogues, have generally no or a very low genotoxic potential  (Jung et al. , 1992). This was confirmed in this study: five sulfonated AAs for which no experimental mutagenicity test data could be found, were shown to be negative in the Ames screening test. S9 TA98 þ S9 TA100 e S9 TA100 þ S9 Remarks TA98 e S9 TA98 þ S9 TA100 e S9 TA100 þ Nitro group substituted AAs are the pre-dominant class of mutagenic AAs in this evaluation, comprising 17 out of the 40 identified substances, i.
Until today, 22 known carcinogenic AAs and their corresponding parent azo dyes are prohibited in the EU. Recently, the European Commission performed a fast-track consultation on a possible restriction of more hazardous substances (CMR 1A and 1B) in textile articles and clothing for consumer use  (EC, 2016c). Beside the regulated AAs, the list contains 6 arylamines, 2 azo dyes, and 6 dyes/colorants with carcinogenic potential, which may cleave to carcinogenic aromatic amines or are carcinogenic, mutagenic or toxic to reproduction. None of the AAs proposed for restriction was found in our database. AAs which are prohibited in clothing textiles in Annex XVII of REACH must be classified as carcinogen category 1A or 1B and/or as mutagen category 1A or 1B. The mutagens and carcinogens classified as category 2 identified within the present study do not have to be restricted based on the existing REACH regulation.
We notice that substances in clothing textiles needs more comprehensive data requirements regarding the mutagenicity for their restriction due to their carcinogenic and/or genotoxic effects. Substances for other consumer products such as food contact materials (FCMs) for instance, cannot be authorized, when they show positive Ames test results unless there is an overruling negative in vivo genotoxicity test result  (EFSA, 2008 (EFSA, , 2011 (EFSA, , 2016)). For most of the AAs representing cleavage products of azo dyes in textiles, no such comprehensive genotoxicity dataset is available. Data requirements depend on the production amounts being mostly low in Europe and therefore, the data on mutagenicity is not available or insufficient. Dyes for textiles on the European market are mostly produced in Asia (e.
Acknowledgements The authors want to thank  Dr. Steffi Chang  and her team from  Envigo CRS GmbH (Rossdorf, Germany ) for the good cooperation in performing the Ames screening tests. They kindly thank  Heinz Rupp  and  Isabel Niederhauser  (both  FSVO ) for support in ordering and shipping of the test substances, as well as  Dr. Roger Marti  ( University of Applied Sciences, Fribourg Switzerland ) for finding further suppliers and for characterization of some test material. Many thanks go to  Dr. Reinhard Jung  ( Taunusstein, Germany ) for his valuable input in the development of the prioritization and testing strategy and to  Caitlyn O'Brien  ( FSVO ) for her valuable input and proof-reading of the manuscript.
Study population The study population was drawn from the Causes of Death Register (Statistics Finland). It consists of all those who died at the age of 70 years or over in 2002-2013 in Finland. The cutoff age of 70 was chosen because both the risk of death (Statistics Finland) and healthcare expenditures per resident start to increase at around 70 years of age in Finland  [18]. Use of LTC was examined for the last 730 days of life. Thus the data include decedents for 12 years and service use for 14 years since 2000.
Data sources The data on LTC use were derived from the Care Register for Healthcare and Care Register for Social Welfare (National Institute for Health and Welfare). The information from these registers was linked using unique Personal Identification Codes. A more detailed description of the data collection has been given elsewhere  [19]. [21]  The unit costs for the care of old people reported by Kapiainen et al. [20]  are mainly based on this report b Costs per day in LTC provided by municipality  [20]  c Costs were converted to their 2013 equivalent values according to the price index of public expenditure for health and social services (Statistics Finland) d  [22]  e COCTEL data: medicine costs per day in the last two years of life among those who died in 2013 and were community-dwelling for at least one day Days in care were calculated for each individual on the basis of dates of admission to and discharge from care.
Analyses Analyses were performed for the whole study population and by age group (70-79, 80-89 and 90+ years). Chisquare tests were used to test the differences in the proportions of service users and genders between the years of death. One-way ANOVA was used to assess the change in the mean age at death during the study period. Independent samples median tests were used to test the differences in the number of days in care, and in the costs between those who died in different years. The number of days in and costs of LTC were analyzed among the users of services. In addition, the sum of days in LTC is presented. Binary logistic regression analyses were performed to assess the probability of using LTC by year of death. Age and gender were adjusted for. The costs of LTC in our data include a lot of zeroes, and the distributions are U-shaped, meaning that there were many individuals with few or no days in care (for health centers 0 or ≥90) and many who were in care for 730 or almost 730 days.
Descriptives The data included 427,078 persons. The mean age at death increased from 82. 3 to 83. 8 years between 2002 and 2013 (p <. 001), and the proportion of women decreased from 59. 1% to 56. 5% (p <. 001). The number of people who died at the age of 70-79 years decreased and that of older people increased during the study period (Fig. 1 ).
Dead counts In September 2009 (1 st and 8 th Sept. ), two surveys for dead Gentoo penguins (n = 111) were carried out at Bird Island (at Landing Beach, Iceberg Point, Freshwater Beach, Stinker Point and Everman Cove), due to the appearance of numerous dead Gentoo penguins on the beaches of Bird Island, South Georgia. After identifying a body of a Gentoo penguin, the bill length and bill depth were measured, as above. Sex was estimated using equations from Williams  [85]. It was also reported if it was recently dead or an old carcass. To avoid duplicating the identification of dead animals in the second survey, stock marker was used to paint the bill of the penguins. Data were statistically analysed using Minitab statistical software (Sowers Printing Company, PA, USA) and R  [86]. The values are as mean ± standard deviation, unless stated (significance threshold: 0.
Results The feeding ecology of 55 Gentoo penguins from Bird Island (South Georgia) was studied during the austral winter of 2009 (Table  1 ; S1 Dataset). Of these, 12 had empty stomachs (or only minor residues of food), which were removed from further analyses. Based on individuals whose stomach contents were analysed, our study showed that male Gentoo penguins (N = 17; body mass = 6666 ± 653 g) were significantly heavier than females (N = 26; body mass = 5334 ± 520 g; Mann-Witney U test, U = 409, P < 0. 01). The mean solid proportion of Gentoo penguin food samples ranged from 4 to 414 g (141 ± 101 g), with females having significantly more solids than males (Mann-Witney U test, U = 114, P < 0. 01).
Acknowledgments Pierre Richard ,  Richard Phillips ,  Miguel Guerreiro ,  Pedro Alvito ,  Vitor Paiva  and  Jon Watkins  for providing valuable contributions in the laboratory during the planning and execution of the project. David Thompson  for reviewing a final version of the manuscript. We also thank  Marcella Libertelli  for aiding the identification of small otoliths. This work is part of  SCAR AnT-ERA , ICED, BAS-CEPH programs. JX is supported by the  Investigator FCT program  ( IF/00616/2013 ) and  FRC  is supported by the  Foundation for Science and Technology (Portugal)  and the  European Social Fund (POPH, EU)  through a  post-doc grant  ( SFRH/BPD/95372/ 2013 ). RPV is currently supported by a doctoral grant from the  Portuguese Science Foundation  ( SFRH/BD/84030/2012 ). This study benefited from the strategic program of MARE, financed by  FCT  ( MARE-UID/MAR/04292/2013 ).
Micro-computed tomography Trabecular bone parameters were measured at the left proximal tibia (200 slices distal of the growth plate) using microCT analysis (Skyscan 1172 instrument (Brucker, Belgium) set at 60kV and 150A  [41]. Images were reconstructed by the Skyscan NRecon program and analysed using Skyscan CTAn software (Brucker, Belgium)  [41].
Bone histomorphometry Bone histomorphometry was performed on the proximal tibial metaphysis from mice as previously described  [34, 42]. Briefly bones were fixed, sectioned using a Leica microtome (Solms, Germany) and tissue sections were stained with von Kossa, Paragon, tartrate-resistant acid phosphatase (TRAcP) and aniline blue. Static and dynamic bone histomorphometry were performed on trabecular bone (0. 1 and 1mm distal to the growth plate) and three sections per samples were analysed using computer software based on the Aphelion Image Analysis tool kit (Adcis, He0rouville-Saint-Clair, France).
DGCR8 binds Tcf7l1 mRNA, a component of the core pluripotency network To understand the underlying molecular mechanisms responsible for the block of pluripotency exit in Dgcr8_KO mESCs, we first assessed the transcriptome of WT and Dgcr8_KO  mESCs by RNA sequencing (RNA-seq). We identified many differentially expressed genes implicated in several biological pathways (Fig. 3  A, Fig. S3  A, and Table  S3 ). The intersection of our RNA-seq data with a DGCR8 HITS-CLIP dataset (mRNA bound by DGCR8;  Macias et al. , 2012)  and a list of candidate genes involved in the exit from pluripotency  (Leeb et al. , 2014)  highlighted only four potential candidates that might be responsible for the observed phenotype: Ext1, Nav1, Akt3, and Tcf7l1 genes (Fig. 3 B ). We decided to confirm the direct binding of DGCR8 to the stem-loop structure present in intron four of Tcf7l1 mRNA because of its well-established role in the core pluripotency network  (Pereira et al.
For the quantification of AP positive colonies number, entire sixwell plates used for AP staining assays were first scanned to capture the total plate area in a single image. Then, images were processed using the ImageJ software  (Nanes, 2015). The number of AP-positive colonies was calculated on threshold intensity (default parameters) of inverted regions that were user-selected (full well or identical areas between conditions) using the Analyze Particles tool (default parameters). The total number of AP-positive colonies is depicted in the graph as a clonal assay.
FeatureCount (v1. 4. 5-p1; Liao et al. , 2014)  was used to count reads for each gene (Ensembl GRCm38. 78), ignoring reads on overlapped regions and multiple-hit reads. Differentially expressed genes were defined by both edgeR (v3. 12. 0; Robinson et al. , 2010) and DESeq2 (v1. 10. 0; Love et al. , 2014) , with fold change greater than two and false discovery rate <0. 1. Differentially spliced events were identified using DEXSeq (v1. 16. 7) with false discovery rate <0. 01  (Anders et al. , 2012). A volcano plot was generated using ggplot2 (1. 0. 1; Ginestet, 2011). Pathway analysis was performed using the Consensus PathDB-mouse database  (Kamburov et al. , 2013).
Acknowledgments We thank  Professor Wutz  and  Dr. Beyer  for critical reading of the manuscript and  Dr. Freimann  for single-cell sorting. We deeply acknowledge  Professor J. Steitz  for the DGCR8 plasmids and  Professor A. Wutz  for gift of the TCF7L1 antibody. Technical help by  H. Wischnewski , the  Functional Genomics Center Zurich , and the  Eidgenössische Technische Hochschule Zürich (ETH) Flow Cytometry Core Facility  is gratefully acknowledged. This work was supported by a core grant from  ETH  (supported by  Roche ). D. Cirera-Salinas  is supported by the  National Centres of Competence in Research RNA and Disease , funded by the  Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung. M. Bodak  is supported by  ETH Zurich  ( ETH-21 13-1 ) and  R. P. Ngondo  is supported by  Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung  grant  31003A_153220.
Codon-optimization of mScarlet wrmScarlet was generated by gene synthesis (Gblock, IDT) based on the mScarlet sequence (Genbank KY021423;  (Bindels et al. 2017) ; Figure  3  Supplement 1). Codon-optimization was performed using the "C. elegans codon adapter" service  (Redemann et al. 2011 ) with the following parameters: "0 introns", "optimize for weak mRNA structure at ribosome binding site", and "avoid splice sites in coding region". The Gblock fragment library was combined by isothermal ligation with left and right homology regions flanking the d10 sequence in twk-18(bln213) to generate the repair template pSEM87. The wrmScarlet cDNA sequence is available upon request.