dataesr/scientific-paragraphs-categorization huggingface dataset:
We would like to acknowledge  Gondar ELFORA Abattoir  authorities and staff and Ethiopian  Public Health Institute (EPHI)  for allowing us to use their data and budget. Funding The program was funded by  TDR , the  Special Programme for Research and Training in Tropical Diseases  and the  Institute of Tropical Medicine Antwerp  and supported by the various implementing partners. Ethiopian  Public Health Institute (EPHI)  provided funding for data collection.
We here apply a novel, intraspecific approach of comparing old and young seabirds for improving our understanding of aging and neoplastic processes in natural settings. We used the long-lived common gulls (Larus canus) for studying the age-related pattern of expression of cancer-related genes, based on transcriptome analysis and databases of orthologues of human cancer genes. The analysis of differently expressed cancer-related genes between young and old gulls indicated that similarly to humans, age is potentially affecting cancer risk in this species. Out of eleven differentially expressed cancer-related genes between the groups, three were likely artifactually linked to cancer. The remaining eight were downregulated in old gulls compared to young ones. The downregulation of five of them could be interpreted as a mechanism suppressing neoplasia risk and three as increasing the risk.
, 2019). From this perspective, seabirds are a promising, albeit currently completely ignored candidate for studying cancer defense mechanisms, as they have a longer maximum life span than expected from their body size and rates of energy metabolism  (Holmes & Ottinger, 2003). In this study, we used a known-age breeding colony of common gull (Larus canus, maximal recorded life span 34 years) to study the links between age and cancer defenses at the genetic level. To gain insight into the key molecular mechanisms underlying cancer defenses, we characterized the transcriptome of birds and compared the results with databases of known human cancer-related genes to assess whether cancer-related genes are differently expressed between old and young gulls, and between males and females. Additionally, we analyzed whether the cancer-related genes differently expressed in our analyses overlap with aging-associated genes in other species, to understand whether the age-related patterns of cancer resistance in gulls are more likely phylogenetically conserved or unique to long-lived seabirds.
| Transcriptome assembly and annotation The sequencing resulted in 320M PE reads of 95 bp. Read cleaning and adapter trimming using Trimmomatic 0. 38  (Bolger, Lohse, & Usadel, 2014)  kept 99. 5% of raw reads. Subsequent de novo transcriptome assembly was generated with Trinity 2. 8. 4  (Haas et al. , 2013). Downstream analyses for aligning reads to assembly and differential expression were performed within Trinity using Salmon  (Patro, Duggal, Love, Irizarry, & Kingsford, 2017)  and edgeR  (McCarthy, Chen, & Smyth, 2012) , respectively. The edgeR package calculated false discovery rate (FDR) using Benjamini-Hochberg multiplicity correction method. The assembly quality was assessed by Benchmarking Universal Single-Copy Orthologs BUSCO  (Simão, Waterhouse, Ioannidis, Kriventseva, & Zdobnov, 2015).
To annotate the obtained transcriptome, we used Dammit  (Scott et al. , 2019). We modified it to use the latest orthologous genes database (OrthoDB) version 10. 0  (Kriventseva et al. , 2019). The Dammit protocol comprises of several steps. First, TransDecoder version 5. 5. 0  (Haas et al. , 2013)  was used to determine open reading frames (ORFs). Then, the discovered protein domains were annotated using HMMER  (Johnson, Eddy, & Portugaly, 2010)  on the Pfam database  (El-Gebali et al. , 2019). Finally, Dammit maps the transcripts to the OrthoDB and returns the best matching OrthoDB cluster ID. To identify the human orthologues, we took this OrthoDB cluster ID and then used it on the OrthoDB API to retrieve human orthologues for each transcript. The OthoDB API returned several possible human orthologue genes for each OrthoDB cluster ID.
We included all genes returned from the OrthoDB API in further analyses irrespective of the reported mapping accuracy. The advantage of this approach is that all possible matches are found. The disadvantage is that some included matches are redundant. Human cancer-related genes were obtained from the COSMIC cancer gene database (September 2019 version,  Sondka et al. , 2018)  and joined with the OrthoDB. The joining was made using transcript human orthologues (human gene symbols and their synonyms) retrieved from the OrthoDB API and the gene symbols from the COSMIC cancer gene database. To check for OrthoDB version 10. 0 redundant matches, the final list of cancer genes was checked manually using the OrthoDB version 10. 1 website and matches that were removed between version changes were identified (listed in Table  S3 ).
To identify possible genes that vary with age, we used the list of mammal age-related genes from the GenAge database  (Magalhães & Toussaint, 2004). Custom database joins were performed using either SQLite version 3. 24. 0 (Hipp, Kennedy, & Mistachkin, 2018)  or dplyr package in R version 3. 5. 3 (R Core Team, 2017). To determine the differentially expressed genes in the final dataset, we applied an FDR.
Age group Hatch year of 0. 05 that was calculated based on all of the assembled transcripts not only using transcripts that were left in the dataset after joining the databases. Ggplot2 package  (Wickham, 2009)  was used for producing graphs. Source code of the updated version of Dammit protocol using the version 10 of OrthoDB can be accessed from (https:// github. com/rix13 3/dammi t/tree/Ortho DBv10). The raw sequencing data along with the assembled transcriptome are openly available in EMBL-EBI European Nucleotide Archive under the primary study accession number PRJEB35479. S1 ) of those had a human orthologue in the OrthoDB and 3,305 (Table  S2 ) could be matched to a gene in the COSMIC cancer gene database. After correcting for possible false positives, there were 39 differentially expressed transcripts by sex (9 matched genes, see Table  S1 ) and 431 by age (220 matched genes, see Table  S4 ).
After removing the transcripts that had low abundance, no matches were found in the COSMIC database for sex-related differentially expressed genes. Two hundred of genes differentially expressed between age groups were downregulated and 20 upregulated in old birds compared to the young birds. Thirty of the transcripts that displayed differential expression by age were also present in the COSMIC cancer gene database (Table  S2 ). After removing transcripts that had low abundance (logCPM < 1) and grouping similar transcripts, this list was reduced to eleven cancerrelated genes (Table  S3 ). Out of the eleven differentially expressed cancer-related genes between young and old common gulls, three were likely artifactually linked to cancer after checking with latest OrthoDB version 10. 1. The remaining eight were downregulated in old gulls compared to young ones.
This is further supported by the finding that none of the identified cancer-related genes expressed differently between age groups has been associated directly with aging in humans or model species. We did identify seven differently expressed transcripts that could be linked with known human aging-associated genes as curated in the GenAge database, but there was no overlap with cancer-related genes revealed by our analysis. These results suggest that cancer suppression might represent an evolutionary adaptation that enables these seabirds to achieve longevity, despite their high metabolic rate and small body size. For example, when looking at telomere shortening, which is considered a mechanisms mediating trade-offs between senescence and cancer susceptibility  (Nesse, 2017) , previous studies indicated that unlike many other species, older gulls do not have shorter telomeres when compared to younger gulls  (Rattiste et al.
Study Design/Procedure At inclusion visit, we assessed patients' CFTR mutation, past history (meconial ileus, distal intestinal obstruction syndrome (DIOS), number of hospitalizations, number of exacerbations and number of intravenous (IV) antibiotic cures within the year before inclusion), the presence of chronic colonization (Pseudomonas aeruginosa and Staphylococcus aureus), cystic fibrosis-related diabetes (CFRD) and treatments (pancreatic enzymes replacement therapy (PERT), ursodeoxycholic acid, inhaled antibiotics (ATB), and oral ATB prophylaxis, IV ATB). We also assessed clinical data (weight, height and body mass index (BMI) expressed as Z-scores) and the last lung function testing results (percent of predicted value of forced expiratory volume in one second (ppFEV 1 ), forced vital capacity (ppFVC), forced mid-expiratory flow between 25% and 75% of forced vital capacity (ppFEF 25-75 ), lung total capacity (ppLTC), residual volume (ppRV), residual volume capacity (ppRCF), and resistance of airways (ppRaw)) measured within the past month before inclusion and obtained from patient's medical file.
Statistical Analysis We used Graphpad Prism 5 (GraphPad Software, La Jolla, CA, USA) for the analyses. Results were expressed as median with 95% confidence interval (95% CI) or with minimum and maximum (median [min; max]) for continuous variables or in absolute values and percentage (n/N (%)) for categorical variables. Comparison between groups was performed using the Mann-Whitney test for unpaired nonparametric variables. Scores obtained from children and those obtained from their parents were compared using the Wilcoxon's test. The Fisher's exact test was used for categorical variables. Correlation coefficients were assessed using univariate Spearman's test. A p value under 0. 05 was considered significant. Two-way mixed absolute single measure intraclass correlations were computed to express test-retest reliability between children and parents.
Statistical analyses of phenotypic data In order to undertake genome-wide association analyzes, a collection of 365 pea accessions, here after referred to as the association mapping collection or the collection, was phenotyped for frost damages (FD) under field and controlled conditions. Statistical analyses of frost damages scores showed highly significant genetic variation for all studied traits and the coefficient of genetic variation ranged between 37. 6 for FD_Field_date4 and 74. 4 for FD_Field_date2 (Table  1 ). The estimates of broad sense heritabilities (H 2 ) were high for all traits, varying from 0. 84 to 0. 89. In addition, frost damages observed in controlled conditions and in the field for the date 3 and 4 showed the highest mean scores of 2. 8, 2. 4 and 2. 4 respectively (Table  1 ).
SNP genotyping of the association mapping collection After quality control, the genotyping data comprised a total of 10,739 polymorphic SNPs with imputed missing data and a minor allele frequency (MAF) greater than 5%. Each linkage group contained 1533 SNPs on average. The distribution of SNPs varied within and between LGs (Additional files 2 and 3). Linkage group III showed the highest number of SNPs (1888 SNPs), while LG I was the least dense (1220 SNPs). Mean MAF in the association mapping collection varied from 0. 29 on LGI to 0. 31 on LG V and LG VI (Additional file 3). Only 751 of SNPs had a MAF less than or equal to 0. 1 (Additional file 4). The distribution of SNP markers across the different LGs was dense and no gap between adjacent SNPs exceeded 1. 7 cM, except on LG I and LG V which presented gaps of 2.
Population structure and kinship analyses To avoid false positive results in association analysis, structure and kinship of the association mapping collection were analyzed using 2962 non-redundant positions markers. The collection structure was studied using the discriminant analysis of principal components (DAPC) method. Following the analysis of the Bayesian Information Criterion (BIC) profile and using the 'ascore' criterion, the optimal number of clusters was fixed to 7 (Fig. 1a ) and the optimal number of principal components (PCs) was set to 6 (Fig. 1b ). Therefore, these 6 PCs and 7 clusters were used for discriminant analysis of principal components. The distribution of individuals into the 7 clusters is represented along the two first axes of DAPC (Fig. 1c ). The main passport data (Additional file 6) seemed to be related to the discrimination of the clusters.
After frost, a recovery period was applied with a temperature regime of 16 °C/5 °C and 10 h of daylight during 8 days. Frost tolerance was evaluated by scoring frost damages at the end of the recovery period with the same scoring scale as the one used to evaluate the frost damages in the field experiment, except that scores was attributed to single plants instead of plots. Overall, 5 traits constituted the phenotyping data for the GWAS, abbreviated as follows: FD_CC: frost damages in the controlled conditions experiment, FD_Field_date1, FD_Field_date2, FD_Field_date3 and FD_Field_date4: winter frost damages evaluated in the field experiment at the first, second, third and fourth date respectively.
Phenotypic data analyses The phenotypic data were analyzed with the R 3. 5. 0 software  [56, 57]  using a linear mixed model to obtain estimates of variance components, heritability (H 2 ), as well as best linear unbiased predictions (BLUPs) of adjusted means. The following Linear Mixed Model (LMM) was used: Y ij = μ + geno i + rep j + e ij , where Y ij is the value of frost damages recorded for the genotype i at the replicate j. μ is the mean, geno i is the random genetic effect of the genotype i, rep j is the fixed replicate effect of the replicate j and e ij is the residual effect. The model was carried out using the R function "lmer" of the package 'lme4'  [58, 59]. Heritability (H 2 ) was estimated using the following formula: H 2 ¼ V g =ðV g þ V res. n rep Þ, where V g is the genotypic variance component, V res is the residual variance component and n rep is the number of replicates taking into account the missing values.
1. 9 software  [61, 62]. Accessions and SNP markers with a call rate below 0. 90 as well as SNP markers with a minor allele frequency (MAF) below 0. 05 were excluded from the GWA analysis. After quality control checking, a genotyping matrix consisting of 10, 739 SNPs and 363 accessions with 0. 6% missing data was kept for further analyses. The resulting data set was further imputed using Beagle v. 3. 3. 2 software  [63]. Beagle applies a Markov model to the hidden states (the haplotype phase and the true genotype) along the chromosome using an EM (Expectation-Maximization) algorithm that iteratively updates model parameters to maximize the model likelihood up to the moment where convergence is achieved. Finally, a genotyping matrix consisting of 10,739 SNPs and 363 accessions with no missing data was used for GWAS.
Linkage disequilibrium estimation The estimates of linkage disequilibrium (LD) within the collection were determined by the squared allelefrequency correlations (r 2 ) for pairs of loci as described in Weir  [65]. Linkage disequilibrium analysis between pairs of SNP markers was calculated in a sliding window of 900 markers using Plink v. 1. 9 software  [61, 62]. Then, intrachromosomal LD quantification and graphical representation of LD decay were accomplished using R 3. 5. 0 software  [56, 57]. The LD decay was measured as the genetic distance (cM) where the average r 2 decreased to half its maximum value.
Population structure and individual relatedness To control false positive associations, population structure and individual relatedness (kinship) among accessions of the collection were taken into account by fitting markers based structure and kinship matrices in the association models  [66]. Kinship and population structure were estimated using a matrix data composed of 363 accessions and a set of 2962 markers without any missing data and corresponding to non-redundant genetic positions randomly selected on the consensus map. The coefficients of kinship between pairs of accessions were estimated using the realized relationship matrix kinship estimation approach implemented in FaST-LMM software  [67]. Two alternative approaches were considered to estimate the kinship matrix as described by Rincent et al.
First, the optimal number of genetic clusters (k) was determined through the 'K-means' method using the function "find. clusters". The number of clusters was allowed to vary from one to 20 during the determination of the optimal value of k, based on the Bayesian Information Criterion (BIC). The most likely number of clusters was chosen on the basis of the lowest associated BIC. Then, the principal component analysis (PCA) step of DAPC was performed through maximization of the 'a-score' criterion and the optimal number of principal components (PCs) was obtained after 100 iterations using the function "optim. a. score" implemented in 'adegenet' package of R software. Finally, DAPC was performed considering the most likely number of clusters (k) and the optimal number of PCs identified using the function "dapc" implemented in adegenet Rpackage  [70] [71] [72].
To confirm the allocation of accessions to clusters by DAPC analysis, a Nei genetic distance matrix  [73]  was calculated with the function "stampp-NeisD" implemented in 'StAMPP' package of R software  [74]  using the genotyping data composed of 363 accessions and a set of 2962 SNPs. Then the resulting matrix was plotted as a dendrogram using the ward method with the package 'cluster' implemented in R software  [75]. The two first coordinates of DAPC results were used as covariates (Q matrix) in the GWAS to correct the association tests for false positives.
The threshold to declare an association significant was set at a probability level of the p-value inferior to 4. 65E-06, i. e. -log10 (p) > 5. 33, which corresponded to the Bonferroni threshold (0. 05/ number of tested SNPs). To represent the association results, Manhattan plots and their corresponding quantile-quantile plots were drawn using the package 'QQman' implemented in R software  [77].
Local LD block estimation and favorable allele identification Local LD analysis was used to define the LD blocks around significant associated markers detected by GWAS using Plink 1. 9 software  [61, 62]. For each associated marker, markers in strong linkage disequilibrium (LD; r 2 > 0. 8) with this one, were identified to define a LD block. By this way, a LD block was defined as the interval including all markers in LD (r 2 > 0. 8) with the targeted associated marker(s). Unique associated markers which didn't constitute a LD block were kept for further analyses. Thus, for each identified genomic region, LD blocks and unique associated marker(s) composed a significant locus related with frost tolerance. At each significant locus, haplotypes were identified, among the accessions of the collection, according to the nonimputed genotyping data corresponding to the list of markers significantly detected by GWAS and linked markers.
Acknowledgements The authors would like to thank the  INRAE experimental units of Estrées-Mons, Dijon and Theix, France , for their contribution to the field experiments. They are also grateful to  Catherine Desmetz  ( Agroécologie, INRAE ) for additional genotyping of the reference collection, to  Marianne Chabert-Martinello ,  Anthony Klein  and  Jean-Bernard Magnin-Robert  ( Agroécologie, INRAE ) for providing informations about the constitution of the reference collection and to  Renaud Rincent  ( GDEC, INRAE ) for his advices in genome wide association analyses.
Funding This work was supported by the PhD fellowship of Sana Beji which was cofunded by the University of Lille, doctoral school for materials, radiation and environmental sciences (France) and by the region Hauts-de-France (France). The acquisition of the phenotyping data was funded by the SNPEA project (ANR06-GPLA019). The acquisition of the genotyping data was funded by the GENOPEA project (ANR-09-GENM-026). The analyzes, interpretation of data and writing of the manuscript were realized in the frame of the Pea-MUST project (ANR11-BTBR-0002). The three project fundings come from the French government and were managed by the Research National Agency (ANR). The funding agency did not contribute to the design of the study or collection, nor to the analysis and interpretation of data or to the writing of the manuscript.
The concentrated emulsions were diluted and spread between glass plate and cover slip for microscopic observation in transmission mode and image analysis. The size distribution was determined using the analysis image software AnalySIS™. Droplets size measurements of o/w emulsions were carried out at small-angle light scattering using a Mastersizer 3000 instrument (Malvern, UK). The refractive indexes of the optical model used for data processing were 1. 332 for water and 1. 552 for NB. The electrical conductivity of emulsions was measured with a Radiometer CDM 210 conductivity meter at 25°C. Contact angles of NB/photocatalyst/water were measured by the sessile drop method on the flat photocatalyst surface using Drop Shape Analysis System DSA10Mk2 (Krüss, Germany). A pellet of the photocatalyst was pressed under 7 kPa pressure.
Acknowledgements We thank  Guillaume Barraquand  for countless discussions on the problem of the Kardar-Parisi-Zhang equation in a half-space. PLD acknowledges early discussions with  A. Borodin ,  I. Corwin  and  P. Calabrese , and a fruitful ongoing collaboration on these and related topics with  J. de Nardis  and T. Thiery. We finally acknowledge support from  ANR  grant  ANR-17-CE30-0027-01 RaMaTraF.
Interestingly, these differences were visible 6 h after treatment. The PI fluorescence across the spheroid depth was plotted on an hourly basis during the first 24 h. The plots are shown in supplementary Figure  S1 , where each solid line corresponds to a time point (from 1 to 6, and 24 h after treatment). For both treatments, two peaks were observed at the edge of the spheroids, with the highest peak area when P-A PBS treatment was performed compared to P-A NaCl treatment. From these analyses, we set a data analysis protocol to remove the out-of-focus PI fluorescence, as described in section H of the "Materials and Methods" and SI (supplementary Figure  S2 ). The kinetic of PI uptake was obtained from images (Figure  2C, D ). For both cell lines, it clearly appeared that P-A PBS led to a faster penetration of PI compared to P-A NaCl.
Growth Follow-Up After treatment, the plate was placed in IncuCyte Live Cell Analysis System Microscope at ×10 magnification (Essen BioScience IncuCyte™, Herts, Welwyn Garden City, UK). Spheroid growth was followed during 7 days after treatment using bright-field and GFP channels. Micrographs exported from IncuCyte software (U. S. National Institute of Health, Bethesda, MD, USA) were analyzed with ImageJ; the area of the spheroids was determined from green-fluorescent micrographs and was plotted as relative fold change of the initial area, as a function of time.
Image Analysis Micrographs were analyzed using ImageJ software as described in the supplementary Figure  S2. PI signals from out-of-focus planes were collected using wide-field microscopy. The out-of-focus PI fluorescence at the center of the spheroid was the result of a field depth of the microscope objective that is larger than the object diameter. In order to remove this out-of-focus fluorescence, we applied a threshold on both PI (red fluorescent) and GFP micrographs. Areas of viable spheroids were obtained from GFP fluorescence and removed from the PI micrographs. Measurements of the integrated PI fluorescence intensity were done on the final images that represented focal plane PI fluorescence.