dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank the crew and science party on board  M136  and  M138  for their help in sample collection and instrument operation. Thank you to  SiaoJean Ko ,  Dominik Jasinski ,  André Mutzberg  and  Mario Esposito  for their laboratory assistance. We thank two anonymous reviewers and the associate editor,  Marilaure Grégoire , for their constructive comments. The project, cruises,  Insa Rapp ,  Jan Lüdke  and  Ruifang C. Xie  were funded by the  German SFB 754 program  ( Climate-Biogeochemistry Interactions in the Tropical Ocean );  Ruifang C. Xie  additionally by a  DFG research grant  (project number  432469432 ); and  Frédéric A. C. Le Moigne  by a  DFG  fellowship of the excellence cluster "The  Future Ocean " ( CP1403 ). This manuscript benefited from stimulating discussions at the BIARRITZ ("bridging international activity and related research into the twilight zone") workshop held in Southampton, UK in 2019.
Financial support. This research has been supported by the  DFG SFB 754  (project no. 27542298 ) and a  DFG grant  to  Ruifang C. Xie  (project no. 432469432 ). The article processing charges for this open-access publication were covered by a  Research Centre of the Helmholtz Association. Review statement. This paper was edited by  Marilaure Grégoire  and reviewed by two anonymous referees.
The canine infectious respiratory disease complex (CIRDC) is an endemic worldwide syndrome involving multiple viral and bacterial pathogens. Traditionally, Bordetella bronchiseptica (Bb), canine adenovirus type 2 (CAV-2), canine distemper virus (CDV), canine herpesvirus (CHV) and canine parainfluenza virus (CPiV) were considered the major causative agents. Lately, new pathogens have been implicated in the development of CIRDC, namely canine influenza virus (CIV), canine respiratory coronavirus (CRCoV), canine pneumovirus (CnPnV), Mycoplasma cynos and Streptococcus equi subspecies zooepidemicus. To better understand the role of the different pathogens in the development of CIRDC and their epidemiological relevance in Europe, prevalence data were collected from peerreviewed publications and summarized.
, 2014). To better understand the role of the different pathogens in the development of CIRDC and their epidemiological relevance in Europe, prevalence data were collected from peer-reviewed publications and summarized. These data included serological findings and detection or isolation results from healthy dogs and dogs with respiratory disease published since the year 2000. The literature was searched using Google and PubMed databases from 2000 to 2019 with search terms including: 'dog', 'canine', 'respiratory disease', 'respiratory pathogen' and the names of the specific organisms described in this review.
Records of 3,884 dogs, mostly purebred (79. 4%), were included in the survey. The prevalence of upper respiratory tract disease was 5. 7% without a significant difference between purebred (5. 6%) and crossbred (6. 4%) dogs. A pet owner survey was performed in 2014 on 43,005 purebred dogs from the UK  (Wiles et al. , 2017). The advantage of such studies is that disease episodes for which no veterinary advice was sought are included in the dataset. On the other hand, misinterpretation is possible as disorders were not always clinically confirmed. Furthermore, owners may forget disease episodes as all data are based on memory recall. Respiratory disease was reported in 2. 8% of dogs, with kennel cough (0. 26%), regular reverse sneezing (0. 15%), unspecified respiratory signs (0. 14%) and brachycephalic airway obstruction syndrome (0.
(2014)  selected health data on 51 client-owned dogs at a teaching hospital in Italy in 2012. Cases were included in the study if owners agreed to have their pets sampled non-invasively (i. e. rectal swabs and spontaneous urinary samples). The majority of dogs were adults (76%) and purebred (71%). Just over half of the sampled dogs showed no clinical signs (55%). Respiratory signs were observed in 7. 8% of dogs. A Danish study from 1997 collected, among other information, health data on 4,295 purebred dogs registered with the Danish Kennel Club  (Proschowsky et al. , 2003). Nearly 11% of dogs had shown a respiratory disease event during their lifetime, but the responses were not validated by a veterinarian. Male dogs were significantly more likely to be affected by respiratory disease (12.
Although important, the univariate analysis does not control the redundancy of the chosen In the BC study set no incomplete or outlying samples were found. In the HNC study set, two samples were excluded prior to the normalization procedure due to technical reasons and one sample was found as an outlier with a different proteomic profile on principal component analysis (PCA) plot (Figure  S1b ). On the same plot, no significant difference between the two study sets and sample plate (Figure  S1c ) was found. Finally, data for 100 patients remained and were used for further analysis: 29 were BC patients (12 RR and 17 RS) and 71 were HNC patients  (35 RR and 36 RS). There was no imbalance in RS status.
To validate the strongest candidate of RS found in the exploratory bead array assays, we developed dual binder sandwich immunoassays for THPO and STIM1. We used combinations of six antibodies for THPO and five antibodies for STIM1 and a dedicated protocol as described elsewhere  [22]. Analyzing the HNC samples, the protein profiles of the different capture antibodies against the same target were highly correlated (Figure  3a  and Figure  S4 ). Concordant trends and significant differences were observed for three STIM1 antibodies (HPA011018, HPA011088, HPA012123) and three THPO antibodies (HPA019596, HPA048828, HPA051629), hence confirming our previous observations (Figure  3b  and 3c ). Although important, the univariate analysis does not control the redundancy of the chosen candidate predictors.
35, Figure  S3b ). Annotation of the shortlisted protein using KEGG pathways showed several biological functions related to cancer signaling, immune response, cell proliferation, metabolic pathways and DNA repair. To validate the strongest candidate of RS found in the exploratory bead array assays, we developed dual binder sandwich immunoassays for THPO and STIM1. We used combinations of six antibodies for THPO and five antibodies for STIM1 and a dedicated protocol as described elsewhere  [22]. Analyzing the HNC samples, the protein profiles of the different capture antibodies against the same target were highly correlated (Figure  3a  and Figure  S4 ). Concordant trends and significant differences were observed for three STIM1 antibodies (HPA011018, HPA011088, HPA012123) and three THPO antibodies (HPA019596, HPA048828, HPA051629), hence confirming our previous observations (Figure  3b, c ).
Patient Genotype and its Association with Radiosensitivity In addition to the analyses of plasma proteins, we analyzed 55 SNPs in both study sets by DNA genotyping of 31 genes that were reported in the literature to be involved in RS  [23] [24] [25] [26] [27] [28] [29]. Among these, we found corresponding protein profiles for 8 genes from which only VEGFA was found significant in univariate analysis of the proteins, but without correction for multiple testing (Table  S3 ). Five out of 55 genotyped SNPs were located in the VEGFA gene region. Six additional SNPs were selected for statistical testing by searching for VEGFA transcription factors (TFs) using TRRUST v2 database  [30]. We found two TFs, for which we had genotyped SNPs: HIF1A with five SNPs in our data and TP53 with one SNP in our data.
Patient Genotype and its Association with Radiosensitivity In addition to the analyses of plasma proteins, we analyzed 55 SNPs in both study sets by DNA genotyping of 31 genes reported in the literature to be involved in RS  [23] [24] [25] [26] [27] [28] [29]. Among these, we found corresponding protein profiles for 8 genes of which only VEGFA was found significant in univariate analysis of the proteins, but without correction for multiple testing (Table  S3 ). Five out of 55 genotyped SNPs were located in the VEGFA gene region. Six additional SNPs were selected for statistical testing by searching for VEGFA transcription factors (TFs) using TRRUST v2 database  [30]. We found two TFs, for which we had genotyped SNPs: HIF1A with five SNPs in our data and TP53 with one SNP in our data. In total, 11 SNPs of three genes were analyzed.
Some genetic variants in the VEGFA gene may influence the risk of high-grade late rectal toxicity after radiotherapy for prostate cancer  [25]. We have previously seen an increase in both hypoxia and VEGFA gene expression patterns in both radiated adipose and vascular tissue from BC and HNC patients, respectively  [44, 45] , but have never studied genetic variants related to this biology. In the present study, the plasma levels of THPO and STIM are elevated in BC and HNC RS data sets indicating positive predictors for RS. THPO and STIM1 are two interesting multifunctional proteins, both involved in the regulation of skeletal homeostasis  [46, 47]. THPO promotes megakaryocytic differentiation, regulates self-renewal activity and the pluripotency of hematopoietic stem/progenitor cells  [48]  and plays a role in non-homologous end joining DNA repair (NHEJ)  [49].
SERPINC1 is an anti-thrombin protein and has an anti-inflammatory function  [60]. Elevated plasma CHIT1 and SERPINC1 in the RS study sets are therefore indicative of elevated inflammatory response. In parallel, elevated SLC4A1 (AE1) and PR2 in RS data sets indicate elevated oxidative stress. SLC4A1 (AE1) belongs to anion exchanger family which is involved in the regulation of intracellular pH [61] and is probably involved in response to oxidative stress  [56]. There are presently no data available on the role of RP2 in radiosensitivity. However, a relation between hypoxia, oxidative stress and elevated PR2 has been described in the literature  [62, 63]. The clinical impact regarding the identification of radiosensitive subjects cannot be overestimated in a steadily increasing population of cancer survivors.
One can ask if HNC cancer patients with osteonecrosis have an active necrotic process in their body. The results could be biased due to this process. To address the question, we collected the blood samples weeks after removal of necrotic ORN by surgery and we included several proteins such as fibronectin and TNF alpha to investigate if any of the patients had an active necrotic process  [61]. The study size, limited phenotypic data and blood samples that have been collected up to six years after finishing RT are indeed other limitations. Theoretically, biomarkers of RS could be constantly expressed and/or expressed only after irradiation. As our results show that RS is multifactorial, it could be a combination of the two. Thus bio-sampling before and after exposure would optimize the chance to identify unique biomarkers.
However, this can be tested and verified by analyzing prospective data sets where samples before and after treatments are available. Within a newly supported Euratom project, HARMONIC (www. isglobal. org/en/-/harmonic), such an approach has been initiated with the aim of identifying the biomarkers and mechanisms behind RS. Considering the preselected SNP analysis, the increasing availability and decreasing costs of large panel next-generation-sequencing will help us to enhance genotyping beyond 55 pre-selected functional SNPs and probably improve the RS profile in the next step of the project. Concerns about the study size and limited phenotypic data were in part overcome by combining two study sets, BC and HNC, and given the fact that the study sets consist of carefully selected and matched radiosensitive and non-sensitive patients.
Head-and-Neck Cancer Study Set The characteristics of the head-and-neck study set (HNC) have been described previously  [5]. During the period 2008 to 2010, 64 patients with osteoradionecrosis (ORN), RS group, that were admitted to the Karolinska hospital were asked to participate in the study. Thirty-seven of them accepted. Thirty-seven matched HNC patients (controls), RR group, were recruited from a database of the Stockholm Regional Cancer Center. The controls were matched for tumor site, irradiation dose to the mandible, standard TNM-classification of malignant tumor and sex. The inclusion criterion was the absence of ORN over a time period between RT and recruitment equal or longer than for the matched case. ORN stage was classified according to Schwartz & Kagan  [66]. The patients were given a median tumor dose of 68 Gy delivered in 2 Gy fractions, 5 times per week.
The average age of HCN RS as well as RR groups was 64 years. Data on dental extraction, brachytherapy, sex and chemotherapy are available and were included in our previous publication  [5]. Based on the 3-dimensional dose planning system, patients who received more than 5% above the total dose to mandible were excluded from the study. We also checked if dental extraction could be the cause of ORN in this study set and found no correlation between ORN and dental extraction  [5]. Blood was collected by venipuncture in heparin tubes and kept on ice. The tubes were centrifuged in cold at 300× g for 15 min, and plasma was collected and stored at -20 • C until analysis.
Antibody Validation To evaluate the selectivity of the antibodies targeting the two proteins STIM1 and THPO, a duplex sandwich immunoassay was built, as previously described  [22]. To find matching antibody pairs for the assay, 3 antibodies from the Human Protein Atlas project (HPA011018, HPA011088, HPA012123) and 2 commercial antibodies (S6197 and S6072, Sigma-Aldrich, St. Louis, MO, USA) targeting STIM1, and 6 HPA antibodies (HPA019596, HPA030603, HPA042965, HPA048828, HPA051629, HPA076834) targeting THPO were coupled to a distinct bead population and were combined to form SBAs. The same antibodies were biotinylated as previously described  [69] , and along with one already biotinylated commercial detection antibody targeting THPO (BAF288, LOT: AKZ0415061, R&D, Systems, Bio-techne, Minneapolis, MN, USA), they were evaluated using a dilution series of plasma samples to assess the performance and duplex capacity.
The extracted DNA was quantified with a NanoDrop ND-8000 (ThermoFisher Scientific, Waltham, MA, USA). Genotyping was performed at the Mutation Analysis Facility at the Karolinska University Hospital using iPLEX ® Gold chemistry and MassARRAY ® mass spectrometry system  [70]  (Sequenom, San Diego, CA, USA). Multiplexed assays were designed using MassARRAY ® Assay Design v4. 0 Software and Assay Design Suite v1. 0 (www. integratedgenetics. com). The PCR amplification was performed in a total volume of 5 µL containing 10 ng genomic DNA, 100 nM of each amplification primer, 0. 5 mM dNTP mix, 3. 5 mM MgCl2 and 0. 5 U HotStarTaq DNA Polymerase in 1. 25× Buffer (Qiagen, Hilden, Germany). The reaction was subjected to the following PCR conditions: 95 • C for 15 min, followed by 45 cycles at 94 • C for 20 s, 56 • C for 30 s, 72 • C for 60 s and a final extension at 72 • C for 3 min.
Data Pre-Processing The statistical environment R  [71]  and Matlab  [72]  were used for the evaluation, processing and analysis of the data, unless otherwise stated. Technical assessments of the data, such as calculations of coefficients of variation (CV) within each assay and correlations in between the repeated assays were performed on unprocessed data. Prior to data normalization of the SBA data, outlying samples were detected using principal component analysis (PCA) after excluding the sample-free wells from the data set. To minimize the effects of assay plates we applied multi-dimensional MA-normalization  [73]  on unprocessed MFI values for proteins. The data was subsequently normalized as described in Kato et al. [68]  but using probabilistic quotient normalization and by adjusting the processed data for each antibody individually.
Prediction Model Building Prior to model building, antibodies with p-values from univariate analysis higher than 0. 2 and opposite expression trend in breast and head-and-neck cancer study sets were removed. Next, the most important and robust antibodies were selected using multiple random validation (MRV) approach. In this approach, the data were split 10,000 times into internal training and validation sets with the proportion 75:25. In each iteration logistic regression (LR) model was built on internal training set and RS status prediction was made on internal validation set. Using the results of the prediction of 10,000 LR models, for each protein profile, an importance score was calculated. The importance score is taking into account the predictive power of the particular classifier measured by the area under the curve and the rank of the component in each LR model signature.
The optimal number of antibodies in the model (optimal signature) was found on a sorted importance score curve using the "knee" method. The optimal signature model was built using LR (Model 1). Then, the model was reduced in a backward elimination scheme using Bayes factors calculated from Bayesian Information Criterions (Model 2). The last model was found by adding SNPs data to Model 2 (Model 3). Quality of the model fit was assessed by R2 measure and chi-square test by comparing it to the null model. The threshold value for classifying patients as radio-resistant (RR) or radio-sensitive (RS) was found by minimizing weighted prediction error. Performance of the classifiers was measured by area under the ROC curve (AUC), sensitivity, specificity and weighted classification error. Independent data from the second run of the same assay were used to validate the accuracy and reproducibility of all prediction models.
Sequences were edited using CodonCode Aligner (CodonCode Corporation, Dedham, MA, USA) and contigs were built from forward and reverse sequences generated for each gene. Confl icting base calls were coded using the ambiguity code. Sequences were aligned with  Muscle (Edgar, 2004). Alignments were inspected visually and edited manually using MES-QUITE v. 3. 31  (Maddison & Maddison, 2017 ) when they could be improved. Genetic distances were estimated in Mesquite using the Kimura 2-parameter model (K2p). A partition scheme was defi ned using PartitionFinder  (Lanfear et al. , 2016)  for each phylogenetic analysis, using the Akaike Information Criteria for nucleotide substitution model selection. Prior data blocks were defi ned by codon position. Phylogenetic reconstructions were performed using Bayesian inference with MrBayes v.
RESULTS For this study, 227 new COI sequences were generated (108 by iBOL, 107 by ANTAGENE and 12 by the CEFE), with GenBank accession numbers MH138351 to MH138393, MH138395 to MH138397, MH138399 to MH138474, MH138476 to MH138479 and MN731371 to MN731477 (Table  S1 ). Sequences are also publicly available on the dataset "DS-ANTCOR -Specifi city of the ant fauna of Corsica" of the BOLD -Barcode of Life Data Systems (Doi: dx. doi. org/10. 5883/DS-ANTCOR). This dataset includes 658 characters. 1. Camponotus aethiops and C. universitatis (Fig. 1 ). These two species form two well supported clades with a K2p genetic distance of 9. 4%. For both species, the genetic distance between Corsican and continental samples is lower than 0. 2%, indicating no detectable differentiation. 2. Lasius COR sp1 (Fig.
Hebert (University of Guelph) for providing privileged access to the iBOL sequencing facility and M. -P. Dubois for providing facilities for molecular biology bench work. Two anonymous reviewers made suggestions that substantially improved this article. Data used in this work were partly produced using the GEMEX technical facilities of the Centre d'Ecologie Fonctionnelle et Evolutive with the support of LabEx CeMEB and an ANR Investissements d'avenir program (ANR-10-LABX-04-01). Many ant specimens were obtained through the AntArea project (www. antarea. fr). We thank P. Avoscan, E. Cade, T. Colin, L. Colindre, M. Fichaux, C. Foin, G. Ganem, P. Geniez, C. Lebas, A. Lerch, M. Roffet, A. Rotini, B. Schatz et P. Wegnez for collecting and providing samples of ants. This research was funded by grants from the Fond de Solidarité et de Développement des Initiatives Etudiantes (FSDIE) of the University of Montpellier and from the Offi ce de l'Environnement de la Corse (OEC).
The forest data were compared with data from natural or semi-natural, non-woody vegetation sites. The response of forest net ecosystem productivity to nitrogen deposition (dNEP / dN dep ) was estimated after accounting for the effects on gross primary productivity (GPP) of the co-correlates by means of a meta-modelling standardization procedure, which resulted in a reduction by a factor of about 2 of the uncorrected, apparent dGPP / dN dep value. This model-enhanced analysis of the C and N dep flux observations at the scale of the European network suggests a mean overall dNEP / dN dep response of forest lifetime C sequestration to N dep of the order of 40-50 g C per g N, which is slightly larger but not significantly different from the range of estimates published in the most recent reviews.
Using univariate statistics such as simple regressions of NECB as a function of N r deposition is flawed if N r deposition is co-correlated with any of these other drivers  (Fleischer et al. , 2013) , as can be the case in spatial gradient survey analyses across a wide geographic domain. This is because all of the variability in ecosystem C sequestration across the physical space is only allowed to be explained by one factor, N r deposition. For example,  Sutton et al. (2008)  showed (using forest ecosystem modelling) that the apparently large dC/dN slope in the dataset of  Magnani et al. (2007)  was reduced by a factor of 2-3 when accounting for climatic differences between sites, i. e. when co-varying limitations in (photosynthetic) energy and water were factored out. Similarly, ignoring the growth stage (forest age) and the effects of management (thinning) in the analysis introduces additional uncertainty in the estimated dC/dN response.
There are also potentially large uncertainties in the C and N flux measurements or model estimates used to calculate a dC/dN response. In the companion paper  (Flechard et al. , 2020) , we presented -and discussed uncertainties in -plausible estimates of C and N budgets of 40 forests and natural or semi-natural ecosystems covering the main climatic zones of Europe (from Mediterranean to temperate to boreal, from oceanic to continental), investigated as part of the CarboEurope Integrated Project  (CEIP, 2004 (CEIP, -2008) )  and the parallel NitroEurope Integrated Project  (NEU, 2006 (NEU, -2011)). The NEP budgets were based on multi-annual eddy covariance (EC) datasets following well-established protocols, and in order to better constrain the N budgets, specific local measurements of dry and wet N r deposition were made.
Nitrogen losses by leaching and gaseous emissions were estimated by a combination of measurements and modelling. The data showed that observation-based GPP and NEP peaked at sites with N dep of the order of 2-2. 5 g N m -2 yr -1 but decreased above that and that increasingly large N r losses occurred at larger N dep levels, implying that the net dC/dN response was likely non-linear, in line with an overview of dC/dN response results from various approaches  (De Vries et al. , 2014a) , possibly due to the onset of N saturation as predicted by  Aber et al. (1989) , and associated with enhanced acidification and increased sensitivity to drought, frost and diseases  (De Vries et al. , 2014b). The data also showed that, at the scale of the CEIP-NEU flux tower networks, nitrogen deposition was not independent of climate but peaked in the mid-range for both mean annual temperature and precipitation, which geographically corresponds to mid-latitude central and western Europe, where climate is most conducive to forest productivity and growth.