dataesr/scientific-paragraphs-categorization huggingface dataset:
In a, coloured in red, genes with p-value over 0. 05 and logfold change absolute value over 1. 1, in yellow, genes know to be DE between fresh-frozen and formalin-fixed paraffin embedded samples and in blue, CDR2L gene and related genes. In b, the coloured plot are the genes included in following Gene Ontology terms: GO:0021699 (red), GO:0042088 (purple), GO:0002448, GO:0032817 (green), and GO: 0050861 (blue). Most significant gene names are plotted. c Heatmap of the 1314 differentially expressed genes (rows) and 12 ovarian cancer samples related with an anti-Yo paraneoplastic syndrome (top panel, indicated in brown) and 733 ovarian cancer samples from public databases (top panel in blue). The heatmap to the left indicates z-scales expression values, and two columns to the right (base expr PCD and base expr non-PCD) express the log2 base mean expression of the 1314 genes in the ovarian cancer samples related to an anti-Yo paraneoplastic syndrome and the rest of the samples, respectively.
Pathway analysis Pathway analysis showed 1751 statistically significant Gene-Ontology and KEGG terms, mostly part of biological process, among which 132 terms were of biological interest regarding both the immune system and the CNS (Fig. 4 , Supplementary Figures 7, Supplementary Table  5 ). The upregulated pathways were mostly a part of the innate immune system (p = 0. 01, Odds ratio 6. 2 [95% CI, 1. 6-26. 8]). Forty terms were related to the CNS, and 92. 5% of them were upregulated. Interestingly, within the CNS enriched pathways, three were related to hindbrain maturation, among which the cerebellar cortex maturation (GO:0021699), with genes ARCN1, CEND1 and RERE. To further characterise the brain transcriptomic representation of the DE gene list, we used ABAEnrichement package,  30  to perform gene set expression enrichment analysis in the adult human brain using the Allen Brain Atlas dataset.
The potential impact on survival of CDR2 and CDR2L expression In order to establish the potential role of CDR2 and CDR2L expression on overall survival of OT patients, we have performed a meta-analysis using a large list of public transcriptomic dataset (Supplementary Table  9 ). Interestingly, CDR2 expression was associated with a worse prognosis in Cox univariate (Hazard ratio (HR) = 1. 07 [95% confidence interval, CI, 1. 01-1. 13], p-value = 0. 01 and multivariate analysis (HR = 1. 07 [1-1. 15]), p-value = 0. 045 (Supplementary Figure  1  and Supplementary Fig. 2, respectively ). Conversely, the expression of CDR2L did not have independent prognostic value (Supplementary Figure  3 ).
DISCUSSION We have performed a transcriptomic analysis of 12 anti-Yo PCD OT, compared to a large transcriptomic series of 733 OT control samples from different public databases. The transcriptomic profile of anti-Yo PCD OT was remarkably different. We have identified a prominent transcriptomic overrepresentation of CD8+ and Treg, and also a significant underrepresentation of M2 macrophages in ovarian cancer samples related with an anti-Yo PNS. Interestingly, the DE genes were enriched for AIRE-related genes. Finally, we also found a non-random distribution of DE genes genome-wide, with the highest density of DE genes next to CDR2L in the chr17q25. 21 cytoband. Previously, it has been described that an overexpression of Human epidermal growth factor receptor 2 (HER2) at protein level in breast tumours is associated with anti-Yo PCD.
ACKNOWLEDGEMENTS The authors thank the genomic platform at the  Cochin institute , and the help of  Sebastien Jacques  for performing the microarrays. The results published here are in part based upon data generated by the  TCGA Research Network : http:// cancergenome. nih. gov/. We thank  Dr Gaetane Nocturne  for her insightful comments. Funding This work was supported by the  LYric Grant INCa-DGOS-4664  and by the  Ligue Contre le Cancer Comité du Rhône ; and by  Association pour la Recherche sur les Tumeurs Cérébrales (ARTC) ; and  JNLF-SFN  grant. The funding sources had no role in the study design, in the collection, analysis, and interpretation of data and in the writing of the manuscript.
Availability of data and material: Raw expression data have been deposited to ArrayExpress database (www. ebi. ac. uk/arrayexpress) under accession number E-MTAB-5942. The R scripts of the most important steps of these analyses, and some of the supplementary data are available online with the following link (https://figshare. com/projects/CVDP_antiYo_PCD_transcriptome/23503).
It is not possible to construct a unique dust-layer height from the CALIOP data. Thus two CALIOP-derived layer heights were used: the cumulative extinction height defined as the height where the CALIOP extinction column is half of the total extinction column, and the geometric mean height, which is defined as the geometrical mean of the top and bottom heights of the dust layer. In statistical average over all IASI data there is a general tendency to a positive bias of 0. 5-0. 8 km against CALIOP extinction-weighted height for three of the four algorithms assessed, while the fourth algorithm has almost no bias. When comparing geometric mean height there is a shift of -0. 5 km for all algorithms (getting close to zero for the three algorithms and turning negative for the fourth). The standard deviation of all algorithms is quite similar and ranges between 1.
0 and 1. 3 km. When looking at different conditions (day, night, land, ocean), there is more detail in variabilities (e. g. all algorithms overestimate more at night than during the day). For the solar sensors it is found that on average SCIAMACHY data are lower by -1. 097 km (-0. 961 km) compared to the CALIOP geometric mean (cumulative extinction) height, and GOME-2 data are lower by -1. 393 km (-0. 818 km).
Furthermore we account for differences in satellite overpass times using trajectory model analysis. Finally, for the first time, by utilizing data from GOME-2, SCIAMACHY and IASI with their respective spectral and spatial resolutions, results from the different passive infrared and solar algorithms are compared for the same dust episodes to identify strengths and weaknesses. Note that this work focuses on the comparison of dust-layer heights retrieved from active and passive sensors. Comparisons of aerosol dust amount are outside the scope of this work and is discussed elsewhere  (Popp et al. , 2016). The remainder of the paper is organized as follows: in Sect. 2 the data and data analysis methods are presented. The results from the aerosol-layer height comparison are given in Sect. 3. The results are discussed in Sect.
Data and methodology To allow the inclusion of data from the SCIAMACHY instrument that ceased operation in 2012, four desert dust events in 2010 were selected (total 40 days): -18-27 March (10 days), -22 May-1 June (11 days), -1-12 July (12 days), -14-20 September (7 days). The comparison focuses on the region between 0-40 • N and 80 • W-120 • E (see Fig. 2 ) and is mainly affected by dust from the Sahara but is also influenced by dust from the Middle East, India and western China.
, 2009). Of the six types the dust aerosol is largely non-spherical, implying a relatively large depolarization ratio and hence relatively unambiguous classification. Numerous data products are available from CALIOP. We use the 5 km profile product from CALIOP data version V4-10. Only dust profiles with CALIOP cloud aerosol discrimination (CAD) values between -100 and -20 are included  (Winker et al. , 2013). Profiles containing polluted dust and water and ice clouds are excluded. Furthermore we only include dust layers that are continuous; thus, multilayered dust clouds are excluded from the analysis. The dust-layer height is estimated from the extinction coefficient at 532 nm (Extinction_Coefficient_532). The extinction coefficient is a retrieved quantity and we only include profiles for which the quality control flag Extinction_QC_Flag_532 equals 0 (unconstrained retrieval; initial lidar ratio unchanged during solution process) or 1 (constrained retrieval).
(2007)  and a set of 2311 atmospheric situations. These were selected using statistical methods from 80 000 radiosonde reports and stored in the Thermodynamic Initial Guess Retrieval (TIGR) climatological database  (Chédin et al. , 1985; Chevallier et al. , 1998). The PSD is modelled by a monomodal log-normal distribution described by the effective radius (R eff ) and the standard deviation of the distribution σ g. Following the results of previous sensitivity studies (Appendix A,  Capelle et al. , 2014; Pierangelo et al. , 2005) , fixed values are taken for the effective radius (R eff = 2. 3 µm) and for the standard deviation of the size distribution (σ g = 0. 65). The aerosol vertical distribution is supposed to be concentrated within a single homogeneous layer. While this assumption cannot correctly describe observations that are in general more complex, the height retrieved here can be defined as an average weighted height for which half of the dust optical depth is below and half of the optical depth is above.
Indeed, AEROIASI provides valuable information, not only on the mean height of the dust layers but also on their vertical extent (i. e. layertop heights and whether the layers reach the ground or are elevated). In this study AEROIASI retrievals from version 2 of the algorithm are used. They mainly differ from the previous version described by  Cuesta et al. (2015)  in the a priori desert dust model and the surface emissivity database. Using these new databases, we obtain lower spectral residuals with respect to IASI measurements than with the previous version and higher adaptability for covering the large region analysed in this paper (i. e. the tropical dust belt). The climatological desert dust model consists of refractive indices, a singlemode log-normal particle size distribution and an a priori vertical profile of dust.
In Fig. 2  examples of data from steps 1-5 are shown. The pixels identified as dust from IASI data by the BIRA-IASB (Royal Belgian Institute for Space Aeronomy; top plot, Fig. 2 ), DLR (second plot, Fig. 2 ), LMD (third plot, Fig. 2 ) and LISA (bottom plot, Fig. 2 ) algorithms are overlaid by CALIOP cumulative extinction heights, which are derived from profiles identified as dust (step 3, red dots) that are within the temporal and spatial requirements. CALIOP data are recorded after the IASI overpass. To account for possible movements of dust between the overpasses, the CALIOP dust heights were moved in longitude, latitude and height using the FLEXTRA model (step 4,  Stohl et al. , 1995). FLEX-TRA calculated mean wind trajectories with meteorological input data from the ECMWF. Here operational data with a 1 • latitude × 1 • longitude resolution, 91 vertical levels and a time resolution of 3 h were used.
The black dots in Fig. 2  are CALIOP dust height pixels that have been moved from their original location (red dots) to the nearest IASI pixel (step 5). As the cumulative extinction and geometric mean CALIOP dust heights are different they will be moved by FLEXTRA to different locations. An example of this is seen in Fig. 4 , where the cumulative extinction (black circles) and geometric mean (red circles) heights from the passive instruments sometimes overlap (not moved or moved to same location and height) and sometimes do not overlap (moved to different location and/or height). It is also seen in the difference in the number of co-located points (Table 2). For the full data period the CALIOP dust heights were on average moved upwards by 0. 015 (cumulative extinction) and 0. 020 km (geometric mean), both with standard deviations of 0.
This step reduces the number of IASI data points to between 0. 58 and 1. 8 % of those in step 1. The number of GOME-2 and SCIAMACHY points are reduced to 17. 3 and 73. 0 % respectively. The movement of CALIOP dust heights to the MetOp-A and Envisat overpass times and the final co-location of CALIOP heights and dust pixels gives the final number of dust heights to be compared to CALIOP dust heights; see values for step 5 in Tables  2  and 3. Inspection of IASI-retrieved dust heights shown in Fig. 2  reveal differences in dust detection and dust height between the various algorithms. While differences in dust detection is not the subject of this paper, we do, however, note that there are substantial differences in the pixels identified as containing dust by the various algorithms. In particular the DLR al-gorithm detects very little dust over the ocean regions; the BIRA-IASB and LISA algorithms detect dust over the ocean west of 40 • W and north of 20 • N, whereas the DLR and LMD algorithms do not detect dust in this region; for the example swath plots in Figs.
The LMD algorithm heights are generally similar to CALIOP cumulative extinction heights, while LISA algorithm heights are in better agreement with the geometric mean heights. For this transect, BIRA-IASB and LISA algorithms capture the rather monotonous decrease of dust-layer heights from about 4 km in altitude near 30 • N to 2 km in altitude at 19 • N depicted by CALIOP geometric mean heights. The LMD algorithm retrieves dust heights near 1. 5 km at 24-27 • N, similar to the CALIOP cumulated extinction estimates. The behaviour for this single overpass is also present in the full IASI data set as shown in Figs. 5-6 and Table  3. However, note that there are substantial differences when comparing the passive methods with the CALIOP cumulative extinction and geometric mean methods. Overall, the CALIOP geometric mean method gives a larger CALIOP dust height (Table  4 ).
078 km, cumulative extinction 0. 590 km; LISA: -0. 045 km, 0. 507 km), while the LMD algorithm agreed better with the CALIOP cumulative extinction height of -0. 053 km (geometric mean: -0. 607 km). This is believed to be caused by the differences in the aerosol profile used for the radiative transfer simulations: BIRA-IASB and LISA use and retrieve vertically extended and resolved profiles, while LMD place all the aerosols in one single homogeneous layer. Far fewer data points were available for the solar sensors due to their larger pixel size and lack of night-time data. The heights retrieved from the solar sensors on average underestimate the CALIOP geometric mean (cumulative extinction) heights by -1. 393 km (-0. 818 km) (KNMI, GOME-2) and -1. 097 km (-0. 961 km) (IUP, SCIAMACHY). This may be caused by the large pixel size and the assumption in the retrieval that the pixels are fully covered by aerosol.
The IASI instrument was first flown in 2006 and was the first of several to be launched. Thus data from IASI have the potential to provide long global time series of ECVs. There is considerable variation between the IASI-retrieved dust heights. Nevertheless, if careful consideration is taken for differences in temporal and spatial characteristics of the observations, it might be feasible to construct a global data set of quality-controlled IASI-retrieved heights against CALIOP. The quality control will allow uncertainties on a pixel-by-pixel basis, which again may be used for sensitivity studies. This dust height data set may be used to further our understanding of dust on the climate system. However, several open questions should be answered to have a better understanding of the quantities measured and their accuracy.
A list of open questions are given at the end of the discussion section and includes both studies requiring large data sets and time periods, and studies looking at algorithm specifics. Finally, the various algorithms and instruments are different in their approaches to retrieving dust height. In the comparison with CALIOP no single algorithm is found as the best overall. Different methodologies may give best results in different locations and situations. Thus it seems fruitful to continue the development of all algorithms and encourage comparison exercises. In Figs. A1-A4 statistics are shown for all data, and landday, ocean-day, land-night and land-day subsets for all IASI dust height retrieval methods compared with the CALIOP geometric mean heights. Figures  A5-A8  show similar data but using the CALIOP cumulative extinction heights.
Over land the DLR height data are clumped at a single height for the day subset and at two heights for the night-time data subset. For LMD (Figs. A3  and A7 ) the agreement is monomodal for the land day, land night and ocean night subsets when compared with both the CALIOP cumulative extinction and geometric mean heights. For the ocean day subset a bimodal distribution may be present. Overall the agreement is better when compared with the cumulative extinction height. The LISA data (Figs. A4  and A8 ) also have a bimodal ocean day distribution compared with the CALIOP heights. For the ocean the mean difference with the CALIOP cumulative extinction height is significantly larger at night than during the day. This difference is nearly a factor of 2 smaller when compared with the geometric mean height.
Acknowledgements. This work was supported by the  European Space Agency  as part of the  Aerosol_cci  project ( ESA  contract no. 4000109874/14/I-NB ). Comments on the manuscript by  Andreas Stohl  is greatly appreciated. Thanks to  Sabine Eckhardt  for help with running the FLEXTRA model. The anonymous referees are thanked for their constructive comments. Edited by: Alexander Kokhanovsky Reviewed by: three anonymous referees.
0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http:// creativecommons. org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated. example, lice on cattle have effectively been treated with a variety of insecticide chemistries, including topically applied formamidines, organophosphates, and synthetic pyrethroids, as well as topical or injectable macrocyclic lactones  [10]. Nowadays, the availability of louse genome opens new promising perspectives in understanding their biology and their vector competence, which could lead to more efficient and better ways of louse control  [11].
In total, 735 adult parasites, whose 689 Souk-Ahras (94%) and 46 in Guelma (6%) were collected (Table -2). In Guelma, the lice collected were all of the ischnocera Damalinia ovis species but in Souk-Ahras, the latter represented around 99. 7% and other species of sucking lice (Anoplura) Linognathus ovillus represented the rest, i. e. , 0. 3%. The parasitic load by D. ovis was 9. 2 in Guelma and 29. 9 in Souk-Ahras. Data of Table-3 show that 25 out of 47 in Souk-Ahras and 9 out 30 in Guelma examined goats were found to be infested with lice. In total, 356 adult parasite, whose 251 in Souk-Ahras (70. 5%) and 105 (29. 5%) in Guelma (Table -3) were collected. Among the lice collected in Guelma, the chewing louse Damalinia caprae represented 71% and the sucking louse Linognathus africanus represented 28%.
Relative abundance and parasitic burden in farmyard chickens (in both Souk-Ahras and Mila study areas) Data of Table-4 show that 22 out of 32 in Souk-Ahras and 31 over 31 in Mila of the farmyard chickens examined were infested with lice. In total, 5212 adult parasites, whose 2001 in Souk-Ahras (38%) and 3211 in Mila (62%) (Table-4) were collected. In Mila, the amblycera Menopon gallinae accounted for 58. 4% of the lice collected. The rest comprised eight other species: Menacanthus stramineus (35%), Menacanthus pallidulus (0. 25%), Menacanthus cornutus (0. 19%), Goniocotes gallinae (5. 8%), Goniodes gigas (0. 03%), Goniodes dissimilis (0. 09%), Cuclotogaster heterographus (0. 03%), and Lipeurus caponis (0. 09%). Among the five lice species identified in Souk-Ahras, the amblycera M. gallinae was the most predominant (79%).
Acknowledgments The authors would like to express their deepest gratitude to the  Department of Veterinary Sciences, Chadli Bendjedid University, El Tarf (Laboratory of Animal Production, Biotechnology and Health. Grant Project No. F03120110016 ) and  Institute of Veterinary and agronomic Sciences, Mohamed Cherif Messaadia University, Souk-Ahras , for their technical support and  Dr. Kamel Miroud  for the English revision of the manuscript.
SNPs located on the mitochondrial chromosome and the ungrouped fraction of the genome were excluded. Finally, we applied a stringent filter on individual genotype quality (>30) and only retained variants without any missing or excluded genotype. Our final dataset consisted of 2,628,725 SNPs phased into chromosome-wide haplotypes from 14 individuals. Detection of introgressed haplotypes. We used Chromopainter v0. 04  67  to perform local ancestry inference and identify migrant tracts resulting from introgression between Atlantic and Mediterranean lineages. The program uses a hidden Markov Model to estimate the probability of Atlantic and Mediterranean ancestry at each variable position along the genome using patterns of haplotype similarity. Each individual was separately considered as a "recipient" and compared to "donor" individuals present in reference Atlantic and Mediterranean populations.
saxatilis to the D. labrax genome and generate a list of scaffolds matching to each D. labrax chromosome. Matching scaffolds were properly ordered and assembled along each chromosome to generate 24 pseudo-chromosomes using the software Abacas version 1. 3. 1  71. We then aligned M. saxatilis pseudochromosomes to the D. labrax genome using the algorithm progressiveMauve in order to identify insertions in the M. saxatilis genome that were removed to only conserve homologous sites present in D. labrax. After whole-genome alignment, mapped scaffolds were evenly distributed among the 24 sea bass chromosomes, providing outgroup information for 52% of the D. labrax genome. The genomewide average nucleotide divergence between D. labrax and M. saxatilis was 4. 35% (s. d. = 2. 36%). Population genomics statistics.
Acknowledgements This work was supported by the  ANR  grants  LABRAD-SEQ 11-PDOC-009-01  and CoGeDiv  ANR-17-CE02-0006-01  to P. -A. G. We thank  Véronique Dhennin  and  Julien Derop  from the sequencing platform of  UMR 8199 Génomique Intégrative et Modélisation des Maladies Métaboliques , as well as  Alain Vergnet  from the experimental aquaculture facilities of  Ifremer Palavas  for their precious help. We are also grateful to  Lamya Chaoui ,  Hichem Kara ,  Lilia Bahri-Sfar , and  Filipe Martinho  for providing samples from  Algeria, Tunisia , and Portugal.
Microarrays, cDNA labelling and hybridisation RNA samples from fry from the three isogenic lines after 5 weeks of feeding with either the M diet or the V diet were analysed with microarray technology. Microarray analyses were performed on an Agilent-based microarray platform with 8 X 60 K probes per slide. This platform is based on a high density rainbow trout oligonucleotide microarray resource, which has been enriched with oligonucleotides utilizing recent NGS data from rainbow trout  [27] (Agilent Technologies, Massy, France; Gene Expression Omnibus (GEO) accession no. GPL15840). The platform has been employed and validated in several previous studies  [25, 28]. For each sample, 150ng of total RNA was amplified and labelled using Cy3-CTP according to the manufacturer's instructions (Agilent).
Primers were designed with Primer 3 software (Supplementary File 1). Thermal cycling was initiated with the incubation at 95˚C for 10 min. Forty five steps of PCR were performed, each one consisting of heating at 95˚C for 15 sec for denaturing, and at 60˚C for 10 sec for annealing and a third extension step at 72˚C for 15 sec. Melting curves were systematically monitored (with a gradient of 0. 5˚C/10 sec from 55˚C to 94˚C). Samples without reverse transcriptase and samples without RNA were run for each reaction as negative controls. Primers sequences, along with their efficiencies are presented in the Supplementary file 1.