dataesr/scientific-paragraphs-categorization huggingface dataset:
We used qRT-PCR to validate this miRNA expression profile for the 13 differentially expressed miRNAs. We used the same rat pup samples than in the microarray experiments. The microarray and real-time PCR data were highly correlated, with r 2 = 0. 669 (p = 0. 0003), thus inferring high reliability of the microarray data (Fig  2 ). Again the correlation was greater for P10 than P21 data. At P10, the differential expression of the three miRNAs was statistically confirmed, with miR128-3p and miR34c-5p downregulated and miR434-3p upregulated. At P21, the differential expression of 5 of 10 of the miRNAs was statistically confirmed, with miR378a-5p, miR127-3p, and miR184 downregulated and miR30e-5p and miR449a-5p upregulated. https://doi. org/10. 1371/journal. pone. 0190445. g001 regulation, IFN gamma signaling pathways, protein kinase cascade and Jun kinase kinase activity pathways, and 20 that have been implicated in nervous signalization (Fig  3 ).
Among these 51 genes, we selected 4 target genes because no data were available on their expression in the rat developing lung: MAP3K9, shared within cellular communication, nervous signalization and kinase pathways; NTRK3, involved in cellular communication and nervous signalization; IFNGR2, in IFN gamma signaling; and TNFSF9, in protein kinase intracellular cascade. We used qRT-PCR to examine the deregulation of E2F3, CTHRC1, MAP3K9, NTRK3, IFGNR2, and TNFSF9 genes at P10 and P21 in lungs during alveolarization after LPD-induced IUGR. Three genes, CTHRC1, NTRK3, and MAP3K9, showed significant upregulation at P10, with no change (CTHRC1) or significant downregulation at P21 (NTRK3 and MAP3K9) (Fig  4 ). E2F3 was significantly upregulated at P21 in LPD-induced IUGR lungs.
05). MiRNAs with significantly different expression between LPD-induced IUGR and control-diet gestation (fold-change ! 2; p < 0. 05) are highlighted in red; miRNAs from total lungs of 5 animals/group were analyzed. We used qRT-PCR to validate this miRNA expression profile for the 13 differentially expressed miRNAs. We used the same rat pup samples than in the microarray experiments. The microarray and real-time PCR data were highly correlated, with r 2 = 0. 669 (p = 0. 0003), thus inferring high reliability of the microarray data (Fig2). Again the correlation was greater for P10 than P21 data. At P10, the differential expression of the three miRNAs was statistically confirmed, with miR128-3p and miR34c-5p downregulated and miR434-3p upregulated. At P21, the differential expression of 5 of 10 of the miRNAs was statistically confirmed, with miR378a-5p, miR127-3p, and miR184 downregulated and miR30e-5p and miR449a-5p upregulated.
hemisphere but underestimate CO in the free troposphere and on average in the column. Positive biases in MOPITT retrievals are identified in the Northern mid-and high latitudes, highlighting the importance of proper bias-correction of those satellite retrievals. Biases in representing vertical CO profiles are found over the ocean and most significantly in the Southern hemisphere, suggesting errors in the vertical 30 distribution of CO chemical sources/sinks or in the vertical mixing to be improved in future modelling studies. Varying model-data differences are found in the vertical between CTM and MOPITT retrieved vertical profiles after having assimilated MOPITT X CO ; these bias structures indicate that the posterior model differences to insitu observations would be even larger if the near surface retrievals were assimilated 35 instead of X CO.
Surface station measurements 275 In-situ CO measurements are accessed from the World Data Centre for Greenhouse Gases (WDCGG) database (http://ds. data. jma. go. jp/gmd/wdcgg/, accessed on Dec. 2015). We include CO surface measurements from 88 stations to evaluate modelling results within the boundary layer, calibrated to WMO scale. The geographical distributions of the stations are shown in Figure  1 , and a list of stations 280 is available in Table  S1.
Comparing model results against MOPITT profile data at the surface level 545 (higher than 900 hPa), the absolute biases in the prior simulation are significantly reduced after assimilating MOPITT X CO. The prior surface layer zonal biases (MR) are -23. 0 ppb, -4. 4 ppb, -1. 5 ppb, and -8. 1 ppb for the four latitudinal bands from the north to the south, whereas the posterior biases (MR) are reduced to -6. 5 ppb, +2. 5 ppb, 1. 6 ppb, and -5. 4 ppb, with the signs changed over the tropics (Figure  7 ). The 550 zonal average posterior model difference to MOPITT profiles remain negative for the mid-high latitudes in both NH and SH from the surface to ~700 hPa; On the contrary, the posterior model difference to MOPITT profiles become positive in the tropics (Figure  7 ). 555 For regional detail, the relative bias and the model-data correlation with MOPITT profiles for each pressure level are shown as pseudo Taylor diagrams in Figure  8.
For all regions over land, the posterior simulations (shown by filled markers) agree better with MOPITT profiles than the prior (shown by empty markers). The posterior model-data correlation coefficients are higher than 0. 9 for all regions for both X CO 560 and the near-surface level of the profiles. The model-data correlations decrease significantly with increasing altitude, suggesting that the model ability to represent the spatial distribution of MOPITT-based CO concentrations degrades towards the upper atmosphere. The poorest model-data correlations are found around 300 hPa, except for 200 hPa over the tropical ocean, in agreement with the comparison to aircraft 565 measurements. The posterior model CO profiles match the MOPITT profiles reasonably well over North America, Europe, Boreal Eurasia, Middle East, and Australia (Figure  8 ).
Atmos. Chem. Phys. Discuss. , doi:10. 5194/acp-2017-166, 2017   Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 21 March 2017 c Author(s) 2017. CC-BY 3. 0 License. The same applies for TCCON X CO retrievals as well. For each dataset, the observed and simulated values are averaged per month for further analysis in this study, e. g. the 330 model-data biases are calculated using monthly averages of model-equivalences minus corresponding observations.
Introduction The objective of the global seismology team inside the SPACEINN collaboration, namely Working Package 4. 1, is to discuss the current problems in global seismology of the Sun and solar-analogue pulsating stars. Naturally, attending to the type of observations, i. e. Sun-as-a-star or imaged ones, the work and the discussions related were divided in low and high-degree modes. In this proceedings I will concentrate on the activities of the group concerning the low-degree modes obtained in Sun-as-a star observations and in the studies of solar analogues. During the past three years, the discussion of highdegree global modes were focused on the use of different fitting codes and the differences obtained when several datasets of different instruments were used. Some of the works done in this direction are summarized in [e.
g. 1, 2]. Briefly, it has been found that some of the p-mode parameters (for example the mode asymmetries) are inconsistent when different fitting methods are used, while they are consistent when a single methodology is used in different datasets, e. g. , with GONG  [3]  or with MDI and HMI  [4, 5]. Moreover, inversions of the internal rotation profile showed that the solar magnetic cycle 24 is different from cycle 23. The work with different datasets and methods evidenced that the twist appearing at high latitudes with time in several helioseismic variables is very likely to be a numerical artifact [for more details see 2]. Concerning the low-degree modes, the two main axes of the work in the WP4. 1 were concentrated on the development of new statistical tools to study these modes and the study of the temporal evolution of their properties during the evolution of the solar magnetic activity cycle.
Real and simulated solar time series A huge effort has been led by the WP4. 1 in order to provide properly calibrated datasets obtained from the Sunas-a-star instruments on board the Solar and Heliospheric Observatory (SoHO  [7] ), as well as from the Mark-I instrument, a solar spectrophotometer located and operated at Observatorio del Teide (Tenerife, Canary Islands, Spain). It provides precise radial velocity observations of the Sun-as-a-star  1   Photometric light curves of the Sun Photometers (SPM) of the Variability of solar IRradiance and Gravity Oscillations instrument (VIRGO  [8] ) are now available at the SPACEINN portal 2. These are 60 s cadenced time series starting on April 11, 1996 of the three channels of 5 nm bandwidth centered at 402 nm (blue), 500 nm (green) and 862 nm (red).
An example of the application of the code to the GOLF velocity time series can be seen in figure  1. Because of the particularities of the GOLF measurements, the p-mode power excess has been fitted using a double Gaussian following  [32]. A full analysis of the GOLF and VIRGO/SPM datasets using this tool will be soon available (Corsaro et al. in preparation). A lot of discussions and efforts were employed to study low-frequency p modes and g modes in the Sun. Although the signature of the g-mode period spacing was found using GOLF observations  [33, 34] , it has been impossible to unambiguously detect individual g modes with a high confidence level  [35] [36] [37]. Special effort has been spent to find better ways to combine contemporary datasets as it was already outlined in  [30]. Proper covariant matrices have been developed (Broomhall et al.
7-cm radio flux (F 10. 7-cm ) in 10 -22 s -1 m -2 Hz -1 ; and the frequency shifts ∆ν of the acoustic low-degree p modes in µHz. Adapted from  [51]. compromise to study the evolution of the magnetic activity cycle. These solar datasets have been computed from the raw time series of VIRGO/SPM and GOLF applying the calibration procedures developed for Kepler  [53]  using a high-pass filter with a cut-off period at 60 days.
Conclusions The global helioseismology working group (4. 1) in the SPACEINN project has been a success because of the quality and quantity of the work done as well as for the tools and datasets delivered to the community through the SPACEINN web portal. But the work of this team will not finish at the end of 2016 with the official end of the project. WP4. 1 is still working on several scientific papers (e. g. Broomhall et al. ,  Corsaro et al. , Salabert et al. )  and it will continue working together in some other on-going analysis of helioseismic data. It is also important to mention that the deliverables provided go beyond what was originally proposed in the project. WP4. 1 has also been a success reinforcing the synergies between helio-and asteroseismology with a continuous transfer from one to the other, in particular, on the studies of solar analogue stars, which are allowing us to better understand the Sun compared to its siblings.
The community composition was evaluated using MiSeq v3 (Illumina, San Diego, California, USA) with 2 9 300 bp paired-end reads at the GenoToul platform (http://www. genotoul. fr). Final sequences sets were retrieved after demultiplexing, cleaning, clustering (97%) and affiliating them using Mothur  (Schloss et al. , 2009). Sequences have been submitted to GenBank under the accession numbers No. KX031993-KX032512.
All these assumptions were converted into one linear equation system and one inequation system that are detailed in the Appendix S2. In summary, a linear system of 108 equations and 165 inequations was obtained to estimate the 135 unknown parameters. This under-determined linear problem was solved using the function 'lsei' of the package 'limSolve' version 1. 5. 5. 1  (Soetaert et al. , 2009)  on the R 3. 2. 3 software (R Development Core Team 2010). The function used pseudo-inverse matrices to solve the linear system and provided a unique solution that corresponded to the least square solution.
Statistical analysis To help analysing the results obtained from the linear model, a clustering of the OTUs was performed based on their normalized bulk abundances. A value of k = 4 provided clusters that could be easily interpreted. The hierarchical clustering was made with the function 'kmeans' of the package 'stats' with the R 3. 1. 3 software with 100 iterations. The Pearson correlations and significance calculations were made with the R 3. 1. 3 software (R Development Core Team 2010). For correlation coefficient calculations, the function 'rcorr' of the package Hmisc was used. Significance levels were assessed using 9999 random permutations with the function 'sample' of the package combinat.
Scale insects (Sternorrhyncha: Coccoidea) are one of the most invasive and agriculturally damaging insect groups. Their management and the development of new control methods are currently jeopardized by the scarcity of identification data, in particular in regions where no large survey coupling morphological and DNA analyses have been performed. In this study, we sampled 116 populations of armored scales (Hemiptera: Diaspididae) and 112 populations of soft scales (Hemiptera: Coccidae) in Chile, over a latitudinal gradient ranging from 18°S to 41°S, on fruit crops, ornamental plants and trees. We sequenced the COI and 28S genes in each population. In total, 19 Diaspididae species and 11 Coccidae species were identified morphologically. From the 63 COI haplotypes and the 54 28S haplotypes uncovered, and using several DNA data analysis methods (Automatic Barcode Gap Discovery, K2P distance, NJ trees), up to 36 genetic clusters were detected.
Morphological and DNA data were congruent, except for three species (Aspidiotus nerii, Hemiberlesia rapax and Coccus hesperidum) in which DNA data revealed highly differentiated lineages. More than 50% of the haplotypes obtained had no high-scoring matches with any of the sequences in the GenBank database. This study provides 63 COI and 54 28S barcode sequences for the identification of Coccoidea from Chile. Scale insects (Hemiptera: Coccoidea) are key pests on crops and ornamental plants worldwide. The three most important families in terms of economic damage and number of genera are Diaspididae (419 genera), Pseudococcidae (272 genera) and Coccidae (170 genera) 1. Scale insects can cause economic damage directly, through sap sucking, the injection of toxins and the transmission of viruses that weaken the plant, lowering fruit quality and yield.
information for the barcoding of scale insects  18, 19. However, most of these studies have focused on the insects of North America, Asia and Europe. Morphological studies have been performed on scale insects from South America  [20] [21] [22]  , but the marked lack of genetic information for scale insects from this continent is problematic for the efficient management of these pest species in this region. Previous DNA barcoding studies on scale insects have focused on the Pseudococcidae in Chile  14, 23  and Brazil  24. The Diaspididae and Coccidae families remain poorly characterized and underexplored. We present here a survey of the Diaspididae and Coccidae over a latitudinal gradient in Chile, with the aim of providing a comprehensive genetic barcoding database. We collected scale insects from various fruit crops, ornamental plants and trees and shrubs growing next to crops and sequenced their COI and 28S genes.
The minimum interspecific distance for 28S (0. 9%) was found between Ceroplastes sp. I and C. sinensis. Species identifications corresponded to the scientific names of the best GenBank hits, with a percentage similarity exceeding 99. 6% for the COI haplotypes of Parasaissetia nigra, Parthenolecanium corni, Protopulvinaria pyriformis and one of six haplotypes of Coccus hesperidum (Supplementary Data 2). Two haplotypes and their best GenBank hits were mismatched, with a percentage of similarity exceeding this threshold: two of the six COI haplotypes of Coccus hesperidum were matched with Unaspis yanonensis (Kuwana) (Hemiptera: Diaspididae) (GenBank accession: KP981079). For 28S, species identifications corresponded to the scientific names of the best GenBank hits, also with a percentage similarity exceeding 99.
6% for the haplotypes of Parasaissetia nigra, Parthenolecanium corni, Protopulvinaria pyriformis and Saissetia coffeae. No mismatches with a percentage similarity exceeding this threshold were observed. The best GenBank hits for the nine 28S haplotypes of Saissetia oleae corresponded to Saissetia miranda (Cockerell & Parrott), with a percentage similarity of 98. 7% to 99. 4%. For the 28S haplotypes of Ceroplastes sp. I and C. sinensis, the best GenBank hit had a percentage similarity below 94%. The results of Blastn queries for each haplotype are provided in Supplementary Data 2. The most widespread Coccidae were Coccus hesperidum, which was sampled from 20°S to 39°S (at less than 200 m above sea level), and Saissetia oleae, which was sampled from 19°S to 36°S (at elevations of up to 1,000 m above sea level) (Table  1 ).
By contrast, Saissetia coffeae was found only in the northernmost regions of Chile (from 18°S to 29°S, at up to 830 m above sea level) and Pulvinariella mesembryanthemi was found only in the southern regions of Chile (from 39°S to 40°S). Parasaissetia nigra was found at only one site (30°S). The Coccidae with the largest numbers of host plants were S. oleae and C. hesperidum, which were collected on 26 host plants (from ten orders) and 12 host plants (from six orders), respectively (Supplementary Data 1 and 3). Ceroplastes sp. I was found on Vaccinium corymbosum (Ericaceae) and Berberis sp. (Berberidaceae), whereas Ceroplastes sp. II was collected from Schinus molle (Anacardiaceae), a tree endemic to South America. The complete list of host plants for each species is provided in Supplementary Data 1 and 3 (by host plant family and order).
The interspecific distance ranged from 6. 3% to 27. 9% for COI and from 1. 5% to 17. 4% for 28S. The interspecific distance for COI interspecific distance ranged from 7. 7% to 8. 2% within the two linages of A. nerii, and from 6. 3% to 6. 5% within the cryptic species of H. rapax. The 28S interspecific distance ranged from 0 to 0. 01% within the two linages of A. nerii, and was 0. 3% within the cryptic species of H. rapax. For 18 out of 40 COI haplotypes, the species identification corresponded to the scientific names of the best GenBank hit with a percentage of similarity over 90. 8% (Supplementary Data 2). The lowest values corresponded to the haplotypes of Aspidiotus nerii I (~93%) and Hemiberlesia lataniae (~91%). Without taking into account these haplotypes, the percentage of similarity was over 98.
5%. For 20 out of 34 28S haplotypes, the species identification corresponded to the scientific names of the best GenBank hit with a percentage of similarity over 99. 3%. Three GenBank hits mismatched with a percentage of similarity higher than this threshold: Aonidomytilus sp. matched with Dactylaspis sp. (100%); Aonidiella aurantii with Aonidiella sp. (100%) and Hemiberlesia palmae with Abgrallapsis cyanophylli (99. 4%). The result of Blastn request for each haplotype is provided in Supplementary Data 2. Each lineage of A. nerii had specific COI haplotypes, but shared at least the haplotype H28S33 (n = 83). Individuals of A. nerii I (n = 33) displayed the haplotype HCOI28 or HCOI29. Individuals of A. nerii II (n = 49) displayed five haplotypes: HCOI30 to HCOI34. Individuals of A. nerii III (n = 15) had the COI-like pseudogene.
- - - - - - - - - 1 1 4 Aspidiotus nerii I - 1 - 2 5 1 3 2 2 - - 37 Aspidiotus nerii II - - - 3 4 3 - - - - - 15 Aspidiotus nerii III - 1 2 6 8 1 - 1 1 - 1 49 Chrysomphalus dictyospermi - - - - - - 1 - - - - 6 Diaspidiotus ancylus - - - 1 - - - 1 - - - 2 Diaspidiotus perniciosus - - 1 3 2 2 1 - - - - 32 Diaspis chilensis - - - - 2 - 1 - - - - 7 Diaspis echinocacti - - - - 1 - - - - - - 1 Epidiaspis leperii - - - - - 1 - 1 1 - - 5 Furchadaspis zamiae - - - - 1 - - - - - - 2 Hemiberlesia lataniae - - - 4 10 1 1 - - - - 51 Hemiberlesia palmae 1 - - - - - - - - - - 1 Hemiberlesia rapax I - - 1 1 2 1 - 1 2 - - 14 Hemiberlesia rapax II 1 1 - 1 2 - 1 1 - - - 9 Hemiberlesia rapax III - - - 2 2 - - 1 1 - - 7 Lepidosaphes beckii The Diaspididae found on the greatest number of host plants were Aspidiotus nerii and Hemiberlesia rapax (considering all three groups for both species), collected on 18 host plants (from nine orders) and 13 host plants (from seven orders), respectively (Supplementary Data 1 and 3).
Discussion This survey reported COI and 28S barcode sequences for 11 putative species of Coccidae and 19 of Diaspididae from Chile, among which eight Coccidae and 17 Diaspididae species could be assigned a name based on morphological examination. Except for Parasaissetia nigra which is a new mention for Chile, all these species were already reported in the country  21, [26] [27] [28]. Three species of Coccidae could not be morphologically identified because only immature stages were collected and the GenBank matches were not associated with high similarity percentages. Two species of Diaspididae were not morphologically identified at species levels and belonged to the genus Pseudoparlatoria and Aonidomytilus, and were found on native plant hosts. In Chile, only Aonidomytilus espinosai (Porter) is mentioned for this genus  1.
The K2P distance between the 28S sequences of A. espinosai (GenBank accession: DQ145290 and DQ145291) and the haplotype H28S07 was 7. 1%. This value is superior to the maximum intraspecific distance observed (0. 4%) but it is included within the range of the interspecific distance (from 1. 5% to 17. 4%). This species of Aonidomytilus is probably a new species for Chile. No reference sequence was available for Pseudoparlatoria. Four others endemic species of Diaspididae were collected: Aonidiella ensifera, Diaspis chilensis, Melanaspis sitreana, Pseudoparlatoria chilina  26. They were collected mainly on endemic plants, such as Quillaja saponaria or Nothofagus spp. (Nothofagaceae), and on Hedera helix (Araliaceae) and Olea europaea (Oleaceae) for A. ensifera. For Coccidae, sequences of the recently recorded Pulvinariella mesembryanthemi  21  were provided.
Most COI and 28S sequences are new barcodes for the morphologically identified species. More than 50% of haplotypes (39 for COI and 28 for 28S) match with sequences from the GenBank database with low percentage of similarity, probably due to the scarcity of sequence data for these groups, especially in South America. The previous molecular systematic studies included few individuals from Argentina, Colombia and Chile (n = 1) for Diaspididae  18, 19, 29, 30  and from Colombia for Coccidae  31. Futhermore, the COI region we sequenced (the Folmer region of COI, i. e. the "Barcode region") does not overlap with the COI region sequenced in a set of previous studies, notably those using a fragment overlaping the 5′ end of COI and the 3′ end of COII. However, the low amount of GenBank matches at 28S can hardly be explained by the use of different 28S regions since the one sequenced in this study and in previous ones overlap over more than 650 bp.
In addition, we noticed an inconsistency between the sequences deposited by Wei et al. (data deposited on GenBank without reference to a published article) as Unaspis yanonensis (Diaspididae) and the haplotypes HCOI16, HCOI18, HCOI19 and HCOI20 identified in our study as Coccus hesperidum (Coccidae). It is difficult to deal with inconsistencies between databases when all studies do not carry out morphological vouchering of specimens from which DNA has been extracted. To enable future revisions of the samples processed in this study, we deposited all our slide-mounted vouchers in the collections of ANSES (Montferrier sur Lez, France) and/or MNHN (Santiago, Chile). These new sequences will be useful to future identification of these species. However, we noticed that the COI sequences gave more relevant information for the delineation of the Ceroplastes species as observed by Deng et al.