dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantitative real-time PCR assays. In order to validate the differential expression of a set of immune response mediator genes, qRT-PCRs were carried out on IPPs, JPPs and MLNs from a group of 70-day-old pigs. Three genes, β-2-microglobulin (B2M), hydroxymethylbilane synthase (HMBS) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH), were used as reference genes because of their stable expression between samples as evaluated using the geNorm algorithm  26. All gene primers (Supplementary Table  S3 ) were designed using the Clone Manager 9 software (Scientific & Educational Software, Cary, NC, USA). For cDNA synthesis, one µg of total RNA was reverse transcribed for 90 min at 37 °C in a 20 µl volume containing 0. 25 mM dNTP of OligodT, 25 U of MuMLV reverse transcriptase in 4 µl 5X MuMLV buffer (Eurogentec, Liège, Belgium).
Results Global transcription in GALT and peripheral blood. The transcriptome analyses were carried out by RNA-Seq on the same four male piglets for blood samples (60-day-old), and for MLNs, IPPs and JPPs (70-dayold). The global expression data structure was explored with a multi-dimensional scaling plot (MDS plot, Fig. 1a ) based on the TMM normalized gene expressions for each sample. In this two-dimensional plot, samples are positioned according to the statistical distance of their expression profiles, highlighting here that samples are grouped rather by tissue than by individual. Similar results were obtained from normalized antisense read counts (Fig. 1b ). For both sense and antisense transcription levels, we observed a clear separation between MLNs, PB and PPs, whereas IPPs and JPPs grouped together, thus revealing strong similarities between PP types at this scale of comparison.
Globally, 4,138 loci annotated as genes on one strand in Ensembl were detected as transcribed on the other strand in at least one tissue. As presented in Table  1 , the GALT and PB showed an antisense transcription for over 21% and 12% of expressed genes, respectively. The five genes with the largest number of antisense overlapping reads were the same in all tissues: GYS1 (ENSSSCG00000003154) with 48% of antisense reads in blood, NDUFB10 (ENSSSCG00000030328), RNF167 (ENSSSCG00000017906), ND6 (ENSSSCG00000018092) and FIB (ENSSSCG00000012969). Of note, NDUFB10 and ND6 are both members of the NADH: ubiquinone oxidoreductase enzymatic complex. Besides, we detected an antisense transcription level for 598 non-expressed genes in our dataset (see full list in Supplementary Table  S5 ). Interestingly, GALT shared 230 common annotated genes harboring only antisense transcription, and 101 antisense expressed events were shared by the four tissues.
Differential gene expression analysis between GALT. We performed a differential expression analysis within the GALT to explore differences in the transcriptome between PPs and MLNs as well as between IPPs and JPPs, based on the 14,502 genes found to be expressed (sense transcription with CPM > 1) in at least one of the three tissues (Fig. 2a ). JPPs, IPPs and MLNs shared the same five most highly expressed genes: cytochrome   In a first exploratory approach, an MDS plot for the GALT (Fig. 2b ) showed that IPP and JPP samples were separated by intestinal segment, revealing differences in gene expression between IPPs and JPPs. We subsequently tested whether gene expression was significantly different between the GALT tissues using the edgeR package (Table  3 ). The complete lists of DE genes is available in Supplementary Tables S6 to S8.
They also affected metabolic processes such as lipid (GO:0006629) or organic metabolism processes (GO:0006082) (Fig. 3 ). The DE genes between IPPs and JPPs were found to be involved in 60 pathways referenced in the KEGG database  25  , where each of these pathways contained more than ten DE genes (Supplementary Table  S9 ). The pathway that included the most DE genes (106) was "Metabolic pathway", the second (44 DE genes) was "Pathways in cancer", and the third (40 DE genes) was "Cytokine-cytokine receptor interaction". The "Th1 and Th2 cell differentiation" pathway was also enriched and included 19 DE genes, of which 18 and one were under-and over-expressed in IPPs compared to JPPs, respectively (Fig. 4 ). By comparing these results to our previous study that targeted transcriptome analysis along the small intestine (duodenum, jejunum, ileum)  12  , we found an overlap of 431 genes between the set of DE genes previously identified between jejunum and ileum and the 1,105 DE genes between IPPs and JPPs.
Acknowledgements We are grateful to the  Genotoul bioinformatics platform at INRA Toulouse Midi-Pyrénées  (http://bioinfo/ genotoul. fr)for providing computing and storage resources. We are grateful to the  INRA MIGALE bioinformatics platform  (http://migale. jouy. inra. fr)for help and support. We acknowledge the  INRA @bridge platform  for the secure storage of samples and the team at  Le Magneraud (INRA, GENESI)  for their implication on the generation of animals and samples. We acknowledge  Isabelle Oswald  ( INRA, TOXALIM, Toulouse ) who contributed to the design of the  SUS-FLORA  project. TM was financially supported by a PhD fellowship co-funded by the  INRA.
3. 4. 1. Rheological Experiments. The linear viscoelastic responses were probed by small-strain amplitude oscillatory shear measurements with an ARES-2KFRTN1 straincontrolled rheometer equipped with a force rebalance transducer (TA Instruments, USA). The TTS principle was applied to build master curves at the reference temperature of T ref = T g + 44 °C for all the samples, where T g is the glass transition temperature of the sample considered. The reference temperatures for the precursor and the network were chosen in order to compare both sets of data at the same distance from their respective T g (please see Supporting Information). For the precursor this amounts to T ref = -15 °C and for the network to T ref = 0 °C. 8 mm Invar parallel plates with low expansion coefficient were used and reproducibility of the measurements was checked by going down in temperature by a step of 10 °C as a first measurement and up to check the reproducibility of the data at 2 or 3 temperatures (see Supporting Information for details).
Such records are of exceptional value for the fundamental researches in the field of geomagnetism assuming the study of the evolution of the Earth's magnetic field and the associated dynamic processes in the outer core over longtime intervals. The development of science in the study of the magnetic fields of the Earth and the Sun requires a permanent improvement of the quality of the data provided by the observatories as the adequacy of reproducing the characteristics of the magnetic field outside the points of actual measurements using appropriate mathematical models depends directly on it. Over the past decades, the highest international standard for the quality of geomagnetic observations was elaborated within the framework of the INTERMAGNET program  (Love and Chulliat 2013). To date, this standard corresponds to about 130 observatories united into a single network and providing continuous, quasi real-time data online (http://www.
intermagnet. org). The methods for the data preparation established in INTERMAGNET are the de facto standard for any geomagnetic observatory included in the network. The quality of the magnetic field component records relates to the two main components: the hardware used for the measurements and the processing of the collected data. The main instrument of the magnetic observatory, providing the continuity of the measurements, is a quartz-of fluxgate-type vector magnetometer, also called a variometer. At the INTERMAG-NET observatories, variation measurements are carried out with a sampling rate varying from ~ 250 to 0. 05 Hz. Despite the development of methods for measuring the magnetic field, the present high-precision vector magnetometers do not allow automatic measurements of the total values of the magnetic field components in an autonomous mode.
According to the INTERMAGNET rules for the data processing, such regular series should represent daily values resulting from the interpolation of the observed baseline values. The interpolation algorithm is selected by each observatory independently, ensuring the smoothness of the resulting baseline. Most observatories solve this problem by using cubic smoothing splines or the approximation with polynomials. Nevertheless, the described approach has some serious disadvantages. First of all, application of interpolation to obtain the baseline values for the periods of the absence of the absolute observations does not take into account the physical effects caused by the behavior of the magnetic field during the corresponding time intervals. That is, the common agreement that the baseline variations should be described by smooth functions (quadratic polynomials, splines, etc.
Calculation of regular baselines The traditional method for the definitive data preparation does not require a continuously recording scalar magnetometer at the observatory. Despite this, many INTER-MAGNET observatories (more than 85 according to the 2015 definitive data statistics) (http://www. intermagnet. org) perform continuous recordings of the total intensity of the magnetic field by means of a proton-type scalar magnetometer. The difference ∆F between the calculated value of the total Earth magnetic field intensity according to the baseline-corrected vector magnetometer data and the scalar magnetometer record is the one of the basic quality criteria provided by the observatory  (Reda et al. 2011 ). Thus, the presence of approximate full values of the three components of the field vector and the time-synchronized values of its modulus allows the operational calculation of the ∆F record.
Usually, the vector and the scalar magnetometers sample geomagnetic field with different frequencies; therefore, they must first be brought to a single timeline by averaging. In particular, time-synchronized min data are calculated by averaging the original, noise-cleared data using a Gaussian filter (St-Louis 2012). Let us analyze the calculation procedure of the intermediate (regular) baseline values using the proposed method. For a calculation, the following initial data sets are used: 1. Three orthogonal component variations of the EMF � V = (X v , Y v , Z v ) , measured with a vector magnetom- eter with a frequency l 1 ; The EMF total intensity values EMF F v , measured with a scalar magnetometer with a frequency l 1 ; 2. The observed baseline values � B 0 = X 0 b , Y 0 b , Z 0 b , calculated using the results and at time moments of the absolute observations carried out with a non-regular frequency of an average value l 2 ≪ l 1.
Validation of the method on synthetic data Let us analyze the application of the method to partially synthesized data. As the initial variation values � V = (X v , Y v , Z v ), we use the variations of the three orthogonal field components registered by the vector magnetometer at the Saint Petersburg INTERMAG-NET magnetic observatory (IAGA code SPG, Leningrad Region, Russia), cleaned from anthropogenic noise. The initial digital registration frequency is 1 Hz, so for our purposes the data are preliminarily averaged to 1-min values according to the Gaussian filer using the weight factors recommended by INTERMAGNET (St-Louis 2012), with centering to the beginning of the UTC min. Also, it is necessary to define a priori a certain synthetic regular baseline � B(t) = (X b , Y b , Z b ). In the future, the accuracy of the solution found by the algorithm is estimated from its proximity to this series.
1 (t) is an additive white Gaussian noise with the RMS σ = 0. 1 and the mean μ = 0. Now let us analyze the results of the algorithm application to the initial data V , F a and B 0 on the obser- vation interval T = 21 days with λ = 0. 5. In Fig. 1 , the baseline values of the EMF components are presented. The synthetically generated regular baseline is marked with green, and the selected pseudo-observed values are marked with an asterisk. The obtained hourly baseline values are marked with blue. The step of the grid S in the φ function minimization is taken 0. 25 nT for each component. An important indicator of the quality of the calculated baseline values is the difference function G (Fig. 1 ). As it is seen, its values are within the [-0. 5 0. 5] interval, which indicates the high definitive data quality according to the INTERMAGNET regulations.
Validation of the method on real data To validate the method in real conditions, we used initial data from the Saint Petersburg geomagnetic observatory (IAGA code SPG), obtained during 2015. The observatory is located 100 km north from the city of Saint Petersburg (60. 542N, 29. 716E) and based at the site of the former Krasnoe Ozero (Red lake) observatory, where continuous observations were carried out from 1960 to 1990. Through the efforts of the GC RAS in 2014, it was equipped with the modern magnetometric instrumentation, and in 2016 the first definitive data set for 2015 was produced. These data were accepted by INTERMAG-NET, and the observatory was officially included into the network  (Soloviev et al. 2015; Sidorov et al. 2017). The continuous data recording at the observatory are provided by the following instruments:  • FGE 3-axial fluxgate magnetometer  (DTU, Denmark)  for recording the variations of the EMF components; • GSM-19 scalar magnetometer (Gem Systems, Canada) for recording the modulus of the EMF vector.
The fluxgate magnetometer is installed in the separate pavilion and provides measurements with the frequency of 1 Hz. It is oriented in such a way that the sensor axes are aligned along the directions to the geographic north, east and vertical line, so the measured components of the EMF are X, Y and Z, respectively. The scalar magnetometer carries out measurements with the frequency of 0. 33 Hz and is installed in another pavilion. In the same pavilion, absolute observations of magnetic declination and inclination are carried out by operator 1-2 times per week, using the MinGeo 020B declinometer/ inclinometer (MinGeo, Hungary). The distance between the pavilions is about 25 m. The data from both magnetometers are continuously transmitted to the Magrec data logger (MinGeo, Hungary), where their temporal synchronization, min averaging by Gaussian filter, preliminary processing and further transmission to GC RAS are performed  (Soloviev et al.
2013; Gvishiani et al. 2014 Gvishiani et al. , 2016a, b), b). To demonstrate the advantages of the described method, herein we provide its comparison with the traditional method used by the INTERMAGNET community by the example of the SPG data over 2015. Let us remind that the data obtained by the traditional method have the official status of definitive data, published on the INTER-MAGNET Web site (http://www. intermagnet. org). According to the ΔF record constructed for the 2015 definitive data, the processed data from the magnetometers have a high level of coherence, as the ΔF amplitude does not exceed 1 nT during the whole year. The baselines over the whole period of absolute observations in 2015, which correspond to the definitive data, have the following maximal amplitudes: 5 nT for X component, 10 nT for Y component and 2 nT for Z component.
Thus, the comparative analysis shows that the definitive data obtained using the developed algorithm meet the INTERMAGNET requirements. Moreover, due to the new method, the data quality indicators are essentially improved, comparing to the classical approach, and also a relevant estimation of the measurement quality during the analyzed time interval is provided. Taking this estimation into account, it becomes possible to set the minimal quality level required for the final data and, when processing the observations from several observatories, to rank the individual data fragments according to their quality.
Moreover, the selection of the parameter can be automated, for example, by the analysis of statistical features of the initial data. An important feature of the algorithm is the possibility of a quantitative estimation of the resulting data precision and, thus, determination of the problematic fragments in initial data without operator intervention. The algorithm operation was evaluated both on synthetic examples and on real data registered in 2015 at the Saint Petersburg INTERMAGNET observatory. The data obtained using the proposed method were compared with the definitive data officially accepted by INTERMAGNET. The advantages of the new method are demonstrated, and it is shown that it allows to essentially improve the resulting data quality as compared to the classical approach. The obtained results prove that the baseline variability in time should not necessarily be smooth.
Acknowledgements The results presented in this paper rely on data collected at the INTERMAGNET magnetic observatories. We express our gratitude to the national institutes that support them,  INTERMAGNET community  for promoting the high standards of magnetic observatory practice, and Russian-Ukrainian Geomagnetic Data Center for making the data freely available online. The authors thank  Dr. Roman Sidorov  ( GC RAS ) for his help in preparing the materials. We are also grateful to the three anonymous reviewers for their valuable remarks that helped us to improve the paper.
1c ). To assess the effect of RNA-binding in the functional activity of UP1, we mutated conserved Phe residues within RNP motifs of the two RRM domains that directly contact the RNA and are required for the RNA-binding activity in hnRNP A1  17. Substitution of Phe with Asp within RRM2 (UP1-M9_FD2) or with Asp or Ala in both RRM1 and RRM2 domains (UP1-M9_FD12, UP1-M9_FD12a, or UP1-M9_FA12b) is sufficient to abolish the activity of UP1-M9 in our in vivo pri-mir-18a processing assay without affecting the nuclear localization of the protein constructs (Supplementary Fig. 1a, b ; Supplementary Table  1 ). This indicates that the RNA-binding activity of hnRNP A1 is essential for enhancing miR-18a biogenesis. The in vivo functional data further confirm that the stimulatory function of hnRNP A1 in processing of pri-mir-18a requires both RRM domains, as mutations that affect RNA binding in one domain (or deletion of one domain) abolish the activity of hnRNP A1.
Virtually the same domain arrangement and salt bridges are observed in the monomeric solution structure of UP1 free  19  and a crystal structure bound to a short RNA  22  (Supplementary Fig. 4f ), indicating that this arrangement of RRM1 and RRM2 stabilized by a number of interactions is preformed and may be retained in free and ligand-bound structures of UP1. To confirm the domain arrangement of RNA-bound UP1 in solution, we measured NMR and small angle X-ray scattering (SAXS) (Supplementary Fig. 4b-d ). NMR paramagnetic relaxation enhancements (PRE) were recorded for UP1 spin-labeled at position 66 (UP1 Glu66Cys mutant). The PRE data provide longrange (up to ~20 Å) distance information and can thus report on domain/domain arrangements  [24] [25] [26]  (Supplementary Fig. 4c ). The PRE profiles of free and RNA-bound form of UP1 are similar, suggesting that the domain arrangement does not change significantly in the presence of the 12-mer RNA.
3c ; Supplementary Fig. 4a-e ). Validation of the UP1/12-mer RNA structural model. The structural model of the 1:1 UP1/12-mer RNA complex was confirmed by mutational analysis of protein and RNA. ITC data with 12-mer RNAs where the first or second UAG motif has been replaced by UUU, 12-mer-mut1: AGUUUAUUAGCA and 12mer-mut2: AGUAGAUUUUCA show 10-fold and 20-fold (K D = 154 nM and 330 nM) reduced binding affinity, respectively, compared to the wild-type sequence (Table  1 ; Supplementary Fig. 2b ). This confirms that both motifs are recognized by the protein. Additional RNA variants with an AG→UU mutation or lacking the initial AG dinucleotide, that precedes the two "canonical" UAG motifs, have the same binding affinity as the wild-type 12-mer RNA (Table  1 ; Supplementary Fig. 2b ). As mutation or deletion of the AG dinucleotide at the very 5′ end of the 12-mer RNA has essentially no effect, this establishes that UP1 has a preference for the recognition of two closely spaced UAG motifs.
These results are corroborated by a recent SHAPE and chemical probing assays performed on the complete oncomiR-1 cluster  32. Importantly, upon addition of UP1 and hnRNP A1, distinct changes in SHAPE reactivities are observed compared to the free pri-mir-18a indicating that residues around the terminal loop and flanking stem region (nts 170-186) become protected by protein binding (Fig. 5d ; Supplementary Fig. 5a ). This region includes the two UAG motifs that bind to RRM1 and RRM2 of hnRNP A1/ UP1. Analysis of individual replicate SHAPE data sets reveals a modest increase in SHAPE reactivity down the RNA stem in the presence of protein (nts 50, 51, 53-55). However, these changes are not visible after averaging the three SHAPE replicate data sets, and are thus not statistically significant.
7 ). Drosha efficiently processes all but one of the pri-mir-16/18a chimerae analyzed here, where only the processing of pri-mir-16/18a[5GC] was reduced. This shows that conformational changes in the apical part of the pri-mir stem can directly affect Microprocessor activity. By contrast, the processing of pri-mir-16/18a[5GC_internal]) was efficient in the context of this chimera (Fig. 7 ), unlike what we observed with the processing of pri-mir-18a[5GC_internal], which was abolished (Fig. 6b ). Therefore, these data strongly support our hypothesis that binding of hnRNP A1 to the terminal loop of primir-18a results in a partial unwinding of this pri-miRNA that spreads from the terminal loop towards the stem and is essential for the stimulatory role of hnRNP A1 in miR-18a biogenesis.
As the terminal loop corresponds to the sequence that we have identified as the main hnRNP A1-binding site in pri-mir-18a, it is tempting to speculate that binding of hnRNP A1 to the miR-17-92 cluster is associated with a conformational change in the RNA, which can facilitate Microprocessor assembly and Drosha cleavage. We propose that the tertiary structure of pri-mir-18a in the context of the miR-17-92 cluster, as well as sequences in the stem and loop region, are important determinants of miRNA processing by Drosha. The recognition of the pri-mir-18a stem-loop by hnRNP A1 thus adds an additional layer for the regulation of pri-miRNA processing by an RBP, beyond features that have been recently identified  39, 43, 47. In conclusion, our data demonstrate that recognition of a conserved terminal loop RNA sequence in pri-miRNAs by an RBP can strongly modulate miRNA biogenesis by conformational changes and dynamic destabilization induced by RNA binding.
Samples were observed on a Zeiss Axioskop microscope and the images were acquired with a Photometrics CH250 cooled CCD camera using Digital Scientific Smartcapture extensions (IP Lab Spectrum software). The immunofluorescence figures show representative data, and each experiment was reproduced in multiple independent transfections. Analysis of miRNA levels in living cells. HeLa cells were cultured in standard DMEM medium (Life Technologies) supplemented with 10% FBS (Life Technologies). Plasmids were transfected into HeLa cells using Lipofectamine 2000 reagent, as described before  49. Mouse monoclonal anti-T7 tag HRP conjugate (1:10000, 69048, RRID-AB10807495, Novagen) was used to detect T7-tagged proteins. Mouse monoclonal anti-tubulin (1:10000, T6199, RRID-AB_477583, Sigma-Aldrich) was used as loading control.