dataesr/scientific-paragraphs-categorization huggingface dataset:
Cyanine 3 fluorescence was scanned on a MS200 scanner (Roche Nimblegen). Raw intensities were obtained from the NimbleScan software (Nimblegen). The ANAIS web interface  (Simon and Biot, 2010)  was used to normalize data and detect significantly expressed genes, i. e. , genes expressed more than random probes included on the chip (p < 0. 01). Raw and normalized data were deposited on the NCBI GEO repository under accession GSE99940. Biological triplicate data obtained with each polysaccharide were compared to glucose condition using empirical Bayes statistics implemented in the eBayes function from the limma package v 3. 26. 9 in R 3. 2. 2 (R Core Team, 2013) and Bonferroni correction for multiple tests. Genes with an adjusted p-value FWER ≤ 0. 05 were considered as differentially expressed.
Expression Profiling Allows the Detection of Transcription Units The global transcription shift induced by the different tested conditions offered an opportunity to investigate transcription units in Z. galactanivorans. In prokaryotes, many genes are organized into operons, defined as multiple open reading frames transcribed from the same promoter to a single mRNA transcript. Operons allow co-regulation at the transcriptional level for genes that often act as part of the same pathway. These structures can be predicted based solely on sequence information, using distances between adjacent genes, presence of promoters and terminators, and conservation in other genomes  (Ermolaeva et al. , 2001). Such a sequence-based search applied to the Z. galactanivorans genome predicted 1,046 operons containing at least two genes, as reported on the Database of Prokaryotic Operons DOOR 2.
0  (Mao et al. , 2014). However, these predictions need to be validated experimentally. The microarray dataset was therefore used to infer transcription units (GTSegments), defined as groups of proximal genomic elements displaying a coordinated expression in the tested conditions. The computation of dominant GTSegments in Z. galactanivorans using a custom script produced 278 transcription units of more than two and up to 17 genes (Figure  3 , also available as high-resolution file in Supplementary Figure  5 ; Supplementary Table  5 ). These transcription units represented 192 unique regions distributed over the entire genome. A large proportion of the 1,046 total operons predicted by DOOR was not detected from the co-expression network. This does not necessarily invalidate the DOOR predictions, but likely suggests that the set of tested growth conditions did not induce sufficient perturbations of the gene expression in all predicted operons to produce a detectable signal  (Sabatti et al.
Furthermore, genes encoding proteins involved in transcriptional regulation were also significantly enriched among the up-regulated genes (Chi-squared test, χ 2 = 15. 0; p < 0. 001). A total of 35 genes belonging to this category were up-regulated in at least one condition (Figure  4 ). This includes genes annotated as transcriptional regulators of the AraC, AsnC/Lrp, BlaI, HxlR, LysR, MarR, FIGURE 3 | Display of detected transcription units (GTSegments) on Z. galactanivorans Dsij T genome. The figure contains three main parts from the outer part to the inner part: the GTSegments, the predicted operons from DOOR2 database, and the expression values of genes and GTSegments in different growth conditions. The most outer band displays the sequence of genes existing in the chromosome of Z. galactanivorans.
The color of each gene depicts its expression profile based on the co-expression network. Two genes with the same color have then the same expression profile. Black and white segments over the genes illustrate detected GTSegments. White segments are GTSegments similar to predicted operons in the DOOR2 database, whereas black segments are those that are not similar to predicted operons. Gray parts of a GTSegment indicate genes from the same locus that are omitted from the GTSegment. The second most outer band illustrates predicted operons from the DOOR2 database, where successive parts with the same color are genes from the same operon. Scatter plots on the inner rings depict expression values of genes and GTSegments in different growth conditions on a minimal medium with, from the most inner part to the outer part, glucose, ι-carrageenan, κ-carrageenan, porphyran, agar, laminarin, and alginate.
These genes might be involved more generally in the adaptation of cells toward interaction with algal biomass. A striking example is the gene ZGAL_1768, encoding a putative PadR-type transcriptional regulator that was strongly up-regulated with all six polysaccharides tested (log 2 FC > 3), together with the downstream gene of unknown function ZGAL_1769 (Supplementary Figure  6 ). Both genes were found as part of a transcriptional unit in the coexpression network analysis (Supplementary Table  5 ). By contrast, the upstream gene paaE (ZGAL_1767), encoding a putative phenylacetic acid (PAA) degradation NADH oxidoreductase, was down-regulated with all six polysaccharides tested and negatively correlated with the expression of the ZGAL_1768 PadR-type regulator (Supplementary Figure  6 ). This is reminiscent of the role of some members of the large and diverse PadR-like family (PFAM accession PF03551) for the response to phenolic acids, including the prototypical PadR phenolic acid decarboxylation repressor that inhibits the expression of an inducible phenolic acid decarboxylase in gram-positive bacteria  (Barthelmebs et al.
Laminarin induced the expression of the transcriptional unit ZGAL_209 to ZGAL_214, which encodes two TonB-dependent receptors (TBDR) with their associated surface glycan-binding protein (SGBP) of the SusD-like family and a CBM4-containing hypothetical protein localized in the outer membrane (Figure  5 ). Tandems of TBDRs and SusD-like SGBPs are considered a hallmark of PULs in Bacteroidetes genomes  (Grondin et al. , 2017). To date, binding of CBM4 modules has been demonstrated with β-1,3-glucan, β-1,3-1,4-glucan, β-1,6-glucan, xylan, and amorphous cellulose (CAZY database, http://www. cazy. org/). Therefore, this operon-like structure could be involved in the binding of laminarin to the cell surface. The three genes ZGAL_587, ZGAL_588, and ZGAL_589 were also detected as a transcriptional unit and were significantly upregulated when cells were grown on laminarin compared to glucose (Figure  5 ).
, 2012; Williams et al. , 2013). In addition, the genes ZGAL_2990 and ZGAL_2991, encoding, respectively, glycosyltransferases of family GT4 and GT2, were up-regulated by both laminarin and alginate together with adjacent genes of unknown function (Figure  5 ). Similarly, the expression of the detected transcriptional unit ZGAL_631-636, encoding proteins of unknown function, was significantly enhanced with alginate or laminarin. The  Asterisks denote significant up-regulation compared to glucose (FWER < 0. 05). The y-axis scale reports normalized expression values and is conserved for each genomic region. Genes from the same genomic region and shaded with the same color were found to belong to the same transcription unit by the network approach. presence of Thrombospondin type 3 repeats (IPR028974, four in ZGAL_631, one in ZGAL_633) and Calx-beta domains (PF03160, one in ZGAL_631, two in ZGAL_636) suggests a role in calcium binding.
, 1995). However, the two sequences only share 25% identity (data not shown), suggesting different substrate specificities. ZGAL_4688 encodes a protein that likely adopts a beta-propeller fold (InterProScan domain IPR011042), which might be reminiscent of the structure found in several known GH families. Both κ-and ι-carrageenan induced the expression of ZGAL_3150, annotated as a glycoside hydrolase of family GH127 (Figure  6B ). The only characterized activities in the GH127 family are the hydrolysis of β-L-arabinofuranose from plant extensins  (Fujita et al. , 2014)  and of 3-C-Carboxy-5deoxy-L-xylofuranose from pectin  (Ndeh et al. , 2017). These sugars have not been found in red algae, suggesting a new substrate specifity on carrageenan motifs for ZGAL_3150. Both carrageenans also strongly induced the expression of a SusD-like SGBP (ZGAL_3580, log 2 FC > 3) adjacent to a TBDR gene, suggesting a role in binding of carrageenan to the cell surface.
, 2007). Finally, the expression of the locus ZGAL_2985-2988 was induced by ι-carrageenan, but not by κ-carrageenan (Figure  6B ). It encodes three proteins of unknown function and a glycoside hydrolase of family 5, subfamily 42 (ZGAL_2986). GH5 is one of the largest of all CAZy families, encompassing a variety of activities. To date, GH5_42 counts 54 members in the CAZy database (http://www. cazy. org/GH5_42_all. html) but their substrate specificity remains unknown since all of them lack experimental characterization  (Aspeborg et al. , 2012). Our results could therefore guide future activity tests on ι-carrageenan related substrates. In summary, this study provided an unprecedented view of genome-wide expression changes in an alga-associated flavobacterium during polysaccharide degradation.
FUNDING This work was supported by the project  GIS Marine Genomics, Blue Enzymes  ( ANR-14-CE19-0020-01 ),  Fondap 15090007, Basal program  PFB-03  CMM, IntegrativeBioChile INRIA Assoc. Team, CIRIC-INRIA Chile (line Natural Resources) , and  IDEALG  ( ANR-10-BTBR-04 ).
RNA sequencing Three flasks containing Jurkat cells (30 mls at 0. 8x10 6 /ml) were independently infected with VSV-G pseudotyped LAIΔenv WT or N74D at an MOI of 0. 2 in the presence of DMSO or 400nM digoxin. An aliquot of the cells was analysed by FACS 36 hours post-infection and nucleic acids were extracted from the remaining cells using Qiagen RNAeasy Blood & Tissue kit following the manufacturer's instructions. 250 ng of total RNA was prepared for sequencing following the Illumina TruSeq mRNA (unstranded) protocol, multiplexed, and sequenced (V2 High Output kit, 43bp PE) on an Illumina Nextseq to yield an average of > 15 million reads per sample. Data was de-multiplexed using bcl2fastq v 2. 16. Read QC reports were generated in Illumina Basespace. Fastq files passing read QC were analysed on the UCL Genomics cluster using an in house pipeline (Python/R).
Briefly, sequencing reads were aligned to an appropriate reference genome using TopHat v2. 0. 13 and PCR duplicates removed using Picard tools (v1. 100). GTF files describing genes features were obtained from the Ensembl website (http://www. ensembl. org/info/data/ftp/index. html) and read count data for each genomic feature were obtained using the python scripts provided as part of the Deseq2 package. Read count data was subsequently normalized and differential expression computed using Deseq2. Normalized read counts (normalized using the Deseq2 library size procedure) was visualised in Genespring 12. 0 (Agilent) and analysed using Ingenuity Pathway Analysis. The set of scripts used is freely available at https://github. com/plagnollab/RNASeq_pipeline. Sequence data can be downloaded from NCBI SRA accession PRJNA321856.
Genomic positions of genes were retrieved from UCSC table browser  [81]  or published data. Specific gene lists (i. e. genes downregulated by digoxin) were compiled by deriving a non-repetitive list of genes from Ingenuity pathway analysis output and matched against UCSC gene tables to derive genomic coordinates. Integration sites were annotated with these genomic positions using the R Bioconductor package hiAnnotator  [82]. Statistical analysis was carried out using R version 2. 15. 2 (http://www. R-project. org/). qPCR For Taqman qPCR, approximately 1x10 6 cells were washed twice in PBS and total DNA was extracted with the Qiamp DNA Minikit (Qiagen, Manchester, UK). Quantitative PCR reactions were carried out as previously described  [74]  using 0. 3pmol each primer and 0. 15 pmol of the probe in 25 μL volume containing 100-300ng total DNA using an ABI Prism 7000 Sequence Detection System (SDS).
Acknowledgments We thank  Richard Jenner ,  David Selwood ,  Kathy Kettleborough  and  Yorifumi Satou  for helpful discussions,  Jane Rasaiyaa  for  HIV-1 BaL ,  D. Selwood  for the mass spec analyses, MRCT for the  NINDS  library,  Mike Hubank  and  Kerra Pearce  for support with the RNAseq,  Annie Gosselin  for technical help with cell sorting and viral stock production,  Paul Kellam  and  Astrid Gall  for the sequencing of HIV-1 viruses,  Mahad Noursadeghi  and  Lucy Bell  for the initial experiments in primary cells,  Clare Jolly  and  Shimona Starling  for the cell-to-cell infection assays,  Alan Engelman  and  Greg Sowd  for the gift of CPSF6 KO cells. We thank  Heather Niederer  for providing primers and  Nirav Malani  for bioinformatics advice. We thank  Laurence Game  and  Marian Dore  ( Genomics Laboratory, Medical Research Council Clinical Sciences Centre, Hammersmith Hospital, London, UK ) for helpful support.
Animals. Long-Evans rats (n = 5, male, 250 -400 g) were housed in pairs (individually after surgery) in stable conditions of temperature and humidity with a constant light-dark cycle and free access to food and water. Here we analyzed the electrophysiological recordings in DLS from 5 animals: rats 1 and 4 completed at least 127 sessions before the start of recordings, rats 5, 15 and 17 were used in the hand-guided control task and were implanted immediately after the habituation period. After eight hand-guided sessions, rat 17 was also trained and recorded in the regular version of the task and reached the learning criterion after 41 sessions. Data acquisition and processing. Under deep isoflurane anesthesia, a 2 × 4 tetrode array (nichrome wires, 12. 5-μm diameter, California Fine Wire) loaded on a NLX-9 micro-drive (Neuralynx) was implanted above the dorsal striatum through a craniotomy centered at ML = ±3.
6 mm and AP = +0. 6 mm from bregma. Tetrode positions in the DLS were verified using cresyl violet staining on paraformaldehyde fixed coronal brain sections  (Rueda-Orozco and Robbe, 2015). Wide-band 11/20 neurophysiological signals from the tetrode arrays were amplified and continuously acquired at 20 kHz. Spike sorting was performed semi-automatically using KlustaKwik (http://klustakwik. sourceforge. net) clustering software and the graphical spike sorting application Klusters (http://neurosuite. sourceforge. net). The animal position was tracked using a CCD camera (sampling rate = 60 frames s-1) positioned laterally to the treadmill and a marker attached to the left forelimb.
Data analysis All the analyses were performed using the Python language taking advantage of the Jupyter Notebook web interface. The behavioral and neuronal data and the notebooks used to generate the figures are available upon request to the corresponding author. Sessions with less than 30 trials were excluded from the analyses. We considered in total 55 sessions for ST animals (23, 19 and 13 sessions in each animal) and 22 sessions for HG animals (7, 8 and 7 sessions in each animal) leading respectively to 395 and 256 isolated neurons with an average number of neurons per session of 7 (std = 3, min = 2 and max=17 neurons) for ST and 11 (std = 5, min = 4 and max = 25 neurons) for HG animals. Firing rates modulation during running sequence. Trials and intertrials (periods during which the treadmill was on and off, respectively) were divided in 250 ms-long non-overlapping windows.
Acknowledgments We thank  Drs Laurent Perrinet  and  Arnaud Malvache  for critical reading of this manuscript; Members of the  Robbe lab  for stimulant discussions during the development of the project and  Typhaine Moreau  for helping to restructure the pre-processing routines in python. This work was supported by the,  European Research Council  ( ERC-2013-CoG -615699_NeuroKinematics , D. R. ) and the  Mexican Consejo Nacional de Ciencia y Tecnología (P. R. O).
The effective resolution is 13. 8 km at 60 • S, increasing to 3. 8 km at 86. 5 • S, where a pure Mercator grid would have a resolution of 2. 2 km. The model uses 75 vertical levels with thicknesses varying from 1 m at the surface to 200 at 6000 m depth, giving a vertical resolution ranging from 10 to 150 m beneath the ice shelves. See Sect. 3. 3 for the effect of this resolution on ice shelf melting in an idealised case. The bathymetry used for the model domain north of the Antarctic continental shelf is that described by  Megann et al. , (2014). Over the Antarctic continental shelves the IBCSO dataset  (Arndt et al. , 2013)  is used. The two bathymetry datasets are merged between the 1000 and 2000 m isobath along the Antarctic continental slope. Under the ice shelves, bathymetry (included in the IBCSO dataset) and ice draft are taken from BEDMAP 2  (Fretwell et al.
Observations come from  Rignot et al. (2013). Geometry column indicates the main modification to the BEDMAP2 bathymetry/ice shelf draft as follows: GL means the GL is moved seaward, "shallow" means the ice shelf is too shallow away from the grounding line and "narrow" means the narrowest passage into the cavity is one cell wide. ++/+/0/-/-is a summary of the ocean temperature condition at the closest non-extrapolated cell in the WOA2013 observational dataset (Fig. 14 ). ++ for ocean temperature differences with regard to WOA2013 of more than 1 • C, + differences in the range 0. 5 and 1 • C, 0 differences in the range 0. 5 and -0. 5 • C, -differences in the range -0. 5 and -1 • C and -for ocean temperature differences greater than -1 • C.
In R_MLT, the number of wet levels in the cavities varies from ∼ 10 levels near the ice fronts to two levels at the grounding line, while in FESOM there are 21 levels everywhere. This allows for better resolution near the grounding line and in the top boundary layer. Shodlok et al. (2016)  and the sensitivity experiments performed   R_MLT (year 10, 1988)  and observations from World Ocean Atlas 2013  (Locarnini et al. , 2013; Zweng et al. , 2013). Grey area represents ice sheet, ice shelves or ocean shallower than 300 m. The hatched area limited by the green line represents where the observational dataset is obtained by extrapolation. in Sect. 3. 3 show that some ice shelves (West, Dalton, Totten, George VI, Larsen C and FRIS for example) are highly sensitive to the vertical resolution, which affects the ocean properties on the continental shelf, the representation of the top boundary layer beneath the ice shelf, and the ability to resolve details of the cavity geometry.
Limitations In addition to the inter-model differences described above, ice-shelf-ocean models in general are still subject to several limitations. Most of them are specific to our model set-up as well as the large uncertainties in geometry and forcing data, and critical gaps in our knowledge of dynamics at the iceocean interface. The most recent bathymetry and ice shelf draft reconstruction of the Amundsen Sea  (Millan et al. , 2017)  shows features that are missing in the BEDMAP2 data-set. In BEDMAP2, for many ice shelves, there are only indirect observations of ice draft, based on satellite surface elevation data, while the sub-ice bathymetry data are often poorly constrained. For some ice shelves (Getz, Venable, Stange, Nivlisen, Shackleton, Totten and Dalton ice shelves, some of the thickest areas of the Filchner, Ronne, Ross and Amery ice shelves and for the ice shelves of Dronning Maud Land), the flotation condition had to be enforced by lowering the seabed arbitrarily from a level that itself was based on nothing more than extrapolation of cavity thickness from surrounding regions of grounded ice and 100 m thick cavity.
Finally, a conservative wetting and drying scheme needs to be developed to allow for the grounding line (and calving front) to move back and forth. Code and data availability. The model code for NEMO 3. 6 is available from the NEMO website (www. nemo-ocean. eu). On registering, individuals can access the FORTRAN code using the open-source subversion software (http://subversion. apache. org/). The branch used for both configurations used in this study is the 2015 development branch named dev_r5151_UKMO_ISF at revision 5204. The ice shelf module is now included in the public NEMO distribution. The ISOMIP configuration is distributed in NEMO version 3. 6 as an unsupported configuration. No file is required to run ISOMIP configuration. For the circum-Antarctic configuration, the input files (cpp keys, namelist, bathymetry, ice shelf draft, iceberg runoff, initial condition, river runoff, tidal mixing and weights for the surface forcings) could be requested from the authors.
Figure14. Shown are 300-1000 m mean temperature differences betweenR_MLT (year 10, 1988)  and observations from World Ocean Atlas 2013 (Locarnini et al. , 2013; Zweng et al. , 2013). Grey area represents ice sheet, ice shelves or ocean shallower than 300 m. The hatched area limited by the green line represents where the observational dataset is obtained by extrapolation.
Example applications include the use of selforganizing maps (SOMs) reconstruct chlorophyll profiles from ocean color images  [10] ; or for the completion of a database of hydrographic profiles obtained from ocean gliders  [11]. In this letter, we tackle the problem of reconstructing subsurface velocities from surface data using a methodology based on self-organizing maps. We restrict our attention to the Southern Ocean, the region of the world that encircles the Antarctic continent and lies south of Australia, South America and Africa. We focus on this region as it hosts an energetic and complex flow field that presents a challenge for prediction schemes, and due to its remote location and harsh environment, is one of most data sparse ocean basins. Hence, a robust reconstruction of the deep flow from satellite data could be of immense benefit to the oceanographic community.
II. DATA AND METHODOLOGY A. Data The data used in this study consists of surface data that are used as predictors, and sub-surface measurements of current speed that are used to train the SOM and for validation. 1) Surface Data: In order to reconstruct the sub-surface current velocities, we use as predictor variables the surface velocity, surface absolute dynamic topography and the sea surface temperature obtained from satellite data. The dynamic sea-surface topography used in this study is obtained from the Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO) weekly gridded sea level anomalies (SLA) (http://www. aviso. altimetry. fr/), a "level 4" product. We use SLAs for the 5 year period 2005-2011, mapped to a 1/4 degree Mercator grid using optimal interpolation of along-track data series using at least two satellite missions [TOPEX/Poseidon-ERS or Jason-1-Envisat or Jason-2-Envisat] with consistent sampling over the time period.
This dataset provides estimates of the sea-surface height and velocity anomalies, relative to the 20 year period from 1994 to 2014. The time mean sea-surface height and current velocity are obtained using the mean dynamic topography, reconstructed by combining data from the Gravity Recovery and Climate Experiment (GRACE) mission, satellite altimeters, and drifting buoys  [12]. An example of the surface velocity from these datasets is shown in 1(a). The sea-surface temperature (SST) data used are daily averages of Version 2 of the NOAA combined AVHRR-AMSR optimally interpolated SST product (https://www. ncdc. noaa. gov/oisst) [13]. The combined use of infrared and microwave instruments in cloud-free regions reduces systematic biases as the errors of each sensor are independent. 2) Sub-Surface Velocity Data: In order to estimate the ocean current velocity at depth, we use the velocity data provided by autonomous lagrangian drifters called Argo floats  [2].