dataesr/scientific-paragraphs-categorization huggingface dataset:
Reduction of IRDIS data In section 4 and 5, we use SpeCal to reduce two datasets as examples. In section 4, we consider one sequence recorded during the SPHERE GTO on 2016 September 16 observing HIP2578 in IRDIS H2/H3 mode. There are 80 images of 64 s exposure time and the field of view rotates by 31. 5 degrees. The seeing was about 0. 5 arcsec and the average wind speed was 7. 4 m. s -1. All algorithms are applied on the same datacube provided by the first part of the SPHERE pipeline (background, flat-fielding, bad pixels, registration, wavelength calibration, astrometric calibration;  Pavlov et al. 2008; Zurlo et al. 2014; Maire et al. 2016b ). The datacube is a 1024 × 1024 × 80 × 2 array. The last dimension stands for the two spectral channels (H2 and H3). We also added three fake planets to the data (see Table  3 ) using the recorded stellar PSFs.
Reduction of IFS data We now use SpeCal to reduce one sequence recorded during the SPHERE GTO on 2016 September 16 observing HD206893 in IFS YJH mode. There are 80 images of 64 s exposure time and the field of view rotates by 75. 6 degrees. The seeing was about 0. 7 arcsec and the average wind speed was 8. 4 m. s -1. As for the IRDIS data, all algorithms are applied on the same datacube provided by the first part of the SPHERE pipeline  (Pavlov et al. 2008; Mesa et al. 2015; Maire et al. 2016b ). The datacube is a 290 × 290 × 80 × 39 array. The last dimension is the number of IFS spectral channels. We also added three fake planets to the data (see Table  6 ) using the recorded stellar PSFs. As for IRDIS, we chose the position and spectra to be representative of three common cases. The spectra of planets 1 and 2 show strong variations between 0.
Conclusion The Speckle Calibration tool (SpeCal) was developed by the SPHERE/VLT consortium in the context of a large survey (SHINE), the main objective of which is to search for and measure the astrometry and spectrophotometry of exoplanets at large separations (> 5 au). SpeCal provides high contrast images using a variety of algorithms (cADI, PCA, Loci, TLoci) enabling the study of exoplanets, brown dwarfs, and circumstellar disks. SpeCal has been intensively tested on SPHERE guaranteed time observations (GTO) and calibration data since 2013. It is implemented in the SPHERE data center  (Delorme et al. 2017b)  to produce the final reduction for public data releases. The final reductions will be available in the SPHERE target database (TDB). Finally, SpeCal and the DC are able to process all GTO data obtained with IRDIS/SPHERE (dual-band imaging) and IFS/SPHERE (integral field spectrometer) automatically.
SpeCal delivers major outputs for the survey and feeds the SPHERE database with final images, contrast curves, signal to noise maps, astrometry, and photometry of detected pointlike sources in the field (exoplanets, brown dwarfs, background sources, and all sub-stellar or stellar candidates). This material has been used for the study of exoplanets and circumstellar disks primarily based on SPHERE data  (de Boer et al. 2016; Ginski et al. 2016; Lagrange et al. 2016; Maire et al. 2016a; Mesa et al. 2016; Perrot et al. 2016; Vigan et al. 2016; Zurlo et al. 2016; Benisty et al. 2017; Bonavita et al. 2017; Bonnefoy et al. 2017; Feldt et al. 2017; Maire et al. 2017; Mesa et al. 2017; Pohl et al. 2017; Samland et al. 2017). In this paper, we investigated the astrometric and photometric performance for point-like sources considering objects at a contrast of ∼ 3 × 10 -6 in the separation range of 3 to 26 fwhm.
Acknowledgements. SPHERE is an instrument designed and built by a consortium consisting of  IPAG (Grenoble, France ),  MPIA (Heidelberg, Germany) ,  LAM (Marseille, France), LESIA (Paris, France), Laboratoire Lagrange (Nice, France) ,  INAF -Osservatorio di Padova (Italy) ,  Observatoire astronomique de l'Université de Genève (Switzerland) ,  ETH Zurich (Switzerland) ,  NOVA (Netherlands) ,  ONERA (France) , and  ASTRON (Netherlands) , in collaboration with  ESO. SPHERE was funded by  ESO , with additional contributions from the  CNRS (France) ,  MPIA (Germany) ,  INAF (Italy) ,  FINES (Switzerland) , and  NOVA (Netherlands). SPHERE also received funding from the  European Commission Sixth and Seventh Framework Programs as part of the Optical Infrared Coordination Network for Astronomy (OPTICON)  under grant number  RII3-Ct-2004-001566  for FP6 (2004)(2005)(2006)(2007)(2008), grant number  226604  for FP7 (2009)(2010)(2011)(2012), and grant number  312430  for FP7 (2013)(2014)(2015)(2016).
This work has made use of the SPHERE data center, jointly operated by  Osug/Ipag (Grenoble), Pytheas/Lam/Cesam (Marseille) ,  OCA/Lagrange (Nice), and Observatoire de Paris/Lesia (Paris)  and supported by a grant from  Labex OSUG@2020  ( Investissements davenir   ANR10 LABX56 ). We acknowledge financial support from the  French ANR GIPSE ,  ANR-14-CE33-0018. Dino Mesa  acknowledges support from the  ESO-Government of Chile Joint Committee program  " Direct imaging and characterization of exoplanets ".
Experiments were executed on a computer with an Intel Xeon X7460 2. 66 GHz featuring 24 cores and 128 GB of memory, running a Linux 4. 4. 13, along with g++ compiler version 6. 3. We compare MiniSym using the occurrence order, value symmetries, and without lex-leader forcing, against: -MiniSAT, as the reference solver without symmetry handling  [10] ; -Shatter, a symmetry breaking preprocessor described in  [2] , coupled with the MiniSAT SAT engine; -breakID, another symmetry breaking preprocessor, described in  [8] , also coupled with the MiniSAT SAT engine. Each sat solution was successfully checked against the initial CNF. For unsat situations, there is no way to provide an unsat certificate in presence of symmetries. Nevertheless, we checked our results were also computed by the other measured tools.
Conclusion This paper presented an approach dealing with the symmetries when they appear in SAT problems. It borrows from the state-of-the-art static-based approaches their basic principle, i. e. , the adding of symmetry breaking predicates to the original problem, but performed in an incremental and dynamic way. This is possible thanks to the dynamic tracking of symmetries status and on-the-fly generation of effective symmetry breaking predicates. Our approach outperforms other state-of-the-art static methods, as shown by an extensive evaluation on the symmetric problems gathered from the last six SAT competitions. This approach is implemented in the C++ library called cosy. It is an off-theshelf component that can be interfaced with virtually any CDCL SAT solver. cosy is released under GPL licence and is available at https://github.
Another work shows that miR-124 target genes are involved in morphogenesis and neuron differentiation in the olfactory bulb  (Li & Ling, 2017). However, analysis of miRBase (Griffiths-Jones, 2006) (http://www. mirbase. org), the most common database referencing miRNAs, shows a large difference between the number of miRNAs referenced in the mouse (Mus musculus, 1,915 mature miRNAs and 1193 precursors), the human (Homo sapiens, 2,558 mature miRNAs and 1,881 precursors), and in the rat (Rattus norvegicus, 765 mature miRNAs and 495 precursors). Thus, it seems that a large percentage of the miRNAs expressed in the rat are not yet uncovered. Rat is a gold standard model in neuroscience, and it is thus of prime importance to get an extensive knowledge of miRNA expression in the central nervous system  (CNS).
We focused our attention on the olfactory bulb, the cortex, the hippocampus, the striatum, and the spinal cord and found that of the 495 miRNAs referenced in miRBase, 365 are expressed in these five CNS structures. In addition, we discovered 90 novel miRNAs, some of them having orthologous sequences in the mouse or human that play a crucial role in the regulation of the functions of neurons. We also showed that each CNS structure expresses a particular miR-Nome, and interestingly, the novel miRNAs seem to play a key role in defining structure-specific miRNomes. In addition, we quantified the expression of all mRNAs using mRNA-Seq on the very same samples used for miRNA quantification. Then, we correlated miRNA and mRNA expression data using target predictions and selected the top 20 regulated mRNAs for further analysis.
Results Small RNA sequencing of five structures of the CNS We performed small RNA-Seq to reveal the miRNome of five structures of the CNS: the olfactory bulb, the cortex, the hippocampus, the striatum, and the spinal cord. To perform a statistical analysis of the miRNome, we dissected these structures in triplicate from three male adult Wistar rats. Thus, we synthesized 15 independent libraries that were finally pooled for sequencing. RNA sequencing performed well, and we obtained a total of 454,577,868 reads from which 1,150,734 (0. 3% of total) were filtered out because they were too short (size < 15 nt). To assess the quality of the reads, we used FastQC software (version 0. 11. 5, default parameters), and all sequences were flagged as good quality (Table  S1 ).
The CNS expresses known and novel miRNAs We first checked the number of miRNAs known and referenced in miRBase that were expressed among the five CNS structures. From the 495 miRNAs referenced in rat miRBase, 365 miRNAs were expressed in our samples, corresponding to 73. 7% of the miRBasereferenced miRNAs  (Fig 1A). To discover novel miRNAs expressed in the brain, we used the miRPro software  (Shi et al, 2015). Based on miRDeep2, miRPro analyzes the sequences obtained from small RNA-Seq experiments seeking for novel miRNAs using the following pipeline. First, miRPro aligns sequences of potential novel mature miRNAs on the genome. Then, the algorithm excises the corresponding potential pre-miRNAs from the genomic sequence. Finally, miRPro determines the most relevant pre-miRNA, in particular by the analysis of pre-miRNA folding with the RNAfold tool  (Hofacker, 2003).
In this regard, our results are in accordance with the literature. Indeed, using miRDeep2 as miRNA-prediction algorithm, recent studies found from 71 novel miRNAs in the mouse brain after an infection with Japanese encephalitis viruses  (Li et al, 2016) , up to 171 novel miRNAs for the Yin's study  (Yin et al, 2015). In comparison with the miRNA database (miRBase) of the mouse (Mus musculus, 1,915 mature miRNAs and 1,193 precursors) and human (Homo sapiens, 2,558 mature miRNAs and 1,881 precursors), the content of the rat miRNA database is low (Rattus norvegicus, 765 mature miRNAs and 495 precursors). This may reflect the fact that the human and the mouse are two species intensively studied from a genetic point of view compared with the rat. Even if we implement our novel miRNAs in miRBase, the rat miRBase would still be more than half less referenced than the mouse one and three times less referenced than the human one.
Therefore, we selected as depleted miRNAs, miRNAs which had a log 2 of the fold change lower than -4, corresponding to, at least, a 16 times decrease in miRNA expression. This analysis revealed specific miRNAs for each structure except for the striatum. To evaluate the potential impact of these specific miRNAs, we measured the expression of their predicted target mRNAs. Thus, we quantified the expression of all mRNAs using mRNA-Seq on the very same samples used for miRNA quantification. After statistical analysis of the differentially regulated mRNAs, we correlated miRNA and mRNA expression data (A) Concerning specifically enriched miRNAs, cumulative frequency distribution of expression changes of all predicted targets is significantly different from nontarget genes (blue line versus black line, Kolmogorov-Smirnov test).
(D) Individual gene statistical analysis revealed that 91 target genes of the depleted miRNAs are upregulated (DESeq2 algorithm and Wald test, P < 0. 05); only the 20 most regulated are depicted. Interestingly, some regulated genes are predicted to be the target of multiple miRNAs (gene names written in italics). Data shown are mean ± SD form three biological replicates. (E) GO term enrichment analysis of the selected targets. using target predictions as determined by TargetScan for known miRNAs and miRDB tools for novel miRNAs. To evaluate whether the predicted target genes were more regulated than nontarget genes, we analyzed the cumulative frequency distribution of expression change of all miRNA target genes versus nontarget genes. This analysis showed that the expression of predicted targets is significantly different from the one of nontargeted genes.
1% of unfiltered reads) -not annotated reads, aligned in unannotated regions: 72,073,770 (16. 5% of mapped reads) -annotated reads: 363,720,564 (83. 5% of mapped reads) -miRNA reads: 363,720,564 (100% of annotated) The row data were uploaded in the European Nucleotide Archive under the accession number PRJEB24026. The sequence quality was assessed with FastQC software (www. bioinformatics. babraham. ac. uk/projects/fastqc/).
Identification of conserved and novel miRNAs by bioinformatic analysis To obtain clean reads, the adaptator sequences (from the preparation of libraries) and the four nucleotides inserted between the adaptor sequence and the sequence of the miRNAs were removed using the Cutadapt tool. The Rattus norgevicus genome reference was downloaded from UCS Genome Browser assembly ID:m6. All the bioinformatic analysis were performed using the program miRPro  (Shi et al, 2015) , which uses the main algorithm of the software miRDeep2  (Friedländer et al, 2012). The main advantage of miRPro compared with miRDeep2 is that it proposes consistent and unified names for novel precursors and their mature miRNAs in all libraries of the samples. The reads issued from the sequencing are aligned against the genome of reference using Novoalign  (Li & Homer, 2010).
Sequences that matched known rat small RNAs such as rRNA, scRNA, snoRNA, snRNA, and tRNA or degradation fragments of mRNAs were excuded in further analysis, and sequences that perfectly matched the rat genome along their entire lenght and recognized as miRNAs were subjected to subsequent analysis. The small cleaned RNAs aligned against the miRNA database that cannot be annotated to any previous category were subjected to novel miRNA prediction software miRPro  (Shi et al, 2015). Briefly, after the matching of the reads with the genome of reference, the genome is scanned from 59 to 39 to excise potential precursor sequences. Then, the software creates an index of potential precursors. Afterwards, the reads are aligned with the potential precursors. Finally, the predictive structures are performed by the software RNAfold.
Identification of differentially expressed miRNAs and specific miRNAs We compared the miRNA expression profiles between the nervous system structures, using DESeq2  (Love et al, 2014)  under software R. We considered miRNAs as differentially expressed between structures when P < 0. 05. We considered an miRNA as specific of a structure when it was mainly overexpressed or underexpressed in one structure compared with all the others, following these criteria: log2 fold change > 4 or < -4 and P < 0. 05.
miRNA target prediction To understand the function of selected known miRNAs, based on their abundance, and/or their ability to be differentially expressed, we used the algorithm of the database Targetscan 7. 1 (http://www. targetscan. org/vert_71/) (Shin et al, 2010; Agarwal et al, 2015). For novel miRNAs, we used miRDB (http://www. mirdb. org/). We selected mRNA targets that have particular known functions in the nervous system. The signaling pathway of mRNA targets was analyzed using GO Enrichment analysis (http://www. geneontology. org/).
Available tools As discussed throughout this section, it is in principle straightforward to compute Lagrangian particle trajectories by time stepping the trajectory Eq. (  21 ). One merely needs to save the velocity field and update the trajectories using available software like Matlab or Python, invoking either rudimentary schemes or built-in functions such as the Matlab ode suite. Several research groups have developed their own virtual particle codes tailored to specific model output format, model grid and boundary conditions. Examples include a 3D Lagrangian Matlab code for the MITgcm used by  Koszalka et al. (2013b)  and von Appen et al. (  2014 ) and a 2D Matlab code of The Nonlinear Dynamical Systems Group at ETH Zurich  (Farazmand and Haller, 2012 , http://georgehaller. com/software/software.
html). However, significantly more effort is required to develop an analysis code that features a user-friendly interface and thus can be utilized across the modelling communities. Further work is needed to ensure that the code is efficient on data Input/Output. The suite of available tools can roughly be separated into two sets. First, there are large community based Lagrangian codes such as Ariane, TRACMASS, the Connectivity Modelling System (CMS), and the new Parcels code. These are model-independent, run offline (i. e. , on stored velocity data) and provide extensive control on particle behaviour. The second set includes Lagrangian codes tied to (and sometimes distributed with) specific models, such as MITgcm, HYCOM, NEMO, ROMS and MPAS-O. These model-specific codes can be run online (i.
A case for standard tests of particle tools Most of the Lagrangian particle tracking tools described in Section 3. 5 have never been compared against each other, which makes it hard to assess their skill and fidelity. While most codes are designed for very different purposes, we propose to develop a set of test cases that we suggest code developers to use when debugging codes. This set of test cases would then also serve to highlight differences in explicit versus analytical time stepping codes, for example, or differences between particle tracking on A, B and C grids. While we envision the set to grow over time, the following would be a minimum requirement. A first set of tests to consider are those where analytical expressions are known for trajectories. 1. Radial rotation with known period.
, 2013)  to appropriately quantify sub-inertial motion, e. g. , loopers. 7. For codes that include diffusivity, a simulation of Brownian motion with a given K h and K v to test for sub-grid parameterizations of diffusivity. A second set of tests can be considered that do not have an analytical solution, but that test for speed and efficiency of the code in more realistic idealized test cases corresponding to eddy resolving simulations, e. g. , as are becoming standard in modern climate models. 8. Zonally-periodic baroclinic channel  (Ilıcak et al. , 2012; Berloff et al. , 2009; Abernathey et al. , 2013; Ringler et al. , 2016; Wolfram and Ringler, 2017a; 2017b)  to explore unconstrained eddy and mean flow interactions, e. g. , in an idealized Antarctic Circumpolar Current. 9. Eddying double-gyre flow (Shevchenko and  Berloff, 2015; Wolfram et al.
Highlighted processes internal to the ocean transform water particle density: deep convection; entrainment; enhanced abyssal mixing; eddy stirring; weak background mixing. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) E. van Sebille et al. Ocean Modelling 121 (2018) 49-75  across a broad suite of analysis software, we encourage developers of tools to make the trajectory data CF-compliant, as stated at http:// cfconventions. org/Data/cf-conventions/cf-conventions-1. 6/build/cfconventions. html#discrete-sampling-geometries.
Stable water isotopes are a cornerstone in paleoclimate reconstructions, since their concentrations can be used to infer the source of the water. For example, water vapour that has evaporated from the ocean has low concentrations of heavy isotopes. Likewise, ice on Antarctica also has very low concentrations of heavy isotopes, so it is likely that the ice originates from evaporated water that precipitated over the continent  (Gat, 1996). Hence, an observed decrease in heavy isotope concentrations in the ocean could be due to either less evaporation, or advection of meltwater from ice sheets or land  (Roche, 2013). If we could trace the isotopic composition of water as it moves between oceans, atmosphere, land and ice we could reconstruct the hydrological cycles of past climates. In general, if a Lagrangian code is to exchange particles between ocean and other components of the earth system, it must first deal appropriately with sources and sinks of water within the ocean itself.
A1. 2. TRACMASS The TRACMASS Lagrangian trajectory code was originally developed by  Döös (1995)  and a thorough documentation was given by  Döös et al. (2013)  and  Döös et al. (2017). TRACMASS has been used to calculate trajectories using velocity and tracer fields from a variety of ocean models. TRACMASS has also been used to study the atmospheric Hadley and Ferrell Cells using ERA-Interim as input  (Kjellsson and Döös, 2012a). Hence, TRACMASS can handle a wide variety of vertical grids and data formats. TRACMASS solves the path of a trajectory through a grid box analytically (see Section 3. 2. 3) Trajectories are thus unique and if a trajectory is calculated forward and then backward the solution will be the same up to numerical noise due to round-off errors. There are two algorithms for calculating the trajectories.
A1. 3. Octopus Octopus is an offline particle tracking code first written to conduct offline particle simulation using the Southern Ocean State Estimation  (Mazloff et al. , 2010) , which makes use of the MITgcm. The code was used to study tracer evolution  (Wang et al. , 2016)  observed during the Diapycnal and Isopycnal Mixing Experiment in the Southern Ocean (DIMES;  Gille et al. , 2007; Ledwell et al. , 2011). It was later used in simulating Argo floats as a component of observational system planning for the Southern Ocean Carbon and Climate Observations and Modeling project (SOCCOM, http://soccom. princeton. edu) and in studies of watermass pathways in the Southern Ocean. The interpolation scheme is linear in time and trilinear in space. The RK4 scheme is used for time integration. The boundary condition is reflective at the surface and solid walls.
A1. 4. LAMTA software package The LAgrangian Manifolds and Trajectories Analyser (LAMTA) consists of a set of functions developed for gnu-octave and intended for the analysis of two-dimensional velocity fields, in particular for oceanic current datasets. The source code is freely available and distributed under a GPL license upon direct request to the authors (d'  Ovidio and Nencioli). The package provides routines to compute particle trajectories and Lagrangian diagnostics based on user defined velocity fields (which include analytical test cases, numerical model results and altimetry-based surface geostrophic currents). The trajectories are computed using a Runge-Kutta fourth order advection scheme (Section 3. 2. 1). Particle advection can be performed either forward or backward in time.
, 2015). LAMTA has been recently included in the SPASSO (Software Package for an Adaptive Satellite-based Sampling for Ocean campaigns) software package 14  developed to guide the in-situ sampling strategy as well as the interpretation of collected observations from (sub)mesoscale oriented field experiments. The package has been used to support experiments in the NW Mediterranean (LATEX, e. g. Nencioli et al. , 2011; 2013) , tropical North Atlantic (STRASSE,  Reverdin et al. , 2015)  and Southern Indian ocean: KEOPS2 (d  'Ovidio et al. , 2015)  and LOHAFEX  (Martin et al. , 2013). The code has also been integrated in the package used by Cnes/AVISO to produce global maps of Lyapunov exponents and vectors from altimetry data.
A1. 6. Other Biotic-particle models While the CMS discussed above is also used for physical oceanography applications, the code has been developed as a so-called Individual Based Model (IBM), which serve predominantly biophysical applications. Another widely-used example of an IBM is ICHTYOP  (Lett et al. , 2008 , http:// www. ichthyop. org/). We will not cover IBMs in this discussion, as a very good recent overview can be found in  Lynch et al. (2014).