dataesr/scientific-paragraphs-categorization huggingface dataset:
Fertilizers application N and P fertilizers are applied to the first soil layer and depend on the country, the crop type and the cultivated area. N and P fertilizers are applied twice or thrice each year depending on the crop fertilization calendar (Table  1 ). The IFADATA database (IFA, 2016) provides N and P fertilizer data, we consider that half of the inorganic N inputs from fertilizers enters the nitrate pool in the first soil layer, and the remaining half feeds the ammonium pool. On fertilizer days, we estimate the amount of , NH4, and PO4fertilizers applied to each stand on the first soil layer (l=0), though equation 15 as in  Potter et al. (2010) : FERT N,IFA and FERT P,IFA are the annual inputs of N and P fertilizers obtained from the IFADATA database IFA (2016) for each country. AREA is the area of the stand, while AREATOT is the total cultivated area in the country, both obtained from the land use input data of the LPJmL-Med model (cf.
AREATOT pasture and AREATOT nonpasture correspond to the total area of pastures and other cultivated areas, respectively, in the country, obtained from the land use input data of the LPJmL-Med model (Fig. 4 ). In contrast to inorganic fertilizers, we consider that manure is applied twice each year for all crop types except pastures where it is supposed to be spread) uniformly over the grazing period 6 months long in the northern hemisphere, all year long in the southern hemisphere, as described in the crop fertilization calendar (Table  1 ). As data for P manure application are not available from the FAOSTAT database, we estimate it using a constant country-specific (P M AN : N M AN ) ratio provided by  Potter et al. , (2011a Potter et al. , ( , 2011b) )  (cf. equation 17): MAN P = MAN N • (P M AN : N M AN ) (17) where P M AN and N M AN in equation 17 are the total N and P in manure produced in  1994 -2001 , obtained from Potter et al.
Four categories of treatment are considered: no treatment (i = 0), primary/mechanistic treatment (i = 1), secondary/biological treatment (i = 2), and tertiary/advanced treatment (i = 3). The fraction of the total population connected to public sewerage system with treatment i is determined by FRAC sew,i. REM N,i and REM P,i correspond to the fractions of N and P removed from wastewater with treatment i (cf. Table  A1 ). When no data is available concerning the access of population to the different types of sewerage systems, we consider that population can only access primary treatment. The per capita human emissions of N (E N,hum ) and P (E P,hum ) are estimated from the national per capita gross domestic product based on purchasing power parity (GDP P P P ) as shown in equations 21  (Van Drecht et al.
Rivers, dams, and lakes LPJmL-Med divides the soil column into five hydrological active layers of 0. 2, 0. 3, 0. 5, 1, and 1 m thickness  (Sibyll Schaphoff et al. , 2013). Water holding capacity and hydraulic conductivity are derived for each grid cell using soil texture from the Harmonized World Soil Database  (Nachtergaele et al. , 2014)  and relationships between texture and hydraulic properties from  Cosby et al. (1984). Water content in soil layers is altered by infiltrating rainfall, gravity (percolation), and the plant water uptake. The infiltration rate of rain and irrigation water into the soil depends on the current soil water content of the first layer. The surplus water that does not infiltrate is assumed to generate surface run-off. The lateral exchange of water discharge between grid cells through the river is computed via the river routine module implemented in LPJmL-Med model.
Model input The model domain encompasses the catchments of rivers ending in the Mediterranean Sea (see Fig. 2 ). We must note that, since the assessment of some of the simulated variables requires data available at the administrative level, we run the model over the entire area of the Mediterranean countries, i. e. also for catchments not ending in the Mediterranean Sea. The required input data for LPJmL-Med are: (i) gridded climate variables (temperature, precipitation, and radiation); (ii) atmospheric CO 2 concentrations; (iii) gridded soil texture as described in Schaphoff et al. (  2018 ); (iv) gridded land use and crop distribution dataset; and (v) for the present application we have implemented three new inputs quantifying the nutrients load from agricultural management and wastewater release (see below).
Climate and soil inputs The model runs on a daily time step. For the present study, we used monthly climate inputs data (i. e. precipitation and temperature data) from the regional climate model CNRM-ALADIN  (Herrmann et al. , 2011; Farda et al. , 2010; Déqué and Somot, 2008)  in the framework of Med-CORDEX programme (https://www. medcordex. eu/). Here we use the high-resolution version at 12 km (converted to a resolution of 5 by 5 arc-minutes) for the area covered by the domain of the CNRM-ALADIN model (i. e. between 27°N and 57°N in latitudes and between 10°W and 40°E in longitudes). Monthly temperature is linearly interpolated, while a generator based on monthly precipitations and monthly number of wet days provide daily precipitation values  (Gerten et al. , 2004 ). To complete the missing ares (i.
e. southern 27°N) we have used the CNRM-CM5 global model  (Voldoire et al. , 2013) ; from the ESGF data set, https://esgf-data. dkrz. de/projects/esgf-dkrz/). Incident solar radiation is internally computed from the cloudiness data of the Climate Research Unit's (CRU) time series (TS) 3. 1 data  (Mitchell and Jones, 2005). LPJmL works with soil hydrological parameters that depend on soil texture. We use the 13 USDA soil texture classes that are provided by the Harmonized World Soil Database v 1. 2 (FAO, 2012) at 30 arc-second spatial resolution. The original data have been agregated at 5 arc-minute resolution.
Land-use and river network data The land use data for the crops in LPJmL-Med had been compiled from different sources  (Portmann et al. , 2010; Monfreda et al. , 2008; Klein Goldewijk et al. , 2011) , as explained in  Fader et al. (2015 Fader et al. ( , 2010)). Decadal cropland data from HYDE were interpolated to derive annual values and then used for extrapolating the land use patterns of ∼2000 to the past, until 1700. Historical irrigation fractions were determined as explained in  Fader et al. (2010). Further information is given by  Fader et al. (2010 Fader et al. ( , 2015)). Runoff is simulated by the model for each grid cell, and a drainage direction map gives the transport directions of the flows towards the different rivers. The river-routine scheme is derived from the Hydrosheds database  (Lehner et al.
, 2008)  and is updated at 5 arc minute resolution  (Siderius et al. , 2018). The GRanD database  (Lehner et al. , 2011)  provides detailed information on water reservoirs that includes information on storage capacity, total area, and main purpose. Furthermore, information on natural lakes is obtained from  Lehner and Döll (2004). Fertilizers, manure , and wastewater release.
For nutrients, the correlation with data is less favorable, and certainly some aspects in the simulation still need to be improved, regarding both the representation of processes and input data (see below). The poor correlation could also be partly ascribed to the limited number of available long time series of in-situ data, which is well below the number of data for the water discharge. The only long-term data presented in  Ludwig et al. (2003 Ludwig et al. ( , 2009) )  are produced by the combination of in-situ data and the NEWS-DIN model, which makes more complicated the development of modelling approaches and their evaluation. In the case of the Rhone river, the MOOSE observatory system provides regular monitoring of nutrient fluxes in the Rhone. However, in-situ data from the MOOSE program show lower concentrations during the peak of PO 4 in the 1980s as compared to data from  Ludwig et al.
Moreover, the module to simulate nutrient discharge was implemented for the first time in LPJmL-Med model, and it was tested for the first time in this work. Several mismatches between model and observations could be associated with our approach as described above (see Sect. 2), but also to the input data (of fertilizer, wastewater, manure, and land-use patterns used in this study) described in section 2 and 3. In particular, the IFA input time-series considers only national data. Gridded data sets exist, but they are available for specific periods only. Their combination with yearly gridded crop areas and country-level and (when possible) sub-national fertilizer data could enable the establishment of fertilizer input data that better reproduce the spatio-temporal dynamics which could allow a better representation of the spatial differences in fertilizers inputs.
Acknowledgements. This project is part of the  Labex OT-Med  (no. ANR-11-LABX-0061 ) funded by the  French Government "Investissements d'Avenir" program  of the  French National Research Agency (ANR)  through the  A*MIDEX project  (no  ANR-11-IDEX-0001-02 ). This study has been conducted using E. U. Copernicus Marine Service Information. This work has benefited from HPC resources from  GENCI-IDRIS  (Grant  2020-0227 ). We acknowledge  Anne-Sophie Auguères  for her design of the first concept of the land-sea nutrient transfer within LPJmL-Med during her stay at IMBE.
Code availability. The original code of the LPJmL model is publicly available through PIK's gitlab server at https://gitlab. pik-potsdam. de/lpjml/LPJmL. The source code of the adjusted LPJmL-Med version developed in this study and described here are available through osupytheas's gitlab server at https://gitlab. osupytheas. fr/mayache/lpjml-med_version1 The output data from the model simulations described in this study can be obtained from the corresponding authoron reasonable request, and should be referenced as Ayache et al. Data availability. The data associated with the paper are available from the corresponding author upon request. All the data used in this.
Altered transcription in a lamin mutant can be reversed by cec-4 deletion We next examined transcriptional changes in the LMN-Y59C worms and asked whether they were rescued by cec-4 deletion. We performed RNA-seq on whole worm L1 larvae in wild-type, cec-4Δ, LMN-Y59C and the LMN-Y59C; cec-4Δ double-mutant strains (Fig. 7 ), determining significant transcript level changes by edgeR with an FDR < 0. 01 and FC > 1. 5 (Materials and Methods). The transcriptional changes in LMN-Y59C and in the double mutant, while not dramatic, were reproducible between replicate sample pairs (Fig. 7A ), allowing us to identify 187 genes with >1. 5-fold change in transcript levels in each replica of the pairwise comparisons indicated in the heat map legend (Fig. 7B ). There were not many transcript changes meeting this stringent criteria, 49 genes were up-regulated and 48 genes down-regulated >1.
Microscopy Microscopy was carried out on spinning-disk multipoint confocal microscopes (AxioImager M1 [Carl Zeiss] + Yokogawa CSU-22 scan head, Plan-Neofluar 100×/1. 45 NA oil objective, EM-CCD camera [Cascade II, Photometrics], and MetaMorph 7. 7. 2 software or Olympus IX81 + Yokogawa CSU-X1 scan head, PlanApo 100×/1. 45 TIRFM objective, 2× back-illuminated EM-CCD Evol-veDelta (Photometrics), and VisiView software GFP) and mCherry fluorophores were excited using a Toptica iBeamSmart 491-nm and 561-nm lasers. For live microscopy, plates were washed twice with M9 to remove adults and larvae and leave only eggs on the bacteria layer. L1 larvae hatched during 2-3 h and were collected by M9 wash. For live imaging, animals were mounted on slides coated with 2% agarose pads, supplemented with 0.
Image analysis Single-or multiplane (maximum intensity Z projections) images and analysis were generated using Fiji/ImageJ software 6. Quantitation of gwIs4 and cals3 reporter distribution on focal stacks of images was done with plug-in PointPicker (http://bigwww. epfl. ch/thevenaz/pointpicker) as described previously  (Meister et al. 2010a). For high-throughput image analysis, 3D stacks were processed and analyzed as described previously  (Gonzalez-Sandoval et al. 2015; Hauer et al. 2017). For statistical analysis and data representation, only nuclei with two foci were taken into account. Nuclei touching the image boarders at the X-, Y-, or Z-axis were excluded from the analysis. Table operations, data filtering (two foci per nucleus), and normalization were performed with KNIME  (Dietz and Berthold 2016).
0. 153) using the damid. seq. R pipeline  (Sharma et al. 2016 ; available at http://www. github. com/damidseq/ RDamIDSeq). This pipeline identifies and maps specifically those NGS reads that contain the DamID adapters (adapt. seq = "CGCGGCCGAG") and corresponds to genomic fragments flanked by GATC sites (restr. seq = "GATC"). Mapping was performed to the C. elegans genome release WBCel235 (species = "BSgenome. Celegans. UCSC. ce11"). The pipeline provides absolute and relative read counts both per GATC fragment and per user-defined bin size. The number of reads obtained for each replicate in each strain ranged from 35,000 to 32. 3 million (specifically, one was ∼35,000 and all others were >200,000), but all were well above the level required for robust results (10,000 reads) for 10-kb DamID resolution  (Sharma et al.
RNA-seq analysis For each genotype the RNA-seq data from two biological replicate samples were mapped to the C. elegans genome (ce10) using the R package QuasR v1. 22. 0. QuasR includes the short read aligner bowtie to consider only uniquely mapping reads. Count tables of reads mapping within annotated exons in WormBase (WS220) were normalize by division by the total number of reads in each library and multiplied by the average library size. Transformation into log 2 space was performed after the addition of a pseudocount of eight in order to minimize large changes in abundance fold change (FC) caused by low count numbers. The edgeR package v3. 24 was applied to select genes with differential transcript abundances between the indicated genotypes (contrasts) based on false discovery rates (FDR < 0.
Transcription factor motif analysis A compendium of transcription factor weight matrices  (Narasimhan et al. 2015)  was downloaded (http://hugheslab. ccbr. utoronto. ca/supplementary-data/CeMotifs) to examine the set in the file "TF_Information. txt. " These were used to scan the C. elegans ce10 genome using the matchPWM function in the Bioconductor package Biostrings. Only hits with a minimum score of 10 were considered unless the maximum obtainable score by the weight matrix was <10. In that case, the maximum obtainable score was required. The resulting binding sites were then intersected with gene promoter annotation (WS220, 1500 bp 5 ′ , and 500 bp 3 ′ of transcription start site) to determine the number of sites for each transcription factor and gene.
Acknowledgments We dedicate this paper to our dear colleague and coauthor,  Michael Hauer , who passed away during revisions. We thank the  Caenorhabditis Genetics Center (CGC)  funded by  National Institutes of Health Office of Research Infrastructure Programs  ( P40 OD010440 ) for strains. We are grateful to  P. Sartre  for technical help and to  Y. Gruenbaum ,  I. Katic , and members of the  Friedrich Miescher Institute Genomics  and  Microscopy Facilities  for advice and support. J. C. H. was supported by an  FP7 Marie Curie Action Intra-European Fellowship  and by a grant from the  Foundation Suisse de Recherche sur les Maladies Musculaires. A. M. was funded by the same and by a  Marie Heim-Vögtlin  grant from the  Swiss National Science Foundation. C. M. -J. , R. R. -B. , and P. A. thank the  Spanish State Research Agency  and the  European Regional Development Fund  ( BFU2016-79313-P  and  MDM-2016-0687 ) for support.
SciPy is an open source scientific computing library for the Python programming language. SciPy 1. 0 was released in late 2017, about 16 years after the original version 0. 1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics.
Introduction SciPy is a library of numerical routines for the Python programming language that provides fundamental building blocks for modeling and solving scientific problems. SciPy includes algorithms for optimization, integration, interpolation, eigenvalue problems, algebraic equations, differential equations, and many other classes of problems; it also provides specialized data structures, such as sparse matrices and k-dimensional trees. SciPy is built on top of NumPy  1, 2  , which provides array data structures and related fast numerical routines, and SciPy is itself the foundation upon which higher level scientific libraries, including scikit-learn  3  and scikit-image  4  , are built. SciPy is relied upon by scientists, engineers, and others around the world. For example, published scripts  5, 6  used in the analysis of gravitational waves  7, 8  import several subpackages of SciPy, and the M87 black hole imaging project directly cites SciPy  9.
Yet the philosophical motivations behind a fully open tool stack, combined with an excited, friendly community with a singular focus, have proven auspicious in the long run. They led not only to the library described in this paper, but also to an entire ecosystem of related packages  13  , and a variety of social activities centered around them  14. The packages in the SciPy ecosystem share high standards of implementation, documentation, and testing, and a culture eager to learn and adopt better practices-both for community management and software development. In the background section that follows, we capture a selective history of some milestones and important events in the growth of SciPy. Despite what we might highlight here, it is important to understand that a project like SciPy is only possible because of the contributions of very many contributors-too many to mention individually, but each bringing an important piece to the puzzle.
In 1995, Jim Hugunin, a graduate student from MIT, wrote the first message in a new Python Matrix Special Interest Group (Matrix-SIG) mailing list  17  : There seems to be a fair amount of interest in the Python community concerning the addition of numeric operations to Python. My own desire is to have as large a library of matrix based functions available as possible (linear SciPy is an open source package that builds on the strengths of Python and Numeric providing a wide range of fast scientific and numeric functionality. SciPy's current module set includes the following: Special Functions (Bessel, hanker, Airy, etc. ) Signal/Image Processing 2D Plotting capabilities Integration ODE solvers Optimization (simplex, BFGS, Netwon-CG, etc. ) Genetic Algorithms Numeric -> C++ expression compiler Parallel programming tools Splines and Interpolation And other stuff.
algebra, eigenfunctions, signal processing, statistics, etc. ). In order to ensure that all of these libraries interoperate, there needs to be agreement on a basic matrix object that can be used to represent arrays of numbers. Over the next several months, conversations on that mailing list by, among others, Jim Fulton, Jim Hugunin, Paul Dubois, Konrad Hinsen, and Guido van Rossum led to the creation of a package called Numeric with an array object that supported a high number of dimensions. Jim Hugunin explained the utility of Python for numerical computation  18  : I've used almost all of the available numerical languages at one time or another over the past 8 years. One thing I've noticed is that over time, the designers of these languages are steadily adding more of the features that one would expect to find in a general-purpose programming language.
This remains a distinguishing feature of Python for science, and one of the reasons why it has been so successful in the realm of data science: instead of adding general features to a language designed for numerical and scientific computing, here scientific features are added to a general purpose language. This broadens the scope of problems that can be addressed easily, expands the sources of data that are readily accessible, and increases the size of the community that develops code for the platform. The availability of a standard numerical array data structure in Python led to a sudden, rapid growth in the number of scientific packages available for solving common numeric problems. A number of these packages were written by graduate students and postdoctoral researchers to solve the very practical research problems that they faced on a daily basis.
SciPy Begins By the late 1990s, discussions appeared on Matrix-SIG expressing a desire for a complete scientific data analysis environment  19. Travis Oliphant, a PhD student at the Mayo Clinic, released a number of packages  20, 21  that built on top of the Numeric array package, and provided algorithms for signal processing, special functions, sparse matrices, quadrature, optimization, Fast Fourier Transforms, and more. One of these packages, Multipack  22  , was a set of extension modules that wrapped Fortran and C libraries such as ODEPACK  23  , QUADPACK  24  , and MINPACK  25  , to solve and minimize nonlinear equations, integrate differential equations, and fit splines (the latter implemented by Pearu Peterson). Robert Kern, then an undergraduate student (and currently a SciPy core developer), provided compilation instructions under Windows.
Around the same time, Pearu Peterson, a PhD student from Estonia, released F2PY 26 , a command line tool for binding Python and Fortran codes, and wrote modules for linear algebra and interpolation. Eric Jones, while a graduate student at Duke, wrote a number of packages to support his dissertation, including a parallel job scheduler and genetic optimizer. Gary Strangman, a postdoctoral fellow at Harvard Medical School, published several descriptive and inferential statistical routines  27. With a rich programming environment and a numerical array object in place, the time was ripe for the development of a full scientific software stack. In 2001, Eric Jones and Travis Vaught founded Enthought Scientific Computing Solutions (now Enthought, Inc. ) in Austin, Texas. In an effort to simplify the tool stack, they created the SciPy project, centered around the SciPy library which would subsume all the above-mentioned packages.
The new project quickly gained momentum, with a website and code repository appearing in February  28  , and a mailing list announced in June  29. By August, a first release was announced  30  , an excerpt of which is shown in Fig. 1. In September, the first documentation was published  31. The first SciPy workshop  32  was held in September 2002 at Caltech-a single track, two day event with 50 participants, many of them developers of SciPy and surrounding libraries. At this point, scientific Python started attracting more serious attention; code that started out as side projects by graduate students had grown into essential infrastructure at national laboratories and research institutes. For example, Paul Dubois at Lawrence Livermore National Laboratory (LLNL) took over the maintenance of Numeric and funded the writing of its manual  33  , and the Space Telescope Science Institute (STScI), which was in charge of Hubble Space Telescope science operations, decided to replace their custom scripting language and analysis pipeline with Python  34.