dataesr/scientific-paragraphs-categorization huggingface dataset:
The amplifiers are designed for THD better than 78 dB, input-referred in the MDAC closed loop. The designed circuits are verified in the transistor and postlayout levels to validate the proposed digital calibration performance. The very short calibration cycle eases the verification of the calibrated ADC and enables the simulation of the whole ADC in circuits and post-layout levels with a relatively affordable simulation time. To provide more flexibility in the test setup, the digital calibration engine is implemented off-chip, where the digital outputs of the split ADC channels are extracted from the circuit simulations, and then imported into MATLAB to perform the digital post-processing. As the proposed digital  calibration technique requires no feedback from the analog circuits and merely processes the ADC outputs, operating on the off-chip digital outputs of the split ADC verifies the calibration without any loss of generality.
ACKNOWLEDGMENT The authors would like to thank the design team from  STMicroelectronics , including  Roger Petigny ,  Gerard Bret ,  Christophe Curis ,  Fabien Reaute ,  Beatrice Lafiandra ,  Reiner Welk , and  Chandrajit Debnath. The authors are thankful to the TCAS review committee for the valuable comments. This work is supported by the  Research Program Nano2012.
RSAT (Regulatory Sequence Analysis Tools ) is a modular software suite for the analysis of cisregulatory elements in genome sequences. Its main applications are (i) motif discovery, appropriate to genome-wide data sets like ChIP-seq, (ii) transcription factor binding motif analysis (quality assessment, comparisons and clustering), (iii) comparative genomics and (iv) analysis of regulatory variations. Nine new programs have been added to the 43 described in the 2011 NAR Web Software Issue, including a tool to extract sequences from a list of coordinates (fetch-sequences from UCSC), novel programs dedicated to the analysis of regulatory variants from GWAS or population genomics (retrieve-variationseq and variation-scan), a program to cluster mo-tifs and visualize the similarities as trees (matrixclustering).
INTRODUCTION The Regulatory Sequence Analysis Tools (RSAT) is a software suite integrating a wide variety of programs to analyse cis-regulatory elements in genomic sequences. Since its initial development in 1998  (1, 2) , RSAT has provided uninterrupted service and has broadened its applications (Figure  1 ), following advances in the field of regulatory ge-Nucleic Acids  Research, 2015, Vol. 43, Web  Server issue W51 nomics. The suite is organized in a modular way: programs can be accessed individually or interconnected into pipelines to perform more complex analyses. The Web interface combines 52 tools enabling to perform distinct types of analyses (Table  1 ): obtaining sequences, discovering motifs ab initio, scanning sequences to predict transcription factor (TF) binding sites, comparing and clustering motifs, analyzing conservation and divergence of TF binding sites, detecting inter-individual regulatory variations and building control sets based on a wide variety of probabilistic models.
De novo motif discovery in genome-wide data sets RSAT core programs focus on finding putative regulatory signals by detecting exceptional motifs in a set of sequences. These sequences can correspond, for example, to regulatory regions of co-expressed genes obtained from transcriptome profiling (e. g. microarrays, RNA-seq) or regions revealed by epigenomic experiments (e. g. ChIP-seq, ChIP-exo, DNaseI, ATAC-seq) to be likely bound by a given TF or associated with open chromatin. RSAT provides tools to retrieve promoter sequences (retrieve-seq, retrieve-ensembl-seq (6); Table  1 ) from a list of genes. For genome-wide epigenomic data sets, a new program extracts the sequences corresponding to a list of genomic coordinates specified in BED format (fetchsequences from UCSC). The UCSC database is used for this task, as its programmatic access for sequences via DAS is very efficient, although it does not support repeat-masked sequences.
In the future, we will add support for the new programmatic access to Ensembl via REST, which does support repeat-masked sequences. Sequences are then used to perform ab initio motif discovery, based on a variety of criteria: over-represented oligonucleotides (oligo-analysis (1)) or spaced pairs (dyad-analysis (  7 )), positionally biased oligonucleotides (position-analysis (8), local-word-analysis) and differential motif representation between two data sets (oligo-diff). To facilitate analysis of genome-wide data sets, we provide a predefined pipeline (peak-motifs, (9,10)) that performs motif discovery with multiple algorithms, compares the predicted motifs with databases and enables visualization of putative binding sites in the UCSC genome browser. The computing efficiency of 'peak-motifs' enables online analysis of full data sets (several tens of megabases), without size restriction, within a few minutes.
The discovered motifs are usually used in a second step to scan the original set of sequences and locate putative binding sites ('dna-pattern', 'matrix-scan'  (11) ). TF binding sites often form clusters, potentially corresponding to enhancers. Identifying such cis-regulatory modules is achieved in RSAT by predicting cis-regulatory enriched regions (CR-ERs)  (11). Initially embedded within 'matrix-scan', detec-tion of CRERs has been re-designed as an independent program (crer-scan) to increase its computing efficiency and expand its scope. Initially limited to binding sites predicted with RSAT 'matrix-scan', it now takes as input any set of feature coordinates (e. g. annotated sites, ChIP-seq peaks) and detects windows significantly enriched in these features. This quicker version now enables the scanning of genomewide data sets.
Comparing and clustering motifs RSAT proposes an extended support for detailed analysis of motifs represented as position-specific scoring matrices (PSSMs). First, it comprises a program to assess the quality of a PSSM on user-defined sequence data sets by comparing theoretical and empirical score distributions (matrixquality  (12) ). This program has also proven its usefulness to measure the enrichment of genomic regions (e. g. ChIPseq peak sets) for one or several TF binding motifs. Second, there is an increasing demand for comparing motifs of various sources: discovered motifs versus collections of annotated matrices such as JASPAR  (13) , motifs discovered by different algorithms, or in different biological data sets. We have thus increased the computing efficiency of our motif comparison program (compare-matrices).
Detecting regulatory variations Population genomics has given rise to large amounts of genetic variation data in human populations, in some model organisms and in several plants. Furthermore, for Human, Genome-Wide Association Studies (GWAS), which aim at discovering loci and genes associated with diseases, peculiar phenotypes or quantitative traits, have produced over 10 000 single nucleotide polymorphisms (SNPs) with reported trait associations, accessible via the NHGRI catalogue  (14). GWAS results are also available for other organisms (https://easygwas. tuebingen. mpg. de/). Of note, a large fraction of reported SNPs are located outside of protein coding sequences  (15, 16). RSAT now provides a tool to extract genetic variants together with their flanking sequences (retrieve-variation-seq), which can then be scanned with a collection of motifs to predict the impact of the variation on TF binding (variation-scan).
Compatibility with other programs and resources As far as we know, there is no other software suite dedicated to cis-regulation and covering an as wide scope of functionalities as RSAT. The main alternative to RSAT is the MEME suite  (19) , which is limited to motif analyses. MEME and RSAT can be used in a complementary way, as RSAT encompasses the utility tool 'convert-matrix' supporting as input MEME-formatted results. RSAT actually includes several utility tools to ensure inter-conversion between alternative formats for different types of objects: 'convert-matrix', 'convert-seq', 'convert-features', 'convertvariations', 'convert-background-model', 'convert-classes' and 'convert-graph'. These simple programs facilitate interconnections between RSAT and complementary methods, extending the potential usages of the tools.
Taxon-specific public web sites In order to cope with the exponential increase of available genomes, the new RSAT release presents a reconfigured organization of the public Web sites based five servers dedicated to specific taxonomic groups. It is important to note that some types of analyses have become taxon-specific. For example, motif discovery approaches by phylogenetic footprints have proven powerful with Bacteria and Fungi taxa, but remain delusive with Metazoa. Similarly, the methods for detecting regulatory variants depend on the availability of primary data about genetic polymorphism, which is currently essentially available for a few metazoans (human and model organisms) and one yeast (Saccharomyces cerevisiae) genomes. To address the specific needs of user communities and better guide them to the appropriate tools, we set up taxon-dedicated servers, organized according to Ensembl Genomes divisions into Prokaryotes (regrouping Bacteria and Archaea), Fungi, Protists (a multi-clade grouping), Metazoa (merging metazoan species from ensembl.
org, which mainly hosts vertebrates, and ensemblgenomes. org, which hosts non-vertebrate metazoans) and Plants. These taxon-specific servers also provide adapted collections of reference motifs, extracted from specialized databases: Reg-ulonDB for Bacteria  (20) , JASPAR for Metazoa  (13)  and footprintDB for Plants  (21). Furthermore, we are dedicating one server to teaching courses and tutorials (see below), which will provide access to all tools and support representative sets of organisms from each taxon.
Extension of supported organisms In addition to the previously available organisms imported from the NCBI and Ensembl database, we have added support for Ensembl Genomes  (22). As of January 2015, RSAT public servers support 3314 genomes (including 2941 Bacteria, 170 Archaea, 123 Fungi, 40 Metazoa, 18 Plants and 22 Protists).
Installing RSAT in user's operating system The whole software suite can also be downloaded (RSAT Download page: http://teaching. rsat. eu/downloadrequest form. cgi) and installed on Unix-type operating systems (e. g. Linux, Mac OSX). The local installation enables to directly call each program on the command-line interpreter. This presents several advantages: (i) access more tools than presented on the Web sites; (ii) install custom collection of genomes; (iii) automate analyses by integrating the tools in custom scripts; and (iv) parallelize analyses on multi-processor configurations. The downloaded tools also enable to set up a custom Web server, to support the needs of local communities. The drawbacks of the local installation are (i) the requirement to install several programs and libraries whilst ensuring their compatibility with other local resources, and (ii) the need for substantial disk space to store the genomes of interest.
CONCLUSIONS RSAT is possibly the most comprehensive academic suite of programs for the analysis of cis-regulatory sequences. In addition to the core motif discovery programs, which are scalable to genome-wide analyses, RSAT has been expanded to diversify its applications, including comparison and clustering of motifs, regulatory variants analyses and comparative genomics. A key strength is its interoperability with other databases (supports for many motif collections) and web tools (thanks to inter-conversions between file formats). Contrary to many programs that are dedicated to few model organisms, RSAT offers access to thousands of genomes from all kingdoms, facilitated by a new taxonspecific organization of the public servers. Its various modes of access and comprehensive documentation suit the needs of various types of users, from experimental biologists wishing to analyse their data sets without programming skills, to bioinformaticians wishing to integrate RSAT within their automated analysis workflows.
ACKNOWLEDGEMENTS We are particularly thankful to the colleagues who help us in installing and maintaining RSAT servers:  Victor del Moral Chavez ,  Romualdo Zayas-Lagunas ,  Alfredo José Hernández Alvarez  ( Centro de Ciencias Genomicas, Cuernavaca, Mexico ) and  Erik Bongcam-Rudloff  ( BMC, Uppsala, Sweden ). We especially acknowledge  Julio Collado-Vides , who initiated the project in 1997 and supported it during the last 17 years. We thank  Mauricio Guzman  for designing all logos for RSAT and  Benjamin Bardiaux  for valuable technical ideas. We acknowledge  Lionel Spinelli  for his valuable advice about software design and his active participation in discussions about RSAT organization.
The output file can be scanned with the tool 'variation-scan'. Motif discovery oligo-analysis Sequences Over/under-represented Analyses oligonucleotide occurrences in a set of sequences oligonucleotides + PSSM and detects over/under-represented oligonucleotides, using various background models and scoring statistics. dyad-analysis Sequences Over/under-represented Detects over-represented dyads (spaced pairs of dyads + PSSM oligonucleotides) within a set of sequences. NGS ChIP-seq peak-motifs Sequences Discovered motifs + Discovers motifs in ChIP-seq peak sequence sets and predicted sites returns detailed information on sequence composition and discovered motifs, with correspondences in databases and predicted binding sites. Pattern matching * crer-scan Transcription Cis-regulatory enriched Given a set of cis-regulatory elements (predicted sites, factor binding regions (CRER) annotated sites, ChIP-seq peaks), detects regions sites presenting a significant enrichment in CRERs.
The program FRED  [11]  behaves as a fully rigid tool during the sampling step, but it uses a pool of conformers for each ligand. Most other tools explicitly sample ligand conformations during the docking process. Simultaneously, the search engine explores the different positions of the ligand within the rigid binding site, using translational and rotational degrees of freedom of the ligand. FlexX  [12] , Glide  [13] , DOCK  [14]  and Surflex  [15]  are widely used software of the semi-flexible category. Still, the hypothesis of a rigid target is often penalizing, since ligand binding often implies structural rearrangements of the target site, ranging from side chain to whole loop/subdomain displacements. There are different ways to include target flexibility. The most intuitive approach is to use an ensemble of different conformations of the receptor, and to perform semi-flexible docking on each target structure.
Various conformations of the binding site obtained by X-ray or snapshots from molecular dynamics simulation can be used to create the collection of inputs. An alternative way is to generate a single united protein model, as in the FlexE strategy  [16] , from an ensemble of superimposed structures. In this model, the non-conserved regions are treated as alternative locations. Eventually, programs like Autodock  [17, 18]  and Gold  [19, 20]  may explicitly sample target DoF local flexibility, from rearranging polar hydrogens to sidechain rotations. Programs like RosettaLigand  [21] , FlipDock  [22] , FITTED  [23] [24] [25]  and IFD  [26]  allow for both backbone and sidechain flexibility. The real challenge remains to develop algorithms able to screen a database with flexibility in order to simulate conformational changes of the binding site (a phenomenon known as 'induced fit').
These often include hydrogen bonds, ionic bonds and hydrophobic contributions, clashes or even entropic penalties. Weights of each term of this QSAR equation are obtained by a regression analysis on a reference set of receptor-ligand complexes with known binding affinities, or from relative occurrence likelihoods in experimental structural databases. S4MPLE does so far not implement any free-energy scoring function: preferred poses are selected with respect to their force field energies only. This paragraph was inserted because S4MPLE will be compared to state-of-art docking problems which do use them.
Redocking Protocol Using FlexX For direct benchmarking purposes, the program LeadIT (version 2. 0. 2), developed by BioSolveIT including the docking tool FlexX  [12]  was used. Protein site files prepared by MOE lead however to unexpectedly bad results in FlexX simulations (results not shown). In particular, the broad definition of the site seemed to prevent FlexX from efficiently discovering native poses, as no docking constraints were imposed. Therefore, a FlexX-specific site preparation protocol was adopted. Hydrogens were now added with LeadIT, but occasional protein protonation states diverging from the MOE predictions used with S4MPLE were manually restored to the latter. FlexX binding sites included all complete residues with at least one atom within a distance of up to 6. 5 Å with respect to the reference ligand.
It should be noted that this complex is often reported as a failure  [66, 67]. Furthermore, authors of the Astex compilation dataset reported this complex as borderline in term of quality of the ligand's electronic density  [55]. Hence, this case should be most likely classified as 3(b) rather than a FF problem (3(a)). • 1YGC: the most solvent-exposed moiety (arylsulphonamide) adopts a wrong alternative conformation (direct hydrogen-bond with the C58 backbone instead of two water-mediated hydrogen bonds with Y94 and T98), whereas the rest of this large ligand is properly docked. Most of these cases are often described as common failures in other docking papers  [55, 66, 67]. However, all had the native pose visited by S4MPLE, albeit not top-ranked. Yet, only few amongst the above cases are obviously due to FF errors.
Thus, the theoretical applicability range of S4MPLE is broad: from small peptide folding and ligand sampling, to rigid site docking, to classical flexible docking (with moving side chains), to extreme flexible docking (allowing larger movements of protein loops, backbone included), to multiple ligands docking. Therefore, the fact that the herein reported "classical" benchmark tests showed that S4MPLE is neither better nor worse than state-of-the-art software is good news-at equal classical docking performances, S4MPLE is better in terms of its much larger applicability domain. Care was taken to adapt the genetic operators to the specific nature of the energy landscape. Herein used 'driven' mutations or fragment recombinations are much more likely to produce relevant geometries, by contrast to the typical random tinkering with torsional angle values, bound to lead to clash-rich, impossible geometries.
Methods We developed a modelling complex to initially predict the combined impact of environmental parameters and fisheries on global seafood production, and initially evaluated the model's performance through hindcasting. The modelling complex has a food web model as core, obtains environmental productivity from a biogeochemical model and assigns global fishing effort spatially. We tuned model parameters based on Markov chain random walk stock reduction analysis, fitting the model to historic catches. We evaluated the goodness-of-fit of the model to data for major functional groups, by spatial management units and globally.
Here we describe some of the steps used to build a global ocean modelling complex, and evaluate model performance with regard to fit to historic seafood landings. Previously, we developed a global ecosystem modelling complex, EcoOcean  (Alder et al. , 2007) , that was used for global assessments (e. g. Brink, 2010). EcoOcean relied on 18 regional models that jointly covered the world ocean, and while this provided a versatile attempt at global ocean ecosystem modelling, it was a cumbersome approach to work with given the need to populate and analyse 18 food web models in sequence. As a follow-up, we developed a new approach for databasedriven ecosystem model generation in order to construct and evaluate models for each of the world's large marine ecosystems (LMEs;  Christensen et al. , 2009).
A major advantage of this database-and rule-driven approach was automation of much of the model construction and evaluation, and it allowed for inclusion of extensive global data layers, notably as developed by the Sea Around Us project  (Pauly, 2007 , http:// www. seaaroundus. org). Here, we build on the database-driven modelling (i. e. LMEs;  Christensen et al. , 2009)  in order to develop a data and modelling framework as an updated version of the EcoOcean complex. The new model is global and temporal with a variable spatial grid resolution of 0. 5°latitude by 0. 5°longitude; a notable addition is that is driven by temporal-spatial data. For the initial model tuning and testing we aggregated the model processes to 1°latitude by 1°longitude to speed up the development. The model as implemented here covered the period from 1950-2050 with monthly time steps, but in this study we focus on tuning the model so as to obtain capability for hindcasting for the period  1950-2006.
The model was run on a global domain with a spatial resolution of 1°apart from at the equator where resolution was 1/3°. We resampled the model output to 1°l atitude by 1°longitude throughout. Atmospheric forcing was from the Common Ocean-Ice Reference Experiment (CORE-II) data set  (Large & Yeager, 2009)  and covered the period from 1948 to 2006 after a 60-year spin-up. COBALT uses 50 vertical layers, but we aggregated over those, as the spatial food web model is two-dimensional -the depth dimension is considered implicitly through food web interactions and habitat preference patterns. From COBALT, we obtained spatial-temporal output of production rates for three functional producer groups in the model, i. e. for large phytoplankton (nlgp in COBALT), small phytoplankton (nsmp) and diazotrophs (ndi).
Fleet effort distribution We derived effort from a global spatial effort database  (Anticamara et al. , 2011; Watson et al. , 2013)  which covered the period from 1950 to 2006 with country-and fishing gearspecific fleets for a total of 1365 fleets. The effort was standardized across gear types and years in kWh. The database operated with 14 gear types (see Table  2 ), and we used these as 'fleets' in the global food web model. The 'effort creep' , i. e. increase in effort that is related to technological development (e. g. echo sounders and GPS systems) was set to 2% year -1 , which is at the low end of global estimates  (Pauly & Palomares, 2010)  but higher than what was estimated for Greek fisheries  (Damalas et al. , 2014). The global effort database is still under development, and by checking the effort by country and by fleet we found a number of issues that needed consideration.
In some cases, this resulted in changes to the database, with notable issues being: Modelling life and fisheries in the global ocean Global Ecology and Biogeography, 24, 507-517, © 2015 John Wiley & Sons Ltd • Effort for the purse seine, non-tuna boat (fleet 10) in Peru was vastly underestimated, not allowing for the vast expansion that has taken place in anchoveta fisheries since the 1960s. • Where there were missing effort values for certain years by country and by fleet, the missing values were interpolated linearly. • For tuna fleets (fleets 12-14) there were very low efforts in the 1950s; the effort was therefore scaled with 1960 as the base year for these fleets. The relative effort by fleet is shown in Figure  S1  of Appendix S3. In order to consider regional differences in effort for the non-tuna fleets (fleets 1-11 in Table  2 ), the relative fleet efforts were distributed spatially by large marine ecosystems (LMEs;  Sherman et al.