dataesr/scientific-paragraphs-categorization huggingface dataset:
Although impressive progress has been made in this research area with the help of moderately deep observations of cluster lenses (e. g. ,  Postman et al. 2012) , a dedicated in-depth observational effort is needed if the scientific promise and potential of gravitational lensing by clusters is to be fully exploited. This important next step forward is now being taken in the form of the Hubble Frontier Fields (HFF), a recent initiative launched by the Space Telescope Science Institute. As part of the preparations for these unprecedented observations of lensing clusters, five independent teams have analysed the existing imaging and spectroscopic data to provide the community with accurate mass models on each cluster. We describe in this paper the work performed by one of these groups, the CATS (Clusters As TelescopeS) team.
Spectroscopy of strong-lensing features Whenever possible, we use spectroscopic redshifts of multiply imaged systems to constrain the lens model. This information is important since the source redshifts are degenerate with the absolute values of the model parameters characterising the mass distribution. All our models are anchored by spectroscopic redshifts for two to eight different multiple-image systems. We use spectroscopic redshifts from the literature, results shared among the different HFF mass modelling groups, and values obtained by us as part of dedicated spectroscopic follow-up observations. In the latter case we provide below the details of the observations, data reduction, and analysis of these spectra.
AS1063 We have obtained spectroscopy of images 1. 1 and 1. 2 using multiobject spectroscopy with Magellan/LDSS3 on the night of October 11th 2009. We used LDSS3 with 1. 0"-width slits and the VPH-All grism, which altogether provide a resolution of 650 and a dispersion of 1. 9 Å per pixel while covering the wavelength range 3800-9900 Å. The seeing was good (0. 7 -0. 9 ) during the 5. 4 ksec total exposure time of these observations. The LDSS3 spectra were calibrated, combined and extracted using standard IRAF and IDL routines. Both images AS1063-1. 1 and 1. 2 show a strong emission line at wavelength 8307 Å and other fainter emission lines, compatible with [OII] and [N eIII] at a spectroscopic redshift z = 1. 229 ± 0. 005. Additional spectroscopy was obtained on VLT/FORS2 the night of August 21st 2013, as part of the ESO program 291.
A-5027 (PI:Richard). We designed a multi-slit spectroscopic mask covering images 2. 1, 3. 1, 4. 3, 6. 2, 6. 3, 6. 4  in the strong lensing region and we obtained 4. 5 ksec of exposure time under bright time but good seeing conditions (0. 8"-0. 9"). We used the 600z grism, OG590 order filter and 1. 0"-wide slits to cover the wavelength range 7200-9800 Å at a resolution ∼1500-1900 and a dispersion 1. 6 Å per pixel. The FORS2 spectra were reduced using version 4. 9. 11 of the FORS2 data reduction software, and combined using standard IRAF and IDL routines. Both 2D and extracted spectra were visually inspected for faint emission lines and traces of continuum. We identified a clear emission line doublet (also visible in sin- gle exposures) for images AS1063-2. 1, 3. 1 and 4. 3, corresponding to [OII] nebular line at z = 1.
Weak Lensing constraints The background galaxy catalogues for the 6 clusters were derived following the method presented in Jauzac et al. (  2012 ) (hereafter J12). Therefore we here give a brief summary of the different steps. The weak lensing analysis is based on shape measurements in the ACS/F814W band. Following a method developed for the analysis of data obtained for the COSMOS survey, and described in  Leauthaud et al. (2007)  (hereafter L07), the SEXTRACTOR photometry package  (Bertin & Arnouts 1996)  is used for the detection of the sources, using the 'Hot-Cold' method  (Rix et al. 2004,L07). This detection configuration combines an optimal detection of the brightest objects (cold step), and of the faintest ones (hot step). The resulting catalogue is then cleaned by removing spurious, duplicate detections, and any sources in the vicinity of stars or saturated pixels.
Therefore, the identification and the removal of the contaminating unlensed galaxies is crucial. Thanks to the existing HST data for all 6 clusters, a minimum of two colours is available for each of them. Therefore, following the methodology described in J12, we used the existing spectroscopic and photometric redshifts  (Owers et al. 2011; Ebeling et al. 2014)  to calibrate a color-color selection to identify the foreground galaxies and cluster members (the combination of filters used for each cluster is given in Tab. 3. 3). The measure of galaxy shapes is done using the RRG method  (Rhodes et al. 2000) , which was developed for the analysis of data obtained from space, and is thus ideally suited for use with a small, diffraction-limited PSF as it decreases the noise on the shear estimators by correcting each moment of the PSF linearly, and only dividing them at the very end to compute an ellipticity.
Galaxy catalogues We create object catalogues for each HFF cluster using SExtractor  (Bertin & Arnouts 1996)  for the areas shown in Fig. 1 , i. e. , the region within which HST imaging data are available in at least three well separated passbands. Using the F606W images (F625W in the case of A370) as the primary detection band, we run SExtractor in dual-image mode on the images in each passband. The resulting catalogues are combined to create for each cluster a master catalogue of objects. Star-galaxy separation is performed by identifying stars in two separate parameter spaces, namely peak surface brightness vs. flux, and half-light radius vs. flux. We remove from our catalogues all stars and all spurious sources found either to feature a higher peak surface brightness or to be more compact than stars.
Fig. 10  shows the galaxies selected by this process as well as all spectroscopically confirmed cluster members. We compile the latter from various catalogues of spectroscopic redshifts: for A2744, we consult  Owers et al. (2011) ; for MACS J0416, MACS J0717, and MACS J1149, we use the redshifts published by  Ebeling, Ma & Barrett (2013) ; and for AS1063 and A370 we rely on spectroscopic redshifts compiled in the NASA Extragalactic Database (NED). We also use redshifts obtained by Balestra et al. (2013, in prep)  for MACS J0416. As shown in Fig. 10 , the galaxies selected by the dual red-sequence criterion fall into a well defined triangular region of (mF435W-mF606W) vs (mF606W-mF814W) colour-colour space. Galaxies featuring lumi- nosities exceeding MF814W = 0. 01 L * are included in our stronglens mass model as small-scale perturbers (see following section).
Model parametrization Producing a magnification map involves solving the lens equation for light rays originating from distant sources and deflected by the massive foreground cluster. This is ultimately an inversion problem for which several sets of codes and approaches have been developed independently. Our collaboration uses Lenstool 2  (Jullo et al. 2007 ), an algorithm we developed collectively over the years. In this software, the cluster mass distribution can be described as a combination of physically motivated mass components, both for the individual galaxies and the smoother, large-scale haloes. As a basis function for our models, we adopt the dual pseudoisothermal elliptical mass distribution (dPIE, also known as trun-2 publically available on the dedicated web page ID α δ z prior z model [deg] [deg] Table  6.
To this end, we define the total likelihood L = LSLsrc × LW L as the product of the strongand the weak-lensing likelihoods. The strong-lensing likelihood in the source plane is, in turn, defined as the product of M systems of Ni multiple-image likelihoods LSLsrc = M,N i i,j 1 2πµ -1 ij σ 2 SL exp - 1 2 |βij -βi | 2 µ -1 ij σ 2 SL (2) where βij are the source positions of the multiple images, and βi are the barycenter of these positions for system i. For each image, the positional uncertainty in the image-plane σSL is multiplied by the amplification µij. We conduct this optimisation in the source plane as the much more involved computations of an image-plane optimisation would have unduly strained the available computing resources. We find that, for strong-lensing datasets only, sourceand image-plane optimisations yield similar reconstructions for the clusters studied in this work.
ACKNOWLEDGMENTS JR acknowledges support from the  ERC  starting grant CALENDS and the  CIG  grant  294074. MJ, ML and EJ acknowledge the  Mésocentre d'Aix-Marseille Université  (project number : 14b030). This has also benefited from the facilities offered by  CeSAM (Centre de donneS Astrophysique de Marseille 3. ML acknowledges the  Centre National de la Recherche Scientifique (CNRS)  for its support. JPK and HA acknowledge support from the  ERC  advanced grant LIDA. This work was supported by the  Leverhulme Trust  (grant number  PLP-2011-003 ) and  Science and Technology Facilities Council  (grant number  ST/F001166/1 ).
The emergence of Next Generation Sequencing generates an incredible amount of sequence and great potential for new enzyme discovery. Despite this huge amount of data and the profusion of bioinformatic methods for function prediction, a large part of known enzyme activities is still lacking an associated protein sequence. These particular activities are called "orphan enzymes". The present review proposes an update of previous surveys on orphan enzymes by mining the current content of public databases. While the percentage of orphan enzyme activities has decreased from 38% to 22% in ten years, there are still more than 1,000 orphans among the 5,000 entries of the Enzyme Commission (EC) classification. Taking into account all the reactions present in metabolic databases, this proportion dramatically increases to reach nearly 50% of orphans and many of them are not associated to a known pathway.
We extended our survey to "local orphan enzymes" that are activities which have no representative sequence in a given clade, but have at least one in organisms belonging to other clades. We observe an important bias in Archaea and find that in general more than 30% of the EC activities have incomplete sequence information in at least one superkingdom. To estimate if candidate proteins for local orphans could be retrieved by homology search, we applied a simple strategy based on the PRIAM software and noticed that candidates may be proposed for an important fraction of local orphan enzymes. Finally, by studying relation between protein domains and catalyzed activities, it appears that newly discovered enzymes are mostly associated with already known enzyme domains. Thus, the exploration of the promiscuity and the multifunctional aspect of known enzyme families may solve part of the orphan enzyme issue.
Review New progress in sequencing technologies generates thousands of new sequences each day. With the large public sequence databases combined with efficient bioinformatic methods, it is possible to predict the function of some new proteins mainly by comparative genomics approaches. Nevertheless, millions of protein entries are not assigned reliable functions due to the lack of trustworthy annotations and the drawbacks of homology-based predictions  [1]. This shortcoming illustrates our limited knowledge of the functional diversity in the protein world and restricts the analyses of an organism starting from its genome. This is particularly the case for enzymatic activities that can be predicted by gene functional assignments and used as a starting point to reconstruct genome-scale metabolic models.
He noticed that many EC numbers (38% among 3,736 entries) were lacking an associated nucleic or protein sequence in public databases, a problem that hadn't been really considered before by the scientific community. He observed that his estimation could be biased as the EC classification does not cover all known enzymatic activities. Indeed, in sequence databases, some entries are missing an EC number even if a correct textual description of the enzymatic activity is annotated. He proposed to take advantage of the numerous accessible sequenced genomes and to cross this genetic information with published experiments that have characterized the enzymatic activities. This first data mining step should identify some candidate proteins which could be experimentally validated. In 2005, sequence-lacking enzymatic activities were named "orphan enzymes" by Bernard Labedan and Olivier Lespinet in an open letter  [9].
They conducted a similar analysis to that of Peter Karp and showed that 42% of the EC numbers were orphan enzymes (1,625 EC numbers among 3,820). One of the main surprises of this study was the fact that 200 organisms had orphan enzymes, despite the availability of their complete genome. They also noticed that, in several cases, the protein catalyzing the enzymatic activity had been identified but not sequenced. The following year they published two exploratory articles on orphan enzymes  [10, 11]. The proportion of orphan enzymes was updated, giving a slight decrease of 3% (39% of orphans, 1,525 EC entries among 3,877). They pointed out that a number of pathways (~100) had at least one orphan enzyme. They also made several remarks on the use of EC numbers. Moreover, they created a public database, called ORENZA, listing all orphan enzymes present in the EC nomenclature and allowing users to perform queries by tracking them between organisms and pathways  [10].
In 2007, Lifeng Chen and Dennis Vitkup carried out a very detailed review on the historical accumulation of orphan enzyme activities and a wide range of statistical analyses on their distribution across different classifications  [12]. They found 1,360 orphans, representing 34% of the 4,003 valid EC entries. They investigated the number of biochemical characterizations per year of discovery and noticed that it decreased in the 1970s and 1990s. A study of the relation between orphan enzymes and their pathway neighbors was conducted: 39% of network neighbors for orphan activities were orphan themselves, compared with 29% for neighbors of non-orphan activities. They also noticed that a majority of orphan activities were found in the most studied organisms. Finally, they pinpointed a possible bias in the EC classification because many reactions in metabolic databases were not associated with any EC number.
Considering this limitation, they estimated that up to 50% of all know biochemical reactions were orphan. Here, we present an extended review on orphan enzyme activities by updating previously conducted surveys and performing new analyses. We first update the estimation of the number of orphan enzymes and interpret their decrease in the light of past and recent enzyme activity discoveries. As the EC classification does not totally cover all known activities, we briefly introduce two main metabolic databases and analyze their content to estimate orphans at the reaction level. Also, an analysis of their connectivity in metabolic network is made. The concept of orphan enzymes is then extended to local orphans (i. e. activities which have no representative sequence in a given clade, but have one in other organisms) and an analysis is made at the superkingdom level to estimate their number and to evaluate if candidate proteins for local orphans could be retrieved by sequence homology.
An updated view of orphan enzymes In this study, we estimated the number of orphan enzymes by using EC numbers present in the IntEnz  [13]  and UniProt  [14]  databases (versions of February 2013). UniProt is a resource of proteins where enzymatic activities are described using the EC classification. Only valid and complete EC entries were considered without taking into account deleted or transferred entries. We also considered as valid entries the nearly 100 provisional EC numbers of IntEnz waiting to be confirmed by the IUBMB. It appears that 22. 4% of the enzymatic activities are orphans; among the 5,096 EC numbers, 1,143 entries have no associated protein in UniProt. As noticed previously  [12] , the proportion of orphan enzymes is not uniformly distributed across the different classes of the EC nomenclature: in EC class 1 the fraction is 25%, 26% in class 2, 19% in class 3 and 4, 15% in class 5 and 13% in class 6 (Additional file 1: Figure  S1.
Before the year 2000, the EC classification was not updated regularly each year, whereas new EC numbers are now created several times a year, suggesting that the Enzyme Commission tries to minimize the time between the publication of a new activity and its EC attribution. Nevertheless, many of these new EC entries correspond to older biochemical characterizations as depicted in Figure  3 , where the delay between activity discoveries and corresponding EC creations is shown. This pitfall limits the search of enzymes in public databases since EC numbers are the only standardized way for scientists to publish an enzymatic activity associated with a protein sequence. Moreover, many recently characterized activities have no associated protein entries, see Figure  4. We can suppose that the annotations of the corresponding proteins were not updated accordingly with the correct complete EC numbers.
This delay of knowledge in databases, which was reported by Yannick Pouliot and Peter Karp in 2007  [15] , remains the case today and it impacts the evaluation of orphan enzymes because numbers of recently discovered enzymes are wrongly considered as orphans. These authors defined a strategy in order to determine which orphans might be salvageable and extrapolated that around 18% of them can be solved with a literature search. At the time of writing, this type of analysis was applied to a wide list of orphan EC numbers  [16]. The authors found protein sequences for about 270 activities among 1,122 putative orphan enzymes that were extracted from databanks in 2009. Using their results and the current knowledge in databanks, protein entries for 112 false orphans could be updated with the corresponding activities and literature evidences.
Unfortunately, since the year 2000 the number of newly discovered activities is not maintained at the established level and starts to dramatically decrease (Figure  5 ). It may be due to difficulties in publishing such biochemical characterizations, and also to the fact that funding is now directed towards other priorities. The gap between the number of sequences present in public databases and the number of characterized enzymes continues to increase dramatically  [17] [18] [19]. In 2010, Hanson et al. pointed out the dual problem of increasing number of proteins of unknown function produced by genome projects, facing the orphan enzymes missing sequence information  [20]. They suggested using experimental data and comparative genomics in order to predict candidate genes.
Besides, a same chemical transformation may be represented by different  Figure  5  The dynamics of enzyme discovery. The solid red line represents the number of enzymatic activities by their year of discovery, which is estimated by using the earliest publication linked to the corresponding EC entries in IntEnz database. If no publication is mentioned, the year of creation of the EC entry is used instead. The dotted green line represents the number of activities associated to a biological sequence for the first time. The year of protein-EC number association is estimated using UniProt's PubMed cross-references and by selecting only articles with less than ten other cited proteins in order to avoid publications related to the sequencing of large genomic regions. The artefact peak in 1961 is due to large number of created entries during the first EC meeting, where many activities were assigned to an EC number without any tractable publication.
EC numbers when, for example, different cofactors are used. This multiplicity between related activities and EC numbers may lead to discrepancies in databases and mask some orphan enzymes. Another point, reported by Green et al. [21] , is the ambiguity in the use of incomplete EC numbers that could lead to enzyme annotation errors in public databases. This is because incomplete EC numbers don't distinguish between the lack of knowledge of the exact substrate specificity of an enzyme and the lack of an official EC number to describe the given activity. Consequently, the use of EC numbers may have introduced some biases in our survey. It should be noticed that the UniProt consortium is making improvements in the representation of the enzymatic activities through Rhea  [22]  and UniPathway  [23]  databases, which are focused on the definition of chemical reactions and metabolic pathways, respectively.
To complete our survey at the chemical reaction level, we performed a study on orphan enzymes using two metabolic databases, named KEGG (version 65. 0)  [24]  and MetaCyc (version 17. 0)  [25]. The comparison of these two databases was extensively reviewed in a recent publication  [26]. As a difference with EC nomenclature, KEGG and MetaCyc make a clear distinction between the chemical reactions and the enzymatic activities. MetaCyc has adopted a formal representation of the relation between proteins and chemical reactions they can catalyze and thus deals with the multiplicity of enzymatic activity-reaction relations. For example, if an enzyme is able to catalyze the same chemical transformation on a wide range of substrates (i. e. the substrate promiscuity of the enzyme), the different chemical reactions will be explicitly linked to the enzymatic activity description.
In other cases, an EC entry may give only a general description of the overall reaction whereas the different steps of this chemical transformation may be more precisely described using several reaction steps. The results of our analysis are summarized in Table  1. About twice as many reactions are found in the two pathway databases in comparison to the ~5,000 EC entries. This high number of reactions is partly due to the multiple relations between enzymatic activities and reactions described above: in KEGG and MetaCyc, there is an average of 1. 15 and 2. 2 reactions per EC number, respectively. Conversely, a large proportion of these reactions correspond to enzymatic activities not described by a complete EC entry, reflecting the previously mentioned delay between an activity discovery and its official classification by the commission.
In KEGG and MetaCyc, there are 4,588 and 4,497 reactions not linked to a complete EC number, respectively. As a consequence and as noted previously  [12, 27] , the percentage of orphan enzymes may be underestimated using only the EC classification. It increases considerably when the estimation is made at the reaction level using metabolic resources: in KEGG and MetaCyc, 48% and 39% of the reactions are lacking associated protein or nucleic sequences, respectively. Enzymes are classically studied through metabolic pathways, which are groups of activities taking part in a same biological process. In this survey, we studied the orphan enzyme content and their connectivity at the pathway level. As described previously  [26] , there are several key differences between the way the databases represent the notion of a pathway: KEGG pathways are a kind of mosaic of similar pathways predicted in different species; in MetaCyc, the overall reactions in a pathway are supposed to occur in a defined group of species.