dataesr/scientific-paragraphs-categorization huggingface dataset:
Performing a shapelet flexion transformation that is accurate to third order therefore increases n max by 9, meaning that a modest n max = 12 shapelet galaxy requires a far more complex n max = 21 model once flexed. The SHAPELETS software suffers extreme memory demands and reductions in speed beyond n max = 20, and so this method of introducing accurate distortions becomes prohibitively slow (see Appendix A4). Unfortunately, re-truncating the shapelet model back to a more manageable n max is not a simple solution to these problems. This degrades the congruence between the exact and shapelet-approximated lensing transformations in a way that is difficult to describe, as it varies with both the distortion applied and the surface brightness distribution of each individual object. Also of concern is the action of the lensing transformations upon the β scale parameter.
Correlated noise The final ingredient in the creation of simulated images is the addition of realistic measurement noise, due to diffuse background light and finite photon number counts. An important consideration for shape measurement is the impact of spatially correlated noise. This is present due to the standard practice of combining multiple, dithered ACS exposures to generate single 'science' images using software such as MULTIDRIZZLE  (Fruchter & Hook 2002; Koekemoer et al. 2002) , and is generic even for more carefully optimized linear combination schemes (e. g. Rowe, Hirata & Rhodes 2011). Such dithered images were used as the basis for weak lensing measurement in each of GEMS, COSMOS and STAGES  (Rix et al. 2004; Heymans et al. 2005 Heymans et al. , 2008;; Leauthaud et al. 2007; Caldwell et al.
Object detection and shapelet decomposition To test and calibrate shapelet measurements of shear and flexion, we treat the 540 ACS simulation tiles as if they were new telescope data (with properties such as galaxy positions and shapes unknown). The first step is therefore to detect objects in the images from peaks in the surface brightness distribution, and we employ the same techniques as described in Appendix A1 to create a catalogue of galaxy objects in the HUDF. We detect galaxy objects in the simulations using the SEXTRACTOR software with the parameter choices given in Table  A1. An initial cut of V 606 < 27 is then applied to the catalogues, along with requiring FLUX RADIUS > 2. 4 and FLUX AUTO/FLUXERR AUTO > 10 (see  Bertin & Arnouts 1996  for descriptions of these SEXTRACTOR output parameters).
While unrealistic, this simplification will allows any measurement biases to be interpreted cleanly rather than being subject to external factors such as poor PSF modelling. The problem of building accurate PSF models is important enough to be addressed in its own right, and this is increasingly reflected in the literature (e. g. Kitching et al. 2011 Kitching et al. , 2013)). The second important difference is in the choice of the NEIGHBOUR input parameter to the SHAPELETS software, setting NEIGHBOUR = 1. As described in Appendix A2, this causes pixels in the masked areas of each postage stamp (i. e. those associated with nearby objects, or cosmic rays, bad pixels, etc. , in real data) to be given zero weighting at the shapelet modelling stage. Having made these changes to the input settings, the modelling provides a catalogue of shapelet coefficient values for each galaxy in the rotated and unrotated simulations.
A2 Postage stamp image extraction The decomposition of stars and galaxy images into shapelet models must be preceded by the creation of 'postage stamp' images of each object in the catalogue, cropped around the object in question, and masked for neighbours. A postage stamp is also made containing a map of the noise and sky background in the same vicinity. For each object, the SHAPELETS software draws circular postage stamps centred on the SEXTRACTOR-measured centroid. The radius of this circular image is the integer number of pixels closest to a value r PS , defined in terms of the user-specified SHAPELETS input parameter NFWHM as r PS = NFWHM × a + 4, where a is the SEXTRACTOR-output semimajor axis of the object. The name of the parameter NFWHM appears to be from an earlier incarnation of the software in which r PS was defined in terms of the FWHM.
We choose NFWHM = 6 in this analysis, the default value of 5 being found to be often insufficiently large to allow the shapelet modelling of the extended outer profiles of deep, space-based images. The initial version of the SHAPELETS software used in this analysis flagged as a modelling failure any objects for which the effective outer boundary of the shapelet model, defined as a locus of radius θ max = β √ n max + 1 (see  Massey & Refregier 2005 ) around the object centroid, extended beyond the edge of the postage stamp. Small postage stamps led to an unacceptable number of model failures due to this extension of light profiles beyond the postage stamp boundaries, but it was found that drawing overly large postage stamps (NFWHM ≥ 7) around every object was computationally prohibitive. An algorithm for iteratively redrawing the postage stamp in the event of such model failure provided an efficient solution to this problem, and is now part of the SHAPELETS software, documented and available for download online at the web location given in footnote 1.
Unlike in the KSB approach, image pixels at a distance from the object centroid are not explicitly down-weighted Downloaded from https://academic. oup. com/mnras/article/435/1/822/1128681 by guest on 30 July 2022 increasing the importance of careful masking. The SHAPELETS software therefore constructs over each object an elliptical mask defined with semimajor axis MASK_NEIGH × a and semiminor axis MASK_NEIGH × b, aligned with the SEXTRACTOR-defined object ellipse. The factor MASK NEIGH = 4 was found to give better results than the shapelet default value of 2. 75: in the default setting there were often portions of the outer galaxy light profile that were unmasked and clearly visible. These caused an enhanced rate of catastrophic modelling failure for the central galaxy of interest in such postage stamps.
The SHAPELETS software also outputs the diagonal entries of the covariance matrix for all shapelet coefficients, and so these are used to combine the models for each star in an inverse-variance weighted average, giving the resulting PSF model seen in Fig. A2. That this model incorporates no spatial variation across the HUDF field-of-view is not a great concern for the purposes of creating a realistic starter set for lensing simulations. Although the bulk effect of image convolution will be largely corrected for, it does mean that some residual ellipticity and flexion will remain in the starter set images due to the residual anisotropy variation in the HUDF PSF. These faint distortions will therefore be retained in the starter galaxy models, but as will be seen in Section 5 these starter models are then randomly rotated, inverted and further distorted before being used in simulations.
These quantities are chosen by an amoeba-driven optimization process (described by  Massey & Refregier 2005;  we note that other implementations of the shapelet method do not necessarily allow n max to vary, e. g. Kuijken 2006 ). However, the SHAPELETS software does require the choice of a limiting maximum value of n max = N_MAX, and a lower starting point N_MIN from which to begin the amoeba search. The choice of N_MAX is largely motivated by computing resource constraints. In general, a significant fraction of overall processing time is spent modelling a very small subset of large/bright galaxies with complex structure. The time taken and memory required to model a given object increases roughly as n 4 max  (Massey & Refregier 2005). Above n max = 20, the calculation of pre-multipliers for the shapelet basis functions (see equation 16) causes numerical overflow in the unsigned, 64-bit integers the SHAPELETS software uses for the calculation and tabulation of factorial terms in the numerator and denominator of P n, m.
For n max > 20, the SHAPELETS software therefore calculates these numbers as double-precision floating point numbers, as and when they are required. This leads to significant processing overheads, albeit ones which might plausibly be averted with some changes to the internal processing design of the SHAPELETS software itself. Table  A2. SHAPELETS software input parameters. Upper section: input parameter settings that influence the construction of postage stamps. Lower section: parameters that control the shapelet modelling and deconvolution. No default value is indicated for parameters added as a modification to the publicly available software. However, the largest, brightest and best resolved galaxies are also the most likely to be local and only very weakly lensed in real data, and this reduces the motivation for dedicating significant amounts of development effort and processing time to producing high-order shapelet models of this population.
We therefore choose N_MAX=20, which nonetheless allows a high degree of galaxy substructural complexity to be realized (e. g. Fig. 5 ). We choose N_MIN =0 so as to give the SHAPELETS code freedom to model galaxies with a simple circular Gaussian if there is no strong statistical support for a more complex model. The other input parameter chosen to differ from the default choice was THETA MIN GEOM = 0. 5. This parameter sets a lower limit on the quantity defined by  Massey & Refregier (2005)  as θ min = β/ √ n max + 1, which is the minimum geometric scale on which the shapelet model varies. The requirement upon the shapelet modelling amoeba of only accepting models with θ min ≥ 0. 5, also suggested by  Melchior et al. (2007) , prevents excessive subpixel modelling that can lead to unphysical 'ringing' in shapelet models of galaxy images.
AC K N OW L E D G E M E N T S The authors would like to thank  Dave Goldberg  for interesting discussions in the course of the work that led to this paper, and the anonymous referee for many helpful suggestions and improvements. BR and CH acknowledge support from the  European Research Council  in the form of a Starting Grant with numbers 240672 (BR) and 240185 (CH). JR and BR were supported by the  Jet Propulsion Laboratory , which is run by  California Institute of Technology  under a contract for  NASA. This work was supported in part by the  National Science Foundation  under Grant No. PHY-1066293  and the hospitality of the  Aspen Center for Physics.
Issues about monitoring hydrological variability Most of the past studies about hydrological variability rely on ship-based CTD or even bottle sample observations being operated only in specific places and on a monthly (at best) to annual basis. The data available since the beginning of the last century (e. g. Painter and Tsimplis, 2003)  revealed that the sea has witnessed interannual and decadal variability as well as abrupt changes. Different timescales were thus involved in those processes. In the light of the above mentioned changes and trends, it is clear that the Mediterranean Sea is a highly sensitive, fast-reacting system. Indeed, the whole Mediterranean region has been defined as a "hot-spot" for climate change  (Giorgi, 2006) , as numerical model projections  (2080-2099 minus 1960-1979)  indicate pronounced mean warming (surface air temperature), large decrease in precipitation, and increase in precipitation variability during the dry (warm) season.
This is a serious limit, given the importance of intense mesoscale dynamics in these areas, which makes the interpretation of data from monitoring programs even more difficult  (Millot and Briand, 2002) , and prevents reliable trend detection from episodic and sparse observations (e. g. Millot and Taupier-Letage, 2005). Even though the task of investigating the long-term variability of hydrological properties by using shipboard CTD data was greatly aided by an EU coordinated action, MEDAR-MEDATLAS (Mediterranean Data Archaeology and Rescue), which managed to facilitate access to the dispersed CTD data sets together with data from bottles, expendable bathythermograph (XBT) and mechanical bathythermograph (MBT) probes and developed a joint comprehensive database of oceanographic data  (1900-2002, Mediterranean and Black Sea)   (MEDAR Group, 2002) , this data set was basically not very suitable to specify long term variations (scarcity of data, high noise levels).
This data set presently constitutes one of the longest, uninterrupted Mediterranean time series (along with the Corsica Channel series, the DYFAMED and the CEFREM -Centre de Formation et de Recherche sur l'Environnement Marin-series). These data were of particular relevance in documenting the propagation of the EMT signature from the eastern to the western basin. The exceptionally large volume of new deep waters of Aegean origin  (Roether et al. , 2007)  produced during the EMT modified the   (Gasparini et al. , 2005). Presently the sub-surface moorings, which measure currents, θ and S at different depths, monitor the exchanges both in the surface and the deep layers. In the bottom layer, moorings are equipped with a high quality CTD probe to detect the hydrological variability in correspondence to the intermediate water layer (Table  2 ).
The recorded time series can be used for studies of long-term changes of the Adriatic response to local climatic forcing and the volume of the dense water formed as a function of the winter heat losses  (Cardin and Gačić, 2003). The amount of the dense water formed also determines the water exchange between the Adriatic and the rest of the EMED  (Manca et al. , 2002; Cardin and Gačić, 2008). The first attempt to deploy a mooring in the area dates back to 2003, when only 9 months of data were recorded. The present mooring has been deployed in November 2006 (Table  2 ) and is equipped also with instruments monitoring the thermohaline characteristics of the intermediate and deep layer. Figure  12  shows the θ time series at 5 different depths. At 1172 m (sampling the resident and the newly formed deep water) a constant increase of θ may be observed between November 2006 and September 2010 (linear trend of ∼ 0.
The joining of a new partner to the network is done on a volunteer basis, and interested new mooring managers just need to contact the coordinators. Missing sites that deserve to be monitored to achieve the objectives of the HC Programme could be for instance: (i) the AW flow between WMED and EMED, in the middle of the Sicily Channel, close to Malta (such a mooring site is already planned by the Physical Oceanography Unit -University of Malta), (ii) other Cretan straits as well as the Bosphorus, (iii) other known or potential DWF sites, such as the Rhodes Gyre  (Malanotte-Rizzoli et al. , 1996) , the northern Adriatic  (Orlić et al. , 1992) , the Gulf of Gabes and the Tripoli canyon  (Gasparini et al. , 2008; CIESM, 2009) , (iv) the along-slope southern sector of the Mediterranean. Existing sensors on long-term observatories should be carefully calibrated and in general there is strong need of increased uniformity within HC, with common sampling strategies (sampling frequency, processing, calibration frequency, archiving) and the set-up of an HC database and meta-database.
Acknowledgements. This is a contribution to the  CIESM HY-DROCHANGES Programme  (http://www. ciesm. org/marine/programs/hydrochanges. htm),which has been initiated by one of the authors (CM). We thank  Frederic Briand , Director General of CIESM (the  Mediterranean Science Commission ), for his continuing support given to the programme since its origin. The authors thank the Collaboration of the  ANTARES deep sea observatory  for providing CTD time series data. In addition, the authors acknowledge the  Observatoire Océanologique de Villefranche sur Mer Service d'Observation and CNRS-INSU  for providing the DYFAMED monthly CTD time series shown for comparison with the HC time series in Fig. 16, as well as the  Royal Navy of Morocco  for their services. Edited by:  N. Pinardi.
Small non-coding RNAs (smRNA) participate in the regulation of development, cell differentiation, adaptation to environmental constraints and defense responses in plants. They negatively regulate gene expression by degrading specific mRNA targets, repressing their translation or modifying chromatin conformation through homologous interaction with target loci. MicroRNAs (miRNA) and short-interfering RNAs (siRNA) are generated from long double stranded RNA (dsRNA) that are cleaved into 20-24-nucleotide dsRNAs by RNase III proteins called DICERs (DCL). One strand of the duplex is then loaded onto effective complexes containing different ARGONAUTE (AGO) proteins. In this review, we explored smRNA diversity in model legumes and compiled available data from miRBAse, the miRNA database, and from 22 reports of smRNA deep sequencing or miRNA identification genome-wide in three legumes: Medicago truncatula, soybean (Glycine max) and Lotus japonicus.
In addition to conserved miRNAs present in other plant species, 229, 179, and 35 novel miRNA families were identified respectively in these 3 legumes, among which several seems legume-specific. New potential functions of several miRNAs in the legume-specific nodulation process are discussed. Furthermore, a new category of siRNA, the phased siRNAs, which seems to mainly regulate disease-resistance genes, was recently discovered in legumes. Despite that the genome sequence of model legumes are not yet fully completed, further analysis was performed by database mining of gene families and protein characteristics of DCLs and AGOs in these genomes. Although most components of the smRNA pathways are conserved, identifiable homologs of key smRNA players from non-legumes, like AGO10 or DCL4, could not yet be detected in M.
Even though genome sequences of three model legumes are not yet completely finished, a very large portion is available in genomic and EST databases to permit gene identification and annotation. However, we cannot exclude that the lack of certain sequences in these databases may be due to its low expression and presence in non-sequenced regions. In the first part of this review, we will focus on smRNA diversity in model legumes, present recent data about the miRNA functions in nodulation and define a novel category of siRNAs, the phased siRNAs, likely associated with defence reactions in legumes. In the second part, we mined public genomic databases and published reports to investigate the conservation and specificities of the main components of the smRNA pathways [DICERlike and ARGONAUTE (AGO) proteins] in the three model legumes.
LEGUME miRNA DIVERSITY AND FUNCTIONS IN NODULATION DEEP SEQUENCING REVEALED A LARGE DIVERSITY OF CONSERVED AND NOVEL miRNAs Since 2006, most miRNAs from animal and plant species have been registered in the miRNA database, called miRBAse (www. mirbase. org/, Griffiths-  Jones et al. , 2006). Taking into account v19. 0 (August 2012) for M. truncatula and G. max and the recently identified miRNAs, from L. japonicus  (De Luis et al. , 2012) , we listed 26 conserved miRNA families (Figure  2 , Table  1 ). Most of them (from miR156 to miR399) corresponded to the set.
We thus searched these miRNAs in available L. japonicus genomic data and identified 5 miR395 and one miR530 genes. In contrast, even after deep sequencing of a large variety of tissues and conditions, miR397 was never reported in M. truncatula. Lack of this miRNA was first noticed by  Sunkar and Jagadeeswaran (2008) , who performed an in silico prediction of conserved miRNAs in genomic sequences from 682 species, including the three model legumes. In agreement with those observations, no miR397 sequence was found in the last version of M. truncatula genome  (Young et al. , 2011). Putative roles of miR397 in G. max and L. japonicas, which develop determinate nodules, will be discussed later. Finally, three conserved miRNAs, first described in A. thaliana, were only found in G. max (miR403, miR828 and miR862) but not in the other legumes.
truncatula (Table  1 ). This was expected as the large soybean genome size, estimated at ∼1115 Mbp, was associated to remnants of a whole genome duplication event, which occurred approximately ∼13 Mya ago in soybean  (Schmutz et al. , 2010). Nevertheless, the ratio between gene numbers in G. max and M. truncatula is generally above two, suggesting that genome duplication was not the only event explaining miRNA diversification in G. max. Surprisingly, for some families, an opposite profile was observed: for instance, miR395 and miR399 genes, generally organized in clusters, were more abundant in M. truncatula than in soybean (18/13 genes for miR395 and 18/8 genes for miR399 according to miRBase). However, searches of miR395 and miR399-like sequences in the G. max genomic database, allowing three mismatches, allowed us to identify 30 and 20 putative members respectively (C.
, 2010). According to miRBase, all, except miR1511, miR1515, miR2111 and miR2118, may be considered as "legume-specific". However,  Zhai et al. (2011)  showed that variants of miR1507 and miR1509 were present in smRNA libraries of non-leguminous species. For instance, miR1507 was highly abundant in grapes. In addition, miR2118 was sequenced in 34 non-legume species, including 4 gymnosperms, suggesting a very ancestral origin (>250 million years). During the last three years, several reports of smRNA library sequencing and genome wide miRNA identification (see Data Sheet 1 for the list) allowed to found hundreds of novel miRNA families in legumes. In miRBAse, 229 and 179 novel miRNA families have been registered for M. truncatula and G. max, respectively, whereas De Luis et al. (  2012 ) reported 35 novel miRNA families in L.
japonicus. In miRBAse, M. truncatula and G. max correspond to the first and third plant species in term of total numbers of miRNA genes, with 675 and 506 genes, respectively. This huge diversity, comparable to, e. g. , rice, may be due to the high number of smRNA deep sequencing analyses performed (9 and 10 reports for M. truncatula and G. max, respectively, listed in Data Sheet 1). In addition, likely "false" candidates have been registered due to low stringency criteria used for miRNA identification in some of the initial studies. For instance, between 2009 and 2011, many miRNAs (including certain identified in our.
Even though their genome sequences are not yet completely finished, a very large portion is available in genomic and EST databases for database mining. For this, we carried out TBLASTX analyses to identify the orthologs of Arabidopsis DCL and AGO proteins in the three legumes for which large genomic databases are available: Lotus japonicus (Miyakogusa. jp 2. 5,  Sato et al. , 2008) , Medicago truncatula  (Mt 3. 5. 1, Young et al. , 2011) , and Glycine max (Glyma1. 181, http://www. plantgdb. org/ GmGDB/). In addition, we compared our results to those already published for rice  (Kapoor et al. , 2008)  and poplar (Populus trichocarpa,  Margis et al. , 2006). The complete list of DCL and AGO genes is given in Data Sheet 3.
truncatula and L. japonicus, while two GmDCL1 genes are present in soybean. The functional significance (if any) of this specific event of DCL1 duplication in soybean remains to be elucidated. In contrast, like in many angiosperms, a duplication of DCL2 was observed both in L. japonicus and G. max. According to  Margis et al. (2006) , this duplication event took place before the divergence between P. trichocarpa and A. thaliana. However, M. truncatula apparently contains a unique DCL2 gene. As the genome sequence is not fully complete in that species, the most probable explanation may be that the second DCL2 locus has not yet been sequenced. However, we were also not able to identify expressed sequences corresponding to a putative second DCL2 homolog, although the M. truncatula EST database is very large (TIGR MtGi11.