dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of calculated GO-Pfam associations Summaries of our calculated GO MF-domain, BPdomain, and CC-domain associations are shown in Tables  2, 3 , and 4, respectively. These tables show the numbers of distinct GO terms and domain entries (in units of thousands) involved in associations for the 6 source datasets, the filtered GODomainMiner predictions and the InterPro dataset of positive associations. In these tables, the total numbers of GO-Pfam associations found by GODomainMiner refer only to most-specific GO terms in each branch of a GO hierarchy. In other words, if a domain is associated to a GO term and to one or more of its parent terms, only the most-specific (non-parent) term is counted as a found association. The overlap between the GODomainMiner predictions and InterPro is shown in the last row of these tables (here, a match at any GO level is counted as a common association).
For BP GO terms, we find fold-enrichments of 20, 50, and 31 for associations with Pfam, CATH, and SCOP domains, respectively, and for CC GO terms the fold-enrichments are 17, 52, and 31, respectively. A comparison with the Pfam2GO associations from the Gene Ontology website was also performed. It reveals that GODomainMiner retrieves 3966, 3541, and 2055 MF, BP, and CC GO-Pfam associations that were provided by Pfam2GO, respectively. On the other hand, it finds 99 out of 187 MF GO-Pfam associations, 108 out of 256 BP GO-Pfam associations, and 29 out of 65 CC GO-Pfam associations which are present in Pfam2GO but which are not in the InterPro database. These results indicate that GODomainMiner discovers many new associations compared to Pfam2Go and Inter-Pro. This can be explained by the fact that our program does not make any consideration about the possible usage of these associations for protein annotation, whereas InterPro policy is to retain only those GO-domain associations that can be transferred to all the proteins containing a given domain  [18].
Similar results for GO-CATH and GO-SCOP associations are shown in Additional file 1. Finally, Table  5  shows the distribution of GODomain-Miner predicted associations according to our Gold, Silver, and Bronze classification, along with the degree of overlap with the InterPro reference dataset. Since the Gold class represents associations with statistically significant p-values, it is interesting to see that the majority (68%) of our predicted MF GO-Pfam associations common with InterPro fall in this class. Overall, we calculate that 47% of the GODomainMiner MF GO-Pfam associations and 33% of the predicted BP and CC associations are of Gold quality. The quality of GO predictions for CATH and SCOP classifications also follow very similar paths (see Additional file 2).
Comparison with GO-domain associations from dcGO In order to compare the GODomainMiner results with those obtained from dcGO  [6] , we extracted the Pfam2GO associations from the dcGO website  [20]. To avoid the complexity of comparing GO annotations at different levels in the rDAG, our comparison mainly focuses on GOdomain associations in which GO terms are leaves of the GO rDAG. GODomainMiner contains a total of 515,582 GO-Pfam associations regardless of their level in GO hierarchy, of which 79,589 involve leaf GO terms (comprising 21,410 MF, 36,814 BP, and 21,365 CC GO-Pfam associations). The Pfam2GO dataset from dcGO contains a total of 720,534 associations, of which 62,779 involve leaf GO terms (comprising 5939 MF, 24,334 BP, and 32,506 CC associations). Thus, the numbers of associations in GODomainMiner and Pfam2GO are broadly comparable.
However, when considering the leaf levels of all 3 ontologies, Fig. 6  shows that only 11,138 GO-Pfam associations are common between GODomainMiner and dcGO (overlap region B, about 14% of the GODomainMiner set and 18% of the dcGO set). Looking at the overlap with InterPro, which contains 2799 leaf level GO-Pfam associations, GODomainMiner shares 2744 associations (98%) with InterPro, while dcGO shares only 724 associations (26%; overlap C). This shows that GODomainMiner gives a greater coverage of the InterPro reference set than dcGO. Although this is perhaps not surprising since InterPro was used to calibrate GODomainMiner, the high agreement between GODomainMiner and InterPro gives a good indication of the reliability of other associations predicted by GODomainMiner. We also compared GO-SCOP associations predicted by GODomainMiner with the SCOP2GO database from dcGO and with InterPro.
(v) The Pfam domain has no known function (8 associations not present in InterPro). These 8 associations are listed in Table  6  as examples of new knowledge discovered by the CODAC approach. We expect that many further novel associations between MF GO terms and yet uncharacterized domains may be mined from the complete MF GO-Pfam dataset which contains more than 3400 associations concerning so-called DUF (Domain of Unknown Function) or UPF (Uncharacterized Protein Family) Pfam domains. Concerning the strict many-to-one MF GO-Pfam associations, we identified 30 such Pfam domains, most of which have only two associated GO terms. This results in 55 associations of which 7 are known in InterPro (6 gold and 1 bronze) and 48 are new (33 gold, 8 silver and 7 bronze). For one Pfam domain only (CobS, PF02654) the two GO terms are known already in InterPro.
For 5 other Pfam domains, one of the GO terms is known in InterPro and the other one is new. New MF GO-Pfam associations generally give lower scores than known InterPro associations. However, in some cases this suggests an alternative substrate for the domain activity which may be interesting to investigate. For example, for Pfam domain Mqo (PF06039 Malate:quinone oxidoreductase), GO:0052589 (malate dehydrogenase (menaquinone) activity) is found in addition to GO:0008924 (malate dehydrogenase (quinone) activity). The remaining 24 Pfam domains all have new GO MF annotations that do not exist in Inter-Pro. Interestingly, in some cases a different more general InterPro annotation exists, as in the case of PF07722 domain Peptidase_C2 which GODomainMiner associates with GO:0034722 (gamma-glutamyl-peptidase activity) and with GO:0033969 (gamma-glutamyl-gammaaminobutyrate hydrolase) activity, whereas the InterPro annotation is simply GO:0016787 (hydrolase activity).
Conclusion We have presented a systematic approach called CODAC for mining associations from datasets that can be represented as tripartite graphs. We have presented one implementation of this approach called GODomainMiner, for predicting associations between GO terms and protein domains. This was achieved by first collecting existing Pfam, CATH, and SCOP domain annotations of protein chains and sequences on one hand and MF, BP, and CC GO term annotations on the other. We then applied our method to find a list of direct associations between GO terms and domains. Considering only the most-specific GO terms, our approach yields an enrichment of about 15-fold in the number of GO-Pfam associations that currently exist in InterPro. A selected subset of one-to-one and many-to-one associations has been analyzed from a biological point of view, and these all appear to be highly meaningful and consistent with available knowledge.
3 ) Dataset GO-Domain Associations BP GO Terms Domain Entries Pfam CATH SCOP Pfam CATH SCOP Pfam CATH SCOP SIFTS 182 90 53 9. 8 8. 5 6. 8 2. 7 1. 1 0. 7 SIFTS-IEA 197 109 70 7. 6 6. 8 5. 7 4. 9 2. 1 1. 5 SwissProt 1336 461 465 20 18 19 8. 6 1. 2 1. 2 SwissProt-IEA 844 267 302 14 12. 5 13 9. 4 1. 4 1. 3 TrEMBL 837 360 337 13 12 12 8. 3 1. 2 1. 1 TrEMBL-IEA 1756 623 548 18 17 17 12 1. 6 1. 3 Merged 2436 872 764 21 20 20 13 2. 4 1. 8 GODomainMiner 75 23 18 14 8. 6 7. 8 9. 1 2. 1 1. 6 InterPro 3. 829 0. 461 0. 586 1. 094 0. 206 0. 244 3. 265 0. 388 0. 491 Overlap 3. 518 0. 448 0. 572 1. 077 0. 205 0. 244 3. 028 0. 376 0. 480.
2654 gold activity Case (v) Domains of yet unknown function GO:1990838 poly(U)-specific PF09749 Uncharacterised conserved protein 0. 0235 gold exoribonuclease, activity producing 3' uridine cyclic phosphate ends GO:0030144 alpha-1,6- PF15027 Domain of unknown function (DUF4525) 0. 5273 silver mannosylglycoprotein 6-beta-N- acetylglucosaminyl transferase activity GO:0030735 carnosine PF07942 N2227-like protein 0. 2705 silver N-methyltransferase activity GO:0010340 carboxyl-O- PF04301 Protein of unknown function (DUF452) 0. 0201 silver methyltransferase activity GO:0016772 transferase activity, PF01989 Protein of unknown function DUF126 0. 0137 silver transferring phosphorus-containing groups GO:0071617 lysophospholipid PF10998 Protein of unknown function (DUF2838) 0. 0072 silver acyltransferase activity GO:0015666 restriction PF12102 Domain of unknown function (DUF3578) 0.
TemplateMonad is an inductive family (Fig. 3 ) such that TemplateMonad A represents a program which will finally output a term of type A. There are special constructor tmReturn and tmBind to provide (freely) the basic monadic operations. We use the monadic syntactic sugar x ← t ; u for tmBind t (fun x ⇒ u). The other operations of the monad can be classified in two categories: the traditional Coq operations (tmDefinition to declare a new definition,. ) and the quoting and unquoting operations to move between Coq term and their syntax or to work directly on the syntax (tmMkInductive to declare a new inductive from its syntax for instance). An overview is given in Table  1. A program prog of type TemplateMonad A can be executed with the command Run TemplateProgram prog. This command is thus an interpreter for TemplateMonad programs, implemented in OCaml as a traditional Coq plugin.
Run TemplateProgram (TC ← tTranslate ([ ],[ ]) "eq" ; TC ← tTranslate TC "False" ; tImplement TC "notFunext" ((∀ A B (f g : A → B), (∀ x:A, f x = g x) → f = g) → False) where tIntro and tSpecialize are special versions of the corresponding intro and specialize tactics of Coq to deal with extra booleans appearing in the translated terms. After this command, the axiom notFunext belongs to the environment, as if it where added with the Axiom command. But as we have inhabited the translation of its type, the correcteness of the translation ensures that no inconsistency were introduced. Note that we could also define another translation, e. g. the setoid translation, in which FunExt is inhabited. This is not contradictory as the two translations induce two different logical extensions of Coq, which can not be combined.
Related Work and Future Work Meta-Programming is a whole field of research in the programming languages community, we will not attempt to give a detailed review of related work here. In contrast to most work on meta-programming, we provide a very rough interface to the object language: one can easily build ill-scoped and ill-typed terms in our framework, and staging is basic. However, with typing derivations we provide a way to verify meta-programs and ensure that they do make sense. The closest cousin of our work is the Typed Syntactic Meta-Programming  [11]  proposal in Agda, which provides a well-scoped and well-typed interface to a denotation function, that can be used to implement tactics by reflection. We could also implement such an interface, asking for a proof of well-typedness on top of the tmUnquoteTyped primitive of our monad.
For these stars, photometry was carried out on board and only light curves were downloaded to Earth. In addition, twenty 10 × 15 pixel windows were used on each CCD to provide sky reference images and monitor the background level. A further 80 such windows, known as imagettes, were assigned to selected targets of interest, and were downloaded as pixellevel data to enable a finer photometric analysis on the ground. Nominally, the targets in the exoplanet channel have magnitude 11 ≤ r ≤ 16, but a number of brighter stars were also observed, despite being saturated. Most of these were assigned an imagette to enable their photometry to be optimised on the ground. A prism was located in the optical path of CoRoT's exoplanet channel, so that each star produced a 'mini-spectrum' on the focal plane.
Some targets were thus observed twice or even three times a few months or years apart, with a slightly different instrumental configuration. As a consequence, the photometric mask used to perform the on-board photometric measurements typically varied from one observation to the next, changing the contamination of the aperture (the fraction of measured flux coming from other stars in the vicinity of each target). Table  1  also provides an estimate of the median photometric precision at R = 14. 0. For these estimates, we applied a one-hour duration non-linear filter to all light curves in a given run, ignoring obvious outliers flagged by the CoRoT data reduction pipeline such as SAA crossing. The scatter on each light curve was then estimated on these detrended light curves as the median from the median of a running window of three hours duration  (Hoaglin et al.
Furthermore, the mission lifetime was extended by three additional years in 2009, and again in 2012 (although this was ultimately cut short by the failure of the second data processing unit, DPU2). This led to the selection of new long and short run pointings, for which no dedicated ground-based observations were available, and once again we had to rely on published catalogue information. The final release of the CoRoT light curves  (Chaintreuil et al. , 2016)   1  was accompanied by a complete update of the database of spectral classifications for all potential exoplanet targets in the continuous viewing zones (ExoDat), which is described in  Damiani et al. (2016). To overcome the incomplete coverage of the dedicated ground-based observing programme, the final version of ExoDat is based on the PPMXL catalogue, which combines USNO-B1.
This was done using a purpose-written software package developed in Oxford and written in Python. This package was initially developed to help prioritise transit candidates and optimise the follow-up. It has been used in this manner since 2010, although it has evolved somewhat since it was first used. For the present paper, we re-ran the latest version of the code on all the transit-like events identified since the start of the mission, resulting in a homogeneous set of transit parameter estimates. The main lines of this analysis are described in the following sections. All results, either plots or tables, are provided online, as html pages per field and per candidate.
It is important to bear in mind, however, that the limb-darkening coefficients used may not be appropriate for some objects. The fit was performed using an implementation of the Levenberg-Marquart algorithm for non-linear least-squares regression adapted for Python from the the Idl program Mpfit. Once these physical parameters have been obtained, the individual transit events are fitted, allowing only the time of transit centre to vary, and the ephemeris is refined using a linear fit to the times of transit centre. We repeated the process of variability removal, global and individual fits, until all the parameters have converged, meaning that their values change by less than their formal uncertainties (which are derived from the covariance matrix of the fit). At that point, we also computed the orbital inclination, stellar density and stellar radius, using the equations of  Seager & Mallén-Ornelas (2003)  and assuming a power-law stellar mass-radius relation with an index of 0.
Follow-up observations of the candidates Ground-based photometric and spectroscopic follow-up observations formed a key part of the CoRoT exoplanet programme. Photometry taken during and just outside the transits (on-off photometry) with larger telescopes at higher spatial resolution, was used to confirm whether the transits occurred on the main target or a fainter nearby star. High contrast imaging helped identify background binaries or physical triple systems further. Radial velocity (RV) measurements allowed us to identify objects with multiple sets of spectral lines, and to measure the masses of any actual planets, together with the eccentricity of their orbits. These or additional spectroscopic data were also used to estimate the fundamental parameters (effective temperature, gravity, mass, radius, and age) of the target stars.
Some would still have had F V or F sec , but these are the few exceptions with genuinely high impact parameter or marginally detectable secondary eclipses. On the other hand, taking at face value the observation that none of the confirmed planets had more than one flag set (except for CoRoT-36b/LRc07 E2 0307,  Grziwa et al. in prep. ) , one might conclude that follow-up observations could have concentrated on the candidates that received at the most one flag. This would have reduced the number of follow-up targets by 36%. However, the more salient conclusion of this exercise is that the flag system that we defined is too blunt a tool for reliably distinguishing between genuine planetary transits and astrophysical false positives reliably. A more nuanced approach is required, including for example a probabilistic assessment of the planetary nature of each candidate, as proposed by  Morton (2012)  and implemented in the vespa package  (Morton, 2015).
Assuming a conservative value of 90% for the detection completeness gives an occurrence rate of 0. 98 ±0. 26%. This result is in agreement with the occurrence rates estimated for Hot Jupiters from radial velocity surveys  (Wright et al. , 2012; Mayor et al. , 2011)  but not with the estimates for Kepler data  (Howard et al. , 2012; Fressin et al. , 2013; Santerne et al. , 2016). The later range from 0. 4 to 0. 5% for F, G, and K dwarfs, that is less than our finding for CoRoT data. We note that separating giants detected in the anticentre fields from those in the centre ones, we found no significant difference in the occurrence rates of the close-in giants which are 0. 96 ± 0. 37 and 0. 99 ±0. 31 respectively. Additionally, three giant planets discovered by CoRoT CoRoT-10b  (Bonomo et al. , 2010) , , and CoRoT-9b  (Deeg et al.
Acknowledgements. The French team thanks the  CNES  for its continuous support on the CoRoT Exoplanet programme, that included grants  0879 ,  98761 ,  426808 , and  251091. The authors wish to thank the staff at  OHP  for their support during their numerous nights on SOPHIE, the staff at  ESO La Silla Observatory  for their support and for their contribution to the success of the  HARPS  project and operation. The team at the IAC acknowledges support by grants  ESP2007-65480-C02-02  and  AYA2010-20982-C02-02 ,  AYA2012-39346-C02-02  and  ESP2015-65712-C5-4-R , all of the  Spanish Secretary of State for R&D&i (MINECO). The CoRoT/Exoplanet catalogue (Exodat) was made possible by observations collected for years at the  Isaac Newton Telescope (INT) , operated on the island of  La Palma  by the  Isaac Newton group in the Spanish Observatorio del Roque de Los Muchachos  of the  Instituto de Astrophysica de Canarias.
Over 3,000 images from the Janelia Farm FlyLight collection were registered using the proposed strategy. These registered expression patterns can be searched and compared with a new version of the BrainBaseWeb system and BrainGazer software. We illustrate the validity of our methodology and brain atlas with registration-based predictions of expression patterns in a subset of clock neurons. The described registration framework should benefit to brain studies in Drosophila and other insect species.
Our results highlight the benefits of statistically representative templates in terms of precision and accuracy and show that the newly proposed atlas outperformed the currently available templates. The new Drosophila adult brain template was applied and validated using thousands of images from the JFRC FlyLight database (http://flweb. janelia. org/cgi-bin/flew. cgi) as well as newly generated images. The resulting atlas and database of registered images are available for browsing, query, and visualization through a user-friendly web interface (http:// fruitfly. tefor. net) and a desktop application providing advanced 3D visualization and querying functionalities.
"Würzburg" Dataset Most of the experiments reported here were performed on 44 brain images from the Würzburg dataset  (Rein et al. , 2002; Jenett et al. , 2006) , consisting of 22 female and 22 male specimens. These were all adult Drosophila brains dissected from 5-day-old flies, stained with nc82  (Wagh et al. , 2006)  and imaged using a Leica TCS confocal microscope equipped with a Leica 20× lens with a numerical aperture of 0. 7. The original data consisted of 8-bit images of 1,024 × 1,024 × 200 voxels with a voxel size of 0. 6 × 0. 6 × 1. 1 µm. A label image was associated with each nc82 image, indicating for each voxel its localization within one out of 14 anatomical regions that had been manually delineated by an expert: left/right medulla, left/right lobula, left/right lobula plate, left/right mushroom body, ellipsoid body, noduli, fanshaped body, protocerebral bridge, and left/right antennal lobe (see Supplementary Figure  1 ).
Confocal Imaging Whole-mount brains were mounted on a 76 × 26 mm glass microscope slide (KnittelGlass) to which pairs of Paper Reinforcement Rings had been applied. The samples were mounted in Prolong Gold mounting solution (Invitrogen). Spacers were covered with a cover glass (KnittelGlass, #1 thickness, 0. 17 mm) held in place by nail polish. Image acquisition was done sequentially on a Leica TCS SP8 upright microscope with a 25× 0. 95 NA plan-apochromat water immersion objective. The original image data consisted of 1,024 × 1,024 × ∼ 200 voxels, with a voxel size of 0. 60 × 0. 60 × 0. 98 µm. Images were acquired with a 12-bit dynamic range. A frame average of two successive scans was applied. Fluorescence emission from the 488, 547, and 647 nm was imaged using the 488, 561, and 633 nm lasers, respectively.
Image Registration and Atlas Generation Group-wise intensity templates were constructed with the Advanced Normalization Tools (ANTs) software  (Avants et al. , 2011) , using an algorithm building an average shaped brain that follows a two-step strategy. First, the intensity images of all brains used for building the atlas were aligned onto a randomly selected brain using rigid transforms (rotations and translations), which were determined by maximizing the mutual information (a criterion measuring the structural congruence between two images) using a multi-resolution iterative gradient descent algorithm. The aligned images were then voxel-wise averaged to create an initial, blurry average brain. Next, all individual brains were warped non-linearly on this average using symmetric diffeomorphic image registration (SyN)  (Avants et al.
, 2008)  with cross-correlation as similarity metric. A new average was calculated by combining the co-registered brains. The nonlinear registration was refined by repeating this step at four resolution levels to converge to an optimal average template. This algorithm was run through the buildtemplateparallel. sh (Avants et al. , 2010)  ANTs script. For combining the co-registered images into a final template image, we experimented with the default ANTs strategy (state-ofthe-art method in MRI), which generates a normalized average image by voxel-wise averaging followed by sharpening with a Laplacian kernel. In addition, we implemented in Matlab an alternative strategy in which the template intensity image was generated by computing a voxel-wise median over the coregistered images, with no subsequent sharpening step.
The anatomical label image associated with the template was obtained by applying to each individual label image the diffeomorphic transformations computed from the corresponding reference (nc82) image, followed by a pervoxel majority voting over all warped label images. Labels were interpolated using the nearest neighbor method. Individual (non atlas) brain images were registered against atlas templates using the antsIntroduction. sh ANTs script  (Avants et al. , 2010) , which performs an initial rigid registration with mutual information as similarity metric followed by non-rigid registration with SyN and cross-correlation as similarity measure.