dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and discussion Two tutorials demonstrating property and bioactivity modelling are available as Additional files 1 and 2, and also within the package documentation. We encourage camb users to visit the package repository (https:// github. com/cambDI/camb) for future updated versions of the tutorials. In the following subsections, we show the results obtained for the two case studies presented in the tutorials, namely: (1) QSPR: prediction of compound aqueous solubility (logS), and (2) PCM: modelling of the inhibition of 11 mammalian cyclooxygenases (COX) by small molecules. The datasets are available in the examples/PCM directory of the package. Further details about the PCM dataset can be found in Ref. [28].
Missing descriptor values were imputed with the function ImputeFeatures. Two filtering steps were then performed: (1) highly-correlated descriptors with redundant predictive signal were removed using the function RemoveHighlyCorrelatedFeatures with a cut-off value of 0. 95, and (2) descriptors with near zero variance and hence limited predictive signal, were removed using the function RemoveNearZeroVarianceFeatures with a cut-off value of 30/1. Prior to model training, all descriptors were centered to have zero mean and scaled to have unit variance using the function PreProcess. After applying these steps the dataset consisted of 1,606 molecules encoded with 211 descriptors. Three machine learning models were trained using 80% of the data (training set), namely: (1) Support Vector Machine (SVM) with a radial kernel, (2) Random Forest (RF), and (3) Gradient Boosting Machines (GBM).
Case study 2: proteochemometrics In the second case study the functionalities of camb are illustrated for proteochemoemtric modelling. The tutorial "PCM with camb" (Additional file 2) reports the complete modelling pipeline for this dataset  [28]. Bioactivity data for 11 mammalian COX (COX-1 and COX-2 inhibitors) was extracted from ChEMBL 16  [2, 28]  (Table  2 ). Only the data satisfying the following criteria was kept: (1) assay score confidence higher than 8, (2) activity relationship equal to '=' , (3) activity type equal to "IC50", and (4) activity unit equal to 'nM'. The mean IC 50 value was taken for duplicated compound-COX combinations. The final dataset comprised 3,228 distinct compounds and 11 mammalian COX proteins, with a total number of 4,937 datapoints (13. 9% matrix completeness)  [28].
A common representation for the compound structures was found using the function StandardiseMolecules with default parameters. Then, two main The area defined by the blue lines comprises 1 LogS units (a) and 1 pIC 50 units (b). Both plots were generated using the predictions on the test set calculated with the Linear Stacking ensembles (Tables  1, 3 ). Overall, high predictive power is attained on the test set for both datasets, with respective RMSE/R 2 0 values of 0. 51/0. 93 (a), and 0. 73/0. 63 (b). Taken together, these data indicate that ensemble modelling leads to higher predictive power, although this increase might be marginal for some datasets (b). descriptor types were calculated: (1) PaDEL descriptors  [14]  with the function GeneratePadelDescriptors, (2) and Morgan fingerprints with the function Mor-ganFPs.
Substructures with a maximal diameter of 4 bonds were considered. The length of the fingerprints was set to 512. To describe the target space, the binding site amino acid descriptors were derived from the crystallographic structure of ovine COX-1 complexed with celecoxib (PDB ID: 3KK6  [42] ) by selecting those residues within a sphere of radius equal to 10 Å centered in the ligand. Subsequently, we performed multiple sequence alignment to determine the corresponding residues for the other 10 COX, and calculated 5 Z-scales for these residues with the function AADescs. Prior to model training, missing descriptor values were imputed (function ImputeFeatures). Two filtering steps were then performed: (1) highly-correlated descriptors with redundant predictive signal were removed using the function RemoveHighlyCorrelatedFeatures with a cut-off value of 0.
95, and (2) descriptors with near zero variance and hence limited predictive signal, were removed using the function RemoveNearZeroVarianceFeatures with a cut-off value of 30/1. Prior to model training, all descriptors were centered to have zero mean and scaled to have unit variance using the function PreProcess. These steps led to a final selection of 356 descriptors: 242 Morgan fingerprint binary descriptors, 99 physicochemical descriptors, and 15 Z-scales. The dataset was split into a training set, which was comprised of 80% of the data, and a test set (20%) with the function SplitSet. Three single PCM models were trained using fivefold cross-validation, namely: GBM, RF, and SVM with a radial kernel (Table  3 ). These models were subsequently combined into model ensembles using (1) greedy optimisation (1,000 iterations), and (2) model stacking (Table  3 ).
Availability and future directions camb is coded in R, C++, Python and Java and is available open source at https://github. com/cambDI/camb. To install camb from R type: library(devtools); install_ github("cambDI/camb/camb"). We plan to include further functionality based on the C++ Indigo API, and to implement new error estimation methods for regression and classification models. Additionally, we plan to further integrate the python library RDkit with camb. The package is fully documented and includes the usage examples and details of the R functions implemented in camb.
Acknowledgements ICC thanks the  Paris-Pasteur International PhD Programme and Institut Pasteur  for funding. TM thanks  CNRS and Institut Pasteur for funding. DSM and RCG thanks Unilever  for funding. GvW thanks EMBL (EIPOD) and  Marie Curie (COFUND)  for funding. AB thanks Unilever and the  European Research Commission  (Starting Grant  ERC-2013-StG 336159 MIXTURE ) for funding.
2005; Kennicutt et al. 2009; Lee et al. 2009; Liu et al. 2011; Hao et al. 2011 ) and investigate the star formation laws and efficiency throughout and across galaxies (e. g. ,  Kennicutt et al. 2007; Leroy et al. 2008; Schiminovich et al. 2010; Schruba et al. 2011; Calzetti, Liu & Koda 2012, among many others). Amid the richness of all these datasets, the limited angular resolution of GALEX (5 ′′ , corresponding to ∼20 pc even in M33 at 840 kpc distance) has prevented linking the largescale star-forming structures to the physical components (clusters, associations, isolated massive stars) that produce the variety of structures observed in galaxies at all redshifts. The HST Treasury Program LEGUS (Legacy ExtraGalactic UV Survey, GO-13364) is designed to bridge the gap between the small-scale and large-scale star formation, by exploiting the combination of high-angular resolution (about 70 times that of GALEX) of HST with the UV capabilities of the imaging cameras aboard the telescope.
Sample Selection The science goals briefly presented in the previous section are the drivers upon which the criteria for selecting the LEGUS sample were built. The 50 LEGUS targets were selected from the ∼400 star-forming galaxies (out of a total of ∼470) in the 11HUGS catalog, which has well-defined completeness properties and is limited within ≈11 Mpc  (Kennicutt et al. 2008; Lee et al. 2011). Use of this catalog as a source of nearby targets offers the advantage that extensive ancillary data are already available in public archives (the NASA Mikulski Archive for Space Telescopes [MAST], and the NASA InfraRed Science Archive  [IRSA] ) and in the NASA Extragalactic Database (NED), which enable leveraging the previous characterization of kpc-scale star formation. The ancillary data include the GALEX far-UV and near-UV images, centered at 0.
As these galaxies tend to be well-known objects, usually with pre-existing wide-field optical images or mosaics in the HST archive, our pointings overlap with and complement the archival ones. NGC 5194 represents an exception to the 'radial' strip criterion: in this case, the location of the pointings was chosen to complement same-Cycle GO pointings in the same or similar filters as those used by our project, in order to maximize the legacy value of the datasets, while still maintaining overlap with the optical mosaic obtained with the Advanced Camera for Surveys (ACS) in previous Cycles. The two remaining extended galaxies, NGC 1291 and NGC 4594, were observed with only one pointing; these are earlytype spirals with lower SFR/area than other galaxies, as determined in the UV from GALEX imaging; the single pointing for each galaxy was chosen to be located on known areas of star formation (outside of the central regions), while overlapping with pre-existing optical images with ACS.
Images and Mosaics The WFC3/UVIS datasets were processed through the calwf3 pipeline version 3. 1. 2 once all the relevant calibration files (bias and dark frames) for the date of observation were available in MAST. The calibrated, flat-fielded individual exposures ("FLT" files) were corrected for charge transfer efficiency (CTE) losses by using a publicly available standalone program 4. These corrections were small because we used the post-flash facility 5 for the F275W, F336W, and F438W exposures to increase the background to a level near 12 e -. At these levels, CTE losses represent a small perturbation on the charge transferred on readout of the CCDs. The resulting "FLC" files were then aligned and combined using the drizzlepac software  (Gonzaga et al. 2012). In summary, we first aligned and combined individual exposures for each filter, then aligned the combined images across filters using the F438W (or F336W) image as the reference frame for the World Coordinate System (WCS).
We then re-combined the exposures for each filter using the solutions determined from the alignments within and across filters to provide the final data products. In detail for WFC3/UVIS data, the individual exposures for each filter were first aligned using the tweakreg routine. The shifts, scale and rotation of individual exposures were solved for using catalog matching to an accuracy of better then 0. 1 pixels. Each catalog typically contains a few hundred sources that are used for determining the alignment solution. The astrodrizzle routine was then used to combine the aligned images for each filter at the native pixel scale. Each image was sky-subtracted 6 , weighted by its exposure time and a Gaussian kernel was used. The resulting cosmic-ray corrected, combined and drizzled images for each filter were then aligned with tweakreg to a common reference frame, using the WCS of the F438W image or the F336W image, if no F438W image was available.
subtraction, exposure time weighting, a Gaussian kernel and the UVIS native pixel scale. The final data products for the WFC3/UVIS data are in units of e -s -1 with a pixel scale of 39. 62 mas pixel -1 and are registered with North up and East to the left. The units of e -s -1 enable a user to convert instrumental measurements to physical units using the WFC3 photometric zeropoints, which are included as keywords in the headers of the data products and are also posted at: http://www. stsci. edu/hst/wfc3/phot zp lbn. Mosaics were made as the final data product for those targets with multiple overlapping pointings (e. g. NGC 4258, NGC 5194+5195, etc. ). The mosaicking was done with tweakreg by aligning the drizzled images in the overlap region and propagating the solutions with tweakback to the FLC images before the final drizzle step.
For ACS/WFC data, the same data reduction procedure was followed. We retrieved the CTE-corrected data from MAST and aligned and drizzled the images using the ACS/WFC native pixel scale of 0. 049 arcsec pixel -1. The combined ACS/WFC images for each filter were then aligned to the UVIS WCS reference frame and re-drizzled to the native UVIS pixel scale to provide final data products that are equivalent to the UVIS data products. It was sometimes necessary to mosaic ACS images together to cover the UVIS field of view because of the different pointings of the archival images. To execute the steps in the data reduction sequence described above, we developed automatic scripts to perform the image alignment. These are based on scripts created for the HST Frontier Fields project  (D. Hammer, priv.
comm. ). The data reduction procedure that was adopted was extensively tested. We verified that data re-sampling through drizzling and rotating the images to N-E has no affect on the photometric accuracy. Differences were well below 0. 1 mag and thus smaller or comparable to (and, for faint sources, smaller than) typical photometric errors. We also compared aperture photometry (performed by subtracting the local background measured in a annulus close to the source) for sky-subtracted and unsubtracted datasets and found no differences above 0. 1 mag.
Stellar Photometry Stellar photometry is being performed on the individual, uncombined "FLC" frames, using the photometric package DOLPHOT 2. 0  (Dolphin et al. 2002) , with the WFC3 and/or ACS module. This package is designed to measure the flux of stars in dithered HST images acquired with the same position angle and small ( 30 ′′ ) shifts between exposures. The aligned FLC files contain the shifts derived by tweakreg as header keywords; we use these as starting points for DOLPHOT, which is then allowed to optimize the shifts among the images using bright stars that are common to all the images. The photometry is carried out independently in each filter. DOLPHOT iteratively identifies peaks and uses PSF models from Tiny Tim  (Krist 1995; Hook & Stoehr 2008)  to simultaneously fit the PSF and the sky to every peak within a stack of images.
25 mag). Using this catalogue, we have performed aperture photometry in all the 5 available bands, with a radius of 4 pixels (corresponding to 0. 16 ′′ , or 4. 1 pc at the distance of NGC 6503, see Table  1 ) and sky annulus with radius of 7 pixels, and 1 pixel wide. This automatic catalogue counts a total of ∼4600 objects, with a CI larger than 1. 2 and detection in at least two contiguous filters with photometric error σ ≤ 0. 3 mag. The photometry is based on the Vega magnitude system. Galactic foreground extinction has been removed using the information available from the NASA Extragalactic Database (NED). Filter-dependent aperture corrections, in the range 0. 7-0. 8 mag, have been estimated using isolated clusters in each frame of NGC 6503, and the published photometry already includes these corrections.
Data Products and Deliverables At the end of the project, a number of high-level products will be delivered to the community. The products will be initially hosted on a dedicated website (legus. stsci. edu) maintained by the LEGUS team; subsequently, the products will be migrated to stable archival platforms: MAST (http://archive. stsci. edu/) and the Hubble Legacy Archive (HLA, http://hla. stsci. edu/), or their successors. For each of the 50 LEGUS galaxies, the high-level data products will include: 1. Combined and aligned images in the 5 LEGUS bands (NUV,U,B,V,I), corrected for CTE losses and registered to a common WCS reference as given by the WFC3/UVIS/B (or U) band. When observations for a given galaxy include a mix of WFC3 and archival ACS images, the registration is performed relative to the WFC3 WCS.
Although the parameters obtained from the fits are provided for all cluster candidates detected in at least four bands, only those parameters derived for the high-fidelity clusters, i. e. , those with class 1, 2, or 3, should be considered reliable. 6. Existing ancillary imaging data for each galaxy. The minimum set of ancillary data includes: GALEX (two bands), SST (seven bands for 47 galaxies; five bands for NGC 1433, NGC 1566, and NGC 6744), WISE (four bands), ground-based R-band and continuum-subtracted Hα (two bands, 41 galaxies). All of these data are already available from either MAST (e. g. , GALEX) or IRSA (SST, WISE, and ground-based). However, the consolidation of the ancillary data will offer a one-stop-shop for the LEGUS galaxies 10.
Summary LEGUS is an HST Cycle 21 Treasury program that is imaging 50 nearby galaxies in 5 broad-bands with the WFC3/UVIS, from the NUV to the I band. The overall science goal is to link star formation across all scales, from individual stars to the multi-kpc scales of whole galaxies, through the full range of structures that newly formed stars occupy. The 'tools' to achieve this goal include, but are not limited to: the investigation of the hierarchical star formation, including dissipation timescales; the evolution and disruption of star clusters; the recent star formation histories of galaxies. LEGUS will also provide a census of the UVbright globular clusters across a range of environments, and a reference database for future identification and study of the progenitors of supernovae. The science results from LEGUS will inform models and investigations of the evolution of the luminous baryonic component of galaxies across cosmic times.
, under NASA contract NAS 5-26555. a Galaxy name, recession velocity, and morphological type as listed in NED, the NASA Extragalactic Database. Inclination, in degrees, derived from the sizes listed in NED. SFR(UV) h M * i M(HI) j Ref k (km s -1 ) (degrees) (Mpc) (PT) (KK) (M ⊙ yr -1 ) (M ⊙ ) (M ⊙ ) (1) (2) (3) (4) (5) SFR(UV) h M * i M(HI) j Ref k (km s -1 ) (degrees) (Mpc) (PT) (KK) (M ⊙ yr -1 ) (M ⊙ ) (M ⊙ ) (1) (2) (3) (4) (5) b RC3 morphological T-type as listed in Hyperleda (http://leda. univ-lyon1. fr), and discussed in  Kennicutt et al. (2008)  for the LVL galaxies, from which the LEGUS sample is derived. In that paper, T-type =11 is adopted for galaxies mis-classified as early types, while being compact irregular or Blue Compact Galaxies (BCGs). Uncertainties on the morphological classification are in parenthesis.
2009 , cyan circles) and of the galaxies with WFC3/UV archival data (magenta circles; including M31; one galaxy, NGC5128, has T=-2. 2, and does not appear in the panel to the left). The LEGUS sample covers the full parameter range of local star-forming galaxy properties, except for the lowest mass bin, which is already well represented in previous HST programs. Fig. 3a. -Footprints of the WFC3 primary observations (magenta), ACS parallels (blue), and, when relevant, of the archival ACS images (red), for the 50 LEGUS galaxies. The footprints are overlaid on the GALEX NUV images of the galaxies, with 20 ′ ×20 ′ size, North up, East left. In the few cases of neighboring/interacting galaxies, two galaxies are shown on a panel (e. g. , NGC1510/NGC1512; NGC5194/NGC5195). The best fits to the 5 LEGUS photometric bands were performed with the algorithm described in  Adamo et al.
These observations are associated with program # 13364. Support for program # 13364 was provided by NASA through a grant from the Space Telescope Science Institute. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. S. d. M. acknowledges support for this work by NASA through an Einstein Fellowship grant, PF3-140105. C. L. D. acknowledges funding from the European Research Council for the FP7 ERC starting grant project LOCALSTAR. D. A. G. kindly acknowledges financial support by the German Research Foundation through grant GO 1659/3-1. A. H. acknowledges support by the Spanish MINECO under project grant AYA2012-39364-C02-1.
J. E. R. gratefully acknowledges the support of the National Space Grant College and Fellowship Program and the Wisconsin Space Grant Consortium. A. W. acknowledges funding from the European Research Council under the European Community's Seventh Framework Programme (FP7/2007-2013 Grant Agreement no. 321323). Properties of the LEGUS Galaxies Sample. Name a v H a Morph. a T b Inclin. a Dist. c Method d Ref e 12+log(O/H) f Ref g.
We can note that this idea of integrating events into processes to make them more flexible is also used in  [8]  for cross-organizational business processes. We have implemented our propositions in PEtALs, an open source Service Oriented Architecture on top of an Enterprise Service Bus technology based on JBI (Java Business Integration), JMS (Java Message Service), XSLT (eXtensible Stylesheet Language Transformation) and web services standards (SOAP, WSDL, BPEL). PEtALs supports mediation and interoperability between software components using adaptors, and provides an engine, called Maestro, for orchestrating BPEL services. In the context of Génépi, PEtALs connects information systems of partners (actors) involved in crisis resolution and orchestrates the CRP. More precisely, partners publish the actions they are able to perform on the impacted world (e.
g. stop a fire) as web services through their information systems. After real world situation analysis, the crisis cell defines a first CRP, which connects the different web services published by partners. This CRP is specified in BPMN and automatically derived in a BPEL process using UML as a pivot model and ATL (Atlas transformation Language) for the derivation  [9]. Then, these elements (i. e. the BPEL process and the Maestro engine) are deployed on PEtALs. When running, the CRP triggers services corresponding to either external services such as web services published by partners, web services implementing interaction protocols as BPEL processes, web services supporting the update of the crisis model after collecting information from actors on the field, or internal services such as the Service Retriever component used for finding services.
ncbi. nlm. nih. gov/sra) under accession number SRP051545. The sequences alignments were done using bowtie  (Langmead et al. 2009 ) based on the remapped QTL region on the genome Galgal4. 0, where about 500• coverage for each of 10 homozygote genotypes was achieved. Furthermore, samtools  (Li et al. 2009 ) was used to perform SNP-calling, and the resulting VCF files were used for further candidate gene analysis (workflow provided in File S1 and sequencing details of the target QTL region provided in Table  S1 ).
2013). Next, SNPs located within the noncoding regions such as RNA splicing sites, and UTR regions were identified and recorded as potential factors affecting gene expression. SNPs in the coding regions that were nonsynonymous mutations were evaluated and ranked according to their effect on the protein function by PASE  (Li et al. 2013 ; https://github. com/xidan21/PASE) (Figure  2 ). Finally, the identified SNPs from each sample were categorized according to their genotypes. The SNPs that are present in, almost, every sample of one QTL genotype group (e. g. , QQ) and absent in the other QTL genotype group are called "consensus SNPs. " These consensus SNPs were chosen as candidate SNPs, and the corresponding genes were prioritized for future analyses.
Data availability Sequence data from this article have been deposited with the Sequence Read Archive (SRA; http://www. ncbi. nlm. nih. gov/sra) under accession no. SRP051545. File S1 contains the script package to perform sequence alignment and SNP calling. File S2 contains the scripts to annotate and prioritize the SNPs while File S3 contains examples of output files and further example scripts. The PASE software is accessible via https:// github. com/xidan21/PASE.
2014). Interestingly, PRDX4, as one member of the antioxidant enzyme family, plays a crucial role in the process of protecting muscle cells against oxidative stress  (Novelli et al. 1991; Leyens et al. 2003). Our previous study has shown that the expression level of PRDX4 is significantly different between the QQ and qq genotype groups  (Nadaf et al. 2014). Subsequently, we used our in-house developed software PASE to evaluate the effect of the nsSNP in PRDX4. As shown in Table  2 , the mutation (T179P) caused by the nsSNP in PRDX4 has a high conservation score (0. 79) and a low physicochemical properties changes score (0. 22). The nsSNP in PRDX4 with a high conservation score is expected to contribute to the considerable difference in PRDX4 expression between the QTL genotypes  (Nadaf et al.