dataesr/scientific-paragraphs-categorization huggingface dataset:
LePhare LePhare  (Arnouts et al. (2002) ;  Ilbert et al. (2006) ) is a public template fitting code that uses a χ 2 minimization of differences between observed and theoretical magnitudes to find the best template (galaxy type) and redshift. The code also provides for each object upper and lower 1-sigma limits for this estimate, a maximum likelihood estimate for the redshift, K-corrections and absolute magnitudes for each band and a probability distribution function. An adaptive method can be used to improve theoretical magnitudes, as well as an N (z) prior to minimize catastrophic errors. The effect of emission lines on the theoretical magnitudes can be estimated and taken into account. Several sets of SEDs and extinction laws are available in the code to be used. For this study we use the five MAGDETMODEL magnitudes.
• Templates: A set of 66 SEDs were used in the analysis of the CFHTLS data by  Ilbert et al. (2006)   • Prior: Although the N (z) prior available in LePhare refers to the (B-I) color from the VVDS survey, we verified that applying the procedure to the gi DES color we achieved a significant minimization of the outlier fraction, and therefore we used this prior. • Training: The adaptive training method available in LePhare was used to obtain a re-calibration (zero-points offsets) in each band. This procedure was first applied to the training sets and the shifts obtained were used when the code was run on the testing samples.
ZEBRA The Zurich Extra-galactic Bayesian Redshift Analyser  (Feldmann et al. (2006) ) is a flexible and powerful photometric redshift code, based around template fitting. The code produces a posterior distribution for each galaxy in redshift and template space, P (z, T ), as well as marginalized distributions for P (z) and p(T ). For redshift computation, the filters were smoothed over a scale of 100 Å, and the templates smoothed over 30 Å. The tests were conducted with a redshift resolution in linear steps of ∆z = 0. 01. The five MAGDETMODEL grizY magnitudes were used in this analysis. • Templates: 81 templates were used in ZEBRA's Bayesian mode. These templates were selected from a super-set of SEDs, and consist of the most frequent best-fit templates at z = zspec for galaxies in the training sample.
In both cases, the smaller the value, the closer are the true redshift distribution and its reconstruction through photo-z's. Figure  9  shows these values for all the codes analyzed in Test 1. As expected, the two metrics are strongly correlated. It can also be seen how having a redshift pdf for each galaxy, instead of a single-estimate photo-z, helps a given code to have a better redshift reconstruction. This can be inferred looking at the cases where both the pdf and the single-estimate are displayed (TPZ, ANNz2, BPZ): in all these cases the pdf version of the code obtains better results in terms of these two metrics. As for the results, TPZ and the nearest-neighbor code, NIP-kNNz, show the best performance in this regard. To summarize the results from Test 1, we note that most of the codes presented in this work fulfill the requirements for σ68 and 2σ outlier fraction, while only a few fulfill the 3σ outlier fraction requirement.
Results for DESDM, TPZ, SkyNet and BPZ photo-z codes So far we have compared a large number of photo-z codes in a variety of situations and configurations. Next we look in greater detail at four photo-z codes: DESDM, TPZ, SkyNet and BPZ. The DESDM photo-z code, a regression artificial neural network, is integrated within the DES Data Management service, so its results will be made available together with all the DES data products, making it a clear choice to be studied in detail here. TPZ and SkyNet are state-of-the-art training-based methods using, respectively, random forests and artificial neural networks to compute photo-z's , and yielding the best performance among all the codes utilized in this analysis. Finally, BPZ is the template-based photo-z code showing best performance in the tests previously shown, and it has been widely used by other galaxy surveys such as CFHTLenS  (Heymans et al.
In Fig. 21  we observe how the full redshift distribution reconstructed from the four photo-z codes compares to the spectroscopic distribution. The DESDM code produces one single value for the photo-z of each galaxy in the testing sample while the other three are P (z) codes, so that they return a probability density function (pdf) for each galaxy to be at a given redshift. This is the reason why the N (z) reconstruction looks smoother for TPZ, SkyNet and BPZ, since these are computed from stacking all individual photo-z pdfs. Quantitatively, one can measure how good an N (z) reconstruction is by looking at the Npoisson and KS metrics in Table  6 : the lower these values are, the better is the agreement between the true N (z) and the photo-z-reconstructed one. As for the advantage of using P (z) codes, one can observe in Table  6    in their P (z) versions than in their single-estimate photo-z versions.
In conclusion, the neural networks with regularization perform better than the un-regularized DESDM network while the fact that SkyNet uses a softmax constrained output in combination with a back-propagation algorithm that uses second order derivative information seems to give it the edge over the rest of ANNs. Aside from ANNs, TPZ, which is a state-of-the-art photoz code using Prediction Trees and Random Forests, performs remarkably well in all the tests in this work. The prediction trees and random forest techniques used by TPZ have the advantage that they have fewer hyper parameters to be chosen compared to neural networks. Neural networks have, amongst others, to choose the amount of hidden layers, the amount of nodes per hidden layer, the learning rate and at least one reg-ularization parameter if present.
BPZ and PhotoZ yield the highest photo-z precision among the methods of this class. The reason for this is probably not the template sets they use, which for BPZ is a combination of  Coleman, Wu & Weedman (1980)  and  Kinney et al. (1996) , and a more complex combination of templates for Pho-toZ (see 4. 1), since other codes include similar libraries. The fact that they both use Bayesian priors calibrated on DES data, and not only on previous datasets, can be relevant here, although this makes the result more dependent on how representative the training sample is of the full DES data sample. In order to set up a context for DES photo-z results, it is worth checking the performance obtained by previous similar surveys. Of particular interest is the comparison to the CFHTLS and CFHTLenS (which are two different reductions of the same survey), photo-z results  (Coupon et al.
SUMMARY AND CONCLUSIONS The Dark Energy Survey succesfully installed DECam during the second semester of 2012, starting its operations in November 2012 with a Science Verification (SV) period lasting until February 2013. Among the 150 sq. deg. covered by SV observations, four different fields, of about 3 sq. deg. each, overlap with areas with substantial spectroscopic coverage such as VVDS 02hr or COSMOS. Using grizY photometry for galaxies matched to the existing spectroscopic data in these four calibration fields, this paper presents the photometric redshift performance of the DES survey in the SV period. Most of the relevant photo-z codes have been used in the analysis. Since spectroscopic galaxy samples are generally shallower, a weighting technique is used to make the calibration sample of galaxies to mimic the DES full sample in magnitude and color space in order to properly estimate the photo-z performance in the DES galaxy sample.
Generally, training-based photo-z codes show the best performance in the tests in terms of photo-z precision and bias. Among them, TPZ, using Prediction Trees and Random Forest, and SkyNet, a state-of-the-art Artificial Neural Network application, seem to yield the most accurate results, achieving a core photometric redshift resolution below σ68 = 0. 08. The fact that these two new codes perform better than others extensively used in the literature shows how there is room for improvement in the photo-z industry. On the other hand, all template fitting methods employed show consistent results between them, although the use of Bayesian priors specifically calibrated on DES data and adaptive template-recalibration procedures appears to help significantly. Finally, in the last part of the paper, we choose four photoz codes, representing different techniques and types, and we present a more detailed analysis of their results.
We show some of the most relevant metrics as a function of redshift and, most importantly, we study the estimation of the true redshift distributions N (z) computed using photo-z's. For these four codes, we obtain the true redshift distributions in six photo-z bins. Figure  21  shows the DES photo-z capabilities in such crucial task, and demonstrates the ability to split the DES full sample in tomographic redshift bins of width 0. 2 already with these early data. Furthermore, the calibration of the true redshift distribution of a photo-z selection is the most important ingredient for cosmological studies involving galaxy clustering or weak lensing, and it is an important outcome from this paper, enabling further science analyses. The photo-z analyses carried out in this work using these early stage DES data will serve as a benchmark for future data releases, and as the survey area grows during the observation period, more spectroscopic data will be available allowing a better calibration and a better sampling for training algorithms.
The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2009-13936, AYA2012-39559, AYA2012-39620, and FPA2012-39684, which include FEDER funds from the European Union. J. G acknowledges a fellowship from DS-CAPES Program. This work was partially support by STFC grant ST/K00090X/1. Please contact the author(s) to request access to research materials discussed in this paper. Table  6. Results of all the photo-z metrics listed in Appendix A for all the codes analyzed in Test 1. The errors are computed from bootstrap resampling with 100 samples. The weighting procedure has been applied, together with a cut on the 10% of the galaxies with the highest estimated photo-z errors for each code. 0. 006 ± 0. 003 0. 011 ± 0. 002 0. 078 ± 0. 002 0. 119 ± 0. 006 0.
ACKNOWLEDGMENTS This paper has gone through internal review by the DES collaboration. We are grateful for the extraordinary contributions of our CTIO colleagues and the  DES Camera, Commissioning and Science Verification  teams in achieving the excellent instrument and telescope conditions that have made this work possible. The success of this project also relies critically on the expertise and dedication of the DES Data Management organization. Funding for the DES Projects has been provided by the  U. S. Department of Energy , the  U. S. National Science Foundation , the  Ministry of Science and Education of Spain , the  Science and Technology Facilities Council of the United Kingdom , the  Higher Education Funding Council for England , the  National Center for Supercomputing Applications  at the  University of Illinois at Urbana-Champaign , the  Kavli Institute of Cosmological Physics at the University of Chicago , Finan-This research uses data from the  VIMOS VLT Deep Survey , obtained from the  VVDS database operated by Cesam, Laboratoire d'Astrophysique de Marseille, France.
A set of potential TR unit profiles was obtained from known protein domains and de novo detection of TRs in the human proteome. As the Pfam database  (Punta et al. 2011; Mistry et al. 2013 ) includes many prominent protein domains found in tandem, we used Pfam annotations to look for potential TRs. Currently, ~40% of the human proteomic sequence carry Pfam-A annotations. For all Pfam-A FIG. 1. Tandem repeat unit evolution. (A) A scenario of TR unit evolution for species A and B represented by TR unit phylogeny, where nodes mark either speciation events or TR unit duplications. Abandoned edges mark a TR unit loss. The ancestral TR region is created through duplications of an ancestral subsequence, that is, the unique TR unit at the root of the phylogeny (black). Immediately following the speciation event, exact copies of the TR reside in orthologous proteins in both species (pink and blue), even after some point mutations in TR units the TR still is perfectly conserved, as long as the amino acid identity remains high.
The kth TR unit in A is the closest to the kth unit in B. Subsequent TR unit duplications and losses diminish the conservation of the TR between species A and B. Without point mutations, the more TR unit losses or gains occur, the more TR units begin to cluster by sequence similarity within the same species. (B) The bi-species TR unit phylogeny of a perfectly conserved WD repeat (PF00400) in the human TORC subunit ENSP00000457870 and its yeast ortholog YNL006W. The TR units are indexed by their order along the protein sequence. The depicted phylogeny allows to reconstruct ancient TR unit duplications leading to the currently observed TR regions in fungi and animals before their divergence ~0. 6-1. 6 byr ago  (Taylor and Berbee 2007). (C) The bi-species TR unit phylogeny of a perfectly separated TR in the human NAC-alpha domaincontaining protein 1 ENSP00000420477 and its mouse ortholog ENSMUSP00000049490.
The ancestral protein presumably contained a TR region with multiple repeat units. Yet, the TR region cannot be reconstructed due to the fast succession of TR unit gains/losses in at least one of the lineages. Schaper et al. doi:10. 1093/molbev/msu062 MBE annotations, we constructed cpHMMs, allowing the possibility that any Pfam-A domain might be repeated in tandem. As short and rare TRs are not expected to be part of the Pfam database, we additionally built cpHMMs from de novo TR detections. Because of algorithmic differences, the TR detection by existing de novo TR detection algorithms is highly disparate  (Schaper et al. 2012). Thus, rather than using any single algorithm, we combined the TR predictions of four available algorithms (Materials and Methods). Finally, the cpHMMs were used to annotate human TRs.
These observations were consistent with our GO-term analysis showing that conserved TRs in particular were significantly enriched with protein binding (60%, fig. 4A ). Further, more than a third of all conserved TRs were part of membrane proteins (40%, fig. 4A ). Among the most common TR types in this group were cadherins, ANKs, LRRs, and WD40 repeats (fig. 4A ). Structurally, these TRs are mostly located in the extracellular matrix, or the cytosol, but not within the membrane  (Angst et al. 2001; Hulpiau et al. 2013). Functionally, these TRs may be involved in protein binding outside the membrane  (de Wit et al. 2011; Hulpiau et al. 2013; Mou et al. 2013 ). The most frequent conserved transmembrane TRs are transmembrane helices (TMH; PF00520) in ion channels.
2007). Taken together, proteins with zinc finger TRs can explain most of the enriched GO terms for strongly separated TRs (fig. 4B ). Most of the zincfinger-containing genes are arranged in clusters, and evolve through tandem gene duplications and losses  (Tadepally et al. 2008). Thus, possibly, the same evolutionary gain/loss mechanisms promote the evolution of the zinc finger TRs. Moreover, we noted that single zinc finger TR units often occupied exactly one exon. Although such zinc fingers clearly are repeated in tandem within the protein sequence, the TR units appear disconnected on the DNA sequence. Among the other TRs strongly separated in at least one mammal was the neuroblastoma breakpoint family (NBPF or DUF1220, PF06758), immunoglobulin I-set (PF07679), the calcium-binding EGF (PF07645), and an EGF-like (PF00008) domain repeats.
The Majority of Human Protein TRs Are Highly Conserved The majority of all human TRs are conserved at least within mammals or further back in time-in stark contrast with the rarity of separated TRs. Most likely, these conserved TRs had avoided any recent TR unit changes, with a unit configuration conserved deep into the eukaryotic tree. Thus, the TR duplications that had lead to the original expansion of the TR should be ancient. Indeed, 52 TRs were conserved even between human and yeast, amounting to 13% of all human TRs that have a detectable ortholog in yeast. These ancient, highly conserved TRs include a range of TR types: for example, domain repeats such as the calponin homology (PF00307) and the prenyltransferase (PF00432), solenoid repeats such as WD40 (PF00400) (fig. 1B ), armadillo (PF00514), and also repeats with other structural configurations such as the EF hand (PF00036) (see Kajava 2012 for a structural classification of TRs).
Annotation of TRs in Human Proteins with Circular HMM The complete set of 20,240 gene trees with associated protein sequences from 61 eukaryotic species including human were obtained from Ensembl Compara v69. For all human sequences, TRs were annotated based on: 1) tandemly repeated PFAM A domains and 2) de novo detections. For each PFAM A domain annotated in the Ensembl human proteins, the corresponding sequence profile HMM was obtained from the PFAM database  (Punta et al. 2011). To detect PFAM domains that occurred as TRs, their profile HMMs were transformed into circular profile HMMs, or cpHMMs (fig. 2 ), so that one motif (described by its sequence profile) could be repeated in tandem via a circular transition from the final to the starting state of the HMM. TRs corresponding to PFAM A domains were annotated in human sequences using the Viterbi algorithm applied to cpHMMs (supplementary fig.
Phylogenetic Analysis of TRs within the Eukaryotic Clade For every human TR, we used its cpHMM to annotate homologous TRs in all orthologous (including 1:1, 1:many, and many:many orthologs) genes from other eukaryotes as represented by the Ensembl Compara gene trees. Next, we built MSAs (Multiple Sequence Alignments) for each ortholog pair (supplementary fig. S1 , Supplementary Material online). For each TR MSA that contained at least four TR units in both orthologs, we reconstructed bispecies maximum likelihood TR unit phylogenies using PhyML 3. 0  (Guindon and Gascuel 2003; Guindon et al. 2010 ) with default options (LG + À model; examples in fig. 1 ).
Function Enrichment Analysis Ensembl protein identifiers were mapped to HGNC symbols. The mapping of HGNC symbols to GO functional  Schaper et al. doi:10. 1093/molbev/msu062 MBE annotations, and the enrichment analysis assuming a hypergeometrical model was conducted with Gorilla  (Eden et al. 2009). All TR-containing human proteins constituted the background distribution, which was independently contrasted with distributions of functions within strongly separated and strongly conserved sets of TRs. The complete enrichment data set including directed acyclic graphs of enriched GO terms is available at http://www. atgc-montpellier. fr/TRE (last accessed February 20, 2014).
Acknowledgments The authors thank  Erich Bornberg-Bauer ,  Andreas Schu ¨ler , and  Gil McVean  for insightful discussions and  Nives S ˇkunca ,  Stefan Zoller , and three anonymous reviewers for their invaluable feedback on the earlier version of this manuscript. E. S. was invited to the  "Institut de Biologie Computationnelle" (IBC)  to carry out the phylogenetic studies. This work was supported by the  Swiss National Science Foundation (SNF)  grant  31003A_127325/1  to M. A. , a SNF doctoral mobility grant to E. S.
Quantitative mass-spectrometry-based spatial proteomics involves elaborate, expensive, and time-consuming experimental procedures, and considerable effort is invested in the generation of such data. Multiple research groups have described a variety of approaches for establishing high-quality proteome-wide datasets. However, data analysis is as critical as data production for reliable and insightful biological interpretation, and no consistent and robust solutions have been offered to the community so far. Here, we introduce the requirements for rigorous spatial proteomics data analysis, as well as the statistical machine learning methodologies needed to address them, including supervised and semi-supervised machine learning, clustering, and novelty detection. We present freely available software solutions that implement innovative state-of-the-art analysis pipelines and illustrate the use of these tools through several case studies involving multiple organisms, experimental designs, mass spectrometry platforms, and quantitation techniques.
Organelle proteomics, also termed more broadly spatial proteomics, is the systematic study of proteins and their assignments to distinct spatial cellular subcompartments and is a field rapidly growing in importance  (6). Current experimental designs and multivariate data analysis techniques permit a researcher to collectively infer and track the localization of thousands of proteins and promises to elucidate the coordinated changes in localization at the whole-proteome level. Experimental organelle proteomics requires both sophisticated experimental designs in order to obtain accurate datasets and elaborate methodologies with which to analyze and make sense of the data  (6). Various experimental designs have been proposed, from those merely focused on the identification of proteins in single organelles through biochemical purification (pure fraction cataloging) to more complex methods that utilize quantitative mass spectrometry to elucidate the broad subcellular diversity of cells (fractionation-by-centrifugation approaches).
Briefly, these techniques start with gentle lysis of the cells and separation of the intact and complete cell content using successive differential centrifugation steps or gradient-based ultracentrifugation, which permits continuous separation of the complete cell content as a function of its density. Several fractions representing differential subcellular enrichments are then collected, and their respective protein complements are identified and quantified by means of high-resolution mass spectrometry. The relative protein abundances within the fractions represent unique organelle-specific distributions among partially enriched fractions. The resulting datasets are then formatted as a matrix, representing the protein quantitation patterns along the fractions, which is then subsequently submitted to further data analyses.
The ad hoc data structures and methodological advances that we describe have been implemented and further developed using a set of flexible software packages for the R (12) programming language, namely, MSnbase  (13)  and pRoloc  (11) , available under permissive open source licenses from the Bioconductor (14) project. Methodological aspects, the understanding of the pipeline, and its critical parameters are explored through applications to empirical case studies. In the third section, we proceed with a novel application of the computational methods presented to the analysis of protein dual-localization and dynamic spatial proteomics, also termed comparative organelle proteome profiling  (15) , in which protein localization is compared and contrasted under different conditions. Material and Methods for Spatial Proteomics Data Analysis-In this section, we present a detailed description of spatial proteomics data and a set of steps leading to trustworthy results, as summarized in Fig.