dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparisons of algorithm performance Performance of de novo tools  Fig 3  shows the distribution of the reconstruction error when a given computational tool is applied several times to the same real trinucleotide matrix. Reconstruction errors show limited variability due to stochastic steps in the algorithms and no variability whatsoever for maftools. All methods under evaluation are roughly equivalent in terms of their ability to properly reconstruct the initial matrix of mutational catalogues. This is not surprising, given that all methods are meant to solve the optimization problem in  (2). In general, the error value appears to depend on the cancer dataset. This is expected because the fours datasets differ with regards to the number of samples, their total number of mutations and the number of operating mutational signatures, making the decomposition more or less difficult.
In order to study the robustness to the presence of outliers, we also estimated the reconstruction error of each method (not only bayesNMF) after pre-treating all datasets with the function get. lego96. hyper. The results reported in S5 Fig show that the performance of each method improves after pre-treating the samples, especially for Melanoma and Breast cancer datasets that are characterised by a few samples with an extremely high number of mutations. For the Melanoma dataset, the gain in performance is considerable for bayesNMF and maftools. We used realistic simulations to evaluate the performance of each method for de novo extraction followed by a classification step in which the extracted signatures are assigned to the most similar COSMIC signature. Figs  4  and 5 , respectively show the specificity and sensitivity of such two-stage procedure as functions of the number of samples G in each catalogue and the cosine similarity cut-off h, while Figs  6  and 7  show the specificity and sensitivity as functions of the number of mutations in each catalogue and h.
Specificity increases with the average number of mutations (Fig  6 ). For Profiles 2,3 and 4 it is close to 1 starting from as low as 1000 mutations, while for Profile 1 it is only for at least 10,000 mutations that we observe a specificity close to 1 for most of the methods, with the notable exception of bayesNMF that performs well even for lower numbers of mutations. Sensitivity increases with the average number of mutations with a large variability according to the cancer profile and method (Fig  7 ). Sensitivity is high for cancer profiles characterised by one predominant signature (Profiles 3 and 4) or two strong signatures (Profile 2) but may become relatively low for datasets characterized by small contributions by several signatures (Profiles 1). This indicates that signatures that act together with other signatures and have small effects may be more difficult to identify.
Specificity and sensitivity slightly deteriorate for higher cut-off values. This is expected because by setting a higher cut-off, the number of found signatures that are not similar enough to COSMIC signatures increases. Because these estimated signatures are considered as novel, they are false positives (that is found signatures not used for simulations), leading to a greater number of false positives and therefore to a lower specificity. Moreover, if the cut-off is too stringent, the number of false negatives will be high because some signatures used for the simulations are correctly found but do not score a high enough cosine similarity and therefore count as false negatives. This will make the resulting sensitivity low. Fig  8  shows the running time when tools are applied to real lung datasets with a varying number of samples.
Some of such signatures were similar to those identified in human tumour DNA. An original aspect of this paper is the empirical performance study and in particular the probabilistic model of mutational catalogues used to generate simulations. This model is based on the zero-inflated Poisson distribution that allows for sparse contribution of signatures and thus makes it possible to build mutation count data that are more realistic than the pure Poisson model previously considered  [9, 14]. As argued in the Simulated data section, our model underrepresents the few samples with extremely large total mutation counts. Because catalogues of this type might hamper the detection of signals from less mutated samples in the same dataset  [7] , it is likely that our results slightly overestimate the methods performance in the presence of hypermutated samples.
We suggest that developers should assess their new methods on simulations based on realistic models such as ours or the latter. The advantage of simulations over real data is that the underlying model generating the synthetic data is known and can be compared to the estimation provided by the method being evaluated. For this reason, we decided not to simulate catalogues from real data using the bootstrap: this would have produced almost real samples but without the possibility to evaluate the performance of methods according to different parametric scenarios. We strongly believe that the mutational signature research could benefit from the development of public realistic datasets that can be used to benchmark old and new detection tools. Another innovative aspect of our work is the assessment of the performance of de novo approaches.
This is in general a slippery problem given the non-supervised learning nature of NMF and concurrent methods. We addressed it by building confusion matrices after comparing the "true" signatures used for simulations with the newly identified signatures in terms of their cosine similarity. Finally, we introduced MutationalCone, a new implementation of existing refitting tools that is based on a very simple geometric model and proved to be faster than all other methods. The R code implementing MutationalCone can be found in the S1 File. The results of our study can be helpful to guide researchers through the planning of mutational signature analysis. In particular, we show that the performance of de novo methods depends on the complexity of the analysed sequences, the number of mutations and to a lesser degree the number of samples analysed.
75 or less). On the contrary, if the aim is to assess the presence of known signatures in mutational catalogues (cancer genomes or exomes), we recommend turning to refitting methods. For wellstudied cancers, refitting approaches are a faster and more powerful alternative to de novo methods, even with just one input sample. As the COSMIC database has been built and validated by analysing tens of thousands of sequences of most cancer types, we recommend borrowing strength from previous studies and using refitting tools when performing standard analysis not aimed at the discovery of new signatures. Our simulation study seems to indicate that de novo probabilistic methods EMu and bayesNMF have an overall better performance as they achieve better sensitivity and specificity with a fair running time.
However, in order to assess the robustness of results, due to the variability of outcomes and the presence of hypermutated samples, we recommend to systematically perform a sensitivity analysis based on the application of one or more alternative methods based on different algorithms. Our analysis also reveals that if the dataset under consideration contains catalogues with a very large number of mutations, all methods achieve better performance by replacing such outliers with the bayesNMF pre-treatment function get. lego96. hyper. Interestingly, the mutation profiles of the synthetic datasets simulated with the ZIP model resemble the profiles of datasets after such pre-treatment, see S6 Fig. Not all the de novo methods we evaluated offer the possibility to automatically choose the number of signatures to be found.
For instance, the popular SomaticSignatures only provides a graphical visualisation of the residual sum of squares for several choices of the number of signatures; the user can choose the optimal number by identifying the inflexion point. For this reason, we did not address this crucial aspect in our empirical assessment. Similarly, we only considered mutation types defined by the trinucleotide motifs, as currently only pmsignature  [16]  can consider more than one flanking base on each side of the substitution. A more recent set of 49 signatures have been estimated from a new large dataset including thousands of additional sequences. The article introducing this new set has not been published yet but the new set of signatures is available on the COSMIC website. In the future it will be interesting to evaluate the performance of the different tools using the new set of signatures.
Supporting information S1 Fig. Comparison  of newly identified signatures with COSMIC signatures. Signatures a-g were identified in a de novo extraction using the maftools R package from the TCGA Lung Adenocarcinoma cohort of 563 cancer genomes. The novel signatures were then compared to the 30 signatures validated in the COSMIC database in terms of cosine similarity. Each signature is then assigned to the most similar COSMIC signature provided that their cosine.
Acknowledgments We acknowledge the support of a grant from  La Ligue contre le Cancer  ( Appel à projets 2016 'Recherche en E ´pidemiologie ', grant recipient: GS). HO is supported by a PhD fellowship from the  French Institut National du Cancer (INCa_11330). VP would like to thank  Gregory Nuel  for the useful discussions around zero-inflated models. The authors would like to thank all the reviewers for the very useful remarks and constructive suggestions.
The real data underlying the results presented in the study are available from the Genomic Data Commons Data Portal. Datasets can be accessed at the following links: Lung adenocarcinoma: https://portal. gdc. cancer. gov/files/6f5cde97-d259-414f-8122-6d0d66f49b74; Breast cancer: https://portal. gdc. cancer. gov/files/b8ca5856-9819-459c-87c5-94e91aca4032; Melanoma: https:/.
com/friend1ws/pmsignature model -Mutation Feature Vector Format -Mutation types defined by one or two flanking bases -Selection of the optimal number of signatures -Transcriptional strand bias -Background signature bayesNMF [4,17-19] R/NMF Mutation counts file -R-based implementation of [2] maftools [5] R-Bioconductor /NMF -Mutation Annotation -Genomic visualization https://bioconductor. org/packages/release/bioc/ -Format -Cosine similarity html/maftools. html -Selection of the optimal number of signatures -Group-wise comparisons (differential analysis) -APOBEC enrichment analysis Helmsman [21] Python/ NMF and PCA -Variant Call Format -Able to run in parallel and designed for large https://github. com/carjed/helmsman -Mutation Annotation Format datasets -Connection to external packages (in R) -may generate mutational catalogues from sequence data SignatureAnalyzer [4] R/ Bayesian NMF Mutation counts file -Automatic selection of the optimal number https://www.
synapse. org/#!Synapse:syn11801492 of signatures -Sparse signature profiles and contributions SigProfiler [2,4] Matlab/ NMF Mutation counts file -Further development of the original https://fr. mathworks. com/matlabcentral/ framework fileexchange/38724-sigprofiler -Two steps: 1) extraction of a minimal set of signatures, 2) estimation of their contributions to individual samples SparseSignatues [22] R/ NMF with Lasso- Mutation counts file -Integration of DNA replication error https://bioconductor. org/packages/release/bioc/ penalized cost function signature html/SparseSignatures. html -Sparse signature matrix -Number of signatures estimated with cross- validations -Scalable to large datasets (Continued ) [20]s://github. com/jburos/bayesNMFhttps://software. broadinstitute. org/cancer/cga/mspR/BayesianNMF Mutation counts file -Selection of the optimal number of signatures -Data pre-treatment with the function get.
org/packages/release/bioc/ Decomposition -Enrichment and depletion html/YAPSA. html decompTumor2Sig [28] R/quadratic programming -Variant Call Format https://github. com/rmpiro/decompTumor2Sig -Mutation Position Format- Mutation Feature Vector Format Web-interface -Variant Call Format -Refitting and de novo identification -Mutation Annotation Format -Bootstrapped cosine similarity -TSV -Comparison with either hg19 or hg38 Mutalisk [32] Web-interface Variant Call Format -Refitting and de novo identification http://mutalisk. org -Transcriptional strand bias -Localization of kaetegis -Histones modifications -Cosine similarity comparison MuSiCa [33] Web-interface -Variant Call Format -Refitting and de novo identification http://bioinfo. ciberehd. org:3838/MuSiCa/? -Mutation Annotation Format -Cosine similarity -TSV -Samples classification -Excel https://doi.
org/10. 1371/journal. pone. 0221235. t001 [16]verts a set of "Alexandrov's signatures"[10]to "Shiraishi's signatures"[16]-Decomposes a mutational catalogue in "Shiraishi's signatures" MutationalCone [See S1 File] R/cone projection Mutation counts file -Fast in comparison to others refitting tools Sigfit [29] https://github. com/kgori/sigfit R/ Bayesian NMF Mutation counts file -Provides a new model for combining de novo and refitting approaches -Possible application to indel or rearrangement count data -Also implements EMu [9] model and allows conversion to genome-or exome-relative signatures Pipelines and web-interfaces Mutspec [30] https://toolshed. g2. bx. psu. edu/repository/view_ repository?id=f5c1f75e9fb33f8e Galaxy pipeline/NMF Variant Call Format -de novo identification -Includes MS analysis in mouse cancer MutaGene [31] https://www.
In this paper  [14] , authors proposed a semantic recommender system approach for elearning use to assist learners in finding suitable Learning Objectives. Moreover, the system can assist the instructors to suggest new resources to enhance the courses syllabus. This system is built with a query keywords extension using the semantic relations and the ontology reasoning. Similar to above two approaches, authors presented in this paper  [10]  a personalized ontology-based recommendation system to represent items and user-profiles in order to provide personalized services using semantic web applications. This paper shows high accuracy when semantically enhanced methods are applied in recommendation systems. Developers experimented this system on the Netflix movie-dataset. The evaluation shows that the semantics-based methods of the recommender system improved the recommendations' accuracy.
Improving recommender systems with machine learning Integrating machine learning techniques in recommender system certainly will improve the recommendation accuracy. In this paper  [13] , authors proposed a courses recommender system to learners based on their profiles. This system used weka open source software which consists of a collection of machine learning algorithms and implemented k-means algorithm to create learners' profiles supported by their data on Moodle. Besides above approach, in this paper  [24]  authors presented the item-based approach and the user based approach to recommend recipes based on preferences of the user ratings. Tanimoto Coefficient Similarity and Log Likelihood Similarity were used to compute similarities between different recipes for the item-based approach.
These classes can be found in the Gaia archive vari_classifier_result table with best_class_name = ARRD, RRD, RRAB, RRC, MIRA_SR, ACEP, CEP, T2CEP,and DSCT_SXPHE, respectively. Each entry has an associated best_class_score between 0 and 1. Additional (non-published) classes were included in the prediction types, see  Rimoldini & et al. (2018)  for all details. Descriptions of the classifier and the published classes can be found in the Gaia archive vari_classifier_definition and vari_classifier_class_definition table, respectively. The sky and magnitude distribution of the published classes, grouped by main type, is shown in the left panels of Figs. The published source samples were cleaned to reduce contamination levels caused by data and processing artefacts, as well as by genuine variability of other types.
Long-period variables Long-period variables are red giants that are characterised by long periods and variability amplitudes of up to a few magnitudes. They are identified by classification as MIRA_SR. On the basis of data available for DR2, CU7 selected 151 761 LPV candidates that fulfil the selection criteria for LPVs, which included a minimum of 12 points in G, a G BP -G RP colour higher than 0. 5 mag, a variability amplitude (quantified with the 5%-95% trimmed range in G) larger than 0. 2 mag, and a correlation between G and G BP -G RP higher than 0. 5. Of these, 150 757 are published in the classification table, and 89 617 have SOS results. The detailed distribution into these two tables is indicated in Fig. 3. The SOS LPV module processes LPV candidates when their period is longer than 60 days.
Both the investigation of transient variability  (Wevers et al. 2018 ) and the further classification of the candidates are foreseen for the future Gaia data releases. As mentioned previously, the identification of the shorttimescale variable candidates published in Gaia DR2 involves particular variability detection methods that are specifically tuned to detect fast photometric variability, complementarily to the general variability detection approach (which may miss some of these events because they are so fast). These methods are applied to a preselected subset of the Gaia data set  (Eyer et al. 2017 ): for Gaia DR 2, the short-timescale variability analysis is restricted to relatively faint sources (G ∼ 16. 5-20 mag) whose G per-CCD time series indicates that they are likely to show rapid variations  (Roelens et al.
Conclusions In this second Gaia data release, a sample of 550 737 variable sources with their three-band time series and analysis results has been released to the astronomical community, showcasing samples of high-amplitude variable candidates distributed over (a large portion of) the whole sky. It demonstrates the immense potential of Gaia data to provide an unbiased all-sky photometric survey together with astrometric and spectroscopic data. Although the variability analyses in DR2 did only mildly rely on the astrometric data and on none of the spectroscopic data, this dependence will become much heavier in future data releases. Several caveats have been outlined in this paper: -Most provided stellar samples are rather incomplete as a result of the current state of data calibration (e. g.
In total, this is about half of the variable sources released in this DR2. Compared to the DR1 example, we increased the sample size about 100 times, which is expected to result in a large number of novel studies, regardless of whether they are combined with existing data sets. The next variability release will be based on data that are better calibrated yet again and on a longer time baseline. It will also include BP/RP spectrophotometry and radial velocities from RVS, which will allow us to improve the quality and quantity of the Gaia DR3 release. For RR Lyrae stars, the percentiles for G BP and G RP were computed for a number of FoV transits > 1, although there are stars with 0 or 1 transit remaining in the band after our cleaning. For the skewness and kurtosis, we included only RR Lyrae stars with > 2 and > 3 transits, respectively.
Acknowledgements. We dedicate this paper to our dear friend and colleague  Jan Cuypers  , whose passing has left a great void in our variability group. Thank you, Jan, for your friendship and lifelong contribution to science. We would like to thank the referee,  Andrzej Udalski , for various suggestions and providing so far unpublished OGLE-IV estimates that improved the contents of this paper. We thank  Xavier Luri ,  Antonella Vallenari , and  Annie Robin  for valuable feedback during the preparation of this paper. We also acknowledge  Mark Taylor  for creating and implementing new requested features in the astronomyoriented data handling and visualization software TOPCAT (Taylor 2005). This work has made use of data from the  ESA space mission Gaia , processed by the  Gaia Data Processing and Analysis Consortium (DPAC).
There is a general scarcity of oceanic observations that concurrently examine air-sea interactions, coastal-open ocean processes, and biogeochemical (BGC) parameters, in appropriate spatiotemporal scales, and under continuous, long-term data acquisition schemes. In the Mediterranean Sea, the resulting knowledge gaps and observing challenges increase, due to its oligotrophic character, especially in the eastern part of the basin. The 15 oligotrophic open Cretan Sea's biogeochemistry is considered to be representative of a greater Mediterranean area up to 10 6 km 2 , and understanding its features may be useful on even larger oceanic scales, since the Mediterranean Sea has been considered a miniature model of the global ocean. The spatiotemporal coverage of BGC observations in the Cretan Sea has progressively increased over the last decades, especially since the creation of the POSEIDON observing system, which has adopted a multiplatform-multiparameter approach, supporting BGC data acquisition.
20 The current POSEIDON system's status includes open and coastal sea fixed platforms, a Ferrybox (FB) system, and Bio-Argo autonomous floats, that deliver remotely Chlorophyll-a (Chl-a), O 2 , pH and pCO 2 data, as well as BGC-related physical parameters. Since 2010, the list has been further expanded to other BGC (nutrients, vertical particulate matter fluxes), ecosystem and biodiversity (from viruses up to zooplankton) parameters, thanks to the addition of sediment traps, frequent R/V visits for seawater-plankton sampling, and of an ADCP delivering 25 information on macrozooplankton-micronekton vertical migration (in the epi-, mesopelagic layer). Gliders and drifters are the new, currently under integration to the existing system, platforms, supporting BGC monitoring. Land-based facilities, such as data centers, technical support infrastructures, calibration laboratory, mesocosms, support and give added value to the observatory.
Between 1999 and 2002 the Adriatic Sea has gradually started to regain its dense water formation role in the Eastern Mediterranean and, finally, the Adriatic Sea produced dense water in 2012. However, Ocean Sci. Discuss. , https://doi. org/10. 5194/os-2018-3 Manuscript under review for journal Ocean Sci. dense water formation conditions have been identified showing that the basin is in the process of slowly returning to the state of the late pre-EMT period  (Velaoras et al. , 2014; Cardin et al. , 2015). The deep-water mass formation in the Cretan Sea has the potential to transfer CO 2 into the deep layers. 5 However, there is a sparseness of carbonate system data (C T , A T , pH and pCO 2 ) in the wider Eastern Mediterranean, despite an increasing effort to study these properties in the last decades  (Sisma-Ventura et al.
The lack of data from Ocean Sci. Discuss. , https://doi. org/10. 5194/os-2018-3 Manuscript under review for journal Ocean Sci. Discussion started: 6 February 2018 c Author(s) 2018. CC BY 4. 0 License. midwater depths severely limits our ability to quantify the efficiency of the biological pump  (Robinson et al. , 2010). Although in the western Mediterranean there have been several studies on deep living zooplankton, much less is known for the Eastern Mediterranean (review by  Saiz et al. , 2014) , although its stock and composition has been investigated  (Siokou et al. , 1997 (Siokou et al. , , 2013) )  and its importance in consuming sinking particles reported  (Koppelman et al. , 2004). 5 In the Cretan Sea, deep water mesozooplankton has occasionally been studied  (Siokou et al. , 2013)  and the vertical flux of zooplankton faecal pellets quantified  (Wassman et al.