dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Description We used public datasets to test the "predomics" algorithm and compare it with SOTA methods. For the classification tasks we used curated metagenomic datasets from ExperimentHub  [25]  (see Supplementary Table  S1  for more information). The code used to query and process the data is provided in the supporting dataset of this article doi:10. 55. 24/100698. In total, 54 datasets were derived (i. e. , 6 different cohorts and for each 6 taxonomic levels, a marker gene, and a pathway table along with a fused taxonomic dataset). Moreover, these datasets were transformed as "presence/absence" for additional experiments (n = 54). Baseline microbiome data were also used to predict the clin- ical outcome of bariatric surgery on morbidly obese patients  [26]. Their microbiome was sequenced at baseline and at 1, 3, and 12 months after surgery (see original paper for methods).
BTR models are sparse and accurate and improve with taxonomic specificity We tested our approach on 6 different public metagenomic datasets (Table  S1 ) and 9 derived types of variables (6 different taxonomic levels, a merged multi-taxonomic level, marker genes, and a functional MetaCyc pathway table, i. e. , a total of 54 datasets; see Methods). We trained and tested models with different numbers of features (i. e. , model size, k #) and noticed an effect on accuracy. As expected, the testing performance on unseen data was lower compared to training performance. How-ever, this difference was more pronounced for the SOTA, indicating a significant overfitting effect, compared with BTR models as discussed previously  [19]. The simplicity and sparsity of the BTR models reduces overfitting on studied datasets (Fig.
2 ). Because BTR models come with an embedded feature selection strategy, we used a Mann-Whitney test to select the k # most correlated features for random forest (RF) and SVM to allow comparison. For ENET we used the embedded regularization path and selected the first k # from it. We applied a model size penalization technique on the empirical (training) accuracy to select the best model. BTR models performed at least as well as the SOTA in 46 of 54 (85%) of the cases. They outperformed SOTA in 19 of 54 (35%) and were outperformed in 8 of 54 (15%) (Fig. 3 ; Fig. S1A-C ). Similar results were observed even when all the variables in the dataset were used (no penalization) for the SOTA (Fig. S2A-C ) or when fixing the same model size for all the compared models (Fig. S3A-C ). Similar results are obtained for additional performance scores including recall, precision, and f1 score (Figs  S6  and S7 ).
When learning from the different types of variables based on taxonomic levels (Cirrhosis Stage 1), the performance of the models varies accordingly. Higher performance is obtained at the gene marker, species, and genus levels and decreases with higher taxonomic levels. Similar results are reported elsewhere  [31]. Moreover, when applied to a multi-taxonomic level dataset (from strain to phylum as generated by Pasolli et al. [25]  with dif-ferent specificity levels mixed together; i. e. , "whole tax"), models displayed surprisingly good performance (Fig. 3B ). Indeed, in this space, models can be powerful because they can summarize more complex rules such as: "if (abundance of all Firmicutesabundance of all Clostridiales order) > threshold then disease. " We tested the generalization of Bin, Ter, Ratio and also Ter-Log models trained in Cirrhosis Stage 1, in a second, independent dataset (i.
e. , Cirrhosis Stage 2). Results illustrated in Fig. S5  indicate very good external validation with an average training accuracy = 0. 89 (SD = 0. 02) and testing accuracy = 0. 85 (SD = 0. 04). Ter and Ratio models generalized better compared to Bin and TerLog. In addition to the abundance datasets described above, we trained and tested similar models on presence/absence binary data derived from the previous 54 abundance datasets. Overall results are similar, indicating that the detection of species alone can be powerful enough in some prediction tasks (see Supplementary Figs S1D-F, S2D-F, S3D-F, and S4). It is noteworthy that, when applied to presence data, BTR models indicate relationships between sub-ecosystem complexity or richness. These can be useful to detect switch-like mechanisms in the microbiome.
The feature importance (FI) score of BTR models correlates strongly with the FI of the well-established but more complex RF model (respectively, R = 0. 68, R = 0. 81, R = 0. 7, with Bin, Ter, and Ratio models; Figs  S11  and S12 ). This information allows prioritizing further exploration of the features in the context of the predicted phenomenon. Predomics generates a family of BTR models with equivalent predictive power in a given model size range (i. e. , family of best models [FBM]; Fig. S8 ; see methods and supplementary material). The FBM is analyzed to identify the common features that are found in the models. For instance, in the Cirrhosis Stage 1 (species) dataset, the 268 models in the FBM with model size < 6 rely on only 67 features (i. e. , 16% of the whole dataset), which can be used to infer a "feature co-presence network in models" (Fig.
5A ). An emerging property of this network is the clustering of phylogenetically related species, such as Firmicutes enriched in patients (blue tones) and Proteobacteria and Actinobacteria enriched in controls (green tones). Co-presence of the features indicate complementarity in prediction (red edges), while replacement of the features by one another indicate redundancy (blue edges). This can also be observed with the inverse relation of feature pairs in the data and in the models (Fig. 5C )-the most correlated pairs in the dataset are those that do not occur together in the models. This network provides precious information to decipher the sub-ecosystem that is associated with the disease (Figs  S8-S10 ).
BTR models provide biological insights We focused on the liver cirrhosis dataset  [11] , where major patient dysbiosis was observed with decreased microbial richness, depletion of gut commensals, and an invasion of oral bacteria. Several markers at taxonomic and functional levels were associated with the disease. Some authors have modelled liver cirrhosis-associated microbiome features using curated information from the literature, such as the ratio of autochthonous (butyrate-producer bacteria) to non-autochthonous (oral bacteria, opportunistic pathogens). They used these taxa to build a cirrhosis dysbiosis ratio (CDR) score  [32]. On the basis of their description we built 3 redun-dant ratio models using family taxonomic features to reproduce their score and applied them in the Liver Cirrhosis Stage 1 (family) dataset  [11]  (Fig.
6B-D ). We searched the same family-level dataset for Ratio models. The models that were identified provided superior performance (accuracy = 0. 86; Fig. 6A ) compared with CDR-based models (i. e. mod1, mod2, mod3; accuracy = 0. 56 in average; Fig. 6F ). The reason for the CDR lower performance can be explained by the inclusion of the Bacteroidaceae family in the liver cirrhosis group by the authors, while we observe the opposite association in the current dataset. Bacteroidetes-related features are enriched in the control group, and this is consistent for different taxonomic levels (Fig. 6E , see supplementary material). At the phylum level, the Ratio model (S6) points at a mutual between Bacteroidetes and the combination of Proteobacteria and Viruses, which is also picked up by the Bin model (S4).
Moreover, we observe the presence of modules related to bacterial peptidoglycan biosynthesis in the FBM (PWY-6470: peptidoglycan biosynthesis V). It has been described as an elicitor of inflammatory response associated with the progression of liver For each feature present in the network we show (left) the prevalence of the features in the whole dataset (grey bar) and in the prediction classes (disease, healthy) depicted as blue and red dots, respectively, and (right) the feature abundance distribution in the prediction classes (disease, healthy) depicted as blue and red box plots, respectively. Grey stars indicate significant differences. C: A scatter-plot indicating for each edge of the network the correlation between the 2 features in the data and FBM, respectively, in the y-and x-axis.
Other issues related to data quantification can affect predictive models. Recent work has shown the importance of data acquisition in subsequent analytical inferences. In particular, microbial loads differ significantly between individuals and are associated with specific types of microbial ecosystems  [42]. Moreover, varying sequencing depth can over-or under-estimate less abundant taxa that may be selected by the models. It is thus advisable to pre-filter rare taxa from the dataset before training the models. However, the sparsity constraint in our approach will force important taxa to be selected by the models, improving their generalization. The simplicity of BTR models may come with the risk of overinterpretation. The existence of k species in a model may correspond to different explanations ranging from simple correlation to causal relation.
They may or may not interact, as in the case of a niche differentiation  [30]. For instance, the oraloriginated species found in the gut of patients with liver cirrhosis  [11]  along with the absence of usual commensals may reflect a global difference in the environment where they live rather than direct interaction  [11]. Explicit identification of covarying species provides important knowledge that can be used to propose functional interactions between species or between the species of interest and the host. Yet, even if BTR models represent real interactions between species, it is not recommended to propose a causal interpretation without further experimental validation. We also stress that the quality of reference datasets used in establishing predictive models is crucial for model interpretability.
The propagation of errors and inaccuracies in genomic datasets is a well-known issue that negatively affects the outcome of automated methods used for functional annotation  [43]. Moreover, orphan enzymatic activities, by definition, cannot be associated with gene sequences while the number of sequences with unknown functions is extremely large and keeps increasing, making error percolation a widespread feature  [44]  (see supplementary material). Another important issue that plagues microbiome studies is the influence of potential confounders modulating microbial ecosystems. For instance, it has been shown that metformin can alter the bacterial ecosystem in such a way that some bacterial species (e. g. , Escherichia coli) are increased in abundance while others are depleted  [15]. In this context, we advise users to filter out confounder-related species from the data or to discard models that are sensitive to confounders.
Those that are not significantly different (P < 0. 05) are considered equivalent. The BTR models are tested on 109 different datasets (see Table  S1 ) and compared with the methods from the SOTA algorithms: SVM with linear and Gaussian kernel (data not shown), random forest, and ENET (an improvement of Lasso, α = 0. 5). All algorithms were evaluated by measuring test accuracy in a cross-validation setting and compared among them using paired t-tests. A specific comparison between TerLog models (i. e. , Ter model with log-transformed data) and the geometric mean balance algorithm is provided in the supplementary material.
Feature importance Similar to RF, "feature importance" is defined as the usefulness of features to be predictive, given all other features and best models of the FBM. During each cross-validation fold, the outof-bag error on each model of the FBM is computed. The importance of the j th feature is measured by permuting all features within the out-of-bag data. The out-of-bag error is computed on these perturbed data for each FBM model. The overall feature importance for the j th feature is obtained by averaging over all FBM models the difference in out-of-bag error before and after the permutation. This is performed on all the features of the dataset that are found in the FBM models, on which errors before and after permutations are computed. Finally, the MDA is computed as the average of these values over all the folds and is displayed along with the standard error of the mean.
Network reconstruction We used Scalenet  [47]  to reconstruct the feature co-presence network in model selection data. Here we used the top 5% strongest edges inferred by bayes hc and aracne methods in the FBM-presence table. ScaleNet first reduces the reconstruction problem to a number of simpler reconstruction problems, then uses SOTA reconstruction methods to solve them. Finally, a consensual voting strategy between the methods is adopted to identify accurate subgraphs, which are then overlapped together. r Project name: Predomics r Project home page: e. g. , https://github. com/eprifti/predomics r Operating system(s): Platform independent r Programming language: R r Other requirements: R (version ≥2. 15. 0). Predomics imports the following R packages: reshape2, plyr, BioQC, foreach, snow, doRNG, yaml, ggplot2, gridExtra, grid, gtools, RColor-Brewer, glmnet, pROC, viridis, kernlab, randomForest.
Availability of Supporting Data and Materials The supplementary material includes additional experiments and results. The datasets used here are public in respective repositories. For the classification tasks we downloaded 5 curated metagenomic datasets from the ExperimentHub  [25]. The raw data were generated in independent studies using shotgun metagenomics (Table  S1 ) and were processed bioinformatically and curated by Pasolli et al. [25]. For the regression experiments, we used shotgun metagenomics data from a recently published study, where morbidly obese patients underwent bariatric surgery  [26]. The code for processing the data is made available in the project's home repository. Snapshots of our code and other data further supporting this work are openly available in the GigaScience repository, GigaDB  [48].
Figure5: Feature co-occurrence network in the family of best models. A: This network displays feature co-occurrence patterns in FBM models. Only the top 5% strongest edges inferred using the ScaleNet network reconstruction approach (parameterized with bayes hc and aracne algorithms; see Methods) are shown. The size of the nodes is proportional to the average importance (MDA) in the Bin, Ter, and Ratio experiments. The colours of the nodes indicate the taxonomic family assignation as indicated in the legend. The red and blue edges indicate co-presence and co-absence in the models, respectively. B: For each feature present in the network we show (left) the prevalence of the features in the whole dataset (grey bar) and in the prediction classes (disease, healthy) depicted as blue and red dots, respectively, and (right) the feature abundance distribution in the prediction classes (disease, healthy) depicted as blue and red box plots, respectively.
Acknowledgements We wish to thank  S. D. Ehrlich  as well as  E. Le Chatelier  for mindful discussions on the early stages of this work and  T. Swartz  for help in language proofreading. J. -D. Zucker also wishes to deeply thank  Professor Hsu Wen-Lian  of the  Institute of Information Science, Academia Sinica, Taipei, Taiwan  for the fruitful discussions he had during his stay in the summer of 2018.
Funding This work was supported by the  French National Agency  through the  national program Investissements d'Avenir  (reference no. ANR-10-IAHU-05 ) IHU ICAN; by the  Funding Support of European Union 's  Seventh Framework Program  under grant agreement  HEALTH-F4-2012-305312 , by the  Assistance Publique-H ôpitaux de Paris promoter of the clinic program , and by  Assistance Publique-H ôpitaux de Paris Contrat  d'interface chercheurs 2015-2018.
In the following we show that Hough circle transform can be used to detect objects with partial circular symmetry regardless of a shell-like or Gaussian-like morphology. We have tested the robustness and the efficiency of the algorithm to reconstruct the position and extent of simulated sources and to detect the firmly identified sources in the HGPS catalogue. We also investigate how the morphological parameters of the detected objects can be used to differentiate the various source populations in the HGPS. We discuss on the identification of new source candidates based on such morphological classification, and/or supported by spatial coincidence search in MW catalogues. The paper is structured as follows: In Sect. 2 we present the data and catalogues used; In Sect. 3 we describe the detection algorithm based on Hough circle transform, the parameters derived in order to classify the detected objects, and the association procedure.
The gradient magnitude provide thick edges of the structures so a non-maximum suppression is applied to obtain 1-pixel thin edges. These first steps are performed following the implementation of the Canny filter in scikit-image python package 9  while the subsequent steps differ. Then we filter the thin edges using conditions on both gradient and significance values. We mask the thin edges where gradient magnitude is lower than its 86. 6 percentile value (∼ 1. 5σ for a Gaussian distribution). The significance maps are filtered by hysteresis thresholding with a high threshold of 4. 5σ and a low threshold of 1σ (pixels between these two thresholds are masked only if they are not connected to a pixel above the high threshold). Contrary to the hard threshold usually applied in VHE γ-ray studies for source detection (as in the HGPS procedure), a hysteresis thresholding better preserves the global spatial coherence of the extended excess even if the whole structure is not strictly above the hard threshold.
Simulated datasets We have performed a bootstrap of the sources catalogued in the HGPS survey. We produced 10 sky realisations, each one including 100 sources, a model for hadronic background emission and a toy-model for diffuse Galactic background emission. Details on the production of the simulated flux, counts, exposure and final significance maps are given in Appendix A. We did not intend to produce a realistic model for the various components of the diffuse Galactic emission, instead we used a simplistic toy-model that we can tweak in order to test how the source reconstruction is affected by an increase of the diffuse background level. To do so, we arbitrarily tweaked the contrast in flux between the source and the diffuse background with one parameter. The contrast parameter C ism has been set to 0.
Analysis products We provide the full list of objects detected on the HGPS significance map together with several short lists for convenience: -list of the objects associated with the HGPS sources (providing a detailed description of their sub-structures) -list of non-HGPS objects with MW coincidences (which could be further studied in dedicated analyses) -list of high-circularity candidates (see Sect. 6. 2) -list of SNR-like candidates (see Sect. 6. 3) In addition to the object positions (in longitude and latitude), these lists contain the morphological information defined in Sect. 3. 2 and the possible association given by the catalogue cross-match described in Sect. 3. 3. All these lists are available in FITS format in the paper data on the editor page 11. A description of the FITS file columns is provided in Table  C.
E. S. S. Collaboration and the CTA Consortium. Meanwhile, we warmly welcome collaboration with anyone interested in writing up follow-up proposals for deeper MW observations and/or in carrying out dedicated analyses with the existing MW archival data towards the most valuable objects presented here, in particular those not catalogued in the HGPS. same flux (column Flux map if available) and the same spatial model than catalogued so the surface brightness and the source population distributions are preserved. However for sources composed of multiple Gaussian components we generated only one Gaussian corresponding to the averaged source given in the HGPS because we do not have the information on the flux of its Gaussian components. This is not an issue as the random re-sampling of the sources will naturally create multiple nested sources.
Observations and data reduction HE 1143-1810 was observed five times simultaneously by XMM-Newton  (Jansen et al. 2001)  and NuSTAR  (Harrison et al. 2013 ) in December 2017. The pointings had a net exposure of ∼20 ks each, and were separated by 2 days from each other. The log of the data sets is listed in Table  1. XMM-Newton observed the source with the optical monitor (OM;  Mason et al. 2001) , the EPIC cameras  (Strüder et al. 2001; Turner et al. 2001) , and the Reflection Grating  Spectrometer (RGS; den Herder et al. 2001). The data were processed using the XMM-Newton Science Analysis System (sas v18). The OM photometric filters were operated in the image mode; the images were taken with the U, UVW1, UVM2, and UVW2 filters, with an exposure time of 5 ks each. The OM data were processed with the sas pipeline omichain, and converted into data suitable for the spectral analysis using the sas task om2pha.
The EPIC-pn instrument operated in the Small Window mode, with the thick filter applied, while the EPIC-MOS instruments operated in the Timing mode. The spectral analysis is based on pn data, because of the higher effective area compared with MOS and to avoid crosscalibration uncertainties. The data show no significant pile-up as indicated by the sas task epatplot. Source extraction radii and screening for high-background intervals were determined through an iterative process that maximizes the signal-to-noise ratio  (Piconcelli et al. 2004 ). The background was extracted from circular regions with a radius of 50 arcsec, while the source extraction radii were allowed to be in the range 20-40 arcsec; the best extraction radius was found to be 40 arcsec for every iteration. The light curves were corrected for instrumental effects and were background-subtracted using the sas task epiclcorr.
The EPIC-pn spectra were grouped such that each spectral bin contained at least 30 counts, and without oversampling the spectral resolution by a factor greater than 3. Finally, the RGS data were extracted using the standard sas task rgsproc. The spectra from the two detectors RGS1 and RGS2 were not combined. The NuSTAR data were reduced using the standard pipeline (nupipeline) in the NuSTAR Data Analysis Software (nustardas, v1. 9. 3), using calibration files from NuSTAR caldb v20171002. Spectra and light curves were extracted using the standard tool nuproducts for each of the two hard X-ray detectors aboard NuS-TAR, located inside the corresponding focal plane modules A and B (FPMA and FPMB). The source data were extracted from circular regions with a radius of 75 arcsec, and the background was extracted from a blank area close to the source.
Spectral analysis We performed the spectral analysis with the xspec v12. 10 package  (Arnaud 1996). The RGS spectra were not rebinned and were analysed using the C-statistic (Cash 1979) to exploit the high spectral resolution of the gratings in the 0. 3-2 keV band. Broad-band fits (UV to X-ray, 0. 3-79 keV) were performed on the rebinned pn and NuSTAR spectra plus the OM photometric data, using the χ 2 minimization technique. All errors are quoted at the 90% confidence level (∆C = 2. 71 or ∆χ 2 = 2. 71) for one interesting parameter. In our fits we always included neutral absorption (phabs model in xspec) from Galactic hydrogen with column density N H = 3. 47 × 10 20 cm -2  (Kalberla et al. 2005). When using optical-UV data, we also included interstellar extinction (redden model in xspec) with E(B -V) = 0.