dataesr/scientific-paragraphs-categorization huggingface dataset:
Continuous wave searches Narrow-band Continuous GWs (CWs) from spinning deformed neutron stars  [169]  have not yet been observed in data recorded by LIGO and Virgo. Although the expected waveform signatures for this type of signal are well known, their small amplitude makes them extremely hard to detect, and any search needs to process long stretches of data. As the number of parameter space points to search grows with the observation time, the sensitivity of current searches is limited by the available computing power. Several established search methods exist with different trade-offs between sensitivity, robustness and computational efficiency. For searches covering wide parameter spaces, analysis pipelines usually follow a hierarchical approach  [170]  with multiple stages. The first stage evaluates a detection statistic over a dense grid of candidate waveforms.
Posterior samples can then be produced by re-sampling the chain of removed points and current live points according to their posterior probabilities. This method can take days to weeks to measure the parameters of a GW signal. Speeding up this process becomes more important as GW detectors become more sensitive and more signals need to be analysed. Some efforts at speeding up GW parameter estimation with ML have already been implemented in LIGO and Virgo data analysis codes. RIFT is a generic algorithm that uses Gaussian process or random forest regression to approximate marginal likelihoods appearing in Bayesian inference for compact binary parameters. By efficiently parallelizing, RIFT can produce inferences at a much faster speed  [212]. Multinest  [213]  is a generic algorithm that implements the nested sampling technique using ellipsoids.
In an ideal situation, Bayesian parameter estimation of GW data can provide the posterior probability distribution of the various source parameters. Cuts based on the maximum neutron star mass can then be applied to compute P(HasNS). Similarly, the fitting formula for the tidally-disrupted matter can be applied to the parameter posterior distributions to infer P(HasRemnant). However, the fastest LIGO-Virgo parameter estimation infrastructures currently available do not meet the low-latency requirements for electromagnetic (EM) follow-up in X-ray and optical wavelengths. Getting the most reliable EM-Bright values in absence of Bayesian posterior samples poses the most important challenge in source properties inference. Matched filter searches give point estimates of masses and spins that can be used for low-latency estimates of EM-bright properties.
Individual mass and spin components from matched filter searches have often large errors with respect to true parameter values. During the O2 run, LIGO and Virgo researchers estimated these errors by implementing a Fisher approximation method to construct an ellipsoidal region of the parameter space around the matched filter point estimates. However, this method is only effective for high SNR signals and it ignores the matched filter search biases in parameter measurements. A supervised ML algorithm was developed to address this issue in Ref. [221]. The algorithm uses the scikit-learn  [222]  KNeighborsClassifier method to classify a source as HasNS or HasRemnant. An input data set is created by injecting simulated signals into GW data and performing a search. A map between true values and matched filter search point estimates is then used to train the classifier.
Identification of electromagnetic counterparts to gravitational-wave sources Detecting EM emission from a GW source can considerably increase the knowledge of the astrophysical source properties and contribute to the validation of cosmological models. The authors in  [234]  applied an Artificial Neural Network (ANN) to localize GW signals from BBH mergers. The input data to the ANN consists of the arrival time delays, amplitude ratios, and phase differences from multiple GW detectors. For the purpose of GW source localization, they divided the sky into grids or sectors. The samples were labelled with the sector number to which they belonged and the ANN was trained to classify them into their correct sector. It was found, when testing their method with simulated BBH signals in Gaussian Advanced LIGO noise, that for coarse angular resolution (18-128 sectors), the model is able to classify unseen GW samples into their correct sector in more than 90% of cases, when a multi-labelling scheme is applied.
Conclusions In this paper, we have provided a review of ML applications to GW science. ML is an exciting area of development in the field of multi-messenger astrophysics. Over the years, LIGO and Virgo researchers have applied a variety of ML algorithms to many challenging problems in GW data analysis and detector characterization. The data from LIGO and Virgo is non-stationary and non-Gaussian. ML methods can be successfully used to improve the quality of these data. The probability of a GW signal candidate being astrophysical or transient detector noise can be determined by applying ML to the data from thousands of environmental and instrumental monitors. The origin of detector noise can be inferred by applying classification techniques to find different types of transient noise. Citizen science projects provide training data for some of these studies.
Acknowledgments We thank  Jess McIver  and  Damir Buskulic  for their feedback on this work. This publication is supported by work from  COST Action   CA17137 , supported by  COST (European Cooperation in Science and Technology). JP, KA and PE are supported by the  Australian Research Council Centre of Excellence for Gravitational Wave Discovery (OzGrav) , through project number  CE170100004. MC is supported by the  National Science Foundation  through award  PHY-1921006. LH is supported by the  Swiss National Science Foundation  with the Early Postdoc Mobility grant number  181461. DW and HG are supported by  Science and Technology Facilities Council (STFC)  grant  ST/L000946/1. RE is supported at the  University of Chicago  by the  Kavli Institute for Cosmological Physics  through an endowment from the  Kavli Foundation  and its founder Fred Kavli.
SM and ZM thank  Columbia University in the City of New York  for their generous support and are supported by the  National Science Foundation  under grant  CCF-1740391. SM and ZM acknowledge computing resources from  Columbia University's Shared Research Computing Facility project , which is supported by  NIH   Research Facility Improvement Grant   1G20RR030893-01 , and associated funds from the  New York State Empire State Development, Division of Science Technology and Innovation (NYSTAR)  Contract  C090171 , both awarded April 15, 2010. TDG acknowledges partial funding from the  Max Planck ETH Center for Learning Systems. Gravity Spy  and SC is partly supported by the  National Science Foundation  award INSPIRE  15-47880. VG is supported by the  LIGO Laboratory, NSF  grant  PHY-1764464.
DK is supported by the  Spanish Ministry of Science, Innovation and Universities  grant  FPA2016-76821  and the  Vicepresidència i Conselleria d'Innovació, Recerca i Turisme and Conselleria d'Educació i Universitats  of the  Govern de les Illes Balears. MB and FM are partially supported by the  Polish National Science Centre  grants no. 2016/22/E/ST9/00037  and  2017/26/M/ST9/00978. LIGO was constructed by the  California Institute of Technology and Massachusetts Institute of Technology  with funding from the  United States National Science Foundation  under grant  PHY-0757058. The authors are grateful for computational resources provided by the  LIGO Laboratory  and supported by the  National Science Foundation  Grants  PHY-0757058  and  PHY-0823459.
Icacinaceae Miers are a well-described family. However, the family lacks a comprehensive guide to endocarp morphology, which would be an important tool for interpreting the family's extensive fossil record of endocarp remains. In this survey, we describe fruits and endocarps of 88 species of Icacinaceae s. str. , four of Icacinaceae s. lat. (now Metteniusaceae) and one of Oncothecaceae. We investigated the value of the endocarp in species recognition. In addition, we generated an Xper 3 database with an associated e-key to increase the availability of raw data and the understanding of the characters used. This study documents great diversity in epicarp hairs, mesocarp thickness, endocarp ornamentation, tubercle shape (if present), endocarp structure and thickness, and the locule lining. Some morphological features appear diagnostic for individual genera.
Ambiguity in terminology / descriptions can lead to 'noise' in taxonomic studies, and different methods of morphological examination can lead to different results, e. g. , in measurements of characters such as endocarp thickness or tubercle length. Thus, taxonomic studies should include clear definitions (and / or explanatory diagrams) for critical characters / character states examined. In this spirit, a manual of leaf architecture was made (Leaf Architecture Working Group 1999;  Ellis et al. 2009)  to establish universal standards for analysis of leaf morphological characters. Similarly, for angiosperm wood, a database was created (InsideWood) to standardize the examination of wood characters and to host raw data  (Wheeler 2011 ). In addition, this database uses the International association of wood anatomists (IAWA) list of characters, which are well illustrated and explained  (Wheeler et al.
1989 ). Recently, new databases have been made using the Xper 2 software (Ung et al. 2010); important botanical examples include Palm-ID  (Thomas 2011; Thomas & De Franceschi 2013) , Pl@ntWood  (Sarmiento et al. 2011 ) and a key to Amazonian ferns  (Zuquim et al. 2017 ). These databases allow us to define items (species) and descriptive models (characters). Diverse functionalities are available, including interactive identification of a specimen starting from any descriptor. These tools can be used to complement descriptions and dichotomous keys, especially in cases like the study presented here. In this survey, we describe fruits and endocarps of Icacinaceae species (ca 56% of the total diversity). We aim to (1) provide a detailed reference for endocarp morphology across the family, (2) provide the first database using Xper 3 (the online version of Xper 2 ) dedicated to the Icacinaceae fruits and endocarps from modern species, and (3) discuss patterns of endocarp diversity and the value of endocarp characters in fossil identification and species assignment.
and dried with silica gel. Samples were then coated with gold-palladium for 5 minutes for examination with a scanning electron microscope (SEM, Jeol JCM6000, 15 kV), facilitating the observation of anatomical features, especially the endocarp wall layers and the locule surface (which sometimes bears papillae). Then, we rehydrated the specimens in boiling water for 10 minutes and removed the exocarp and mesocarp using a scalpel and a binocular microscope (Wild M3Z). Finally, the endocarp surface was studied using a binocular microscope (Wild M3Z) and imaged with a Leica DFC 420 camera. The technique of stacking was used for the macrophotography of the endocarps, and the final image was reconstructed using Photoshop CC 2014 software. When at least two specimens were available for a species, we conducted the anatomical and morphological studies separately using two fruits.
The adhesion and hardness of the mesocarp vary considerably among species and genera. In extreme cases of hardness (e. g. , Rhyticaryum macrocarpum Becc. ), the mesocarp was slashed and reimmersed in boiling water for a few additional minutes. Measurements were taken from the digital photographs using ImageJ software  (Rasband 2016). Measurements of cells were based on 10 cells per row. In addition, fruit size was obtained directly from the herbarium sheets. We measured at least five fruits per species. In many cases, a given fruit measurement is close to the endocarp measurement, because the mesocarp is usually highly collapsed / shriveled in herbarium samples. The sampling comprises 88 species of the approximately 160 accepted extant species of Icacinaceae s. str. (Stull et al. 2015). We added four species of Metteniusaceae H.
Database and E-key online The Xper 3 online database  (Ung et al. 2010 ) was used to record descriptions and observations. The interface includes tabs (Fig. 3. 1) with the following categories: items (specimens), descriptive model (characters and states), description (the link between the two previous tabs) and identification, which generates an interface for assisted identification (Figs  4 5 ). The database also was implemented in the local software Xper 2 , permitting access to the statistical part of the database (power of discrimination of characters, number of pictures, etc. ).
Online database The Xper 3 key for the Icacinaceae (online version) is available at the following link: http://icacinaceae-fruits. identificationkey. org/mkey. html This Xper 3 key, based on the following information, is a convenient means of synthesizing the data obtained from our study, and it offers a useful tool for identifying fruits of Icacinaceae, which is particularly relevant for studies of the fossil record. The database contains 93 items (Fig. 3. 2), with each including associated specimens (Fig. 3. 3) and morphological and anatomical pictures (Fig. 3. 4). The database contains 646 pictures associated with these items and descriptors. Fifty-six descriptors (i. e. , characters) were defined, 52 of them with associated images, and 121 states were used (average number of states per descriptor = 2.
16); among them, 103 are illustrated. Six descriptors were divided into 23 sub-descriptors. Depending on the choice made by the user, some sub-descriptors can be unavailable for the user. For example, the length of the tubercle depends on the presence of tubercles. The dataset includes 18 numerical and 38 categorical descriptors. The descriptors are in a dependent hierarchy (Appendix 1). The discriminative power of all descriptors is available in Appendix 2. In addition, we can apply different descriptor weights (one to three) as a means of emphasizing the most pertinent characters. We gave more importance to the following characters: fruit gynophore, epicarp hairs; endocarp keel, shape in lateral view, apex structure, base morphology, outer surface ornamentation; primary vascular bundle position; locule number, presence of tubercles, and presence of locule hairs.
The database contains 5208 potential combinations. The database is 95. 5% complete with 194 unknown data points. These unknown data mainly reflect fungal degradation of pertinent features on herbarium specimens. However, the species with the most missing data, Miquelia assamica (Griff. ) Mast. ex B. D. Jacks. , is 82. 1% complete, demonstrating the overall robustness of the dataset.
Use of the Xper 3 database The interactive identification part includes the descriptors on the left (Fig. 4. 1) and the remaining taxa in the table on the right (Fig. 4. 2). The user can begin the identification of the specimen with any descriptor. When the user clicks on a descriptor, the different character states appear, among which the user can choose. The choice of a character state induces removal of taxa in the table (Fig. 4. 2). The user can click on names of the remaining taxa (Fig. 4. 2, arrow) and obtain all the available information for the species in one sheet (Fig. 5 ): the species name (Fig. 5. 1), all associated pictures (Fig. 5. 2), the specimens studied (Fig. 5. 3) and all associated character states. Thus, the user can check the accuracy of the identification. A history of characters used is available (Fig.
Acknowledgements We would like to thank the curators of multiple herbaria who helped make specimens available for study:  Sovanmolly Hul  and  Myriam Gaudeul  (P),  Petra De Block (BR) ,  Roxali Bjomer  ( L, U, WAG ),  Timothy Utteridge  (K), and  James Solomon  (MO). We also thank  Sylvain Bouquin  for assistance with the Xper 3 Link. We are grateful to two anonymous reviewers for constructive suggestions and great improvement of this manuscript. This work was supported by a grant from  Agence Nationale de la Recherche  under the LabEx  ANR-10-LABX-0003-BCDiv , in the program " Investissements d'avenir" n_   ANR-11-IDEX-0004-02.
To obtain a deeper understanding of the global Ath-effector protein interactome, we conducted three systematic large-scale screenings with T3Es from Rps and Xcc, the first vascular pathogens screened in this manner. Additionally, we conducted an extensive literature survey to gather published Ath interactors of effector proteins from pathogens from four different kingdoms of life: Bacteria, Chromista, Fungi, and Animalia. Combining all these data allowed us to identify 100 new "effector hubs" (i. e. , Ath proteins interacting with two or more effectors). Together with Ath-Ath protein interactions retrieved from public databases, we generated an Ath-effector protein network that captures the wide diversity of Ath pathogens. This network allowed us to detect general trends of effector interference with the host proteome.
Here the choice of Rps T3Es was constrained by a pool maximum imposed by the screening method (see Materials and Methods). T3Es were picked according to their highest degree of conservation within the species complex  (Peeters et al. , 2013). On average, 10. 7 and 5. 7 Ath interactors were found per Rps and Xcc T3E. These Ath cDNA libraries had been previously used to test interactions with 57 and 32 effector proteins from Hpa and Psy, respectively, (8K space) and 46 effector proteins from Gor (12K space)  (Mukhtar et al. , 2011; Weßling et al. , 2014). The subset of interactions of effectors from Rps, Xcc, and Gor in the 8K space was used to compare with previously published Hpa and Psy data K E Y W O R D S database, effectors, interactomics, network, Ralstonia, Xanthomonas (Figure  1 ). In general, Rps effectors interacted on average with more Ath proteins than the other screened species; however, this difference is only statistically significant when compared to Gor effectors (one-tailed Wilcoxon signed-rank test p <.
9% for Psy, 58. 7% for Rps, 51. 7% for Hpa, 48. 8% for Xcc, and 35. 6% for Gor. The total number of species-specific effector interactors was 221 out of 299 (73. 9%). These data show that most effector interactors are kingdom-and species-specific. To evaluate whether Rps and Xcc effectors interact randomly or converge onto a common set of Ath proteins we performed simulations rewiring effector-Ath protein interactions within the 8K space. In these simulations, each effector was assigned randomly as many Ath proteins as it had interacted with in our screenings. Then, the number of interactors found on all simulations was plotted and compared with the experimental data (Figure  2a ). The number of effector interactors observed in our screenings was significantly lower than the numbers obtained in the random simulations for both Rps and Xcc.
| Manual curation of the literature to compile Arabidopsis-effector protein interactions In order to gather more knowledge on Ath-effector protein interactions, we conducted an extensive literature search compiling data from a wider spectrum of bacterial, fungal, oomycete, and invertebrate effector proteins. We only considered published direct protein-protein interactions that had been confirmed by classic techniques such as Y2H, co-immunoprecipitation, pull-down, F I G U R E 1 Arabidopsis thaliana (Ath) degree of effector proteins from Glovinomyces orontii (Gor), Hyaloperonospora arabidopsidis (Hpa), Pseudomonas syringae (Psy), Xanthomonas campestris pv. campestris (Xcc), and Ralstonia pseudosolanacearum (Rps). Comparison of the Ath degree (i. e. , number of Ath interactors per effector) of effector proteins from Gor, Hpa, Psy, Xcc, and Rps found in the 8,000-Ath-cDNA collection (8K space).
F I G U R E 3 Overlap among effector interactors depending on the origin of the data set. Area-proportional Venn diagram showing the overlap among effector interactors identified in the largescale yeast two-hybrid (Y2H) screenings performed in this study, in similar large-scale Y2H already published, and in the manual curation of the literature. The total number of effector interactors coming from each dataset is indicated in parentheses.
| Construction of an interaction network involving Arabidopsis and effector proteins We constructed an Ath-effector protein interaction network compiling the previously described experimental and literature-compiled data with Ath-Ath protein interactions from public databases and the literature  (Stark et al. , 2006; Dreze et al. , 2011; Orchard et al. , 2014; Smakowska-Luzan et al. , 2018). From the total of 49,500 interactions compiled in this study, 48,597 were grouped into a single connected component constituting what we defined as our Ath-effector interactomic network (Table  S4 ). This network was constituted of 47,314 Ath-Ath and 1,283 Ath-effector protein interactions between 8,036 Ath proteins and 245 effector proteins. Effectors came from 23 different species, including bacteria (128 effectors), oomycetes (61 effectors), fungi (46 effectors), and invertebrates (10 effectors).
| EffectorK, an online interactive knowledge database to explore the Arabidopsis-effector interactomic data In order to facilitate the access and exploration of all the data pre- Additionally, EffectorK also allows users to find the shortest paths between two queried proteins in the network. In order to update, expand, and further improve EffectorK, we encourage users to submit their own interactomic data by filing in and sending a dedicated template available on the site. These data will be verified by the curator team prior to their incorporation in the database. More information about usage, content, and data submission is accessible online, under the tabs "Help" and "Contribute" of the database web server. Please contact us if you have any question or suggestions by email via contact@effectork.
| D ISCUSS I ON In this study we identified systematically Ath interactors of effectors from the vascular bacterial pathogens Rps and Xcc. We combined this information with other Ath interactors identified in similar experimental setups. Additionally, we conducted an extensive literature review to gather published Ath interactors of effectors from a wide variety of pathogens, including other bacterial species and also oomycete, fungal, and invertebrate pathogens. Studying this combined interactomic dataset allowed us to identify new trends of how effectors interfere with the plant proteome and evaluate whether previously described network principles were still supported on a wider scale. We showed that there are no substantial differences in terms of connectivity among the effectomes of five different pathogen species screened systematically (Figure  1 ).
We have reinforced previously described intra-and interspecific convergence of effector F I G U R E 6 Graphical representation of interactomic data on EffectorK. Graphical representation of interactomic data from Xcc effector XopAC (AvrAC). XopAC, in purple, interacts with 36 Ath proteins, in green (only 12 shown for better visualization). The size of a protein node is proportional to its degree (e. g. CSN5B interacts with 50 proteins, BIK1 with six, and APK1A only with XopAC). The thickness of the connecting edges indicates the level of confidence: narrow edges represent physical interaction detected by only one technique, whereas thick edges indicate that the interaction has been detected by at least two independent techniques (e. g. XopAC interaction with BIK1 has been detected by coimmunoprecipitation and pulldown assays, whereas the interaction with APK1A, only by Y2H) targeting with effectors from two new species  (Mukhtar et al.