dataesr/scientific-paragraphs-categorization huggingface dataset:
A two-step hierarchical Hough search The approach used to analyze each year of data is based on a two-step hierarchical search for continuous signals from isolated neutron stars. In both steps, the weighted Hough transform is used to find signals whose frequency evolution fits the pattern produced by the Doppler shift and the spin-down in the time-frequency plane of the data. The search is done by splitting the frequency range in 0. 25 Hz bands and using the SFTs from multiple interferometers. Histograms of the percentage of SFTs that each detector has contributed in the first stage to the all-sky search. These figures correspond to a 0. 25 Hz band at 420 Hz for the first year of S5 data. The vertical axes are the number of sky-patches. In the first stage, and for each 0. 25 Hz band, we break up the sky into smaller patches with frequency dependent size in order to use the look up table approach to compute the Hough transform, which greatly reduces the computational cost.
The post-processing After the multi-interferometer Hough search is performed on each year of S5 data between 50 and 1000 Hz, a top list keeping the best 1000 candidates is produced for each 0. 25 Hz band. This step yields 3. 8 × 10 6 candidates for each year. The post-processing of these results has the following steps: (i) Remove those 0. 25 Hz bands that are affected by power lines or violin modes. A total of 96 bands are removed. These bands are given in table  2. (ii) Remove all the 0. 25 Hz bands for which the χ 2 vetoes more than a 95% of the elements in the top list. Figure  5  shows the χ 2 veto level for all the frequency bands for both years. With this criterion, 144 and 131 0. 25 Hz bands would be vetoed for the first and second year of data respectively. These first two steps leave a total 3548 bands in which we search for coincidence candidates and set upper limits; a total of 252 bands were discarded.
Noise lines were identified by previously performed searches (  [13, 14, 18, 17, 38] ) as well as the search described in this paper. Several techniques were used to identify the causes of outliers, including the calculation of the coherence between the interferometer output channel and physical environment monitoring channels and the computation of high resolution spectra. A dedicated analysis code "FScan"  [39]  was also created specifically for identification of instrumental artifacts. Problematic noise lines were recorded and monitored throughout S5. In addition, a number of particular checks were performed on the coincidence outliers, including: a detailed study of the full top-list results, for those 0. 25Hz bands where the candidates were found -in order to check if candidates are more dominant in a given year, or if they cluster in certain regions of parameter space; and a second search using the data of the two most sensitive detectors, H1 and L1 separately -in order to see if artifacts could be associated to a given detector, consistent with the observed spectra.
25 Hz wide. Each upper limit is based on the most significant event from each year in its 0. 25 Hz band. Our goal is to find the value of h 0 (denoted h 95% 0 ) such that 95% of the signal injections at this amplitude would be recovered by our search and are more significant than the most significant candidate from the actual search in that band, thus providing the 95% confidence all-sky upper limit on h 0. Ratio of the upper limits measured by means of Monte-Carlo injections in the multi-interferometer Hough search to the quantity h 95% 0 /C as defined in Equation  (28). The top figure corresponds to the first year of S5 data and the bottom one to the second year. The comparison is performed by doing 500 Monte-Carlo injections for 10 different amplitude in several small frequency bands. 153 and 144 frequency bands have been used for the first and second year respectively.
This yields a scale factor C of 8. 32 ± 0. 19 for the first year and 8. 25 ± 0. 16 for the second year of S5. With these values we proceed to set the upper limits for all the frequency bands. The validity of equation (  28 ) was studied in  [10]  using LIGO S4 data. In that paper upper limits were measured for each 0. 25 Hz frequency band from 100 Hz to 1000 Hz using Monte Carlo injections and compared with those prescribed by this analytical approximation. Such comparison study showed that the values obtained using equation  (28)  have an error smaller than 5% for bands free of large instrumental disturbances. For an in-depth study of how to analytically estimate the sensitivity of wide parameter searches for gravitational-wave pulsars, we refer the reader to  [30]. The 95% confidence all-sky upper limits on h 0 from this multi-interferometer search for each year of S5 data are shown in figure  10.
Alternative strategies and future improvements The search presented in this paper is more robust than but not as sensitive as the hierarchical all-sky search performed by the Einstein@Home distributed computing project on the same data  [18] , which for example, in the 0. 5 Hz-wide band at 152. 5 Hz, excluded the presence of signals with a h 0 greater than 7. 6 × 10 -25 at a 90% confidence level. This later run used the Hough transform method to combine the information from coherent searches on a time scale of about a day and it was very computationally intensive. At the same time, the Einstein@Home search, due to its larger coherent baseline, is more sensitive to the fact that the second spin-down is not included in the search. The Hough transform method has also proven to be more robust against transient spectral disturbances than the StackSlide or PowerFlux semicoherent methods  [10].
Other strategies can be applied to perform all-sky multi-interferometer searches using the Hough transform operating on successive short Fourier transforms. In this section we estimate the sensitivity of the semi-coherent Hough search for two hypothetical searches to illustrate its capabilities, by either varying the duration of the total amount of data used in the multi-interferometer search, or by lowering the threshold for candidate selection. In the first case we consider a search over the full S5 data with the same criteria of selecting candidates as presented here, i. e. setting a threshold in the significance for a false alarm level equivalent to one candidate per 0. 25 Hz band, but using the full data. This first search would be more sensitive since we increase the number of SFTs to search over.
10 at 50 Hz to 6. 13 at 1000 Hz permitting deeper searchers than in previous Hough all-sky searches  [10]. A set of new features have been included into the multidetector Hough search code to be able to cope with large amounts of data and the memory limitations on the machines. In addition, a χ 2 veto has been applied for the first time for continuous gravitational wave searches. This veto might be very useful for the analysis of the most recent set of data produced by the LIGO and Virgo interferometers (science runs S6, VSR2 and VSR4) whose data at lower frequencies are characterized by larger contamination of non-Gaussian noise than for S5. Although the search presented here is not the most sensitive one on the same S5 data, this paper shows the potential of the new pipeline given the advantage of the lower computational cost of the Hough search and its robustness compared to other methods, and suggests further improvements to increase the sensitivity and overall efficiency of the analysis.
Acknowledgments The authors gratefully acknowledge the support of the  United States National Science Foundation  for the construction and operation of the  LIGO Laboratory , the  Science and Technology Facilities Council of the United Kingdom , the  Max-Planck-Society , and the  State of Niedersachsen/Germany  for support of the construction and operation of the GEO600 detector, and the  Italian Istituto Nazionale di Fisica Nucleare  and the  French Centre National de la Recherche Scientifique  for the construction and operation of the Virgo detector. The authors also gratefully acknowledge the support of the research by these agencies and by the  Australian Research Council , the  International Science Linkages program of the Commonwealth of Australia , the  Council of Scientific and Industrial Research of India , the  Istituto Nazionale di Fisica Nucleare of Italy , the  Spanish Ministerio de Economía y Competitividad , the  Conselleria d'Economia Hisenda i Innovació of the Govern de les Illes Balears , the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , the  Polish Ministry of Science and Higher Education , the  FOCUS Programme of Foundation for Polish Science , the  Royal Society , the  Scottish Funding Council , the  Scottish Universities Physics Alliance , The  National Aeronautics and Space Administration , the  Carnegie Trust , the  Leverhulme Trust , the  David and Lucile Packard Foundation , the  Research Corporation , and the  Alfred P.
Dataset and morphometric analyses We performed geometric morphometric analyses to assess the taxonomic affinity of the four new fossils. Geometric morphometrics is a core of procedures providing quantification of the global shape of a structure  [47] [48] [49]. Geometric morphometrics can provide tools in paleontology for diagnosing fossil taxa at different levels, and for estimating their taxonomic affinities with extant taxa  [31] [32]. Phylogenetic relationships cannot be determined owing to an inability to distinguish symplesiomorphy from synapomorphy, but remains one of the most robust methods for ascertaining gross affinities of fragmentary or problematic fossils (e. g. [18, 31, 32] ). Thus, while taxa can be robustly assigned to higher group any supraspecific taxa established based on geometric morphometrics run the risk of creating paraphyletic groups and therefore should still be tested in future studies by more complete material and cladistic analyses of additional character systems (e.
We attempted to maximize the morphological diversity of our dataset by selecting four species per tribe and five specimens per species, when possible. In addition, we included   1 ). As two of the four new fossils were attributed to the tribes Bombini and Anthophorini, we also assessed their generic attribution based on the dataset described in  Wappler et al. (2012)   [18]  (n = 336 specimens) and  Michez et al. (2009)   [31]  (n = 37 specimens) respectively (Tables 2 and 3 respectively). Forewings were photographed using an Olympus SZH10 microscope coupled with a Nikon D200 camera. Photographs were input to tps-UTILS 1. 56  [52]. The left forewing shape of the 632 specimens and of the four new fossils were captured from photographs by digitizing two-dimensional Cartesian coordinates of 18 landmarks placed on the wing veins (Fig.
1 ) with tps-DIG v2. 17  [53]. The 636 landmark configurations of the reference dataset were scaled, translated and rotated against the consensus configuration using the GLS Procrustes superimposition method to remove all of the non-shape differences and to separate the size and shape components of the form  [47, 54]. The superimposition was performed using R functions of the package ''geomorph''  [55]. The aligned landmark configurations were projected into the Euclidean space tangent to the curved Kendall's shape space to aid further statistical analyses. The closeness of the tangent space to the curved shape space was tested by calculating the least-squares regression slope and the correlation coefficient between the Procrustes distances in the shape space with the Euclidean distances in the tangent space  [56].
This variation amplitude of our dataset was calculated with tps-SMALL v1. 25  [57]. As some cross-veins were difficult to define on the body fossil of Euglossopteryx, the landmarks positions were checked by three authors (MD, DM and TD) and by performing a superimposition of the right forewing on the left forewing to accentuate the veins. Landmarks coordinates resulting from digitalization of wing shape of Anthophorini  [31] , Bombini  [18] , four new fossils and 632 original specimens are available in Tables S1, S2, S3 and S4 respectively. The entomological collections from where the specimens were loaned are available in Table  S4.
Validation of shape discrimination at different taxonomic levels Prior to assignment of the four new fossils, shape variation within the reference dataset and discrimination of the different taxa was assessed by Linear Discriminant Analyses (LDA) of the projected aligned configuration of landmarks. These analyses were performed at suprafamily (i. e. bees versus apoid wasps), family, subfamily, and tribal levels as a priori grouping by using the software R version 3. 0. 2 [58]. The effectiveness of the LDA for discriminating taxon was assessed by the percentages of individuals correctly classified to their original taxon (hit-ratio, HR) in a leaveone-out cross-validation procedure based on the posterior probabilities of assignment. Given the observed scores of an ''unknown'', the posterior probability (pp) equals the probability of the unit to belong to one group compared to all others.
Assignment of the bee fossils Taxonomic affinities of the new fossils were first assessed based on their score in the predictive discriminant space of shapes. After superimposition of the 636 landmark configurations (i. e. corresponding to the reference dataset and the four new fossils), aligned coordinates of the 632 specimens from the reference dataset were used to calculate the LDA. A unique superimposition of both the reference dataset and the assigned specimens is sometimes disregarded while it is of primary importance because GLS Procrustes superimposition is sampling dependent. We included a posteriori the four new fossil specimens in the computed LDA space as ''unknown'' specimens and calculated their score. Assignments of the new fossils were estimated by calculating the Mahalanobis Distance between ''unknowns'' and group mean of each taxa (Table  S5 ).
We also calculated posterior probabilities of assignment to confirm the assignment to one taxon (Table  S5 ). Assignments of the new fossils were performed in four consecutive analyses corresponding to different taxonomic levels of a priori grouping: suprafamily (i. e. apoid wasps versus bees), family, subfamily, and tribe. In order to further assess the generic taxonomic affinity of the two new fossils associated with the tribes Bombini and Anthophorini, the reference datasets assembled in Wappler et al. (2012)  [18]  and  Michez et al. (2009)   [31]  were used. PCA were computed to visualize shape affinities between the fossils and the genera in question.
Morphometric analysis The regression coefficient between the Procrustes distances and the Euclidean distances is close to 1 (0. 9999). This means that the linear tangent space closely approximates the shape space, thereby permitting us to be confident in the variation amplitude of our dataset. Shape variation within the reference dataset. In the morphometrics space defined by the LDA based on suprafamily a priori grouping, the two groups are perfectly discriminated: all 602 bee specimens and the 30 apoid wasp specimens are assigned to their original group by the cross-validation procedure. In the LDA space with family a priori grouping, the 10 families are well isolated from each other and only 10 specimens are not assigned to their original group by the cross-validation procedure, accounting Table  1.
30; pp = 1). Taxonomic affinities of the bombine fossil from the lacustrine beds of Cerdanya were also assessed based on the reference dataset described in  Wappler et al. (2012)   [18]  (Table  3 ). In the morphometric space defined by the PCA, this fossil is undoubtedly clustered with the genus Bombus (Fig. 2a ). A-posteriori assignment of the fossil in the discriminant shape space based on the subgenera of Bombus does not allow a reliable subgeneric attribution. Taxonomic affinities of the anthophorine fossil from Cereste were also assessed based on the reference dataset described in  Michez et al. (2009)   [31]  (Table  3 ). In the morphometric space defined by the PCA, this fossil is undoubtedly clustered with the group of the Habropoda sensu lato, and more particularly with the genus Habropoda (Fig.
An affinity between E. biesmeijeri with euglossines is certainly plausible given the distribution and ecology of the latter. Moreover, orchid bees were certainly present in the Tertiary of North America as evidenced by fossils in Early Miocene amber of the region (e. g. [63, 64] ). We are confident that E. biesmeijeri is near to the Euglossini and may even belong to the crown group, but this will require further and more completely preserved material and it could be a stem-group euglossine. Future studies encompassing a dataset built around a maximal sampling of corbiculate bee genera would be most beneficial for further refining the potential affinities of this enigmatic fossil. In particularly, it would be very interesting to discover whether the jugal lobe of the hind wing was replaced by the distinctive jugal comb, a notable synapomorphy of Euglossini (e.
Indeed, these methods have proven useful in a variety of cases and lineages for discriminating taxa (e. g. [32, [69] [70] [71] ). Here we show that bees can be discriminated based on their forewing shape at a variety of taxonomic levels, ranging from subfamily and above. Discriminant analyses employed in our study discriminated Anthophila from the families of apoid wasps, and even among the currently recognized families and subfamilies with reasonable robustness. Morphological similarities at different taxonomical levels cannot be mistaken for phylogenetic relationships. They are merely similarities and this is highlighted by our own data. Exploration of our dataset representing significant parts of the bee forewing shape variation showed that phylogenetically close taxa are not always close in terms of overall similarity (e.
Acknowledgments We are very grateful for collection access to the curators of the following museum:  David Notton ,  National History Museum (London, UK );  Frederique Bakker ,  Naturalis Biodiversity Center (Leiden, NL );  Jeannine Bortels ,  University of Liege (Gembloux, BE );  Eliane De Coninck, Royal Museum of Central Africa (Tervuren, BE) ; and  Wouter Dekoninck ,  Royal Belgian Institute of Natural Sciences (Bruxelles, BE). We also would like to thank  Maryse Vanderplanck  and  Thomas Lecocq  ( University of Mons, BE ) for their precious help, and  Ahmet Murat Aytekin  ( Hacettepe University, TR ) for providing data on bumble bee wings.
For each of these species we calculated the number of step changes in Red List category, corresponding to the number of changes between adjacent category levels. The following levels were considered: 0, Least Concern; 1, Near Threatened; 2, Vulnerable; 3, Endangered; 4, Critically Endangered; 5, Critically Endangered (Possibly Extinct), Critically Endangered (Possibly Extinct in the Wild), Extinct in the Wild, or Extinct. 'Possibly Extinct' and 'Possibly Extinct in the Wild' are tags applied to Critically Endangered species that are likely to be (but not confirmed as) already extinct  [25]. Data Deficient species are not included. Negative step changes correspond to deteriorations in Red List status (i. e. increased extinction risk; e. g. , changing from Vulnerable to Extinct corresponds to -3 step changes), positive step changes correspond to improvements (i.
Sensitivity analysis to variation in knowledge We investigated how the results per country would be affected if all Data Deficient (DD) species were threatened, and that their Red List status had changed at the same average rate as the threatened species within each country. We obtained a measure of weigthted endemism in DD species per country u, Du, using formula 3 but just for the subset of species classified as DD. We assumed all past changes in Red List status to have taken place solely amongst threated species, and calculated a rate of average change per threatened species per country, C u , as the ratio between W u and R u. We then calculated, for each country, the expected rate of change in Red List status (W u '5D u. C u +W u ) and the the expected weighted threat (R u '5D u +R u ) if it is assumed that all DD species are threatened.
g. patients, cell-lines, tissues, etc. ), which helps the interpretation of the underpinning biology. Results: In the context of underpowered experiments, N-of-1-pathways predictions perform better or comparable to those of GSEA and Differentially Expressed Genes enrichment (DEG enrichment), within-and cross-datasets. N-of-1-pathways uncovered concordant PTBP1-dependent mechanisms across datasets (Odds-Ratios≥13, p-values≤1 × 10 -5 ), such as RNA splicing and cell cycle. In addition, it unveils tissue-specific mechanisms of alternatively transcribed PTBP1-dependent genesets. Furthermore, we demonstrate that GSEA and DEG Enrichment preclude accurate analysis on single paired samples. Conclusions: N-of-1-pathways enables robust and biologically relevant mechanism-level classifiers with small cohorts and one single paired samples that surpasses conventional methods.
We conducted these studies to unveil deregulated mechanisms in the context of the alternative splicing factor protein PTBP1 knockdown (Polypyrimidine tractbinding protein 1). PTBP1 was previously reported as a key player in alternative splicing of many genes associated to lineage-specific cell differentiation  [7]  or tumor genesis  [8, 9] , such as cell cycle. We previously demonstrated that PTBP1 depletion inhibits tumor growth, colony formation and invasiveness in vitro in ovarian tumor cells  [8, 9]. Transcriptome analyses of PTBP1depleted cells uncover deregulated genesets (mechanisms) and therefore, offer potential therapeutic target discovery. We used one previously reported single paired RNA-Seq sample as well as our new datasets derived from breast and ovarian cancer cell lines, and PTBP1-depleted and matched control samples.
Methods Dataset description. Three transcriptome datasets pertaining to PTBP1-depleted cell lines and matched controls were used: Datasets I, II and III. Descriptions are summarized in Table  1  and details of their respective experimental design are described in the first section of the Results. RNA-Seq dataset and preprocessing (Dataset I). The RNA-Seq dataset (Table  1 ) pertains to transcriptomes of PTBP1-depleted mouse neuroblastoma cell line CAD (Cath. A-Differentiated; a variant of CNS catecholaminergic cell line, Cath. A) and matched controls. The read counts are normalized by RPKM (Reads Per Kilobase of transcript per Million mapped reads). All measurements were log2 transformed. If several alternative transcripts referring to the same HGNC gene name were present, only the one with maximum expression was considered for further analysis.
To minimally transform or bias the data, we processed all the experiments without filtering genes with low expression. The entire GEO control and PTBP1-KD RPKM data (1+1 samples) were used for N-of-1-pathways analysis, while the list of 1. 5-fold deregulated genes between control and PTBP1-KD samples was provided and further enriched with the Fisher's Exact Test. Cell lines, culture conditions (Dataset II and III). The epithelial human breast cancer cell line MDA-MB231 (ER-/ PR-/ HER2-) were obtained from the American Type Culture Collection (Manassas, VA). The epithelial human ovarian tumor cell line A2780 was received as a generous gift from Dr. Thomas C. Hamilton (Fox Chase Cancer Center, Philadelphia, PA) Cancer Center, Philadelphia, PA). Cells were grown in DMEM supplemented with 10% fetal bovine serum (FBS), 2mM L-glutamine in a humid environment at 37°C, with 5% CO 2.