dataesr/scientific-paragraphs-categorization huggingface dataset:
DESC uses resources of the  IN2P3 Computing Center (CC-IN2P3-Lyon/Villeurbanne -France)  funded by the  Centre National de la Recherche Scientifique ; the  National Energy Research Scientific Computing Center , a  DOE Office of Science User Facility  supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231 ;  STFC DiRAC HPC Facilities , funded by  UK BIS National  E-infrastructure capital grants; and the UK particle physics grid, supported by the  GridPP Collaboration. This work was performed in part under  DOE  Contract  DE-AC02-76SF00515. We thank the developers and maintainers of the following software used at various stages of our catalogtesting suite: IPython (Perez & Granger 2007), Jupyter (),  NumPy  (van der Walt et al. 2011), SciPy (Jones et al.
After hybridization, microarrays were washed sequentially in wash buffer 1 (Agilent Technologies, 1 minute) and wash buffer 2 (Agilent Technologies, 37°C, 1 minute). Slides were scanned immediately after washing on an Agilent G2505C Microarray Scanner with Agilent Scan Control A. 8. 5. 1 software. The scanned images were analyzed with Feature Extraction Software 10. 10. 1. 1 (Agilent Technologies) using default parameters (protocol GE1_1010_Sep10 and Grid: 074809_D_F_20150624). All subsequent data analyses were done under R (www. r-project. org) using packages of Bioconductor (www. bioconductor. org). Raw data (median of pixel intensity) were imported into R using the read. maimages function from the limma package with the following weight function (assigning a weight of 1 or 0 to each spot): myfunw<-function(x) {okType<-x$ControlType==0; okFoundGreen<-x$gIsFound==1; okPos=x-$gIsPosAndSignif==1; okWellAbove<-x$gIsWellAboveBG==1; as.
numeric(okType & okFoundGreen & okPos & okWellAbove);}. We selected the spots with a minimal weight of 1 for 26 out of 32 microarrays or with a minimal weight of 6 per group from at least 1 experimental group for each tissue. Data were then stored in an ExpressionSet object and normalized by the quantile method using the normalize. quantiles function from the preprocessCore R library. Replicated probes on the array (identical ProbeName) were resolved by taking the median normalized signal of each set of replicated probes. Pathway analysis was performed using the ENRICHR web-based tool  (37) (38) (39). Gene expression data from DNA microarray analyses reported in this study have been deposited in NCBI's Gene Expression Omnibus (GEO GSE179564). After treatment with DNase I (Invitrogen) and reverse transcription of 1 mg of total RNA with Multiscribe Reverse Transcriptase, real-time quantitative PCR was performed with Fast SYBR Green Master Mix or TaqMan Fast Advanced Master Mix and QuantStudio5 real-time PCR system (Thermo Fisher Scientific).
Subsequently, immunoreactive proteins were blotted with anti-rabbit or mouse HRP-labeled secondary antibodies for 1 hour at room temperature, revealed by enhanced chemiluminescence reagent (SuperSignal West Femto, Thermo Fisher Scientific), and visualized using ChemiDoc MP Imaging System; data were analyzed using ImageLab 4. 2 (Bio-Rad). Fatty acid profiles in brown adipose tissue. Fatty acid composition of brown adipose tissue triglycerides was determined by capillary gas chromatography. Tissue samples were homogenized in methanol/ butylated hydroxy toluene (10 mg/L) and stored at -20°C until analysis. Neutral lipids (corresponding to an equivalent of 20 mg tissue) were extracted following the Folch method using chloroform/methanol (2:1 vol/vol), in the presence of the internal standard glyceryl trinonadecanoate (Sigma-Aldrich).
Acknowledgments The  following GenoToul (Toulouse network of technological platforms in life sciences ) facilities contributed to the work: Animal housing and phenotyping ( Xavier Sudre, Caroline Nevoit ),  GeT-TRiX  ( Yannick Lippi ), and  GeT Santé  ( Emeline Lhuillier ,  Frédéric Martins ). This work was supported by  INSERM ,  Université Toulouse III ,  European Foundation for the Study of Diabetes  ( EFSD/Novo Nordisk Programme for Diabetes Research in Europe 2019  to DL),  Fondation pour la Recherche Médicale  ( DEQ20170336720  to DL),  Agence Nationale de la Recherche  ( ANR-17-CE14-0015Hepadialogue  to DL and CP),  Région Occitanie  ( DIALOGUE  project to DL),  Astra-Zeneca France  (to DL), and  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  ( SPHERES, ERC Synergy Grant  agreement  856404  to DL).
large polytomy containing major groups (e. g. , †Scanilepiformes + Polypteridae, Chondrostei, and a poorly resolved neopterygian total group). Parsimony using implied weights: A different phylogenetic picture of 'early' actinopterygian interrelationships emerges when analyzing our phylogenetic dataset with implied weights  (Goloboff, 1993; Goloboff et al. , 2018)  using a gentle concave (K=12; see  Goloboff et al. , 2018). We used 44 best-fit trees (fit score = 68. 77) to produce a strict consensus tree (Appendix 1-figure  5 ). In the strict consensus, †Brachydegma is resolved as a stem neopterygian, on the basis of three synapomorphies of varying fit scores (f): presence of a vertical preopercle  (C. 116 ; f:0. 37); length of the median gular exceeding half the length of the lower jaw (C.
For Sectorial Search algorithms, minimum sector size was set to five and maximum sector size was set to 58, which corresponds to ~50% taxa in our matrix. All other parameters remained unchanged. To ensure an exhaustive search of the dataset, two separate analyses of three rounds each were conducted using an alternation of 1000 iterations of 'Ratchet' and 'Sectorial Search' algorithms. The first analysis comprised a round of Sectorial Search, followed by two rounds using Ratchet. The second analysis started with a Ratchet round, followed by a round of Sectorial Search and then another round of Ratchet. All trees including suboptimals were saved at the end of each round, but only optimal trees (MPTs) were kept in memory for running the following round. Each round in both analyses was always complemented by the 1000 iterations of 'Tree Fusing'.
Characters are assigned fit values (f) between 0 and 1, with a value of 0 indicating that the character is not homoplastic (see, for example, C. 74). We also conducted a Bayesian analysis of our dataset in MrBayes  (Ronquist et al. , 2012) , employing the same outgroup constraint as applied in parsimony analyses. The datatype was set to 'standard' (=morphological). We specified a gamma distribution for rates of character evolution, and indicated that invariant characters were not included in the matrix. We conducted two runs using the default of four chains, one cold and three heated. We assessed convergence by examining: the standard deviations of split frequencies; ESS values; and visual inspection of the trace of log likelihoods. We discarded the first 50% of sampled trees as burnin.
Acknowledgements S Pierce  and  J Cundiff  (both MCZ),  D Nelson  (UMMZ),  BEK Lindow  and  KM Gregersen  (both  NHMD ) are thanked for kindly providing access to fossil and/or recent material. MR  Sánchez-Villagra ,  C Romano  (both PIMUZ),  M Coates  ( University of Chicago ),  T Miyashita  ( Canadian Museum of Nature ),  T Simões  ( Harvard University ),  O Vernygora  ( University of Kentucky ),  A López-Arbarello (LMU) , and  M Véran  (MNHN) are thanked for useful discussions. MR  Sánchez-Villagra  is also thanked for additional administrative support to  TA. A Pradel  and  G Clément  (both  MNHN ) are thanked for facilitating access to the collections of the  MNHN. D Germain  and  P Vincent  (both  MNHN ) and the FOSFO team of the CR2P are acknowledged for further facilitating the research activities of TA at the  MNHN.
Data availability μCT raw and/or derived data are available on Morphosource. Links to parent directories for each studied specimen are given below. †Brachydegma caelatum (MCZ VPF-6503): http://www. morphosource. org/concern/media/000440974 †Brachydegma caelatum (MCZ VPF-6504): http://www. morphosource. org/concern/media/000441020 †Pteronisculus gunnari (NHMD VP 73588A): http:// www. morphosource. org/concern/media/000441157 †Parasemionotidae indet. (NHMD VP 74424A): http://www. morphosource. org/concern/media/000441197 Acipenser brevirostrum (UMMZ 64250): http://www. morphosource. org/concern/media/000441184 Phylogenetic matrix and trees available through Dryad at: https://doi. org/10. 5061/dryad. jsxksn0bz. The following datasets were generated:.
Berlese-Tullgren extracts were washed, arthropods were separated manually from soil debris and stored in 95% ethanol in a freezer at -20 °C until further analysis. Metabarcoding samples were extracted similarly with Berlese-Tullgren with the difference that equipment was thoroughly disinfected with bleach and rinsed with distilled water before and after each sample extraction. Metabarcoding samples were stored at -20 °C until being analyzed at the Hajibabaei laboratory at the University of Guelph, Canada, which occurred approximately three months after collection. Geographic coordinates of the samples are indicated in Ref. 33  and Fig. 1  summarizes the workflow of samples. Processing of traditional samples. Ants, springtails and termites were all identified via morphological and molecular data (individual-based DNA barcoding).
DNA barcoding using Sanger sequencing was conducted at the Centre for Biodiversity Genomics, University of Guelph, using methods described in Wilson  47. When possible, we sequenced a maximum of five individuals per species. In total, we generated 324 new COI sequences, corresponding to 328 specimens of 171 ants, 114 springtails and 43 termites. These sequences were added to BCI projects BCIFO, BCICL and BCIIS of the Barcode of Life Data System (BOLD; http:// www. barco dingl ife. org/ index. php), which summed 2,892 sequences (Appendix S1, Supplementary Table  S1 ). These molecular data were used to confirm identifications based on morphology. Each species was attributed a Barcode Index Number (BIN) according to BOLD, which can be used as a proxy taxonomic unit in absence of binomial identification  14  (e.
Purified second round PCR product was sequenced on an Illumina MiSeq using the v3 MiSeq sequencing kit (300 bp × 2). Bioinformatics. The bioinformatics of metabarcoding remains in flux  27. A wide array of metabarcoding tools exists, and their choice (and relevant parameters) is essential for meaningful results  52. Available tools include, for example, Mothur 53 , Qiime 54 or Obitools  55. However, an important restriction that pertains to all these tools is the use of customized databases for taxonomy classification. We relied on the mBRAVE cloud-based platform ("Multiplex Barcode Research and Visualization Environment"; http:// www. mbrave. net/ 56 ) since it seamlessly integrates BOLD data as reference datasets, which for BCI represent the best curated COI sequence datasets available. mBRAVE employs a typical workflow based on mature bioinformatic methods and is comparable to other available bioinformatics tools.
The sequence reads resulting from metabarcoding were uploaded into the project MBR-BCISOIL of mBRAVE. mBRAVE parameters were optimized for classifying sequences with BOLD datasets which have already been curated and the various sequence errors removed. Hence, the stringent read processing was a bit more relaxed. All mBRAVE parameters are indicated in Supplementary Table  S1 , but the workflow can be summarized as follows. The paired ends of the two overlapping amplicons were pooled (i. e. , treated as one fastq file without any merging) as opposed to merging, since (a) we were expecting very high matches to mBRAVE datasets (see below) with variation within a BIN being small; and (b) a sensitivity analysis, where we varied most mBRAVE parameters one by one to optimize the greatest number of BINs recovered in mBRAVE datasets, indicated an optimal recovery of BINs with pooled sequences (Supplementary Appendix S1 and Supplementary Table  S2 ).
Reads were trimmed at each end (25 bp) to remove primer and adapter sequence, then filtered for a minimum quality score of QV20 and for a minimum and maximum size of 100 bp and 500 bp, respectively (Appendix S1). Denoising, dereplication and removal of chimeric reads, were performed with VSEARCH  57  , integrated into the mBRAVE pipeline (Appendix S1). Erroneous sequences were detected by a divergence range from reference sequences, with parameters as indicated in Supplementary Table  S1. Sequences were identified to BIN level by matching them to reference BINs based on BOLD library datasets tailored for this study, using VSEARCH. These datasets included sequences obtained specifically for this study (2571 BINs for BCI arthropods) and publicly available datasets within BOLD (Appendix S1, Supplementary Table  S1 ).
BINs assignments were realized at 3%, a commonly employed threshold  58. Reads without a match to reference BOLD datasets were clustered into operational taxonomic units (OTU) based on (1) the minimum OTU size (n = 1 read); (2) OTU threshold (maximum distance inside a generated OTU, a conservative 2%); and (3) exclusion from the OTU threshold when sequencing error introduces spurious haplotypes (chimeras and/or sequence errors, 3%). The BOLD datasets that we used for analyses are currently the best ones available for ants, springtails and termites from BCI (Supplementary Appendix S1 and Supplementary Table  S1 ). It is important to note that, although sequence merging is often recommended to remove potentially erroneous sequences, we opted to pool our reads without merging as this retrieved a higher number of BINs.
Question1: similarity between traditional and metabarcoding samples. To compare species richness between sample types (traditional and metabarcoding) and taxa (prevalence data), we computed rarefaction curves and estimates of total species richness with the R package iNEXT  61. To visualize the differences in faunal composition between traditional and metabarcoding samples, we generated differential heat trees using the Metacoder R package  62. These trees help identifying the taxonomic hierarchy with terminal nodes representing either BINs or binomial species. With a prevalence matrix, we used the function 'compare_groups' of Metacoder and the resulting mean difference between treatments to plot the differential heat tree. The differences, however, were formally tested using a Procrustes rotation, which rotates a configuration (for example ordination results) to maximum similarity with another configuration.
First, for each dataset (traditional and metabarcoding), we pooled samples by the 10 locations at our study site (Table  1 ; Fig. 1 ) and considered matrices of species × locations, filled with the number of samples in which the species was recorded for each location (prevalence; maximum possible of 10 samples for each location). We then performed a non-metric multidimensional scaling (NMDS) for each of the traditional and metabarcoding matrices with the function 'metaMDS' of the R package vegan, followed by a Procrustes rotation of the two ordinations with the function 'procrustes' of the same package  63. The function 'protest' of vegan was used to tests for non-randomness ('significance') between the traditional and metabarcoding NMDS (999 permutations). We performed Procrustes rotations separately for ants, springtails and termite data, and either for all species surveyed or restricted to common species.
We then calculated the correlation between the two matrices using a Mantel test, computed with the function 'mantel' of the R package vegan (10,000 permutations  63  ). Question 4: regressions between biomass and read frequency. We considered only ants, for which we had reliable measurements of different variables accounting for body size, as well as an allometric correlation of body length to body weight. Analyses were restricted to ant species present in metabarcoding samples for which we had good measurements (1-5 individuals measured per species, n = 28 species). We tested linear and non-linear relationships with CurveExpert Professional  64  , including, for each species, as dependent variables the total number of reads, the relative read abundance  65  , the total number of sequences and prevalence in metabarcoding samples, and as independent variables either body length (mm), pronotum width (mm), Weber's length (mm) or body weight (g), measured from ant workers in collections of the ForestGEO Arthropod Initiative.
This analysis concerned only Formicidae and the species in categories "probable false negative" and "well surveyed", as sample size was too low for species in other categories (morphological measurements lacked for most of false positives). Discriminant analysis was performed with the function 'discrim' of Systat version 13. 1 (Systat Software, Inc. , Chicago, IL). Given that the barcodes for our false negative samples exist, we used the ecoPCR  66  program to run an in silico PCR to test whether or not our primers could amplify these regions. Barcodes were downloaded from the NCBI database to create a reference dataset to match with our PCR primer pairs allowing up to 15 mismatches between primer and target sequences. To test for differences between taxa (Question 6), whenever possible we kept analyses separate among taxa when investigating Questions 1-5.
Results Traditional samples yielded 1970, 5037 and 226 individuals classified into 80, 111 and 15 morphological species of ants, springtails and termites, respectively. Metabarcoding samples produced 30. 46 million reads, including 15. 96 million post filter reads (mean sequence length = 233 bp). Most of the reads representing BINs included arthropods and more than half of these BINs were assigned to the focal taxa studied (Supplementary Table  S3 ). We validated 396 BINs of arthropods (Supplementary Tables S4, S5) from our library datasets. Diptera, Hymenoptera, Coleoptera, Collembola and Isoptera were best represented in terms of species richness (unique BINs, Supplementary Table  S5 ). Doubtlessly, total species richness in these samples may be higher as most of arthropod species from BCI have not been barcoded yet and, hence, could not be validated with our library databases.
Differences between traditional and metabarcoding samples. Regarding our first question, metabarcoding appears to be rather selective at recording certain taxa and species. For example, the proportion of ant species which are overall common (first quartile of the distribution) in litter samples on BCI (data from the ForestGEO Arthropod Initiative, 2008-2019) and which were also recorded in our metabarcoding samples was 43%, whereas the corresponding figure for termites was 75%. From the viewpoint of the field researcher, metabarcoding represents yet another "sampling method" with inherent biases, but with the obvious benefit that time and expertise needed for handling and sorting samples is greatly reduced. Apart from the sensitivity • •• • • •• • :. • • • •• • • • • nature portfolio of PCR primers, the reasons for the selectivity observed at the species level are not well understood  72.
Explanations for the prevalence of "probable false negative species" are challenging. These species were reasonably well collected on BCI by the ForestGEO Arthropod Initiative during the period 2008-2019, and they all have BINs included in the reference libraries used. Thus, their absence in metabarcoding samples is unlikely to be due to local scarcity. The prevalence of probable false negative species is more problematic for long-term monitoring  23. These errors may be generated by primer biases  39  , but this appears less likely in our study, because metabarcoding samples recorded congeneric species well. Further, the in silico evaluation revealed the primers to be capable of amplifying the COI barcodes of the probable false negative species. Another source of error may occur when genuine metabarcodes are assigned to an incorrect taxon due to incomplete or inappropriate reference databases  39  , or to the use of inefficient assignment methods  41.
Acknowledgements We thank  ForestGEO  and the  Smithsonian Tropical Research Institute  for logistical support. This study was supported by  SENACYT  ( FID16-070  to YB and HB), the  Czech Science Foundation  ( GAČR 20-31295S  to YB) and ForestGEO. Grants from the  Smithsonian Institution Barcoding Opportunity   FY012 ,  FY015  and  FY018  (to YB) and in-kind help from the  Canadian Centre for DNA Barcoding  allowed to sequence specimens. YB and HB were supported by the  Sistema Nacional de Investigación, SENACYT, Panama , and GPAL by a  Junior GAČR grant  ( 19-15645Y ) and a  European Research Council  Grant (No. 669609 ). Arturo García-Gómez  and  Blanca E. Mejía-Recamier  helped refining identifications of Collembola.
Data availability The field protocol was deposited at protocols. io (https:// doi. org/ 10. 17504/ proto cols. io. bj9gk r3w). Sequences and BINs used as reference libraries are publicly accessible in BOLD projects as indicated in the text, and also deposited in GenBank databases under accession numbers KP845288-KP849461, KU745531-KU745532, KX072335-KX072563, MF922335-MF970719, MG030727, MK758129-MK770080, MN345316-MN621065 and MT357731. Details about sampling locations and a full list of species and their BINs are available in Ref. 33. Raw sequences were deposited at the Sequence Read Archive (https:// www. ncbi. nlm. nih. gov/ biopr oject/ PRJNA 668155).
These methods are now used in astrophysics to perform different classification tasks such as identifying bogus from real transient point like sources. We explore this method to provide a robust and flexible algorithm that could be included in any kind of transient detection pipeline. Methods. We built a Convolutional Neural Network (CNN) algorithm in order to perform a real/bogus classification task on transient candidate cutouts (subtraction residuals) provided by different kinds of optical telescopes. The training involved human-supervised labeling of the cutouts, which had been split in two balanced data sets with True and False point-like source candidates. We tested our CNN model on the candidates produced by two different transient detection pipelines. In addition, we provide several diagnostic tools to evaluate the classification performance of our CNN models.
Transient detection pipeline : inputs and outputs To find new transient sources in optical images, a detection pipeline usually acts on the so-called science-ready images or pre-processed images. These images are actually the raw images corrected from the dark, bias, offset and flat field master images and for which the astrometry has been calibrated with the WCS coordinates system using dedicated softwares like SCAMP 1  (Bertin 2006a)  or Astrometry. net 2. A second preprocessing step is usually needed to obtain a photometric calibration of the scientific images. This is done in several steps: 1. Extract the position of the point-like sources using, for example, the SExtractor software  (Bertin & Arnouts 1996)  or the python library SEP (Barbary 2016). 2. Estimate the Point-Spread Function (PSF) model of the image or in different regions of the images (PSF varying model) using softwares like PSFEx  (Bertin 2011 (Bertin , 2013)).
3. Subtract the image background and mask the brightest (saturated) stars that can lead to errors when measuring both their centroid positions and total flux in Analog Digital Units (ADU). This tasks can make use of the Photutils 3 Astropy package  (Bradley et al. 2021a ) and PSFEx. 4. Cross-match the detected sources in the image with known star catalogues and build the photometric model for their instrumental magnitudes. This is usually done using the Xmatch  (Boch et al. 2012; Pineau et al. 2020)  and Vizier services of the CDS, Strasbourg, France. After these steps, the science-ready images can be exploited to search for new transient sources. Some detection pipelines might also add few other steps by already removing most of the cosmic-ray induced-artefacts or identifying moving objects (asteroids and solar system objects).