dataesr/scientific-paragraphs-categorization huggingface dataset:
Prediction Using the movie dynamics, we benefited from all the information available in the calcium imaging movie. This approach allowed us to not rely on a demixing algorithm to produce the neuron's traces. Instead, by working directly on the raw calcium imaging movie, the algorithm has learned to identify a transient and distinguish overlap activity from a real transient. DeepCINAC achieves better performance than CaImAn and is able to achieve human performance level on some fields of view and cells. Additionally, we show that a classifier trained on a specific dataset ('Hippo-dvlt-6s') is able to generalize to other datasets ('Hippo-6m' and 'Barrel-ctx-6s'). DeepCINAC allows training of flexible classifiers whose generalization on new datasets can be improved by adding part of this new dataset to the training (at the cost of slightly reduced performance of the classifier on original data).
However, we show that generalization is not always achieved such as in the case of a classifier trained on 'Hippo-dvt' data and used to predict activity on some very different datasets. This is likely explained by the difference in calcium indicator, imaging rate and imaging resolution. We demonstrated that this limitation can be circumvented by training specific classifiers. Overall, this approach allowed us to create classifiers that scale to different developmental stages (from P5 to adult), different types of neurons (pyramidal cells and interneurons) as well as different indicators (GCaMP6s, GCaMP6m, GECO). On a side note, we observed during the visual inspection of the prediction through the GUI that the network might be able to handle fake transients due to X and Y movements or neuropil activation.
Predictions are fast, with a run-time of around 10 seconds by cell for 12500 frames, meaning less than three hours hours for 1000 cells. However, a GPU would be necessary to train the network on a big dataset. Already widely used by many calcium imaging labs  (Andalman et al. , 2019; Driscoll et al. , 2017; Gauthier and Tank, 2018; Katlowitz et al. , 2018) , CaImAn offers a performing and functional analysis pipeline. Even though the complex fine tuning of CaImAn parameters on the dataset might lead to a suboptimal spike inference from the model, we decided to compare CaImAn against our ground truth. The benchmarks remain limited to a small number of cells for which we established a ground truth and may be extended to more cells. Notably, a future approach could be to use more realistic simulated data such as done in a recent work  (Charles et al.
, 2019). In the model we used, each cell was represented by a segment of the field of view, in our case a 25 by 25 pixels (50 µm by 50 µm) window that allows complete coverage of the cell fluorescence and potential overlapping cells. Consequently, the network is able to generalize its prediction to recordings acquired with this resolution (2 µm / pixel). However, to be efficient on another calcium imaging dataset with a different resolution it would be necessary to train a new classifier adjusting the window size accordingly. Importantly, we trained the model on a selection of cells with valid segmentations; meaning that a cell is not represented by several contours. The inference performance of the classifier might decrease on cells whose segmentation was not properly achieved. Since precise spike inference cannot be experimentally assessed on the data, we chose to infer the activity of the cell defined by the fluorescence rise time instead of inferring the spikes.
Conclusion We built DeepCINAC basing the ground truth on visual inspection of the movie and training the classifier on movie segments. DeepCINAC offers a flexible, fast and easy-to-use toolbox to infer neuronal activity from a variety of two photon calcium imaging dataset, reaching human level performance. It provides the tools to measure its performance based on human evaluation. Currently, DeepCINAC provides several trained classifiers on CA1 twophoton calcium imaging at early postnatal stages; its performance might still be improved with more labeled data. In the future, we believe that a variety of classifiers collaboratively trained for specific datasets should be available to open access.
positive animals injected with both h-SynGCaMP6s and Cre-dependent TdTomato to identify interneurons by the overlap of GCaMP6s and TdTomato signals. Using the GUI, we manually categorized 743 cells from 85 recordings among three categories: interneuron, pyramidal cell and noisy cell. 283 TdTomato expressing cells were categorized as interneurons. 296 cells were categorized as putative pyramidal cells based on their localization in the pyramidal layer, their shape and their activity. Finally 164 cells were categorized as noisy cells, determined by visually estimating their signal-to-noise ratio. We used a total of 643 cells (245 interneurons, 245 putative pyramidal cells and 153 noisy ones) to train the cell type classifier and 100 cells (38 interneurons, 51 putative pyramidal cells and 11 noisy ones, not included in the training dataset) were used to evaluate it.
CINAC_v1 and CaImAn (CINAC_v3 median F1 score=69. 6%, CINAC_ v1 median F1 score=12. 9% and CaImAn median F1 score=63. 5%, Figure8Abottom panel). This increase in F1 score from CINAC_v1 to CINAC_v3 was due to an increase in the sensitivity of the classifier (CINAC_v1 median sensitivity=10%, CINAC_v3 median sensitivity=70. 3%, Figure8Atop panel) with a moderate loss in precision (CINAC_v1 median precision=95%, CINAC_v3 median precision= 81. 2%, Figure8Amiddle panel). Second, we showed that the 'Visual-ctx-6s' datasets specific classifiers (i. e. CINAC_V5 and CINAC_v6) performed better than CINAC_v1 and CaImAn (CINAC_v1 median F1 score=69. 9%, CINAC_v5 median F1 score=73. 9% and CINAC_v6 median F1 score=94. 7%, Figure8-2C). Because CINAC_v5 was trained exclusively on labelled data from the 'visual-ctx-6s' dataset it allows an increase in the classifier sensitivity (CINAC_v1 median sensitivity=59.
2%, CINAC_v5 median sensitivity=100%, Figure8-2A). However this increase was achieved at the cost of a reduced precision (CINAC_v1 median precision=96. 2%, CINAC_v5 median precision=59%, Figure8-2B), and overall a slight increase in the F1 score (Figure8-2 C). To improve the performance on this dataset, we extended the CINAC_v5 training set with 4 cells from the 'Hippo-dvt' to train CINAC_v6. This allows us to increase both sensitivity (CINAC_v6 median sensitivity=94%) and precision (CINAC_v6 median precision=94. 2%) of the classifier, leading to a large improvement of the F1 score (CINAC_v6 median F1 score=94. 7%, Figure8-2C). In a nutshell, when the dataset to analyse has different calcium dynamics, a new classifier specifically trained on this dataset would reach higher performance than CINAC_v1.
, bottom panel). This increase in F1 score from CINAC_v1 to CINAC_V4 was due to an increase in the sensitivity of the classifier (CINAC_v4 median sensitivity=92. 6%, CINAC_v1 median sensitivity=67. 1% , CaImAn median sensitivity=43. 5%, Figure 8C, top panel) with a moderate loss in precision (CINAC_v4 median precision=84. 3%, CINAC_v1 median precision=95. 9%, CaImAn median precision=92. 9%, Figure 8C, middle panel). Overall we confirm here that adding part of a new dataset to the training set of CINAC_v1 classifier allows us to improve the performance on this new dataset. Thus, one could use the training dataset of CINAC_v1 and add part of a new dataset to train a classifier that would achieve better performance than CINAC_v1 on this new data.
Acknowledgments and Funding source We would like to thank  Marco Bocchio ,  Laura Modol ,  Yannick Bollmann ,  Susanne Reichinnek  and  Vincent Villette  for providing us calcium imaging data. " Centre de Calcul Intensif d'Aix-Marseille " is acknowledged for granting access to its high performance computing resources. This work was supported by the  European Research Council  under the European Union's  FP7 and Horizon 2020 research and innovation program  (grants no. 242842  and  646925 ). J. D. was supported by the  Fondation pour la Recherche Médicale  (grant no. FDM20170638339 ). M. P was supported by the  Fondation pour la Recherche Médicale  (grant no. ARF20160936186 ).
Bioinformatic analyses Demultiplexing and. fastq files generation were performed using Bcl2fastq software (v. 1. 8. 4). Trimming of low-quality bases (Phred score < 10) and adaptercontaminated ends was performed with Trimmomatic (v. 0. 6. 1) (http://www. bioinformatics. babraham. ac. uk/projects/trim_galore/). Alignment and variant calling were performed in duplicates, using both an in-house pipeline following the BWA/GATK best practices and the Seqnext software (JSI, Ettenheim, Germany). For our inhouse bioinformatic pipeline, high-quality reads were mapped on the Human reference genome (GRCh37, hg19) using BWA-MEM (v. 0. 7. 1). SAMtools (http:// www. htslib. org/) and Picard (v. 1. 106, https://broadinstitute. github. io/picard/) were run to remove the duplicate reads. Mean depth of coverage was >171x for all samples with > 97.
6% covered at least 30X. Targeted regions with depth under 30X were selected with the Depth Of Coverage GATK tool and manually checked using bam files loaded on two reads viewers, i. e. Alamut Visual (Interactive Biosoftware, Rouen, France) and IGV (Broad institute, Cambridge, MA 02142, USA). For our first-line list of genes, all targeted regions were above 30X, excepted GPX1 and PGD first exons, which were sequenced by Sanger. Exon 1 of PIEZO1 was usually covered ~15X but was validated after a manual check of high-quality reads on bam viewers. Variant calling was performed using the Genome Analysis Toolkit (v. 3. 8) with the Haplotype Caller tool in GVCF mode. VQSR filters were applied according to GATK guidelines (when QD < 2, FS > 60 for snps and > 200 for indels, ReadPosRankSum <-8 for snps and > -20 for indels, when MQ < 40, MQRankSum <-12.
Variant annotation The quality-filtered variant calling files (VCF) obtained were gene-and region-based annotated on GRCh37, hg19 reference genome using Annovar  [36]  and dbSNP141. Functional annotation was made using in silico prediction tools and amino acid conservation scores (SIFT (http://sift. jcvi. org/), PolyPhen-2 (http://genetics. bwh. harvard. edu/pph2/), Mutation Taster (http://www. mutationtaster. org/), align GVGD (http://agvgd. iarc. fr/) and CADD (https://cadd. gs. washington. edu/)). MaxEntScan (http://genes. mit. edu/burgelab/maxent/Xmaxentscan_ scoreseq. html) and Human Splicing Finder3. 0 (http:// www. umd. be/HSF/) were used for splicing effect predictions. Protein domain functions were determined by using the SMART software (http://smart. embl-heidelberg. de/) and are available in Table  S3.
Patients Forty patients suffering from non-immune hemolysis with negative direct antiglobulin tests were submitted to WES sequencing. Clinical and biological data are available in Supplementary Tables  S5  and S6  (normal values in Additional file 1 and Supplementary Table  S7 ). Blood smear results and genetic results are available in Tables  S8  and S11. In silico analysis of genetic variants is available in Additional file 1, supplementary Table  S3. Twenty patients had a HS phenotype, among which four were apparently sporadic cases (P1, P3, P13, P14). The 20 other patients had UH, with either discordant or non-conclusive phenotypical tests. For several patients, EMA test results were discordant with ektacytometry. In the other cases, EMA test and ektacytometry were either normal or atypical.
This patient also carried an apparently homozygous variation of TRPV4 (p. P638L), present in the gnomAD database at an allelic frequency of 0. 03% without any homozygotes recorded (over > 138,000 subjects tested). TRPV4 mutations have been found in skeletal dysplasia, arthropathies and in a familial form of osteonecrosis  [54]. The TRPV4 mutation could explain osteonecrosis but not hemolysis. P26 carried HAMP and HFE variations probably explaining iron overload and CD46 variation for susceptibility to hemolysis. HAMP gene is out of our genes panel analysis. P33 harbored G6PD and SPTB variations, which combination cannot explain the severe phenotype. P34 had UH and iron overload, and variations in 3 genes: a homozygous p. H63D variation in HFE, which may contribute to iron overload even though its implication is not totally clear  [55, 56] , a heterozygous ABCG8 VUS and 2 heterozygous ADAMTS13 VUS.
Genetic results of HS patient (Continued)Table1legend: Variants description and classification according to ACMG guidelines as benign likely benign (LB) variant of uncertain significance (VUS) likely pathogenic (LP) or pathogenic (P). In silico study of missense variations was assessed thanks to Polyphen-2, Mutation taster and CADD score algorithm. HGMD professional and pubmed web interface were used to check for variants description in litterature. Abbreviations: het: heterozygous state; hom: homozygous state; hem: hemizygous state; F: female; M: male; HS: hereditary spherocytosis; gnomAD: genome agregation database https://gnomad. broadinstitute. org; ND: not done; NA: not applicable; DC: disease causing; P: polymorphism; PD: probably damaging; PossD: possibly damaging; B: benign.
0-2. 0 min), 30-48% B (2. 0-2. 5 min), 48-82% B (2. 5-11. 0 min), 82-99% B (11. 0-11. 5 min), 99% B was maintained for 3. 5 min followed by re-equilibration with 15% B for 3 min. Two microliter reconstituted samples were injected. External mass calibration of the Orbitrap prior to sample analysis was performed by flow injection of the calibration mix solution according to the manufacturer's instructions. High resolution data (resolution 70,000) was acquired by full scan from m/z 200-2000 with source voltage of 3500 V electrospray ionisation positive mode (ESI +) or -3600 V ESI negative mode (ESI -), capillary temperature of 275 °C, and sheath, auxiliary and sweep gas flow rates of 40, 10 and 5 arbitrary units, respectively. Data-dependent MS 2 data were collected with a mass resolution set to 35,000 recording a mass range of m/z 200-2000 and maximum trap fill time of 250 ms (full scan mode) or 120 ms (MS 2 scan mode).
External mass calibration of the Orbitrap prior to sample analysis was performed by flow injection of the calibration mix solution according to the manufacturer's instruction. High resolution data (resolution 25,000) were acquired by full scan from m/z 55 to 1100 with source voltage of 4000 V for ESI + and -4000 V for ESI -, capillary temperature of 325 °C, and sheath, auxiliary, and sweep gas flow rates of 40, 10, and five arbitrary units, respectively.
Data processing Raw data files were converted to mzXML format with MSconvert (v 3. 0. 1818) and pre-processed. Three lipid datafiles were corrupted and excluded from analysis. Lipid data were preprocessed with the XCMS package (v3. 0. 2) in the R programming environment (v3. 2. 2)  [40] , whereas polar metabolite data were preprocessed with the ADAP algorithm in mzMINE (v2. 31) (processing parameters provided in Additional file 2: Table  S2 )  [41, 42]. Features not detected in 100% of the QC samples were excluded. For polar metabolite data, features detected in at least one blank sample before peak filling step were removed. For lipid data, blank features were filtered out based on tstat and p-values (sample vs. blank tstat < 1 or those with tstat > 1 but p value ≥ 0. 05) generated by the diffreport function from the XCMS package.
Manual examination of EIC was conducted to filter out poorly integrated peaks, using build-in function in mzMINE for the polar metabolite data and the EICs generated by the diffreport function in the XCMS package for lipid data. After data cleaning, signal drift and batch effects were corrected by LOESS in the W4M Galaxy environment  [43] , and features with QC %CV > 30 were removed. Lipid identification was performed using LipidSearch software v4. 1. 16 on MS 2 datafiles (Thermo Fisher Scientific, USA) as previously described to generate a MS 2 -annotated lipid ID library and matched against the processed lipid data matrices based on parent mass and retention times  [44]. Lipid features without an MS 2 -annotated lipid ID were searched against online database including HMDB (https ://www.
hmdb. ca/), Metlin (https ://metli n. scrip ps. edu/) and Lipid Maps (https :// www. lipid maps. org/) based on m/z with less than 10 ppm mass error. Polar metabolites were annotated using an inhouse library based on authentic standards (AgResearch) analysed through HILIC LCMS analysis under conditions identical to the current study. Metabolic features without a hit in the library were searched against online database including HMDB and Metlin based on m/z with less than 15 ppm error.
The liver is a key organ in the regulation of energy homeostasis and metabolism, and is proposed as the primary site affected by excess VAT deposition  [14]. Hepatic IR may result from release by VAT into the portal vein of lipolytic products such as FFA and adipokines, as well as inflammatory cytokines by infiltrated macrophages  [79]. Importantly, the multivariate statistical modelling revealed improved discrimination between mNFG and mIFG metabotypes than clinical NFG and IFG classification, suggesting a metabolomics-derived signature was more reflective of the integrated changes across a broad range of cardiometabolic risk factors. NFG-mIFG individuals maybe more susceptible to rapid development of T2D than NFG-mNFG despite current normoglycaemia. Validation of these predictions are required through a longitudinal follow up study, or retrospectively on already existing longitudinal public datasets.
After complete gelation, it was extruded with a syringe pump (Razel Scientific Instrument, Fairfax, USA) at a 4 µL/s flow rate, which is similar to the bioprinter flow rate. Extrusion-based bioprinter. The Multiprint bioprinter used was a mechanical extrusion-based 3D printer designed by the Fablab Coh@bit (Technological University Institute of Bordeaux -IUT, Gradignan, France). Straight and non-bevelled Luer-lock needles of 18 and 22 gauge were employed. Computer-aided designs (CADs) were created with SketchUp software ® (Trimble Inc. , Sunnyvale, CA, USA). Multiprint was linked to BioPrinter ® software, and one printing template was used: a lattice with 10 mm strands, 3 m inter-strand space and 2 × 2 mm 2 pores (see Supplementary Fig. S1 ). Each layer of the lattice was composed of approximately 100 µL of hydrogel.
S1 ) was determined using ImageJ software (1. 52o, National Institute of Health, USA, Java 1. 8. 0_112). Lattices were also observed with a manual inverted microscope (DMI 3000B, Leica microsystems, Germany) coupled with the Leica Application Suite software (V3. 8. 0, Leica Microsystems, Germany) at a magnification of ×2. 5. Surfaces of the upper and lower layers of each pore were then determined with the Leica software. Lattice porosity (Pt, expressed as a percentage) was calculated using the formula F1, where Vp (mm 3 ) represents the measured pore volume and Vt (mm 3 ) represents the theoretical pore volume. Pores of a rather spherical shape were assimilated to truncated cones, and the F2 formula was used to determine their volumes (Vp) (h is the height of the lattice (mm), and R 1 and R 2 are the radius of the upper and lower layer, respectively (mm)) (see Supplementary Fig.
Statistical analysis. To handle reproducibility issues, experiments were performed in triplicate or more (n = 3). Rheological data were expressed as the mean ± SD. was used for all statistical analyses. Distribution normality was assessed with a Shapiro-Wilk test. Student's t-test (2-tailed) with Fisher variance testing was performed to determine differences between groups. For lattice strand distance, a Shapiro-Wilk test was used to assess distribution normality. If the distribution was normal, then Levene variance testing was performed. Depending on the Levene result, differences between groups were determined either by an ANOVA test (2-tailed) or a Kruskal-Wallis test (2-tailed). If the distribution was not normal, Kruskal-Wallis testing (2-tailed) was automatically performed. Three tools were used to obtain results: XLstat ® (Addinsoft, Paris, France) software, AnaStats website (http:// www.
-as well as planetary atmospheres -i. e. , lightning electromagnetic pulses. The usual data product for low frequency radio emissions observations is a "dynamic spectrum" (a time varying spectrogram). Other products in use are high temporal resolution waveform snapshots (see, e. g. ,  Briand et al 2016)  and catalogues of events (with a radio bursts classification, see, e. g. Marques et al 2017). In this frequency range, it is not yet possible to build imaging radio telescopes, so that the main source of knowledge is this time-frequency representation of the data. Until recently each low frequency data provider was storing their data products in local formats, or using standard formats with local metadata dictionaries, which prevented interoperability. The NASA space physics community has promoted the Common Data Format (CDF, http://cdf.
The MASER collaboration MASER (http://maser. lesia. obspm. fr) (Cecconi 2018 ) is a collaboration of teams throughout the world, whose aim is to facilitate the open access to science ready low frequency radio data. It is led by Observatoire de Paris (ObsParis) in France including people from LESIA (Laboratoire d'Etudes Spatiales et Instrumentation en Astrophysique) and PADC (Paris Astronomical Data Centre). It gathers scientists and software engineers from other space plasma and radio astronomy labs in France (Orléans, Nançay and Toulouse), and in the USA (University of Iowa, and University of California Los Angeles). Regular collaborations also exist with colleagues in Japan (Tohoku University) and Poland. The MASER team at ObsParis is organized around 4 tasks: (a) data distribution; (b) codes and models; (c) infrastructure and interfaces; and (d) the MaserLib open-source repository.
Task (a) is covering the full data lifecycle: from the production of the data (for ongoing and future data collection), the preparation of their distribution (data formatting, metadata, previews…), their validation (against the selected standards), the implementation of the access interfaces (web portal, virtual observatory, streaming interface…), as well as the curation of data when applicable. The details are described in a regularly updated Data Management Plan, following the open science policies developed at ObsParis (see Figure  1 ). The other French teams are working with ObsParis to implement similar policies on their data collections. The other tasks deal with infrastructure and software development. Task (b) focusses on modelling codes (e. g. , ray tracing code, radio observation modelling…), working with the science team to open the source code, share the simulation runs (through task (a)) and setup run-ondemand capabilities.
Data and Metadata Formats MASER promotes the use of community standards for the data formats and metadata dictionaries. The space physics community is using CDF, whereas the Solar physics remote sensing community is using files formatted in Flexible Image Transport System (FITS, https://fits. gsfc. nasa. gov) (Pence 2010). Interoperability also requires enforcing adoption of standard metadata. MASER thus implements standards from the Heliophysics and Planetary Science communities: Space Physics Archive Search and Extracted (SPASE, http://spasegroup. org) and ISTP metadata for Heliophysics; the Virtual European Solar and Planetary Access (VESPA, http://www. europlanet-vespa. eu) metadata for Planetary Sciences  (Erard 2018).