dataesr/scientific-paragraphs-categorization huggingface dataset:
3. Here we start from an initial condition U (x, 0) = K/2 for x ∈ [-5, 5] ×  [5, 5]. Using an explicit Euler scheme in time, we compute the finite difference approximation with a regular mesh size δx = 0. 05, over a bounded domain with Neumann boundary conditions, i. e. ∇U (x, t) = 0 when x is on the boundary of the domain. Matlab codes are available at https://osf. io/w5utd/. We observe in panels (a,b) that the corresponding random fields tends to be much more clustered than those generated by (S) (compare with Fig. 2a ). Moreover, increasing diffusion coefficients D tend to create larger clusters. As expected from the discussion above, at a fixed time T , U (x, T ) has a very clear bimodal distribution despite the Gaussian random noise W t , with one mode close to 0 and the other one close to K (see Figs.
4. The unknown parameters are Θ = (B(1), B(2), σ, κ). The parameters are estimated by maximising the pseudo-likelihood L(Θ) in (29) when using the full spatial observation data, and by using Lc (Θ) in (30) when the information is restricted to the counting. We assumed the following constraints on the parameter values: Θ ∈ Γ := (-1, 1) × (-1, 1) × (0. 01, 1) × (0. 1, 10). We used the Matlab © built-in gradient-based minimization algorithm fmincon © with default options, and applied to -log( L) to compute the maximum pseudo-likelihood estimators (MPLE). The results are summarized in Table  2. As expected, the observation of the positions of the particles leads to a more accurate estimation compared to counting data. In addition, in this example, the use of the pseudo-likelihood (instead of the likelihood) and the simplifying approximation concerning the initial distribution of particles provides a quite satisfactory estimation accuracy when particle locations are observed.
However, the theoretical mathematical behaviour is unclear or unknown in some cases, but numerical solutions are possible. In this work, the exponent α was set to be less than or equal to 2. While the Fourier definition F(-(-∆) α/2 u)(ξ) = -ξ α F(u)(ξ) allows to consider values α > 2, the mechanistic construction of the fractional Laplacian proposed in Section 2 and the convolution formula (11) cannot be used for such values of α. Another approach uses hypersingular integrals based on higher order finite differences (at least of order α ) instead of the first order finite difference in (11) (see Part 1, Chapter 3 in  Samko, 2001). However, the interpretation of this operator in terms of particle motion is far from obvious. As a second line of research, algorithmic developments are required for these models, in particular in a spatio-temporal context where datasets can be large or massive.
0, 0. 5 and 0. 25 and rounded to the nearest integer (this varies the number of features that a model seeks to identify and use to explain the data). We train each U-Net model on 512 × 512 pixel tiles obtained from the original 2096 × 2096 pixel images with a learning rate of 0. 001 and a weight decay of 0. 005 with a weighted cross-entropy loss using the Adam optimizer  32  , whilst making extensive use of data augmentation to ensure better generalization to other images/datasets (Supplementary Methods). We used the Jaccard index (otherwise known as "intersection over union" or IoU; Supplementary Methods) to assess the performance of these models and tested whether test-time augmentation (TTA)  37  and prediction post-processing (Supplementary Methods) had a positive impact on performance.
Morphometric characterization After detecting and segmenting each cell, we calculate a set of cytomorphological descriptors for each one. The descriptors used here were implemented in a custom Python script, but the large majority are also present in widely used bio-image analysis software programs  39, 40  or described in publications reviewing morphometry in image analysis  41. We characterize every RBC and WBC using the features described in Supplementary Table  S4. Additionally, we characterize the nucleus of each WBC using a reduced set of features, also detailed in Supplementary Table  S4. Overall, we capture 53 features for each WBC (42 for the cellular characterization and 11 for the nuclear characterization) and 42 features for each RBC. To account for the different resolutions (0.
Code availability We have made the Haemorasis pipeline available in https://github. com/josegcpa/haemorasis and as a Docker container in https://hub. docker. com/repository/docker/josegcpa/blood-cell-detection (instructions to run this are provided in Supplementary Methods). The code necessary to run Morphotype analysis (milcomori) and the statistical analysis and plot generation (analysis-plotting) for this paper are available in https://github. com/josegcpa/wbs-prediction. The code necessary for training, testing and inference using the quality control network is available in https://github. com/josegcpa/quality-net. The code necessary for training, testing and inference using the U-Net is available in https://github. com/josegcpa/u-net-tf2. 3 Results.
Acknowledgements J. G. A. is supported by the  NIHR Cambridge BRC  and their opinions are not necessarily those of the NHS, the  NIHR  or the  Department of Health and Social Care. G. S. V. is funded by a  Cancer Research UK   Senior Cancer Fellowship  ( C22324/A23015 ) and work in his lab is also funded by the  European Research Council ,  Kay Kendall Leukaemia Fund, Blood Cancer UK  and the  Wellcome Trust. The authors would like to acknowledge  Drs. Martin Bessner ,  James Russell  and  Duncan Brian  for their efforts in annotating blood cells.
31  , selected for its fast inference and good predictive performance, was trained on 10,000 randomly selected 512×512 pixel images from 93 slides from the MLLS split into training (8,050 tiles from 75 PBS) and testing(1,950 tiles     large (high frequency of overlapping cells) or images were blurred (Supplementary Figure S1). We use the ImageNet-pretrained Densely Connected Convolutional Network DenseNet121, a deep-learning model previously trained on a very large dataset of diverse images, which we fine-tuned on our training and then tested on our non-overlapping testing set. By using a pre-trained model, we guarantee that our model already captures visual features important for human perception of what constitutes a good quality image. Tiles were artificially augmented (computationally altered) during training through random flips/rotations/JPEG compression.
, 1992). Molecular typing was performed using two complementary approaches, namely the multi-locus Variable Number Tandem Repeat (VNTR) and Multi-spacer Sequence Typing (MST) methods. First, VNTR analysis was conducted on loci VNTR4, VNTR7, VNTR10 and VNTR Lb4 as described in  Salaün et al. (2006). In brief, 5-7 μl of DNA were added to 45 μl of PCR mix ( © Qiagen) for PCR amplifications with 40-45 cycles. Annealing temperatures were 52°C for VNTR4 and VNTR10, 54°C for VNTR7 and 53°C for VNTRLb4. Second, samples identified as belonging to the L. interrogans serogroup Icterohaemorrhagiae by the VNTR analysis were further characterized using the discriminating MST3 locus. We relied on the protocol previously described by Zilber et al. (  2014 ) with an annealing temperature of 50°C. Amplification products were sent for sequencing ( © Genoscreen), and the resulting sequences were compared to the MST reference database  (Zilber et al.
For instance, in Madagascar, a probable spillover of L. mayottensis from endemic rodents to the invasive black rat was suggested  (Moseley et al. , 2018). However, currently available data may not be sufficient to rigorously test these respective hypotheses. Indeed, most molecular investigations of Leptospira, such as those conducted in the present study have several caveats in terms of bacterial diversity assessment. First, the characterization of Leptospira genotypes from poorly documented regions, such as Africa, often suffers from a lack of baseline microbiological and serological data which could serve as a reference dataset  (Mgodé et al. , 2015). Here, we combined MST and VNTR profiles, which proved to be quite efficient in obtaining well-discriminated genotypes. However, their complete taxonomic or serologic identification through comparison to already known profiles is very limited due to the absence of reference data from West Africa.
addition, all Praomys individuals, at least one Mastomys individual per site per session, the single house mouse captured (Mus musculus), as well as one giant African rat (Cricetomys) and one shrew (Crocidura) per district were unambiguously identified through cytochrome b gene sequencing from kidney-or spleen-extracted DNA (data are not shown, but see Dobigny et al. , 2008, and Berthier et al. , 2016,  for DNA sequencing and genotyping procedures). Reference datasets for barcoding-based taxonomic identifications were built from hundreds of precisely documented West African rodent specimens housed in the Centre of Biology for Population Management (CBGP) small mammal collections (see Dobigny et al. , 2008)  as well as from published data (e. g. Olayemi et al. , 2012 for Cricetomys; Jacquet et al.
Cell survival and proliferation Cell proliferation was monitored every 4 or 8 h by videomicroscopy (Incucyte Live-Cell Imaging System, Essen BioScience). The area of the well occupied by the cells was determined in several fields for each well using the Incucyte ® ZOOM software (Essen Bioscience) and expressed as a percentage of confluence. The cell proliferation rate was monitored by flow cytometry using either the Guava ® (Merck Millipore) or C6 BD Accuri cytometer using the lipophilic fluorophore PKH67 (Cell Linker Kit PKH67, #MINI67, Sigma-Aldrich). The green fluorescent PKH67 stains lipids of the cell membrane without impairing cellular functions  (35, 36) , and its fluorescence decreases as it is diluted in cells that undergo division. Optimal dye concentrations were determined by following the supplier's protocols and recommendations.
Image analysis In order to determine the impact of TRIAP1 silencing on cells, a set of images was taken by TEM at ×2,900 magnification for each condition. The segmentation of plasma membranes, mitochondria, and nuclei was performed manually in each cell using IMOD software  (39). Quantitative parameters (mitochondria number, size, and perimeter) from models were extracted using IMOD imodinfo prior to statistical analysis. The following parameters were calculated: Mito/cyto area that reflects the ratio of mitochondria number to cytoplasm area and the aspect factor (major axis/minor axis) that reflects lengthto-width ratio of mitochondria. Data are represented as mean +/-SEM of representative analyzed cells. Statistical comparisons using the non-parametric Mann-Whitney test were performed. p < 0.
After fragmentation, cDNA synthesis was performed and resulting fragments were used for dA tailing followed by ligation of TruSeq indexed adapters. PCR amplification was finally achieved to generate the final barcoded cDNA libraries. Libraries were equimolarly pooled and subjected to qPCR quantification using the KAPA library quantification kit (Roche). Sequencing was carried out on the NovaSeq 6000 instrument from Illumina based on a 2x100 mode (paired-end reads, 100 bases) using a S1 flow cell in order to obtain around 35 million clusters (70 million raw paired-end reads) per sample. Raw sequencing reads were first checked for quality using standard tools such as FastQC. Reads were then first aligned on an rRNA database (NCBI U13369. 1) with the Bowtie aligner (v1. 2) in order to evaluate potential rRNA contamination.
Remaining reads were then aligned on the Human hg38 reference genome with the Star Mapper (v2. 5. 3a) and the recommended ENCODE parameters with "-outFilter MismatchNoverLmax 0. 04" (see the STAR manual for details), and the GENCODE (v26) gene annotations. No rRNA contamination was detected (<1. 5%), and in average, more than 90% of sequencing reads were aligned on the genome. Counts per gene were then calculated with STAR using the "-quantMode GeneCounts" option. For exploratory analyses (PCA and clustering), raw counts were normalized with the DESeq procedure and transformed by the vst method. Clustering of samples was performed with correlation-based distance with Ward's linkage criterion using the 5,000 genes with the highest variance. Differential analysis was conducted with the limma/voom framework.
The software interface was Xcalibur (version 2. 1; Thermo Fisher Scientific, Courtaboeuf, France). The mass spectrometer was calibrated before each analysis in both ESI polarities using the manufacturer's predefined methods and recommended calibration mixture (external calibration). The Exactive mass spectrometer was operated with a capillary voltage at -3 kV in the negative ionization mode and 5 kV in the positive ionization mode and a capillary temperature set at 280°C. The sheath gas pressure and the auxiliary gas pressure were set, respectively, at 60 and 10 arbitrary units with nitrogen gas. The mass resolution power of the analyzer was 50,000 (full width at half maximum) at m/z 200, for singly charged ions. The detection was achieved from m/z 85 to 1,000 for RP conditions in the positive ionization mode and from m/z 75 to 1,000 for HILIC conditions in the negative ionization mode.
Data processing Raw files were converted to the mzML or mzXML format with the ProteoWizard software  (42). Mass spectra were processed using the xcms R package deployed in the Workflow4Metabolomics Galaxy platform (https:// workflow4metabolomics. org) (43). In particular, the peak detection and quantification were performed with the centWave algorithm by using a value of 10 ppm and peak width values of 10,40 (Hypersil C18), 15,90 (HILIC), and 15,40 (Phenomenex Kinetex C8).
Metabolite annotation and identification Annotation of metabolite features was performed using an in-house spectral database based on accurately measured masses and chromatographic retention times  (44). Confirmation of metabolite annotation was then accomplished by running additional LC-MS/MS experiments using an UltiMate chromatographic system combined with a Q Exactive mass spectrometer (Thermo Fisher Scientific) under non-resonant collision-induced dissociation conditions using higher-energy C-trap dissociation (HCD). Metabolite identification was based on at least two orthogonal criteria (accurately measured mass, isotopic pattern, MS/MS spectrum, and retention time) to be matched to those of an authentic chemical standard analyzed under the same analytical conditions, as proposed by the Metabolomics Standards Initiative  (45).
Thus, we identified 44 metabolites that significantly changed in TRIAP1-depleted cells relative to control cells. In order to identify biologically pertinent pathways that were significantly enriched in relation with metabolites differentially affected by the loss of TRIAP1, we used the Metabolite Set Enrichment Analysis (MSEA) tool of MetaboAnalyst and KEGG open database (53) (Figure  4C ). The top significantly enriched pathway was related to glutamine/ glutamate metabolism. Overall, the comprehensive combination of lipidomic and metabolomic data indicates that TRIAP1 deficiency results in changes in global lipidic and metabolic profiles of HCT116 colorectal cancer cells. For instance, TRIAP1 loss has a significant impact on the levels of various lipids, the synthesis of which depends on the endoplasmic reticulum (RE) homeostasis  (54).
Funding This research was supported by grants from the  French National Cancer Institute  ( INCa 2017-1-PL BIO-08 ) and the  Fondation ARC pour la Recherche sur le Cancer  ( ARC -PJA 20191209480 ). KN and CR were supported by PhD fellowships from the  French Ministry of Higher Education, Research and Innovation ,  Gustave Roussy Course of Excellence in Oncology-Fondation Philanthropia , and the  Fondation ARC  ( DOC20180507601 ). CR, EL, and GA were supported by fellowships from the  French National Cancer Institute  ( INCa 2017-1-PL BIO-08 ). GA is supported by the  Luxembourg National Research Fund  ( C21/BM/15850547 ). The  ICGex NGS platform of the Institut Curie  was supported by the grants  ANR-10-EQPX-03  ( Equipex ) and  ANR-10-INBS-09-08  ( France Geńomique Consortium ) from the  Agence Nationale de la Recherche  (" Investissements d'Avenir" program ), by the  ITMO-Cancer Aviesan (Plan Cancer III ) and by the  SiRIC-Curie program  ( SiRIC Grant   INCa-DGOS-465  and  INCa-DGOSInserm_12554 ).
High expression of ANXA2 in clinical datasets predicts poor prognosis Finally, to explore the involvement of ANXA2 in promoting cancer progression in humans, we examined whether its expression was correlated with prognosis in various cancers. Kaplan-Meier plots show that high ANXA2 gene expression identifies poor prognosis patients with lung, gastric and ovarian cancer (Figure  9a ). This was true in the whole population and in subgroups with advanced disease defined by high grade or stage. In breast cancer, high ANXA2 expression was only correlated with poor survival in patients suffering from triple-negative breast cancer (Figure  9b ). We conclude that poor prognosis tumors express high levels ANXA2 and infer that they do so to cope with high levels of membrane damage, making them excellent candidates for treatments targeting ANXA2.
p-Cas9HIFIv3 (IDT) with a 1. 2 ratio of gRNA/Cas9. After 10 minutes at RT, 2 x 10 5 MDA-MB-231 cells resuspended in Lonza SE solution were added to the CRISPR mix. Program CH-125 of the 4D-Nucleofector® (Lonza) was applied. Two or three days after transfection, cells were trypsinized and half of them were cultured while the other half was pelleted, lysed and used as PCR template using Phire Tissue Direct PCR Master Mix (ThermoFisher Scientific). PCR amplification of the targeted area was done following supplier instructions with primers 5'-TGGGTAGAGGATGCTGACGA-3' and 5'-CAGAAGCTCTCCCTCCAGGT-3'. Sequencing of PCR products was done by Eurofins Genomics and Sanger data were analyzed to quantify Indels reflecting gene KO with DECODR algorithm (decodr. com  36 ) or ICE algorithm (ice. synthego.
Three independent experiments were performed. Images were analyzed using ImageJ software. Fluorescence microscopy. Cell imaging was performed using a conventional fluorescence microscope IX81 (Olympus, Tokyo, Japan) equipped with a UPLFLN20X/0. 50/WD2. 1mm and a UPLFLN60XO/0. 65/0. 65-1. 25/D0. 12 objectives. DAPI and Hoechst were observed using the U-MWU2 cube containing a band-pass excitation filter (330-385 nm), a dichroic mirror (threshold 400 nm) and a long-pass emission filter (threshold 420 nm). GFP and Alexa488 were observed using the U-MINIBA2 cube containing a band-pass excitation filter (470-490 nm), a dichroic mirror (threshold 505 nm), and a band-pass emission filter (510-550 nm). tdTomato was observed using the cube U-MWG2 containing a band-pass excitation filter (510-550 nm), a dichroic mirror (threshold 570 nm) and a long-pass emission filter (threshold 590 nm).
Around 500 cells/embryos were injected above the duct of cuvier in perivitelline space of the embryo. After checking the implantation with mammalian cells, zebrafish embryos were maintained at 35,5°C. Tumor imaging is done at 3-, 28-and 52-hour post injection (hpi). Survival analysis. The Kaplan-Meier plotter database (https://kmplot. com/analysis/)  39 was used to correlate overall survival rate to the level of ANXA2 mRNA expression. To determine the prognostic value of ANXA2, patients were split by auto-selecting the best cutoff, the period of follow-up was fixed at 120 months, the "only JetSet best probe set" option was selected, and biased arrays was excluded. Hazard ratios with 95% confidence interval and log-rank P value were calculated. The log-rank test calculates the mean chi-square for each event time from each group.
Acknowledgments This work was funded by la  Ligue Contre Le Cancer de Gironde  (grant  191796  and  238171  to A. B. ) and  Ligue Contre Le Cancer du Lot-et-Garonne  (grant  238294  to A. B. ). Christel Poujol  and  Sébastien Marais  are acknowledged for the help in membrane repair assays that were done in the Bordeaux Imaging Center a service unit of the  CNRS-INSERM and Bordeaux University , member of the national infrastructure  France BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ). The authors thank the staff of Vect'UB and CRISPRedit technology platforms (INSERM US  005 -CNRS UAR 3427-TBM-Core ,  University of Bordeaux, France ) for technical assistance. The authors would like to thank  Marie Paul Algeo  and  Marcia Campistron  for her assistance in the animal facility.
A main purpose of this observatory is to assess the long term trend of the main contaminant concentrations and fluxes, and to understand their behavior during extreme events such as floods or dam flushing operations. The dataset presented in this paper contains the concentrations and fluxes of SPM as well as the concentrations and fluxes of several particle bound contaminants of concern (PCB, TME, radionuclides), the particle size distribution and the particulate organic carbon of SPM. Sediment traps or continuous flow centrifuges were used to collect sufficient amount of SPM in order to conduct the measurements.
The current database provides time series of Suspended Solid Concentrations (SSC), SPM fluxes, Particle Size Distributions (PSD), Particulate Organic Carbon (POC) contents, and the concentrations and fluxes of several contaminants of interest  (Thollet et al. , 2021). The hydro-sedimentary and contaminant monitoring data from the Rhône River and the main tributaries are useful for the assessment of: • The annual and inter-annual fluxes of SPM and hydrophobic organic contaminants and TME and radionuclides; • The spatial and temporal variations of the contaminant concentrations; • The impact of extreme events (floods or dam flushing operations) in term of sediment budget and contaminant concentrations. 2 Description of the data and the functionality of the database (BDOH/OSR) In the database, two types of time series are stored.
Discontinuous time series are used for measurements on SPM samples that are collected from a start time to an end time (with no information in between sampling periods). Calculated time series are obtained by several transformations (including data completion) of the discontinuous time series and the SSC (Fig. 1 ). Time series can be interpolated, transformed and multiplied within the database application to derive computed time series from existing time series. The dataset includes the following measured parameters (discontinuous time series): • Particle Size Distribution -PSD (10%, 50%, 90% percentile diameters D10, D50, D90 -in µm); • Particulate Organic Carbon -POC (in g kg -1 dry weight); • Trace metal elements (TME): arsenic (As), cadmium (Cd), chromium (Cr), cobalt (Co), copper (Cu), lead (Pb), mercury (Hg), nickel (Ni), zinc (Zn) (in mg kg -1 dry weight); • Polychlorobiphenyls (PCB) :  PCB28, 52, 101, 118, 138, 153, 180  (in µg kg -1 dry weight); • Radionuclides: Organically Bound Tritium (OBT -in Bq kg -1 dry weight), Carbon-14 ( 14 C -in Bq kg -1 dry weight of carbon) and Cesium-137 ( 137 Cs -in Bq kg -1 dry weight).
The dataset includes the following calculated parameters: • Suspended Solid Concentration -SSC (mg L -1 ) either derived from in-situ turbidity measurements or by filtration of water samples as explained in the Method section. The procedure for data completion are presented in the Method section, • SPM flux (t s -1 ) was calculated in the database by multiplying water discharge (not made available in the dataset because it is released separately by data producers -in m 3 s -1 ) and SSC (Fig. 1 ). The owners of the water discharge data are reported in Tables  2  and 3 , • Contaminant flux (g s -1 or Bq s -1 ) was calculated in the database by multiplying the SPM flux and the contaminant concentration (Fig. 1 ). The procedures for flux computation and data completion of the discontinuous contaminant time series are presented in the Method section.