dataesr/scientific-paragraphs-categorization huggingface dataset:
ONTdeCIPHER requires two configuration files to run; 1) one containing the path to raw sequencing data (fastq and fast5 directories), the path to the sequencing_summary. txt file (optional), the minimum and maximum read lengths, the RaxML output name prefix (optional) and the version of the reference genome used, 2) the second is a tabulated file that contains the barcodes and the associated sample. The main option of the master script is (--step). The user can select one of the following values: pycoQC, pip_core, m_r_p or all. PycoQC will calculate sequencing metrics and generate interactive QC plots, pip_core will run ONTdeCIPHER core pipeline (ARTIC pipeline, seqKit, DeepTools, Sniffles and snpEff), m_r_p will run mafft, raxml, ete3 and Pangolin while all will run all steps. Output sub-directories are generated for each enabled step following a specific architecture (see the github repository for details).
Applications ONTdeCIPHER was tested on a subset of public clinical data of SARS-CoV-2 (Bioproject PRJNA675364) from Bull et al (2020) (see github repository test_data) and the SVs found mainly deletions, were comparable (see Supplementary information Table  S2 ). ONTdeCIPHER was also tested on data from wastewater samples collected in French Guiana (South America) to assess the spatio-temporal dynamics and prevalence of the SARS-CoV-2 viral variants (see Supplementary information Fig. S2 ). ONTdeCIPHER was compared to the closest available tool, porCOV  (Brandt et al. , 2021)  (see Supplementary information Table  S3 ).
Acknowledgements We acknowledge  Dr. Stéphane Calmant , director of the  IRD Centre at Cayenne ,  Dr. Mathieu Chouteau , researcher at  CNRS Guyane , and  Vincent Goujon , director of  CNRS Guyane , for their logistical support and the availability of the laboratory facilities at  Cayenne. We thank our local collaborators in French Guiana for their help to access wastewater samples (the  Cayenne Prefecture, CTG, ARS, CACL , the town hall of  Kourou, Sinnamary, Saint-Laurent, Apatou, Saint-Georges ). We also acknowledge  Marie-Ka Tilak  for her precious advice,.
In Trials I and II, the number of Welch ANOVA with Games-Howell as post hoc, data transformation to square root for normal distribution. 3 Welch ANOVA with Games-Howell as post hoc (data normally distributed, equal variances not assumed). 4 One-Way ANOVA with Bonferroni as post hoc. The growth rate was calculated as fresh weight (FW) at 14 days/FW of inoculum. Embryogenic tissue (ET) weight stands for the FW of all ET on the plate after 14 days of culture. Embryos/ gFW stands for counted cotyledonary somatic embryos adjusted for 1 g (FW) of ET placed for maturation. The mean values are presented with ± standard error of the mean. Different letters (a, b) indicate significant differences between the treatments. The results for each line in each treatment are presented in Supplementary Tables  1 2 3 4.
Statistical analysis ET growth and embryo yield were tested for normality with the Kolmogorov-Smirnov and Shapiro-Wilk tests, and for homogeneity of variance with the Levene test. If the data were not normally distributed, a non-parametric test (Mann-Whitney U-test) was used. Data with unequal variances were analyzed with Welch analysis of variance (ANOVA), with Games-Howell as post hoc, and transformed to square root for normality if required. Normally distributed data with equal variances were analyzed with one-way ANOVA with Bonferroni post hoc. The statistical tests used for each dataset in Trials I and II are specified in Table  2 , and when filter and clump cultivation were compared in Trial III, the Mann-Whitney U-test was used. Greenhouse survival in Trials I and II was analyzed with logistic regression, and treatment, line and placement in growth containers (row, column) were used as covariates.
The location of each plant inside the container was not significant and was therefore omitted from the final models. The biochemical analyses in Trial IV were analyzed with one-way ANOVA and with multiple comparisons of means with Tukey contrasts. The percentage of effect factor was calculated as a percentage of the sum squares. Hierarchical ascendant cluster analysis (HCA) was calculated using Ward's minimum variance clustering method, and the Euclidian distance as a measure of similarity. In all cases, the differences were considered significant when p < 0. 05. The statistical analysis was carried out using IBM statistics 27 or R software (version 4. 1. 3, © 2009-2022 RStudio, PBC).
Clustering was based on the entire available quantitative dataset (fructose, galactose, glucose, maltose, sucrose, myo-inositol, chiro-inositol, stachyose, raffinose, and starch and total protein content). Cold storage induced a slight increase in protein content up to 26 weeks of storage. This slight increase confirms that the accumulation of protein reserves in conifers occurs mainly during maturation  (Joy et al. , 1991; Cui et al. , 2021; Eliaśǒvaé t al. , 2022) , even before the acquisition of desiccation tolerance. In our study, the cold-induced protein content modification was also qualitative, with a significant reduction in certain storage proteins and the appearance of a few bands. While the embryos were stored on their nutrient-rich maturation medium, the decrease in storage proteins did not reveal a consumption of storage reserves for the functioning of the cells, but it marked the initiation of germination for the embryos  (Misra et al.
High resolution (HR) positive mode electrospray ionization (ESI + ) mass spectra were acquired on a LTQ-Orbitrap mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). The instrument settings were as follows: mass scan range m/z 100-1500 Da, capillary temperature 300 • C, sheath and auxiliary gas flow rates at 55 and 10 arbitrary units, spray voltage at 4. 2 kV, source current at 100 μA, capillary and tube lens voltages set at 50 and 120 V, respectively. The mass measurement was externally calibrated before starting the experiment. Each full MS scan was followed by data dependent MS/MS on the three most intense peaks using stepped collision-induced dissociation (CID) (normalized collision energy 35%, isolation width 2 Da, activation Q = 0. 250). Chromeleon Xpress 6. 8 (Dionex), Xcalibur 3.
A preliminary inspection was performed to check the homogeneity of the six chromatograms obtained per sample and of those of the QC samples. Data obtained were converted from. raw to. mzML format with the msconvert tool of ProteoWizard (version 3. 0. 21035) with the peak picking filter set to "vendor". Data were then processed with the W4M platform (Workflow4Metabolomics, https://workflow4metabolomics. usegalaxy. fr/) freely available after registration. Preprocessing of LC- MS data was done with the xcms program to detect (using the centWave algorithm), match and align peaks across samples and fill in missing peak data. Batch correction was performed using an "all loess pool" regression model, and variables were then filtered using blanks and QC. Only variables with a mean intensity higher than 10-fold the mean blank intensity, and a coefficient of variation in the QC lower than 30% were kept (884 variables).
For standard C. longa and C. xanthorrhiza, the data are reported on a dry-mass basis, i. e. in mg of compound/g of dry weight, taking into account their moisture contents. The limit of quantification (LOQ) was estimated in real samples as the concentration for which the selected signal exhibits a signal-to-noise ratio (S/N) of 10, the S/N value being calculated with the NMR processing software package (Topspin). With our experimental conditions, the LOQ was evaluated at ≈ 3. 10 -6 M for curcuminoids and piperine that have well-resolved signals in the downfield region of the spectrum. This value is based on the integration of the right part of the H6 doublet of BDMC accounting for 1 H. For turmerones whose signals used for quantification are located in the more crowded highfield region of the spectrum and less well resolved, the LOQ was ≈ 7.
UHPLC-HRMS profiling Extracts of two standard C. longa samples (S 31 and S 33) were first analyzed by UHPLC-HRMS ESI +. In agreement with published data  [1, 13] , three curcuminoids (curcumin: m/z 369. 1335, 11. 2 min; DMC: m/z 339. 1228, 10. 6 min; and BDMC: m/z 309. 1121, 10. 0 min) and three turmerones (aryl-turmerone: m/z 217. 1590, 18. 6 min; and α and/or β-turmerones: m/z 219. 1745, 21. 8 min) were detected as major compounds (Fig. 1A  illustrating S 31). The turmeric DS were then analyzed in the same conditions. They all contained the three curcuminoids except DS 28 in which BDMC was not observed (Fig. 1B ), and DS 26 and 27 that displayed identical chromatograms with curcumin and only traces of DMC and BDMC (Fig. 1C ). In contrast, LC-MS profiling revealed that nine DS, essentially those prepared from rhizome extracts, do not contain any turmerone (DS 23-28) (Fig.
[4]  that did not explicitly indicate the composition (C. longa extract or powder) of the six DS analyzed. The ratio Sum turmerones/Sum curcuminoids is highly variable from 0% to 207%. It is comprised between 66% and 82% for standard C. longa rhizomes (Table  3 ), 0% and 1. 8% in DS prepared from turmeric extracts except for DS 9 (77%), and between 18% and 93% in DS containing turmeric powders except for DS 6 (2%) and 2 (207%) (Table  4 ). The outof-range values for these three DS will be discussed later. For the two DS 7 and 8 containing a mixture of turmeric rhizome powder and extract, the values of the ratios are intermediary (16% and 10%, respectively) (Table  4 ). The ratio aryl-turmerone/Sum curcuminoids calculated from the data of Avula et al. [4]  is also variable: 15-114% for the three rhizomes analyzed (46-60% in our study) and 0.
We amplified a 625 bp genomic fragment around the loci targeted by the CFTR_1 sgRNAs or CFTR_2 sgRNA. The INDEL analysis performed by ICE software, showed an average INDEL frequency of 40% for CFTR_1 sgRNA, 32% for CFTR_2 sgRNA. The percentage of INDELs reached 44% when both guide RNAs were provided by the same nanoblade preparation. Toxicity was also evaluated by counting the number of organoids that developed nine days after treatment in the presence or in the absence of the nanoblades (Figure  4C ). Here again the nanoblades did not induce toxicity compared to the control conditions. Nanoblade-mediated gene editing was thus also highly efficient for mouse colon organoids underlining the ease and versatility of nanoblades for gene editing and KO generation in the mouse organoid model.
Nanoblades do not induce INDELs at the analysed off-target sites in murine organoids Another advantage of the nanoblades is that we provide Cas9 as a protein. The CRISPR/Cas9 gRNA complexes, so-called RNPs, contained in the VLPs are only transiently present once delivered into the targeted cell  (Gutierrez-Guerrero et al. , 2021). The fact that Cas9 is present in the cell for a limited time should decrease the number of cuts in off-target sites as shown previously  (Mangeot et al. , 2019b). Using the Cas-OFFinder software, we predicted potential off-target sites for each of our sgRNAs (Supplementary Tables  1  & 2 ). We included in our analysis only the off-target sites containing a protospacer adjacent motif (PAM). We assessed two off-target sites for the AR_1 sgRNA with 3 mismatches since no off-target sites with 1 or 2 mismatches were identified by Cas-OFFinder (Figure  5A  -supplementary Table  1 ).
This was confirmed by DECODR analysis. We also predicted the potential off-target sites for the sgRNAs CFTR_1 and CFTR_2 using the open access program Cas-OFFinder (Supplementary Tables  3  & 4 ; no more than 3 mismatches are listed). We proceeded as for AR gRNA off-targets and under these experimental conditions, no off-target gene editing was detected for the 4 different CFTR off-target positions analyzed (Figure  5C&D ).
These PCR products were then subjected to Sanger sequencing. Analysis of the resulting sequences by ICE confirmed that anti-hCFTR NBs resulted in over 90% INDELs (Figure  6B ). The high-level KO of hCFTR in HEK293T was confirmed by almost complete disappearance of hCFTR-HA tagged surface expression (Figure  6C ). As detected previously no cell death was induced by the NBs in 293T cells (Figure  6D ;  (Gutierrez-Guerrero et al. , 2021) ). Finally, the rectal organoids were upon dissociation incubated solely for 10 min with these validated anti-hCFTR NBs. We amplified a genomic fragment around the loci targeted by the hCFTR sgRNA. The INDEL analysis performed by DECODR software, showed an INDEL frequency up to 20% for the hCFTR genomic target site (Figure  6E&F ). Finally, no apparent toxicity was detected since the number of cells developing into human organoids was similar in the absence or presence of anti-GFP or anti-hCFTR nanoblades (Figure  6G ).
Acknowledgements Funding was received from the  CHEMAAV-ANR-19-CE18-0001  grant operated by the  French National Research Agency (ANR). This study was supported by research funding ( CRISPR screen Action ) from the  Canceropôle Provence-Alpes-côte d'Azur , the  French National Cancer Institute (INCa)  and the  Provence-Alpes-côte d'Azur Region. AK and VT received a PhD fellowship from the  French Ministry of Research. M. B. and M. M. E. are supported by an  FWO-SB (Flemish Research Foundation ) doctoral fellowship  1SE8122N  and  1S29917N  respectively; M. S. C. by a senior post-doctoral  FWO scholarship   12Z5920N  and  KU Leuven BOFZAP professorship. Cystic fibrosis research is funded by the  Belgian CF patient Association and Fund Alphonse Jean Forton  from the  King Baudouin Foundation  ( 2020-J1810150-E015 ) and by  FWO (SBO OrganID ;  S001221N ).
801 to 0. 856 for a RMSE estimated to 4. 27 µm. 1A ,B, the segmentation obtained is accurate in healthy areas where muscle fibers have well delimited and regular shapes, whereas segmentation quality drops in inflammatory areas (green arrows), in areas with a high heterogeneity in fiber size and with elongated cells (orange arrows). To address this problem, we took advantage of Cellpose 2. 0 17 new feature that enables to retrain Cellpose ground model using fine-tuning techniques to specialize it to our specific dystrophic muscle dataset. The retraining was performed on nine patches extracted from our dataset with as largest diversity as possible on blue channel, as it revealed to be the most effective according to Table  1. During the retraining process (refer to Section "Methods"), there were always a trade-off to make between large cell segmentation and small fibers segmentation.
The third platform, further referred to as M3-GPU, is composed of 2 AMD EPYC 7502 2. 5GHz processors with 32 cores and 512 GB de RAM and a Nvidia Quadro RTX 5000 GPU with 16 GB of memory. For the 'default' version, we measured the mean Cellpose execution time over all WSIs of the dataset, each computed five times on each platform. For the 'retrained' tiled version, we summed the computation time of each tile to get the computation time of a WSI. As for the 'default' version, we averaged the computation time of all dataset WSIs of five iterations done on each platform. For sake of precision, each WSI is in average 120 MB. The summary of results obtained is available in Fig. 5. Running only on CPU, whether on M1-CPU or M2-CPU, the average computation time is about 20 min per WSI for the default model and about 15 min with the retrained one.
Conclusion and discussion In this work, we describe MyoSOTHES, a tuned workflow designed for quantifying skeletal muscle fiber diameters and CNF on whole slide images of HE stained sections to assess muscle dystrophy. This workflow integrates the generalist Deep Learning algorithm Cellpose, designed for cellular segmentation, which we adapted for myofibers recognition. It is coupled with open-source analysis software QuPath for biological parameters quantification. MyoSOTHES demonstrates its improved performance compared to baseline workflow when evaluating the segmentation quality against manually determined ground truth with an F1-score increasing from 0. 801 to 0. 919 and RMSE improved of 31% in comparison to best performing default model configuration. In order to be widely used, a particular focus was laid on computing consumption.
5 × 10 12 vg/kg, 1. 5 × 10 13 vg/kg, and 4. 5 × 10 13 vg/kg. Animals were euthanized one month after AAV vector administration. Skeletal muscles were dissected out and frozen in isopentane cooled in liquid nitrogen. Transverse cryosections (8 mm thickness) were prepared from frozen muscles, air dried, and stored at -80 • C. HE staining was performed on all sampled muscles. Whole section images of HE staining were acquired on Axioscan Z1 (ZEISS, Germany), using a planapochromat 10 × magnitude 0. 45 NA objective (ZEISS, Germany). Pyramidal images were reconstructed using ZEN software (ZEISS, Germany). The study was performed in compliance with the ARRIVE guidelines. Dataset and ground truth creation. The dataset used in the context of this article is constituted of WSI of 50 mice having undergone the protocol described above.
Scripts description and usage. To perform automated analysis of MyoSOTHES, two python programs, several groovy QuPath-compatible and shell scripts have been implemented. Python scripts are written in python 3. 8. The first script dealing with the segmentation stage uses 2. 0. 4 Cellpose version with numpy version 1. 21. 6 and PyTorch 1. 11. 0. As execution parameters, the input cell diameter and the input channel vary according to configuration studied. In all models, the flow threshold is set to 0. 5 and the resampling option is set to true. The second python script modifies tile masks obtained for reconstruction, as described further in "Tiling and reconstruction strategy" subsection. It requires Pillow library version 8. 0. 1. Concerning groovy scripts, they have been produced using 0. 3.
1 version of QuPath. A first script aims at performing automatically the export of histological whole slide image into TIFF images to be compatible with Cellpose input format. In case of tiling, a script is available to export tiles containing tissue. Another script enables to superimpose Cellpose predicted masks on corresponding sections and assign them the prediction class. This script is available in two versions: the one simply importing one WSI mask, and a second version importing each tile mask and placing them at the right place before removing nested masks using "intersect" function from QuPath. Then, the following script to be executed is the one filtering imported masks. It executes a binary pixel classifier, based on a random forest, trained considering Gaussian, Laplacian of Gaussian and weighted deviation features, on red, green and blue channels, at scale 1.
0. Once masks filtered, their Feret's diameter is measured using the QuPath minimum diameter function accessible via the "Adding shape characteristics" option. Once Feret's diameter estimated, CNF ratio can be computed. To do so, the cell detection plugin with cell expansion set to 0 is applied, which detects nuclei in the entire image and stores them as a detection object. Then, for each mask, the hierarchy between regions of interests and detection is resolved, to assign which nuclei belong to which cell. The cell and nuclei boundaries are converted into polygons to measure the distance between nuclei boundary and cell boundary. If the distance is higher than a threshold of 2. 2 µ m, we consider that the cell is centrally nucleated. Those two operations are run using one script measuring biological parameters.
To perform error estimation, a script is produced to enable the import of ground truth masks stored in a GeoJSON format, which is the one produced by QuPath to export annotations. A final script provides the tools to compute IoU for True Positive, False Positive and False Negative detection, which are then interpreted for the estimation of recall, precision and F1-score. All QuPath tools used in script can be adapted to specific cases following the well detailed QuPath user guide available online  22. Computation support specification. Cellpose computation time measurements were performed using three different machines. The first one is a computer NUC10i7FNH equipped with an Intel Core i7-10710U 1. 10GHz processor, with 6 cores and 32 GB of RAM (Intel, USA). It executes Linux 5. 13. 0 with gcc 9.
4. 0, glibc 2. 31. The second machine is a a computer OptiPlex 7060 having an Intel Core i5-8400 2. 80GHz processor with 6 cores and 32 GB of RAM (Dell Inc. , USA). It runs Linux 5. 4. 0 with gcc 9. 4. 0, glibc 2. 31. The last one is a server AS -2023US-TR4 embedding 2 AMD EPYC 75022. 5GHz processors with 32 cores and 512 GB of RAM (Supermicro, USA) and a Nvidia Quadro RTX 5000 GPU with 16 GB of memory (NVidia, USA). Linux 5. 8. 0 with gcc 8. 4. 0, glibc 2. 31 and Cuda 11. 2 are installed. Segmentation quality measure. The measurement of the segmentation quality must be considered from two angles. Indeed, to have a good quantification of some parameters such as the diameter, it is necessary to be able to detect the largest quantity of fiber possible to avoid biasing the population of detected fiber and also to ensure that the detected fiber has good quality contours.
Consequently, to reconstruct the WSI masks without overlapping masks, we propose a python script which modifies tile masks as presented in Supplementary Fig. S4:  1. All masks belonging to upper and left 200 pixels width stripe will be removed by setting all pixel values to 0, except for those intersecting with lines x = 200 pixels and y = 200 pixels. 2. All masks cut by right and lower tile border are removed by replacing their pixel values by 0. The final step is to place each mask at the corresponding position on the WSI using the position stored in image name, which is made available in a QuPath script. Then, in some marginal cases, two masks are nested due to side effects. To avoid overrepresentation of one mask, the largest of the two nested masks is kept. The detection is performed thanks to the QuPath "intersect" function between annotations.
Acknowledgements The authors acknowledge the financial support of  Hi! PARIS Center  (https:// www. hi-paris. fr). Some of authors are Genopole's members, first French biocluster dedicated to genetic, biotechnologies and biotherapies. They are grateful to the "Imaging and  Cytometry Core Facility" of Genethon  for technical support, to  Ile-de-France Region, INSERM and GIP Genopole, Evry  for the purchase of the equipment.
Super-Resolution and Image Upsampling The task of image upscaling is a well-studied problem. Many methods use a subpixel movement through deliberate changes in the position of the image plane  [36] [37] [38] [39]  or analyze in-scene motion  40, 41  for resolution enhancement through analysis of image sequences. For single image processing, state-of-the-art methods apply data-driven approaches to train and employ deep neural networks (DNNs)  42  to obtain super-resolution images from low-resolution datasets. 4] [45] [46] [47] [48] [49] [50]  Here, we leverage these developments and apply them to the new kind of data provided by a single-photon camera.
51 ach sensor element can detect the advent of a single photon event. The time of an event was measured within a sampling period of 57. 3 ns with a 10-bit resolution of 1024 time bins and a bin width Δt bin of 56 ps. The camera uses a reverse timing to record the event time as the waiting time until the end of the measurement cycle. The sensing process is shown in Fig. 2. During the experiments, we captured two scenes with different motion content: "rotating fan" and "bursting balloon. " For each scene, we recorded data sets of 32 × 32 pixel positions and 5e5 samples (frames). For ease of use, recording and data analysis were performed on two different computers. In principle, however, both processes can be integrated to run on a single machine. Our implementation uses a combination of MATLAB  52  (for capturing data from the camera over USB3.