dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION Here, we succeeded in targeting and correcting by HDR the mutant alleles (in exon 4 and exon 10 of the UROS gene) in hiPSCs from a compound heterozygous CEP patient. To evaluate the genomic correction, we used deep sequencing and ICE/TIDER software that compared Sanger chromatograms. Generally, we obtained good correlations between them and the gold standard deep sequencing. However, low HDR rates are not detected by software analyses (<5%) and we observed some discrepancies between the results from these two pieces of software. These interesting tools, easy-to-use and costless, need to be used with precaution for precise quantification. We obtained full metabolic correction of CEP-hiPSCs by CRISPR/Cas9, by guide design and with disappearance of porphyrin accumulation. Correction of one mutation, either in exon 4 or in exon 10, led to the same partial UROS enzymatic rescue.
, 2013). In this situation, allele-specific correction may be the most suitable approach. To this end, the Pollard lab developed a new software, AlleleAnalyzer, to facilitate allele-specific gRNA design  (Keough et al. , 2019). Despite high versatility and efficacy, the CRISPR/Cas9 nuclease tool has still limitations. First, it bears a risk of off-target effects. The allele-specific guide design has no impact on putative off-target risk. In our model, we observed low percentages of indels in off-target sequences. This flaw could be solved by the new generation higher-fidelity Cas (eSpCas9, spCas9-HF1, and HypaCas9  [Ikeda et al. , 2019] ). For example, HypaCas9 enabled the discrimination of single-nucleotide polymorphisms and the introduction of monoallelic mutations in mouse zygotes  (Ikeda et al.
NGS Deep Sequencing for Allelic Outcomes Genomic DNA was extracted using NucleoSpin Tissue (Macherey-Nagel) according to the manufacturer's protocol. The genomic region flanking UROS exon4 was amplified by PCR (KAPA HiFi DNA Polymerase, Kapa Biosystems, Cape Town, South Africa) with adequate primers (Table  S1 ). PCR products were purified with Nu-cleoSpin Gel and PCR Clean-up (Macherey-Nagel). To prepare sequencing libraries, the Illumina Nextera XT Kit (Illumina, San Diego, CA, USA) was used and nested-PCR using Illumina primers was performed on purified PCR products. An Illumina MiSeq instrument (Illumina) was used for high-throughput sequencing. The average depth of each genome analysis was 30,000. Quality of paired-end reads was checked with FastQC (Galaxy, https:// usegalaxy. org/). Then, quantification of HDR was performed on the restriction site, and percentage of insertion and deletion and base rates were done at the cut site using Alamut Visual software.
Off-target Analysis For sgRNA targeting the exon 4 UROS locus, the top 10 predicted off-target sites, identified by CRISPOR software, were amplified in genome-edited corrected CEP-hiPSCs and subjected to Sanger  (legend on next page) sequencing, followed by comparison with non-transfected cells by ICE analysis. Primers used for off-target analysis are in Table  S1.
The slides were then placed under an Axio Imager 2 microscope with an epi-fluorescence source (Carl Zeiss AG, Oberkochen, Germany). The microscope was linked to the Metafer4 software for automated image acquisition and processing (MetaSystems, Altlussheim, Germany). Genomic DNA was extracted with the Wizard Kit (Promega, Madison, USA) following the protocol validated in the laboratory.
Statistics Statistical significance was inferred when necessary. GraphPad Prism 6 software was used for statistical analysis. Results are presented as mean ± SEM (standard error of the mean). Shapiro-Wilkinson test was done to assess the normality distribution. When it was positive, the two-tailed unpaired t test was done to compare means of two groups and one-way ANOVA, completed with unprotected Fisher's least significant difference test, was used to compare three groups. When normality distribution failed, non-parametric Kruskal-Wallis was used. All comparisons are shown with black bars. Null hypothesis was rejected when p < 0. 05. *p < 0. 05, **p < 0. 01, ***p < 0. 001; ns, non-significant.
ACKNOWLEDGMENTS We thank  Dr Feng Zhang  for sharing plasmids through  Addgene (Cambridge, MA, USA)  and  Vincent Pitard  and  Atika Zouine  from the cytometry platform at  Bordeaux University. This work was supported by the  Association Francaise contre les Myopathies  grant no. 20957 , and  Agence de Biomedecine. We thank  Florence Lichou ,  Beatrice Turcq , and  Nicolas Sevenet  ( Inserm U1218, Institut Bergonie, Bordeaux, France ) for NGS analysis, and Valerie Prouzet-Mauleon for helpful CRISPR discussion. We thank  Catherine Pain  and  Prof. Alain Taieb  for providing CEP patient keratinocytes ( Bordeaux Hospital ). We thank  Katia Obama ,  Isabelle Lamy , and  Sandrine Hamon  for administrative assistance and financial management. Figure 7. UROS Exon 4 Subclone Correction without Karyotypic Instability (A) Exon 4 subclone genotyping.
Data and Code Availability All next-generation sequencing datasets have been deposited in the NCBI database under BioProject accession no. PRJNA645083. The accession number for the exon 4 of CEP-hiPSC nontransfected reported in this paper is NCBI database: SAMN15491845. '', for transfected CEP-hiPSC with nuclease and RNA guide plasmids and ssODN is SAMN15491847 (Figure  5 ), and for corrected cells on exon 4 or porphyrin-negative cells is SAMN15491846 (Figure  6 ).
Then LysoDCs were gated on the basis of their strong MHCII expression and lack of CD4 and were subsequently analyzed for embigin, JAM-A, and CD24 expression. Although a first subpopulation representing 24. 1% ± 2. 7% of total LysoDCs (n = 24) did not express embigin, JAM-A, or CD24 (triple negative  [TN] ), a second one (22. 9% ± 3. 1%; n = 24) expressed only embigin (single positive  [SP] ), a third one (34. 6% ± 3. 7%; n = 24) expressed embigin and JAM-A but not CD24 (double positive [DP]), and finally, a fourth one (18. 3% ± 2. 7%; n = 24) expressed all three markers (triple positive  [TP] ; Figures  1A  and 1B ). We sorted quadruplicates of TN, SP, and TP and a quintuplicate of DP LysoDCs (see Figure  S1C  for gating strategy and post-sorting analysis of cell purity) to analyze their transcriptional profile using whole-mouse genome microarray (GEO: GSE133864).
Single-Cell RNA-Sequencing Reveals LysoDC Transcriptional Plasticity and Dynamics of Differentiation To deepen our knowledge of LysoDC subsets, we next performed single-cell RNA sequencing of LysoDCs (GEO: GSE141776). Through a t-stochastic neighbor embedding (t-SNE) analysis  (Amir et al. , 2013; van der Maaten and Hinton, 2008) , we identified two main groups of cells (Figure  2A ). The minor group represented about 10% of LysoDCs (Figure  2A , clusters 1 and 2), most of which expressing Emb but not F11r or Cd24a (SP LysoDCs; Figure  2B ). These SP LysoDCs displayed a strong cell cycle signature and partitioned into two clusters corresponding to the G1/S (cluster 1; Mcm6 and Ccne1 expression) and G2/M (cluster 2; Cenpa and Ccnb2 expression) phases of the cell cycle (Figure  2B ). These data suggest that SP Ly-soDCs are actively replicating and are the main precursor of LysoDCs.
, 2000; Fo ¨rster et al. , 1996; Gunn et al. , 1998; Legler et al. , 1998; Schaerli et al. , 2000). We thus investigated whether CXCR5 was involved in TN LysoDC location. Cxcr5 was not detected above background in the gene expression database of PP phagocytes (Figure  S3A ). Moreover, when we reconstituted lethally irradiated mice with a 1:1 mixture of bone marrow cells isolated from CX3CR1-GFP À/+ mice and CXCR5-deficient mice, both GFP + (originating from the CXCR5-proficient mice) and GFP À (originating from the CXCR5-deficient mice) monocyte-derived cells were able to reach the follicles (Figure  S3B ). Therefore, location of TN LysoDCs in follicles is likely independent of CXCR5. JAM-A expression by subepithelial LysoDCs was detected from the base to the top of the dome, but its expression increased while getting closer to the epithelium (Figures 3D, S3C, and S3D).
Microarray analysis The total RNA of PP-sorted phagocytes from 3 to 5 independent experiments was extracted with a QIAGEN RNAeasy PLUS micro kit. Quantity, quality and absence of genomic DNA contamination were assessed with a Bioanalyzer (Agilent). Microarray experiments were performed by the Plateforme Biopuces of Strasbourg (Strasbourg, France) using the GeneChipâ Mouse Gene 1. 0 ST array (Affymetrix). Quality controls and normalization of array data were performed as previously described  (Bonnardel et al. , 2015b). Data were normalized with our previous gene array data deposited under the accession number GEO: GSE65514 by Robust Multi-chip Analysis. Single cell RNA-sequencing analysis Next generation sequencing was performed by HalioDX (Marseille, France) using the Chromium Single Cell 3 0 kit from 10X genomics (California, USA) for the library preparation and the NextSeq 500/550 High Output Kit v2 (75 cycles) cartridges from Illumina for the sequencing.
ACKNOWLEDGMENTS We thank the histology, cytometry, and PICsL imaging core facilities for expert technical assistance;  Violaine Alunni  and  Christelle Thibault  from  Plateforme Biopuces et sequencage de l'IGBMC (Strasbourg, France ) for performing the microarray experiments;  Pierre Milpied  and  Laurine Gil  for helpful discussion and technical expertise in single-cell RNA sequencing; and  Benjamin Harding  for correcting the manuscript. This work was supported by institutional funding from  Centre National de la Recherche Scientifique and Institut National de la Santeet de la Recherche Medicale , by the  Agence Nationale de la Recherche  grant  ANR-10-INBS-04-01 France Bio Imaging , by the  I2HD  collaborative project developed jointly by CIML and  Sanofi , and by the  Fondation pour la Recherche Medicale (FRM)  grant  DEQ20170336745.
Blastocystis infection is associated with fecal microbiota modifications in rats. To evaluate the influence of Blastocystis infection on intestinal microbiota, the fecal microbiota composition of both infected and control rats was characterized by Illumina sequencing (SRA accession PRJNA607663). At D0 (i. e. before infection), no difference in fecal microbiota composition was observed between groups (Supplementary Fig. 5 ). At D31, bacterial richness was significantly greater in chronically-infected rats (p = 0. 02) (Fig. 4a, b ). In addition, the PCoA of beta-diversity clearly demonstrated significant differences (Adonis, p = 0. 001) in the evolution of microbial composition between infected and control rats (Fig. 4c ). This was accompanied by an increase in the relative abundance of Proteobacteria and Tenericutes in infected animals (Fig.
Colorectal distension (CRD) test. This test assesses visceral sensitivity by measuring the response of the colon when submitted to distension. The CRD protocol was adapted for rats from Larauche et al. 63. At D31, after 30 min in a restrainer, rats were anesthetized (3% Isoflurane) to introduce the CRD devices into the colon-rectum before being connected to an amplifier (Millar Instruments, Houston, TX, USA). The CRD protocol consisted of a set of distensions at constant pressures from 20 to 80 mm Hg, performed in duplicate as follows: 20 mm Hg steps of 20 sec duration with 4. 5 min inter-stimuli intervals. P-spectrum was extracted from raw data with "smoothsec" at a time constant of 1. 5 sec, followed by "absolute valor" and finally another "smoothsec" at a time constant of 1. 5 sec to exclude the slower and tonic changes.
Rats had ad libitum access to food, water and shelter. Raw data were analyzed with Ethovision XT software (Version 12, Noldus Information Technology, Wageningen, The Netherlands) and various specific behaviors were compared between infected and control rats. Elevated-Plus-Maze (EPM) test. At D29, EPM was performed as previously described  64. Animals were recorded for 5 min by Mediarecorder software. Analysis was performed by Ethovision XT software (version 12).
No. 203646) under the following conditions: denaturation 94 °C/3 min, followed by 28 cycles of 94 °C/30 sec, 53 °C/40 sec, and 72 °C/1 min, with a final elongation step 72 °C/5 min. Pooled PCR products were purified with calibrated Ampure XP beads. Illumina sequencing was performed and DNA libraries built by according to the Illumina TruSeq DNA library preparation protocol. We performed microbiota analysis on Quantitative Insights Into Microbial Ecology (QIIME, version 1. 8. 0) software package  67. In summary, sequences were demultiplexed to remove barcodes and primer sequences. Chimeric sequences were removed with USEARCH61  68. Sequences were clustered with USEARCH61 at a 97% homology threshold  69. Taxonomic analysis was performed with the Greengenes reference database (version 13-8). Alpha diversity measures the richness of single microbial taxa within a sample.
Saint-Quentin-Fallavier, France; Cat. No. CRM46975). Data were collected and peaks integrated with OpenLAB software (Agilent technologies, Santa Clara, SA, USA). Values were expressed in M/g of feces and the ratio was calculated between D31 and D0. Serine protease activity assay. About 200 mg of feces were diluted in 1 ml of distilled water, homogenized and centrifuged at 1000 g for 5 min. One hundred microliters of fecal supernatants were added to 100 µl of buffer (50 mM Tris-HCl pH 8, 1 mM CaCl 2 ) containing 100 µM specific serine protease substrate (suc-phe-ala-ala-phe-pNA) (Bachem, Switzerland; Cat. No. 4013859) and incubated at 37 °C for 6 h. Substrate cleavage was measured at 400 nm with NUNC 96-well plates (ThermoFisher Scientific, Waltham, MS, USA) and normalized to the total feces weight.
Acknowledgements This work was supported by co-financing from the  Region Auvergne-Rhône-Alpes  and  FEDER  in 2015 ("Thématiques émergentes"). Manon Defaye  and  Elodie Baudu  received grants from the  Auvergne-Rhône-Alpes Region. This work was supported by the  « Ministère de la Recherche et de la Technologie », Inserm  and  University Clermont Auvergne  [ UMR1107 ,  UMR1071]; INRA [USC-2018 ]; CNRS [UMR6023]. We thank  Bruno Pereira  for his help with statistical analysis ( Unité de Biostatistiques, Direction de la Recherche Clinique (DRCI ),  CHU de Clermont-Ferrand, France)  and the  "Centre Imagerie Cellulaire Santé" (CICS)  for the colon sections.
RESUMEN Los mosquitos picadores Culicoides son conocidos por ser vectores de transmisión de diversas enfermedades causadas por arbovirus afectando a humanos y animales alrededor del mundo. Un método óptimo y rápido de clasificación para estas y otras especies ha sido un desafío y una necesidad, especialmente en áreas con recursos limitados y problemas de salud pública. En este trabajo, nosotros desarrollamos una aplicación móvil llamada MosCla para clasificar dos especies de Culicoides utilizando el análisis del patrón morfológico de sus alas. La aplicación implementa un método de clasificación automático basado en el cálculo de siete características morfológicas extraídas de las imágenes del ala y una máquina de vectores de soporte como clasificador para producir la clase final de Pusillus u Obsoletus.
ABSTRACT Culicoides biting midges are known for being transmission vectors of various diseases caused by arboviruses affecting humans and animals around the world. An optimal and fast classification method for these and other species has been a challenge and a necessity, especially in areas with limited resources and public health problems. In this work, we developed a mobile application called MosCla to classify two Culicoides species using the morphological pattern analysis of their wings. The app implemented an automatic classification method based on the calculation of seven morphological features extracted from the wing image and a support vector machine classifier to produce the final classification of Pusillus or Obsoletus class. The proposed app was validated using an experimental dataset of 87 wing images with the 10-fold cross-validation method, and the classification performance in terms of the mean of the AUC metric obtained an outstanding score of 0.
The proposed application implements an automatic Culicoides species classification method using the Android development environment to produce the final app. An experimental dataset containing wing images from two BTV transmission vectors species C. pusillus (French Guyana), and C. obsoletus (France) will be used to benchmark the proposed approach. Also, a feasibility study was carried out to know how practical in terms of classification performance, execution time, and battery consumption, the implemented app could be. This study was made using two commercially available devices with limited resources, a Pixel C Tablet, and Pixel 2 phone (emulators). This app will constitute a feasible and practical tool to be used by biologists or entomologists researches on the field. The remainder of this paper is organized as follows: the Materials and Methods section, presents the step by step automatic Culicoides species classification implemented as an Android mobile application, the development environment used on this project, the proposed MosCla app, and the experimental setup designed to validate it.
4. 8 [26] , which will also help to store the images as a MAT object, facilitating the heavy image processing operations and transformations on devices with limited resources, and the LIBSVM (Library for Support Vector Machines) version 3. 24  [27]  for the Android platform. Since both libraries were developed in native C and C++ languages, it was necessary to include the NDK (Native development KIT) version 21. 0. 6113669 to operate with them. Additionally, the Google API play-services-location version 17. 0. 0 will be used in the developed app to estimate the latitude and longitude based localization of newly acquired or existing images. 3) Source Code optimization: although using native C, C++ libraries, the performance of some modules like the digital image processing functions, the watershed transformation, the particles analysis, and the training-test process of the SVM classifier are not optimal enough when performing them sequentially.
C. Proposed MosCla app The proposed application implemented an automatic Culicoides species classification method using the defined development environment to produce an Android application able to classify two Culicoides species on limited resources devices (see Fig. 1 ). The app receives as input the wing images of the Culicoides species. Each entry transits throughout several digital image processing steps such as pre-processing, wing particle detection and zones segmentation, feature calculation to produce a feature vector linked to the entry (wing image). Then, a subset of the computed feature vector is used to feed a SVM classifier, which provides the final classification of Pusillus or Obsoletus class. Additionally, the initial feature vector together with the final classification of each processed entry was saved in a database file, such files were used for a feature selection process using an external wrapper.
D. Experimental setup This section is aimed to evaluate the proposed MosCla app. Therefore, the Culicoides wing images dataset, experimental mobile devices, classifier training and test, validation metrics, and energy measurement protocol are important aspects to be described next. 1) Culicoides wing image dataset: pusillus (n=45) samples were captured at French Guyana (3 ˝59'56"N, 53 ˝00'00" W) and obsoletus samples (n=42) at France (49 ˝59'69"N, 4 ˝01'45"E; 43 ˝58'55"N, 3 ˝42'58"E) by using ultraviolet traps. Samples were stored in 70% ethanol before any morphological analysis. The specimens were separated from other insects according to their wing characteristics using a stereomicroscope  [29]. The digital wing images were obtained using an Olympus BX53 microscope equipped with an Olympus SC100 camera magnified at x10 with stream motion software (Olympus).
For our study, left and right wings were used indistinctly because the systematic selection of one side may bias the results in case of differential directional asymmetry between species. The comparison of wings from catalogs and original descriptions with status (left or right) are mostly unknown and the distribution and color of spots on both wings are similar  [17]. Thus, we formed an experimental dataset with a total of 45 and 42 wing images of Culicoides Pusillus and Culicoides Obsoletus, respectively. 2) Experimental mobile devices: two mobile device emulators were considered for installing and testing the proposed MosCla app. They were selected based on the minimum device specifications required to host the proposed app. It should be noted that some digital image processing functions and the classifier itself demand a certain amount of resources to fulfill the assigned tasks.
5) Validation metrics: the proposed app was validated using a two-step procedure involving (1) the individual assessment of the employed SVM classifier using the area under the curve (AUC) score of the receiving operating characteristic and (2) the feasibility of the implementation using the mean of execution time (mET) and battery consumption (mBC) metrics gathered while running the app. Both validation steps were made on the experimental Culicoides wing's image dataset. 6) Energy measurement protocol: the android debug bridge service (adb command line tool)  [31]  facilitated us a satisfactory way to recover two logs from the emulated mobile devices. The battery stats log, containing important stats regarding the battery consumption of every single process (for our purpose, we took only the information from the process that executed the MosCla app) and the process activity log, containing % stats about the execution time (in nanoseconds) of the developed app.
B. MosCla app feasibility Regarding the maximization of resources on mobile devices, we have not yet implemented the proposed app on the real tablet or phone device, and this is the practical limitation of this work. Instead of that, we approximated the time and battery consumption by using the established energy measurement protocol on two device emulators, in which we implemented the proposed app. Then, the experimental Culicoides wing images dataset was analyzed by the proposed app installed on each device emulator. Results in terms of the mean of execution time and battery consumption for both emulators are shown in Fig. 4. As expected, the tablet Pixel C (red box plot) emulator was faster and has better energy savings than the phone Pixel 2 (blue box plot) emulator. However, both devices were able to reach mean scores under the 15 seconds and consuming only 0.
IV. CONCLUSIONS AND FUTURE WORK In this work, we developed an Android-based mobile application called MosCla to classify two Culicoides species. The proposed app implemented an automatic classification method based on the calculation of morphological features extracted from wing's images and a SVM classifier to produce the final classification between the Pusillus or Obsoletus species. The application of an external wrapper method to reduce the features space from seven to three features improved the SVM classifier performance from an AUC score of 0. 95 to 0. 98, using a 10-fold cross-validation method on the experimental wing's images dataset. Regarding the feasibility, the MosCla app reached satisfactory mean execution values of 12 and 7 seconds and a mean battery consumption values of 0.
11 and 0. 03 mAh for the Pixel 2 phone and the Pixel C tablet emulators, respectively. These results are can be generalized, and with further enhancements, the proposed app could be a valuable tool for those specialists that need a practical tool to classify Culicoides species in the wildlife environment. As future work, we plan to include other Culicoides species such as Foxi and Insignis to the experimental dataset. We also plan to implement other classifiers to explore their performance on limited-resource devices. Finally, we are also interested in testing the proposed app on real mobile devices to compare the time and battery consumption metrics against the results obtained by the emulated environments.