dataesr/scientific-paragraphs-categorization huggingface dataset:
New Phytologist To study the rates of IAA degradation in spruce roots, we then performed a feeding experiment in which [ 13 C 6 ]-IAA was applied to whole seedlings and the degradation of IAA was measured by quantification of labelled IAA metabolites in roots. Feeding labelled IAA resulted in the formation of mainly [ 13 C 6 ]-IAAsp, low concentrations of [ 13 C 6 ]-IAGlu and almost no [ 13 C 6 ]-oxIAA after 6 or 24 h (Fig. 2b ), in accordance with the results obtained with unlabelled IAA (Fig. 2a ). These findings indicate that the high endogenous concentrations of IAAsp and IAGlu observed in spruce originated from de novo synthesis. The spruce genome has several GH3 genes that belong to Group II of the GH3 family Our metabolic data have highlighted the importance of irreversible conjugative pathways for the regulation of IAA homeostasis in conifers.
, 1998; Peer et al. , 2013; P en c ık et al. , 2013; Dr abkov a et al. , 2015; Zi zkov a et al. , 2017). Recent findings have demonstrated that the DAO enzymes catalyse conversion of IAA to oxIAA  (Zhao et al. , 2013; Porco et al. , 2016; Zhang et al. , 2016). These proteins share a high similarity to each other and very low similarity to the other 2OGD family members, making them a novel subfamily  (Zhang et al. , 2016). Surprisingly, our metabolic data revealed that the irreversible oxidative pathway plays a marginal role in maintaining IAA homeostasis in conifers. To verify if the formation of oxIAA in Norway spruce could be ascribed to any DAO-like enzyme, we first checked if DAO-like genes are conserved in spruce by blasting the AtDAO1 protein sequence against the ConGenIE. org (Conifer Genome Integrative Explorer) database.
Acknowledgements This research was supported by grants from the  Swedish research councils FORMAS, VR, Kempestiftelserna , and the  Knut and Alice Wallenberg Foundation  (to KL and CB). We thank  Dr Mikael Lindberg  of the  Protein Expression Platform (PEP, Ume a, Sweden)  for cloning the Pa2OGD genes,  Dr Nicolas Delhomme  for helpful discussions and the  Swedish Metabolomics Center  ( SMC, Ume a, Sweden ) for access to instrumentation.
Obtained sequences were initially analyzed by comparative methods on established public-domain databases using the BLAST (Basic Local Alignment Search Tools) algorithm (http://www. ncbi. nih. gov) (Altschul et al. 1997 ). The BioEdit (http://www. mbio. ncsu. edu/ BioEdit/ bioedit. html) program was used for sequence alignment of the forward and reverse sequence. The resulting alignment was corrected manually. The sequence was analyzed again by BLAST for sequence similarities in order to support identification of the seaweed taxa. The sequences were deposited in GenBank (Table  4 ).
Significantly, the findings of S. ilicifolium and S. ilicifolium var. acaraeocarpum reported here are new records both for Kuwait and the Arabian Gulf, while P. boergesenii and D. dichotoma are new records for Kuwait. C. papillatum, P. boergesenii, D. dichotoma, S. asperifolium and S. aquifolium have previously been reported from the Arabian Gulf  (John and Al-Thani 2014). Likewise, I. stellata and C. sinuosa are known specifically from Kuwait  (Al-Yamani et al. 2014; Silva et al. 1996). It is worth noting that the DNA sequences reported here are the first for any seaweeds from the entire Arabian Gulf, highlighting also how understudied the region is with regards to molecular algal taxonomy. However, this also highlights a general challenge when working in this regiona robust identification of the taxa investigated in this study will only be possible when a larger dataset is available.
Acknowledgments: We are thankful to the sequencing facility in Kuwait University (Project #  GS01/02 ) and  Department of Marine Sciences  for using their facilities and labs. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission. Research funding: We are grateful to the  Kuwait Institute for Scientific Research (KISR)  for PhD funding for  Hanan Al-Adilah. We would equally like to thank the  UK Natural Environment Research Council  for their support to FCK (program Oceans  2025 -WP 4. 5  and grants  NE/D521522/1  and  NE/J023094/1 ). This work also received support from the  Marine Alliance for Science and Technology for Scotland pooling initiative. MASTS is funded by the  Scottish Funding Council  (grant reference  HR09011 ) and contributing institutions.
Image analyses All images were uploaded to a database where they were annotated according to mosquito species and experimental conditions. Images were subjected to bulk analysis as well as manual verification. The annotated image database is accessible to JAVA programming using the Strand Avadis IManage data management software. All data (images and extracted data as text files) are available on cid. curie. fr, Project "Malaria parasite invasion in the mosquito tissues" at https://cid. curie. fr/iManage/standard/login. html. The META data is managed using OpenImadis https://strandls. github. io/openimadis/. Companion scripts are available here: https://github. com/PerrineGilloteaux/MalariaParasiteinMosquito. The api documentation is accessible under API tab https://cid. curie. fr/iManage/api/client/.
The api client jar can be found at https://cid. curie. fr/iManage/standard/downloads. html. For tutorial on access and use of database see: https://youtu. be/mznoB-y99Uo. Companion scripts include segmentation of parasites, nuclei and quantification of intensities (corrected by background) and were performed using a set of ImageJ Plugins in Java. Analysis of the position of parasites relative to cell layer and statistics were performed with MATLAB. The data set for a total of 2,557 parasites was collected from 110 independent experiments. More specifically, As: n = 45 midguts with 1,068 parasites; Ag: n = 34 midguts with 796 parasites; and Ag Tep1KD : n = 31 midguts with 693 parasites. There was no bias in the number of parasites per midgut across the time points and mosquito species (S2 Fig).
As low infection levels affected quantification of parasite distribution (S3 Fig), only the images that contained at least 10 parasites were included in the analyses. To determine the position of parasites relative to the epithelial cell layer, the position of the cell layer was modelled for each image as a 3D deformable mesh. The parameters of this mesh were obtained by minimizing the distance between the mesh and the nuclei centroids obtained after 3D segmentation of the nuclei. All data on the fitted meshes against the nuclei position are available in the database as CellLayerFitted_RecordGuid. jpeg from the user attachment panel, where blue spots are nuclei centroids and red stars represent parasite centroids. The positions of nuclei and parasites can be seen by activating the corresponding overlays.
Position of each parasite in Z was corrected according to Z of the nearest point on the mesh (the mesh Z position was created from the polynomial fit for every pixel of the image to increase the resolution of each parasite projection on the mesh). The cell layer was approximated by the average cell diameter, validated by iteration and returned to the data and the classification of parasites. Each parasite had an identifier, and its properties were stored as an attachment in the database so that any computed value can be validated visually. Parasites with corrected positions raging form -5 μm to +5 μm were considered at the cell layer position. Parasites at a distance greater than 5 μm above/below the cell layer were considered in the blood meal or basal lamina, depending on midgut orientation (metadata "reverse" in the database).
For each record, a 3D projection in XZ and YZ is available for visualization of the parasite positions (result/bookmarks/xz_yz_visualization). The position of parasites found within the cell layer was further defined relative to midgut cells. Each parasite was given a score based on a normalized ratio of the 3D Euclidean distance between the center of the parasite and the center of the nearest nuclei, and the average distance of the nuclei center between them (examples in Fig 4A  and 4B ). The scores were validated by visual examination and are available in the database. For parasite kinetics tracking at short timescales, movies were first compensated for movement based on the 3D nuclei position. To this end, the 3D movies were cropped around each parasite's X-Y trajectories by projection, and the movement of the cropped image was determined by the 3D drift compensation plugin (Fiji) using the DAPI (nuclei) channel as a reference to estimate the movement.
This 3D compensation over time was then applied to the RFP channel showing the parasites. An example of a compensated movie is available at https:// youtu. be/690AIgcAIj0. The compensated cropped movies were used to track the parasite over time, after visual validation that the nuclei were correctly stabilized (the original files can be found in the image database: Results/Bookmarks/Compensated movies). All data extracted from the database and used for figures can be found in S1 Data file.
Acknowledgments GV, JS ˇtafkova, JSoichot and EAL thank  M. E. Moritz  and  C. Kappler  for help with the mosquito colony and parasite cultures; and  E. Marois  for scientific discussions and support. JSalamero and PPG acknowledge the Structure federative de recherche santeFrancois-Bonamy and the SERPICO team, are members of the national infrastructure " France BioImaging ". Authors thank  A. Volohonsky  for graphic expertise.
Funding: EAL received funding by  EC   FP7 Capacities Specific Programme Research Infrastructures ''INFRAVEC"  under grant agreement  228421. EAL received funding from the  International Associate Laboratory  ( LIA -Laboratoire International Associe) grant  from  CNRS. JeS and PPG acknowledge the Structure federative de recherche santeFrancois-Bonamy and the SERPICO team, are members of the national infrastructure " France BioImaging " supported by the  ANR PIA1  ( ANR-10-INBS-04 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
, 2017) , this reporter system should allow for the real-time detection of homeologous recombination during transformation. To test this, we provided competent recipient cells that besides the hlpA-stop-mScarlet-I allele also constitutively expressed sfGFP (strain VL1832) with intact hlpA-mScarlet-I as donor tDNA in the presence of CSP and then performed time-lapse microscopy (see Materials and methods for details). As shown in Figure  3D  and Videos 1 and 2, recipient cells do not display any red fluorescence in the beginning and then gradually start to express red fluorescence. When quantifying the fluorescence signals and superimposing this on a cell lineage tree constructed using a set of new scripts written in BactMAP (van Raaphorst et al. , 2020) (see Materials and methods), it becomes apparent that the initial recipient cell already expresses HlpA-mScarlet-I right after the addition of tDNA before the first cell division as red fluorescent signals above background levels can be detected (Figure  3E ).
Quantitative image analysis For quantitative image analysis of single cells, obtained microscopic images were processed by FIJI software  (Schindelin et al. , 2012). Single-cell segmentation and fluorescence signal intensity measurement were performed by Oufti  (Paintdakhi et al. , 2016). The generated celllist files were analyzed in R (https://www. r-project. org/), using  BactMAP (van Raaphorst et al. , 2020)  for statistical analysis and visualization. After celllist file were imported into R, cells were filtered between 0. 7-1. 2 mm in width length to exclude false events derived from noise or miss-segmentation. Threshold of fluorescence of signal intensity was defined based on negative or positive control for each experiment setting. >500 cells were analyzed at least for each replicate.
To exclude the possibility of overlap in detection of fluorescence (particularly mTurquoise2/msfYFP and msfYFP/mScarlet-I) in multifragments transformation, we ensured that single transformation experiments did not show any signal in the other channels and this was confirmed by looking at the protein localization patterns. For generating cell lineage trees from time-lapse imaging, the stacked time-lapse images were processed by FIJI and stabilization between time frames was performed by Huygens (Scientific volume imaging). Single-cell segmentation and fluorescence intensity acquisition were performed by SuperSegger  (Stylianidou et al. , 2016). The resultant data set was analyzed using BactMAP  (van Raaphorst et al. , 2020).
Flow-cytometry analysis Cells were collected by centrifugation (6000 x g for 3 min) and resuspended in filtered (0. 22 mm) PBS adjusted to a cell density of approximately 1. 0 Â 10 5 to 1. 0 Â 10 6 cells/mL. As encapsulated S. pneumoniae D39V cells tend to form chains particularly during competence, we separated cells by bead beating (BioSpec) without any glass beads. At least >1. 0Â10 4 events were analyzed on a Novocyte flow cytometer (ACEA bioscience) harboring 488 nm and 561 nm lasers. Fluorescence filters used were: FITC (Ex: 488 nm, Em: 530/45 nm) for sfGFP and PE. Texas. Red (Ex: 561 nm, Em: 615/20 nm) for mScarlet-I. Obtained raw data were imported and analyzed in R. Non-bacterial particles were excluded by gating the FSC and SSC values. A threshold was determined so that positive events counted in a negative control strain were <1% and validated with both negative (no DNA control) and positive (parent strain without the point mutation) control for each experimental setting.
Acknowledgements We thank  Jelle Slager  and  Arnau Domenech  for critically reading this manuscript, two anonymous referees,  Juan Carlos Alonso  and all members of the  Veening lab  for stimulating discussions. Work in the Veening lab is supported by the  Swiss National Science Foundation (SNSF)  (project grant  31003A_172861 ), a  JPIAMR grant  ( 40AR40_185533 ) from  SNSF  and  ERC  consolidator grant  771534-PneumoCaTChER. Work in the Polard lab is supported by the  Centre National de la Recherche Scientifique ,  UniversitePaul Sabatier , and the  Agence Nationale de la Recherche  (grants  ANR-10-BLAN-1331  and  ANR-17-CE13-0031 ). Jun Kurushima  was supported by  The Naito Foundation.
The study was registered (ClinicalTrials. gov NCT03823508) and approved by the local ethics committee, and is reported in accordance with CONSORT guidelines. Brain Sci. 2020, 10, x FOR PEER REVIEW 3 of 19 contraindications to tDCS (i. e. , uncontrolled seizure, implanted electronic brain medical device or pacemaker, history of recent craniotomy or cranioplasty in the frontal region) or with premorbid neurological or psychiatric conditions (see Figure  1  for a flowchart). Written informed consent was obtained by the legal representative. The study was registered (ClinicalTrials. gov NCT03823508) and approved by the local ethics committee, and is reported in accordance with CONSORT guidelines.
Data Acquisition In a double-blind, sham-controlled, crossover design, anodal and sham tDCS were allocated in a randomized order and administered in two sessions separated by at least 48 h. Two experienced investigators were involved in data collection. One investigator was in charge of the tDCS application, whereas the other performed the CRS-R assessments. For each patient, the first experimenter received two blinded codes from a third person, one for the active and one for the sham stimulation, allowing the investigator (and the patients) to be completely blind regarding the treatment allocation. Our primary outcome measures were the neurophysiological alterations, as measured by ten minutes of resting-state hdEEG recorded with eyes open directly before and after the stimulation/sham. As a secondary outcome, behavioral improvement was assessed by means of CRS-R assessments performed by an experienced experimenter directly before and after the tDCS/sham (see Figure  2  for the protocol).
Connectivity changes after two sessions of tDCS (sham and active) were analyzed by contrasting changes in active vs. sham conditions ((post active-pre active) vs. (post sham-pre sham)) at the group level using paired t-tests. All preprocessing and processing scripts were written as custom scripts in Python, MNE-Python  [25] , and NICE  [26]. These scripts are publicly available at https://github. com/fraimondo/shock.
Behavioral Statistical analyses of behavioral data were performed using SPSS version 17. 0  [27]. We investigated the presence of a carry-over effect by comparing the CRS-R total scores before active tDCS and before sham tDCS using a Wilcoxon match-paired signed-rank test. In the absence of such an effect, we looked for any treatment effect using the same tests, by comparing the differences in CRS-R total scores as follows: (post active-pre active) vs. (post sham-pre sham), at the group-level and in traumatic brain injuries (TBI) and non-TBI subgroups. As exploratory analyses, we looked at the difference in CRS-R total scores between pre-and post-active, as well as pre-and post-sham.
Funding: This research was funded by the  University  and  University Hospital of Liège , the  Belgian National Funds for Scientific Research (FRS-FNRS) , the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation (Human Brain  Project  SGA2 , under the  Specific Grant  Agreement No. 785907 , and  Human Brain  Project  SGA3 ),  Luminous  project ( EU-H2020-fetopen-ga686764 ), the  European Space Agency (ESA)  and the  Belgian Federal Science Policy Office (BELSPO)  in the framework of the  PRODEX Programme ,  DOCMA  project ( EU-H2020-MSCA-RISE-778234 ),  BIAL Foundation ,  AstraZeneca Foundation ,  King Baudouin Foundation ,  Fund Generet ,  Marie Sklodowska-Curie Actions  ( H2020-MSCA-IF-2016-ADOC-752686 ), the  French Speaking Community Concerted Research Action  ( ARC 12-17/01 ), the  James McDonnell Foundation ,  Mind Science Foundation ,  IAP  research network  P7/06  of the  Belgian Government (Belgian Science Policy) , the  European Commission , the  Public Utility Foundation 'Université Européenne du Travail', and the "Fondazione Europea di Ricerca Biomedica ".
qPCR evaluation of copy number across repetitive element targeting gRNAs The quantitative polymerase chain reaction (qPCR) reactions were performed using the KAPA SYBR FAST Universal 2× qPCR Master Mix (Catalog #KK4602) according to the manufacturer's instructions. The LightCycler 96 machine from Roche was used to perform the qPCRs and the results were extracted using the LightCycler 96 SW 1. 1 software. The following thermocycling conditions were used: 'preincubation' stage = 95 • C for 180 s; '2-step cycling' stage: annealing = 95 • C for 3 s and elongation = 60 • C for 20 s; 'Melting' stage = keep standard. The following primers were used: ZY-JAK2-F AGCAAGTATGATGAGCAAGC; SB- JAK2-R AAAACAGATGCTCTGAGAAAGGC; P1(b) REBE F TAGGAACAGCTCCGGTCTACA; P1 REBE-ilu R AATGCCTCGCCCTGCTTCGG; P5 REBE-ilu F CCAATACAGAGAAGTGCTTAAAGG; P5 REBE-ilu R CTTGGAGGCTTTGCTCATTTCT; P7 REBE-ilu F CCCATCAGTGTGCTGTATTCAGG; P7 REBE-ilu R GGCCTTCTTTGTCTCTTTTG; P13 REBE-ilu F AACAGGCTCTGAAATTGTGGC; P13 REBE-ilu R GCTGGCCTCATAAAATGAGTTA G; P15 REBE-ilu F GTTCTGGCCAGGGCAATCAG; P15 REBE-ilu R CCTGAGACTTTGCTGAAGTTGC.
NGS indel analysis Raw Illumina sequencing data was demultiplexed using bcl2fastq. All paired end reads were aligned to the reference genome using bowtie2  (39)  and the resulting alignment files were parsed for their cigar string to determine the position and size of all indels within each read using a custom perl script (https://github. com/CRISPRengineer/ mutation indel). All indels that were sequenced in both the forward and reverse reads were summed across all reads and reported for each sample along with the total number of reads. Indels within a 30 bp window from the 5 start of the gRNA proceeding through the PAM and extending an additional seven bp's (for a 20 bp gRNA) were counted and summed for each sample.
NGS base editing deamination analysis All paired end reads were aligned to the reference genome using bowtie2, and the resulting alignment files were converted to bam, sorted, indexed, and variant called using samtools  (40). All SNV data within a 30 bp window from the 5 start of the gRNA proceeding through the PAM and extending an additional seven bp's (for a 20 bp gRNA) are reported to analyze the editing window and purity of editing. The custom perl script used for analysis is available at https://github. com/CRISPRengineer/deamination report.
The remainder was centrifuged and resuspended into 100 l of Annexin V Binding Buffer (ref #V13246) diluted into ultrapure water at a 1:5 ratio. Subsequently, we added 5 l of Alexa 647 Annexin V dye (ref #A23204) and incubated samples in the dark for 15 min. We then added 100 l of Annexin V Binding Buffer and added 4 l of Propidium Iodide (ref #P3566) diluted into the Annexin V Binding Buffer at a 1:10 ratio. Samples were incubated in the dark for another 15 min. Cells were washed with 500 l of Annexin V Binding Buffer and centrifuged again to be finally resuspended into 400 l of Annexin V Binding Buffer. All samples were filtered using a cell strainer and were run on the LSR 11 using a 70-m nozzle. Analysis was conducted using FlowJo software.
Whole genome sequencing off-target analysis The top 293T edited clones used for the karyotype analysis were expanded and isolated with the 293T population frozen before initial transfection (pre293T) along with a control 293T population expanded for an equivalent amount of time as the other mutant clones sequenced(post293T). DNA was extracted using the Qiagen DNeasy Blood and Tissue kit (cat-#69506) and were sequenced using Illumina PE 150 to a depth of ∼30×. Alignment and variant calling was provided by the Harvard Chan Bioinformatics Core, Harvard T. H. Chan School of Public Health, Boston, MA, USA using an analysis pipeline based on bcbio framework (https://github. com/ bcbio/bcbio-nextgen). For WGS data, BWA (v0. 7. 17) was used to map sequencing reads to the reference human genome (hg38).
We called SNPs and indels using somatic tumor-normal approach (using a control sample as a normal, and edited samples as 'tumor'), and required 3 variant callers (vardict, v. 2019. 06. 04, mutect2 (from gatk 4. 1. 2. 0), strelka2, v2. 9. 10) to confirm a variant to be called (a similar approach was taken by Zuo et al. (24). In the case of RNAseq data, we used STAR (v. 2. 6. 1d) to align reads and RNAseq specific gatk-based variant calling pipeline, with parameters and filters recommended by GATK best practices for RNA-seq variant calling (https://software. broadinstitute. org/gatk/documentation/article. php?id=3891), followed by filtering out variants at RNA editing sites according to the RADAR (v. 2-20180202) database. We used GATK 3. 8 to call variants in RNA-seq data, because our validation has shown the superior precision of gatk 3.
8 over gatk 4. 1. 2. 0 when using RNA-seq reads. Due to the variability of coverage in RNA-seq data, variants were called in a single batch and only variants called as het, hom or hom ref in all samples were considered for the downstream analysis. We filtered out variants at sites matching gRNA using bedtools (2. 27. 1) and a custom bash script and used R-studio and ggplot2 for the downstream analysis.