dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions The use of micro-CT as a tool for floral organ phenotyping represents an excellent compromise between the classical imaging and the synchrotron facility. On the one hand, as a non-destructive, high-precision imaging tool, it allows a rapid acquisition of 2D and 3D images at the resolution of the cellular. We scanned the intact melon flowers without a tissue fixation and thus shortened the protocol for the acquisition of high-quality 3D images. The resulting scans were used for obtaining geometric measurements of organ sizes and nectar volumes as well as for the respective correlation analyses. The main advantage of this method is the reduced acquisition time for obtaining high-resolution 3D data which provides a higher precision compared to the 2D images in phenotyping floral morphology.
Conclusions The use of micro-CT as a tool for floral compromise between the classical imaging hand, as a non-destructive, high-precision im 2D and 3D images at the resolution of the ce without a tissue fixation and thus shortene quality 3D images. The resulting scans were u of organ sizes and nectar volumes as well as main advantage of this method is the red resolution 3D data which provides a highe phenotyping floral morphology. We believe nique has a great potential to improve the i interactions. Its relative low cost and time high-quality phenotyping data. The establish a comparative analysis of a phenotype-p pollination-related traits while guaranteeing of the floral organs.
Acknowledgments: We would like to thank  Emmanuel Ormea  ( Twevel society ) and  Loïc Anderlesse  ( Synergie4 society ) for their help by technical exchanges and advises to find the best scanning conditions, as well as  Kristina Nikovics  for her help in the transfer of samples from IPS2 to IRBA institutes. We are grateful to  Charles Chapus  for his statistical expertise on R software,  Cécile Enfroy  for providing honey bees and  Nathalie Guatto  for honey bee microtomographic preparation.
Funding: This research was funded by the  ANR Saclay Plant Sciences-SPS  ( ANR-10-LABX-40-SPS )  Institutional Review Board Statement : Not applic Informed Consent Statement: Not applicable. Funding: This research was funded by the  ANR  ( NECTAR ,  ANR-19-CE20-0023 ) and the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-40-SPS ). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Mass spectrometry and proteins' selection strategy Briefly, the gel pieces were destained, subjected to standard tryptic "in-gel digestion" and then analyzed by LC-MS/MS. Proteins identification was performed on XTandem Pipeline. 20 The False Discovery Rate (FDR) levels were estimated to 0. 6% for peptides and 0. 0% for proteins using Xtandem "include reverse" option. The protein quantification was determined after analysis of the total number of tandem mass spectra (SN). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  21  partner repository with the dataset identifier PXD028301. More details are provided in supplementary methods. We applied a selection strategy to search for candidate proteins most useful for the prediction of CE outcomes as follows: Firstly, after eliminating proteins identified with less than 5 spectra and with a foldchange less than 1.
Acknowledgments We are grateful to the  Ministry of Higher Education and Scientific Research, Tunisia , the team of  Pediatric surgery department and the Laboratory of Parasitology-Mycology of the Fattouma Bourguiba Hospital, Monastir, Tunisia  who helped us to accomplish this work. The authors warmly thank the PEA2t platform ( Chronoenvironment, University of Bourgogne Franche-Comté, France ), which manages and maintains the analytical equipment used in this study.
The protocol follows the Consolidated Standards of Reporting Trials (CONSORT) guidelines. 29 30 STIVAL is registered at ClinicalTrials. gov. All patients included undergo the auricular tVNS with the Vagustim or sham treatment with the same device, which has the same appearance but does not deliver any electric current. Patients are randomised to receive the active or sham tVNS for 12 weeks with no crossover design. Each patient is scheduled for a visit at baseline and at 4, 8 and 12 weeks (figure  1 ). At each visit, patients complete questionnaires and undergo a clinical examination for efficacy and safety assessments (table  1 ). An 8. 5 mL blood sample is collected at baseline and at the 12-week visit. Patients followed up in Saint-Antoine Hospital Center undergo non-contrast MRI of the most symptomatic hand at the baseline visit and week 12.
45 46  The additional margin of 20% in recruitment is justified because some patients will not have US-evidenced synovitis. From the study of Vlychou et al, among 22 patients with EHOA, 19 had a thickened synovium; 18 had power Doppler signals; and thus 4 of 22 (18%) had no US features of joint inflammation. 10 ta collection and management Data are collected in an e-CRF, devised by the study coordinator in collaboration with URC-Est. Data are completed by the investigators with the help of a clinical research technician. Data entry checks and a posteriori checks are planned. All information required by the protocol are recorded in physical or electronic report files, and an explanation must be provided for any missing data. All questionnaires used are validated. A CRA will be responsible for the good completion of the study, for collecting, documenting, recording and reporting all handwritten data, in accordance with the standard operating procedures applied within the clinical research and innovation department.
A steering committee with a coordinator, coinvestigators and a methodologist decides what to do in unexpected situations. The committee monitors the progress of the research and meets every 12 months. Source documents (original document or item that can prove the existence or accuracy of a data or a fact recorded during the study) are kept for 15 years by the investigator or by the hospital in the case of a hospital medical file. During and after the research involving human participants, all data collected concerning the participants are rendered anonymous. Under no circumstances are the names and addresses of the participants shown. Only the participant's initials are recorded, accompanied by an encoded number specific to the study indicating the order of enrolment. An audit can be carried out at any time by individuals appointed by the sponsor and independent of those responsible for the research.
Primary outcome Difference in VAS pain (baseline minus week 12) will be compared between groups by using a Wilcoxon rank-sum test. The effect size and its 95% CI will be estimated. Additional analysis will be made to study VAS pain over time using a linear mixed model. In case of non-normal distribution, adapted transformation will be made. Sensitivity analysis will be performed in the ITT population with available data and on the per protocol population defined as all patients randomised without major protocol deviation.
Secondary outcomes Change in AUSCAN subscores and modified FIHOA score will be compared by Student t-test or Wilcoxon rank-sum test (depending on data distribution). Other outcomes will be described in both groups and the difference between them calculated with the 95% CI. The slope over time in VAS pain score, FIHOA score and AUSCAN subscores will be described graphically by randomisation group, and a linear mixed model may be used to compare groups. Missing data replacement strategy for the primary endpoint will be fixed according to the structure of the missing data structure. Other missing values will not be replaced. Statistical analysis will be performed with SAS V. 9. 4. All tests will be two-sided and a p value of <0. 05 will indicate statistical significance.
Acknowledgements The authors thank  Dr Camille Chesnel  and  Professor Gérard Amarenco  for their help in the setting of transcutaneous vagus nerve stimulation in patients with osteoarthritis and for the  ADEPT (traitement de l'Arthrose Digitale Erosive Par stimulation Transcutanée auriculaire du nerf vague ) pilot trial. We thank  Thomas Lobstein  ( Schwa-Medico ) for helpful advice during the development of the project and  Schwa-Medico , which graciously provided the Vagustim, electrode and conductive gel. ESTIVAL study group:  Denis Arniaud ;  Jean-Philippe Bastard ;  Laurence Bérard ;  Francis Berenbaum ;  Julien Champey ;  Roland Chapurlat ;  Grégoire Cormier ;  Alice Courties ;  Camille Deprouw ;  Sandra Desouches ;  Florent Eymard ;  Soraya Fellahi ;  Jacques-Eric Gottenberg ;  Johanna Kalsch ;  Yves Henrontin ;  Eric Lespessailles ;  Emmanuel Maheu ;  Hubert Marotte ;  Sylvain Mathieu ;  Anne Miquel ;  Paul Ornetti ;  Yves-Marie Pers ;  Nicolas Poursac ;  Francois Rannou ;  Anne-Christine Rat ;  Pascal Richette ;  Alexandra Rousseau ;  Christian H.
Second, we tested the role of selected factors using genetic approaches. We found that engineered antigenreactive Tregs protected mice from EAE using IL-10, CTLA-4, and LAG-3 in a nonredundant manner, whereas their expression of AREG was facultative. Collectively, these data document the remarkable capacity of engineered autoreactive Tregs to appropriately provide different suppressive factors acting in a nonredundant manner to prevent disease progression.
Data from the two mice batches were integrated using the Seurat version 3 SCTransform integration  [48]  and dimensionality reduction and clustering were performed using default Seurat parameters. After Principal Component Analysis, cells were clustered using the FindClusters function with a resolution of 0. 5. UMAP was performed using the RunUMAP function on the selected PCs and the UMAP coordinates were used for cell visualization. Clusters annotation was done manually based on the expression of a large number of hallmark genes, for example, Cd3 and Cd4 for T cells, S100a6 for granulocytes and Hexb, Cst3 and Sall1 for microglia. CD4 + T cell clusters were isolated and submitted to another round of clustering to identify subpopulations based on differential gene expression. Cluster of Tregs was manually annotated based on Foxp3 expression.
Heatmap was generated with the Seurat function DoHeatmap using the top 10 markers expressed in each cell cluster. Violin plots were generated with the Seurat function VlnPlot using selected markers per cluster. Differential gene expression analysis was performed using the Seurat FindMarkers function and the MAST package (v1. 8. 2)  [49]. Genes were considered differentially expressed when adjusted p-value was <0. 05 and a log2-fold change was more than 0. 5.
For intracellular staining, cells were fixed and permeabilized with eBioscience Foxp3/Transcription Factor Staining Buffer Set (Invitrogen, #00-5523-00) and stained with mAbs against Foxp3 (FJK-16s, 1:125), CTLA-4 (UC10-4B9, 1:200), CD40L (MR1, 1:100), IFN-γ (XMG1. 2, 1:100), GM-CSF (MP1-22E9, 1:100), IL-17A (TC11-18H10. 1, 1:200). Cell counting in blood, spleen, and brain samples was performed with Precision Count Beads (BioLegend, San Diego, CA, USA, #424902). Antibodies and staining reagents were from BioLegend, Invitrogen-Thermo Fischer Scientific or BD Biosciences. Data were acquired on BD LSRFortessa SORP and analyzed with FlowJo software (FlowJo LLC, Ashland, OR, USA). The gating strategy is explained in Supporting information Fig. S7.
Statistical analyses Data presentation and statistical analyses were performed using GraphPad Prism (GraphPad Software, USA). Graphs show mean + SEM (unless stated otherwise). Data were represented as mean ± SEM. Groups were compared using two-tailed t-test (or one-tail ttest where stated). Cumulative disease scores and data from peptide stimulations were analyzed with one-way ANOVA and subsequent Turkey's multiple comparisons test to compare differences between groups. ns > 0. 05, *p < 0. 05, **p < 0. 01; ***p < 0. 001, ****p < 0. 0001.
Data availability statement: The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information files or are available from the corresponding author upon reasonable request. ScRNAseq transcriptome data are available on ArrayExpress database under the accession number E-MTAB-11294. Transcriptome data are available on GEO database under the accession number GSE164460. https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE164 460 Peer review: The peer review history for this article is available at https://publons. com/publon/10. 1002/eji. 202249845.
I. INTRODUCTION C ONNECTED Electric Vehicles (CEVs) [1] are a new mobility concept that is growing rapidly over the last decades. CEVs are a mix of hardware and software pieces with communication capabilities making them expected to be the main part of the smart city deployment especially in terms of the energy ecosystem, clean mobility service and even for data collect and routing. Compared to traditional transportation system, CEVs are seen as a new form of mobility in cities able to help in the energy transition vision by replying to Net-Zero and COP26 commitments related to environmental issues, the pollution reduction, and the service efficiency for citizens. The success of the CEV concept is depending on its range capability and its battery energy management in terms of capacity, the charging and discharging rate, and the availability of the EV Supply Equipment (EVSE).
In one hand, automakers are already starting manufacturing thousands of new electric cars and trucks which can make pressure on the current power system especially for peak periods where an increased number of CEVs are plugged-in in the same period to charge their batteries. In the other hand, the CEV is co-locating electricity consumption and control based on its specific architecture and its capabilities on Information and Communication Technologies (ICTs) such as intelligent software systems with capabilities of network and Internet of Things (IoT). However, as show in Fig. 1 , all these ICT capabilities create potential cyber vulnerabilities and access points with a high risk of data privacy disclosure which can be maliciously exploited by cyber-attackers. This cybersecurity problem increases especially where decentralized models are considered in the interaction between CEVs for data and energy peer-to-peer (P2P) transaction.
III. PROPOSED MODEL We consider a group of CEVs willing to launch an open electricity market in a parking lot when all supply plug-ins are occupied. The system is presented in Fig. 3. We suppose that the parking lot is equipped by a blockchain server  [1]. We use a consortium blockchain based Ethereum achitecture and any CEV buyer or seller can connect to our system. We take into account a predictive bidding approach (PBA)  [1]  based on stochastic bids that will be run into the smart contract based on a trained model. The smart contract is a digital contract that eases the process of agreement between CEVs by imposing predefined clauses such as bidding and payment functions. In our Blockchain based system, the data integrity, security, trustworthiness, and decentralization approach made it a trustful network.
IV. SIMULATION RESULTS In this section, we present the simulation results and discussions of our proposed scheme performance. We consider a scenario in a real urban area of Ottawa where 20 parking lots are sharing a 4 × 4 km 2. The simulation parameters of this study are summarized in TABLE I. To illustrate our problem, we assume that CEVs can interact wirelessly with one another and each CEV decides the amount of power to be sold or purchased during a certain period. We suppose that all parking lots have a blockchain server that is active, and that the blockchain server can receive all information CEV buyers and sellers. Also, we assume that a bidding auction is launched between CEVs buyers and sellers of electricity. To show the effectiveness of our proposed scheme, we consider a cyber attack: (1) on the CEV buying and selling price data and (2) on the CEV seller power data using for The battery depletion threshold (BDT)  [23]  both our proposed method to compare simulations with and without FDIA.
B. FDIA ON THE AMOUNT OF ELECTRICITY ANNOUNCED BY CEV SELLER In this case we suppose that the electricity profile of the CEV seller is falsified by an FDIA. We study throw simulations the capability of our SVM Algorithm to detect and extract the right electricity profile data. Fig. 8  compares three signals during a week (only day time is considered): the real measured data (blue color), the attacked data ( black color) and the extracted data (red color) using our SVM Algorithm. From Fig. 8 , it is clear that our SVM Algorithm is able the detect and extract the right CVE seller' power profile. To conclude, the FDIA detection remains essential in our system (between CEVs) in order to maintain our liberalized market more and more attractive for CEV owners to exchange energy. Table  3  presents the observation results obtained from Fig.
An innovative glycoproteogenomics approach, integrating transcriptomics-customized datasets and glycomics for protein annotation from nanoLC-ESI-MS/MS experiments, was developed and implemented to identify CD44 variants and associated glycosignatures. The impact of CD44 silencing on proliferation and invasion of BC cell lines and glycoengineered cells was determined by BrdU ELISA and Matrigel invasion assays, respectively. Antibody phosphoarrays were used to investigate the role of CD44 and its glycoforms in the activation of relevant oncogenic signaling pathways. Results: Transcriptomics analysis revealed remarkable CD44 isoforms heterogeneity in bladder cancer tissues, as well as associations between short CD44 standard splicing isoform (CD44s), invasion and poor prognosis. We further demonstrated that targeting short O-glycoforms such as the Tn and sialyl-Tn antigens was key to overcome the lack of cancer specificity presented by CD44.
Still, few studies have addressed the CD44 glycocode in cancer and its functional implications for disease progression  [25] [26] [27]. Moreover, conflicting results have been generated concerning its role in disease and clinical value, which are directly linked to analytical limitations for unequivocal molecular characterization, mostly due to the lack of high-throughput approaches to characterize CD44 at the protein level  [2]. Misguiding nomenclature has also been posing a major limitation for comprehensive data mining and definitive molecular characterization, leading us to propose an uniformization (summarized in Figure  S1 )  [2]. High-throughput proteomics constitutes the gold standard approach to tackle CD44 molecular heterogeneity; however, the success of current workflows is limited by the capacity of databases for protein annotation.
TCGA Dataset Updated clinical information for 413 TCGA Bladder Carcinoma (BLCA) cases corresponding to muscle invasive lesions of different stages (T2, T3, T4) and histopathological natures (papillary and non-papillary), including overall survival and disease-free survival information, were obtained from cBioPortal database (https://www. cbioportal. org/). The level-3 RNA-Seq data from 408 TCGA-BLCA cases was retrieved from Broad GDAC FIREHOSE (http://gdac. broadinstitute. org/) in March 2021.
CD44 transcripts identification by RNAseq CD44 variants were identified based on a protocol described in detail by Peixoto et al. [31]. Briefly, RNA was extracted from 5637 and T24 cell pellets from three independent replicates using the RNeasy Plus Mini kit (Qiagen), pooled together for each cell line, and quantified using Qubit 2. 0 Fluorometer (Thermo Fisher Scientific). RNA integrity was evaluated with Agilent TapeStation (Agilent Technologies). The NEBNext Ultra RNA Library Prep Kit was used to prepare RNA sequencing library for Illumina following manufacturer's recommendations. After validation and quantification, the sequencing libraries were clustered on one lane of a flow cell, loaded on an Illumina HiSeq 4000 instrument and sequenced using a 2 × 150 Paired End configuration. The HiSeq Control Software was used to analyze the obtained results.
The gene hit counts were assessed using the feature Counts from the Subread package v. 1. 5. 2, counting only unique reads that belong to exon regions, and then used for downstream differential expression analysis. A SNP/INDEL and a gene fusion analysis were performed using the Samtools v. 1. 3. 1 program followed by VarScan v. 2. 3. 9 and STAR Fusion v. 1. 1. 0, respectively. For novel CD44 transcripts discovery, Stringtie software was used to extract the transcripts expressed in each cell line. Novel transcripts were pinpointed through the comparison between the reference annotation file and resulting gft file.
Flow cytometry Cells were detached using Accutase™ Cell Detachment Solution (BD TM ), fixed with 2% paraformaldehyde (PFA; Sigma-Aldrich), and incubated with rabbit anti-CD44 polyclonal antibody (ab157107, Abcam; Table  S2 ) using a 1:100 dilution in PBS/2% FBS for 1 h at room temperature (RT). Goat anti-rabbit IgG (H + L) cross-adsorbed secondary antibody Alexa Fluor 488 (Invitrogen) was used for CD44 detection at a 1:300 dilution in PBS/2% FBS for 15 min at RT. Data analysis was performed through CXP Software in a FC500 Beckman Coulter flow cytometer. Results represent the standard deviation of three independent experiments.
0 m/z, isolation offset 0. 0 m/z, HCD first mass at 110 m/z. Mass spectrometer was controlled by Xcalibur 4. 0 and Tune 2. 9 software (Thermo Scientific). The samples were then run on a nanoLC system coupled to an LTQ-Orbitrap XL mass spectrometer (Thermo Scientific) to allow characterization by collision induced fragmentation (CID). Liquid chromatography was performed on an EASY-Spray C18 PepMap, 100 Å, 2 µm, 150 mm × 75 µm (Thermo Fisher Scientific) using the same gradient mentioned above. The mass spectrometer was operated in the positive ion mode over the m/z range 380-1580. Nanospray voltage was set at 1. 9 kV and full scan nominal resolution was 60k (m/z = 400). CID was triggered from a precursor ion list containing the m/z values that presented the HexNAc oxonium ion (m/z 204. 087 within a ± 0.
01 range) in the HCD-MS/MS spectra previously acquired. The 6 most intense ions from the customized parent list were selected for CID fragmentation with NCE = 35%, and MS/MS spectra were acquired in the linear ion trap with an isolation width of 2 Da. Specific parameters were: MS maximum injection time of 500 ms; MS/MS maximum injection time of 50 ms; AGC target 1 × 10 6 for the Orbitrap and 1 x 10 4 for LTQ MS n analysis; dynamic exclusion 45 s; charge rejection: unassigned and 1. Mass spectrometer was controlled by Xcalibur 3. 1 software (Thermo Scientific).
Bioinformatics for CD44 glycoproteoforms identification MS data were first converted to peak lists using Proteome Discoverer version 2. 5. 0. 400 (Thermo Scientific), and then searched against the UniProt Homo sapiens proteome (May 5 2020; 75069 entries), using the MSPepSearch and SequestHT search engines for protein identification and the Percolator algorithm v3. 05. 0 for statistical validation. Prior to the search, the human proteome FASTA database was edited to include CD44 sequences inferred from RNAseq characterization of 5637 and T24 cell lines (Table  S2 ). Searches for HCD tandem spectra were performed with a tolerance of 5 ppm for precursor and 0. 02 Da for fragment ions. For CID, a tolerance of 5 ppm was admitted for precursor and 0. 6 Da for fragment ions. Chymotrypsin was selected as the proteolytic enzyme and up to two missed cleavages were allowed.