dataesr/scientific-paragraphs-categorization huggingface dataset:
Enzyme-linked binding assay RAW264. 7 cells were cultured in a 96-well microplate at a density of 10 5 cells/ml. Cells were incubated for 1 h at 4 C with concentrations of recombinant TAFA-Fc or Fc proteins ranging from 0. 74 nM to 500 nM, in PBS supplemented with 10% FCS. Cells were then washed with PBS, fixed by incubation with 4% paraformaldehyde (PFA) in PBS for 20 minutes and then incubated for 1 h at room temperature with goat anti-human Fc-HRP. The microplates were washed with PBS and peroxidase activity was measured by adding 100 ml/well 3,3',5,5'-tetramethylbenzidine (TMB) and 30% hydrogen peroxide (Sigma-Aldrich) and incubating for 30 minutes at room temperature. Optical density was measured at 450 nm on a TriStar 2 S LB 942, Multimode Microplate Reader (BertholdTech). Results were analyzed with MikroWin software.
Data acquisition and analysis Patch pipettes were connected to the head stage of an Axopatch 200B amplifier. A computer interfaced to a 12-bit A/D converter (Digidata 1322A using pClamp10 (all from Molecular Devices, Sunnyvale, USA) controlled the voltage-clamp protocols and data acquisition. Data were digitized at 20 kHz and filtered at 2 kHz. Series resistances, without compensation, to ensure rapid recording, ranged from 6 to 19 MU (recordings with Ra > 19 MU were excluded). The mean liquid junction potential between the pipette solution and extracellular solution 4 was 12 mV, and was not corrected for. Analyses and curve fitting were conducted in Clampfit 10. 3 (Molecular Devices) and SigmaPlot 12 (Systat Software Inc. San Jose, CA). I h activation (Iht) was fitted for the traces obtained when stepping from À70 to À120 mV and was described as the sum of two exponential terms plus a constant according to the equation Ih(t) = A fast exp(Àt/t fast )+A slow exp(Àt/t slow )+C, where A fast and A slow are the amplitudes of the exponential terms and t fast and t slow are the corresponding time constants (t).
ACKNOWLEDGMENTS We thank the members of the  Moqrich laboratory  for fruitful scientific discussions. This work was funded by the  French National Research Agency  ( ANR-CE16-Myochronic  to A. M. ), a '' Fondation pour la Recherche Medicale (FRM) '' postdoctoral fellowship (to S. Y. ), Equipe  FRM-EQP202003010192  (to A. M. ), and  Biotrail PhD  funding (to C. S. ). This work was also supported by institutional funding from  CNRS  and  Aix-Marseille-Universite (to IBDM).
The data shown are the mean ± SEM for 2-4 independent experiments. (C) Calcium mobilization was measured in Chinese hamster ovary (CHO)-K1 cells stably expressing apoaequorin, Ga 16 , and the receptor of interest. Cells were stimulated with 100 nM WKYMVM (FPR1 and FPR2); 1 mM WKYMVM (FPR3); 100 nM of the chemokines CCL1 (CCR8), CCL2 (CCR2), CCL5 (CCR1, CCR3, CCR5), CCL17 (CCR4), CCL19 (CCR7), CCL20 (CCR6), CCL25 (CCR9), CCL27 (CCR10), CXCL2 (CXCR2), CXCL11 (CXCR3), CXCL12 (CXCR4), CXCL16 (CXCR6), and CX3CL1 (CX3CR1); or 100 nM TAFA4; luminescence was recorded for 30 s.
Simulation results In order to illustrate the performance of the proposed flatness controller in improving the transient stability of the SMIB system, some computer simulations with Matlab and Eurostag are provided here. For comparison purposes, classic K I K d K a T a K g T g K v V ref w ref T c T o Value 3 10 1 0 1 0. 1 25 0. 3 5 1 1 0. 3 3 controllers [3] ,  [4]  have also been implemented and tested, which are described by the following differential equations: u1 = 1 T a (-u 1 + K a (v t -V ref )) (50) u2 = 1 T g (-u 2 + K g (ω s -ω) + T ref ) (51) To enhance the performance of the classic controller, we add a PID controller for the AVR. This PID controller will ensure an exact tracking scheme for the terminal voltage and will speed up the response time of it. Also, the classic governor controller, generating u 2 , is equipped with a Fast Valving Scheme (FVS), which will enhance the transient stability  [31].
Once a sever disturbance is detected, the steam valve is closed with a fast dynamic respecting the rate limitation ( u2 = -1/T c ). This situation is maintained for a time T 2 and after that the valve position is released to meet the dynamic (51) and respect the rate constraints. The parameters of the controllers are given in Table  2. We consider a short-circuit in the middle of the second transmission line. Fig. 2  illustrates the machine speed, mechanical torque and terminal voltage obtained from Eurostag software. The CCT in this case is 585ms. It is clear that the proposed approach shows appropriate robustness against hard faults occurred in the network. On the other hand, the classic controllers reveal a maximum CCT equal to 528 ms. This means that the suggested flatness control algorithm has over 10% improvement in CCT compared to the IEEE common controllers.
Also, Fig. 3  to Fig. 5  illustrate different state evolutions for the proposed controller against the classic IEEE controllers which have been obtained from Matlab. One can see that the proposed controller works well. In fact, the CCT obtained by the proposed controller is larger than that of the classic IEEE controllers. The applied control inputs Efd and Psv are illustrated in Figs. 6  and 7 , respectively. One can see that the control inputs of the proposed scheme are feasible in practice. Also, the proposed controller has the effect of fast valving as it can be observed in Fig. 7 , which is an interresting feature that enhances the transient stability of the power system. Finally, Fig. 8  depicts the dedicated control energies for both the flatness and classic control strategies. It is clear that the flatness controller provides a lower value for the control energy compared to the control energy for the classic control methodology.
Resistance gene screening For molecular characterization of ARG, genomic bacterial DNA was extracted from one colony with the Insta-Gene™ Matrix kit (Biorad, California, USA), according to the manufacturer's instructions. ESBL and tetracycline resistance coding genes were screened by PCR in all E. coli tetracycline-resistant isolates. bla CTX-M multiplex PCR including phylogenetic groups 1, 2, and 9 was performed  [29]. bla TEM gene was screened by simplex PCR  [30]. Amplified PCR products were sequenced (Eurofins, Ivry-sur-Seine, France) and compared with known resistance gene sequences in the GenBank database by multiple-sequence alignment with the Basic Local Alignment Search Tool program for further characterization. Tetracycline-resistant isolates were screened for the presence of tetA and tetB genes with specific tetA primers designed for this study (tetA-F 5′-TAGAAGCC GCATAGATCGCC-3′ and tetA-R 5′-GCTTCATGAG CGCCTGTTTC-3′) and published specific tetB primers  [31].
Combined numerical analysis The combined numerical analysis was performed on ESBL-producing E. coli patterns with BioNumerics® v6. 6 software (Applied Maths NV). Each file with experimental data from AMR and ARG screening was merged as a composite data set in the BioNumerics® database, with the similarity coefficient option taken from each experiment. The matrices from the individual experiments were averaged according to the same defined weight, and an individual similarity matrix was calculated in such a way that all characters had an equal influence on similarity. A dendrogram was drawn by using the unweighted pair group method with arithmetic averages with a tolerance of 1% to show the similarity of combined AMR and ARG patterns of the bacterial isolates.
Acknowledgments The authors would like to thank the farmers who participated in the study for their help in sampling during the fieldwork and the slaughterhouse director for facilitating access. We thank  Jean-Christophe Bambou ,  Jean-Luc Gourdine  ( INRAe, Guadeloupe ), and  Olivier Roy  (Chairman of the association of liberal veterinarians, AVPLG, Guadeloupe) for their useful help in questionnaire designing. We also thank  Professor G. Arlet  ( Tenon Hospital, Paris, France ) who provided positive E. coli control strains for resistance screening by PCR.
Study site and data collection Field work was conducted at 'Los Andes', Suchitepéquez, Guatemala (14°54' N -91°18' W, 1661 m. a. s. l. ) (Figure  1 ). 'Los Andes' is a private reserve, including 607 ha on the southern slope of the Atitlán volcano at an elevation of 840-1830 m. a. s. l. The site is covered by cloud forest on the highest part of the reserve (364 ha) Figure  2. Manual selections of vocalizations were made using the spectrogram display of Raven Pro 1. 5 software (time precision = 0. 0232 s, frequency precision = 21. 5 Hz, dynamic range = 96 dB). All the vocalizations found within 300 s before, during or after a vocalization of P. mocinno were manually selected. Selections were made taking the duration (s) and the highest and lowest frequencies (kHz), between 0. 5 and 2. 5 kHz, resulting in rectangles with x and y coordinates (respectively time and frequency).
Data analysis The vocalizations of P. mocinno were first manually detected by listening and visualizing the 778 recordings through a spectrographic representation with the software Raven Pro 1. 5 (Center for Conservation Bioacoustics 2014) (short-time Fourier transform was calculated with a time precision of 0. 0232 s, a frequency precision of 21. 5 Hz, a dynamic of 96 dB). Pharomachrus mocinno vocalizations were detected in 93 recordings among the 778 30 min recordings. All the vocalizations of P. mocinno in the 93 recordings were then manually annotated and classified as territorial, alarm or courtship vocalizations (contact vocalizations were not found). The vocalizations of all other species occurring in the same bandwidth between 0. 5 and 2. 5 kHz and within 300 s before, during or after a vocalization of P.
timetree. org). Obs-pot overlap: refers to the difference between the observed and the potential overlap respectively for the territorial, courtship and alarm vocalizations of P. mocinno Raven Pro 1. 5 (Center for Conservation Bioacoustics 2014) software directly from on-screen measurement cursors on the spectrogram. Selections were made taking the start (s), end (s), highest and lowest frequencies (Hz), resulting in time x frequency rectangles (Figure  2 ). Frequency and time overlap were quantified by estimating the percentage of the time x frequency rectangle of P. mocinno vocalizations that overlapped with the time x frequency rectangle of other species vocalizations. For each species, an observed overlap (overlap rate) was quantified as the sum of all the overlap percentages with P. mocinno, divided by the times that P.
Acknowledgements. We thank  Sébastien Hardy  and the CEMCA for their important support to conduct the study in Guatemala. We are grateful to the guides  Jesús Lucas  and  Selvin Xiloj  for their useful help during field work and to the  Hazard family  for the support in Los Andes reserve. We thank  Sandrine Pavoine  for the valuable comments and her help on the statistics analyses and  Claire Dallies  for their shared knowledge about the avian community. This work was financed by the  National Geographic Society  [grant number. Supplementary material. For supplementary material accompanying this paper visit https://doi. org/10. 1017/S0266467421000420.
For many years now, communication in the industrial sector has been characterized by a new trend of integrating the wireless concept through cyber-physical systems (CPS). This emergence, known as the Smart Factory, is based on the convergence of industrial trades and digital applications to create an intelligent manufacturing system. This will ensure high adaptability of production and more efficient resource input. It should be noted that data is the key element in the development of the Internet of Things ecosystem. Thanks to the IoT, the user can act in real time and in a digital way on his industrial environment, to optimize several processes such as production improvement, machine control, or optimization of supply chains in real time. The choice of the connectivity strategy is made according to several criteria and is based on the choice of the sensor.
Introduction Whoever designed parallel or distributed software and protocols must have found out how hard it is to get such software correct. 1  Distributed software defies testing, as some errors only occur very rarely, easily less than once in a million of runs. Yet, if such errors occur the software can go awry, with effects that range from confused internal administration, via crashing of the software, to loosing control over safety-critical hardware. The theoretical solution is to prove the correctness, for instance using assertional methods that have been under development since the advent of the first electronic computers  [1]. These days these methods are supported by proof checkers such as Coq  [3]  and Isabelle  [22] , or integrated automatic provers for algorithms such as Dafny  [20].
The more alternative views, the higher the quality of the result, provided they are very precisely compared to each other. Formulating correctness and proving this with a proof checker is also a valid alternative view. We use the modal mu-calculus with data as it is unsurpassed in expressivity  [14, 5]. Fairness can be expressed using alternating fixed-points, and by using data complex behaviour of the model can be tracked and analysed with modal formulas. Alternative property languages, such as CTL/LTL can all be translated linearly to the modal mu-calculus with data  [7]. In this tutorial we first describe mCRL2 and the modal mu-calculus very compactly. Subsequently, we focus on mutual exclusion protocols for shared memory and traverse through the development of such protocols, repeatedly identifying and repairing problems.
Ethics statement According to the French regulations relating to observational database analyses, the collection of clinical E. coli strains and urine samples did not require specific informed consent. The data were analysed anonymously. Mouse UTI experiments were performed in accordance with the European directives for the protection of animals used for scientific purposes (2010/63/EU). The local ethics committee from CREFRE-US006 (Regional Centre for Functional Exploration and Experimental Resources) approved the protocol (number CEEA-122 2014-53).
Sequencing data, sequence alignments and phylogenetic analyses Whole genome sequencing was performed using the Illumina NextSeq500 Mid Output platform (Integragen, Evry, France) to generate 2 x 150 bp paired-end reads, at approximately 80x average coverage. Genome de novo assembly and analysis were performed with the BioNumerics 7. 6 software (Applied Maths) and Enterobase (http://enterobase. warwick. ac. uk/). For SNP-based phylogenetic trees, core genome alignments were generated after mapping raw reads to the E. coli MG1655 genome. The core genome phylogenetic tree was inferred with the Maximum-likelihood algorithm using Enterobase for B2 phylogroup strains. Sequences of all isolates are available in the NCBI Database, Bioproject number PRJNA615384 (https://www. ncbi. nlm. nih. gov/bioproject/PRJNA615384).
05%, Triton X-100. Cells were washed 3 times for 5 min in PBS 0. 05%, Triton X-100 and incubated with secondary AlexaFluor 488 or 568 (Invitrogen) diluted 1:500 in MAXblock medium with 1 μg/ml DAPI (Sigma). Slides were mounted with Fluoroshield (Sigma) and examined on a Leica SP8 laser scanning confocal microscope in sequential mode with LasX software (Leica), while keeping the same laser and detector settings between different wells. Final images were processed with the ImageJ software.
Histological and immunofluorescence analyses of mouse bladder tissue Histological bladder slices were prepared using standard protocols and processed for haematoxylin-eosin staining then scanned using a Pannoramic 250 scanner (3DHistech). Final images were captured using CaseViewer software (3DHistech). For pH2AX, Krt14 and Uroplakin (Upk) staining on paraffin-embedded bladders, 5 μm thick sections were de-paraffinised and re-hydrated in xylene, ethanol and tap water. Unmasking was performed with a 6-min trypsin digestion (Trypsin-EDTA solution, Sigma, T392) followed by 30 min incubation in citrate buffer pH 6 at 80-95˚C. Sections were then blocked and permeabilised in MaxBlock, 0. 3% Triton X100. Slices were stained with anti-pH2AX (Rabbit, Cell Signalling S9718; or Mouse, Millipore #O5-636) at a dilution of 1:200, anti-Krt14 antibodies (Chicken, Ozyme BLE906001) at a dilution of 1:250 and anti-Upk III (Rabbit, Abcam, ab157801) at a dilution of 1:800 in MaxBlock, 0.
For Krt14 positive and Krt14/pH2AX positive cells quantification, the CellProfiler software was used  [55]. Briefly, images were analysed in batch with a pipeline that first identify nuclei as primary objects with Otsu thresholding. Cells were defined by 6 pixels expansion from nuclei with "Distance-N" method and then cytoplasm were deducted by subtraction. pH2AX intensity in nuclei and Krt14 intensity in cytoplasm were measured and recorded for each cell. pH2AX positive cells were defined like after Fiji analysis. Krt14 positive cells were counted when exhibiting a signal above 3 times the average signal on the whole bladder section on the mosaic confocal image. For TUNEL-staining of paraffin-embedded bladders, 5 μm thick sections were de-paraffinised and re-hydrated in xylene, ethanol and tap water.
Acknowledgments The authors thank  Alexandre Perrat , Frederic Auvray and  Laurent Cavalie  ´for their help with genomic DNA extraction and sequence analysis and  Pauline Dragot  for her excellent technical help. We thank the technical microbiology laboratory staff from the  Toulouse University Hospital  for helping to culture and isolate E. coli strains. The authors also wish to thank the staff of the  Tri GenoToul imaging facility , Toulouse (particularly  Sophie Allart  and  Danièle Daviaud ), the experimental histology facility ( CREFRE, Toulouse ) and the lipidomic facility of MetaToul, Toulouse. The authors thank the team members for their helpful discussions and critical reading of the manuscript and  Sebastien Dejean  for his help with data analysis.
This work was supported by grants from the  French National Agency for Research(ANR ; https://anr. fr/)( UTI-TOUL ANR-17-CE35-0010  (EO) and LiBacPain  ANR-18-CE14-0039 (NC) ) and the  French National Institute for Health and Medical Research (INSERM, https://www. inserm. fr/)(twoyears grant "poste d'accueil  INSERM 2018"(CVC) ). The funders had no role in study design, data collection and analysis, decision topublish, or preparation of the manuscript.
The authors confirm that all data underlying the findings are fully available without restriction. Sequencing data presented in the current manuscript have been deposited to the NCBI database, BioProject PRJNA615384 (https://www. ncbi. nlm. nih. gov/ bioproject/PRJNA615384). This project contains the raw reads data deposited in the Sequence Read Archive (SRA) and are publicly available. All other relevant data are within the manuscript and its Supporting Information files.
Both AMLs and ALLs with MLLrearrangements share a common gene signature that is distinct from that of MLL-Germline (MLL-Germ) leukemias  (33) (34) (35). Most of the well studied and validated MLL-r gene targets however are DNA binding proteins like the HOXA gene cluster and its cofactor MEIS1  (36) (37) (38) (39) (40) (41) (42) (43) , which are not suitable targets for immunotherapy. Advances in labeling and mass spectrometry have provided new strategies to more accurately identify cell surface proteins in cancer  (23). However, the focus has been on finding suitable targets for acute leukemias without distinguinshing between underlying cytogenetic mutations  (24) (25) (26) (27). The recently published in-silico human surfaceome provides a highly accurate surface protein dataset based on the experimental evidence from 41 human tissues within the Cell Surface Protein Atlas.
We further validated the specificity of LAMP5 expression in MLL-r leukemias by analysing the 1,109 pediatric leukemia patient samples from the St. Jude PeCan Portal which again revealed LAMP5 as significantly overexpressed in both AMLs and ALLs with MLL-rearragements (Figure  1B )  (30, 31). To determine if LAMP5 expression could discriminate between MLL-r leukemia and MLL-Germ leukemia patients, we performed a Receiving Operating Curve (ROC) analysis. LAMP5 achieved a statistically significant AUC (area under the curve) score in both the Microarray Innovations in Leukaemia (MILE) (GSE13159) and the St. Jude PeCan datasets, with high sensitivity and specificity at the optimal cutoff points (Figure  1C ). Further, a Kaplan-Meier survival analysis of B-ALL and AML patients correlated higher expression of LAMP5 with poor survival (Supplemental Figure  2 ).
High levels of LAMP5 mRNA expression (Figure  1D ) and protein (Figure  1E ) were also observed in human myeloid and lymphoid MLL-r leukemia cell lines (MOLM-13, MV4;11, THP-1, and RS4;11) as compared to MLL-Germ leukemia cell lines (HL-60, Kasumi-1, K562, REH and RCH-ACV) and normal human CD34-enriched cord blood cells (CB-CD34 + cells). Translocations of the MLL locus generate MLL-fusion proteins (MLL-FP) which activate transcription of downstream target genes  (55) (56) (57) (58). To determine if LAMP5 expression was dependent on the MLL-FP, we transformed CB-CD34 + cells with a retrovirus carrying a tetracycline-repressible MLL-AF9 construct. Treatment of transformed cells with Doxycycline led to a simultaneous reduction in the levels of both MLL-AF9 and LAMP5. (Figure  1F ). To determine if the MLL-FP directly activates the LAMP5 gene locus, we interrogated previously published MLL-AF4 chromatin immunoprecipitation sequencing (ChIP-seq) datasets derived from the RS4;11 cell line, and those from CD34 + cells transformed with FLAG-MLL-AF4.
Flow cytometry For apoptosis assays, cells were incubated with allophycocyanin (APC)-conjugated Annexin V (BD Bioscience) for 15 minutes at RT in 1X Annexin V Binding Buffer (BD Bioscience) followed by staining with 7-aminoactinomycin (7-AAD) (eBioscience). For surface LAMP-5 detection, cells were incubated with 3 µg anti-human LAMP-5 therapeutic antibody (TAB-0643CL, Creative Biolabs, NY, USA) overnight and then stained with anti-mouse IgG1-PE (eBioscience). Data were acquired on a FACS Canto I and results were analyzed using FlowJo Version 10 (FlowJo).
ChIP-seq Analysis ChIP-seq data were downloaded from the Gene Expression Omnibus using the listed accession numbers (GSE84116 for MLL-Af4 transformed CB CD34 + , GSE95511 for ML-2 data, GSE79899 for MV4;11 and THP-1 data, GSE38403 for RS4;11 data, and GSE38338 for SEM data) and visualized with the UCSC Genome Browser, assembly hg19. For consistency, data for RS4;11 and SEM were converted from their originally mapped hg18 to hg19 using the liftOver tool from the UCSC Genome Browser Utilities.
Acknowledgments: We would like to thank  Daniel Starczcynowski, PhD , for his intellectual input. We would like to acknowledge the assistance of the  Research Flow Cytometry Core in the Division of Rheumatology at Cincinnati Children's Hospital Medical Center. All flow cytometric data were acquired using equipment maintained by the  Research Flow Cytometry Core in the Division of Rheumatology at Cincinnati Children's Hospital Medical Center. M. W. was supported by an  NIH  grant ( R50 CA211404 ). L. H. L. is a St. Baldrick's  Foundation Scholar  and is supported by grants from  CancerFree KIDS  and the  NIH  ( L40 HL143713-01 ) A. R. K. was supported by a  Hyundai Hope on Wheels  grant.