dataesr/scientific-paragraphs-categorization huggingface dataset:
Figure1. Phylogenetic tree highlighting the position of Timonella senegalensis strain JC301 T relative to several type species of suborder Micrococcineae. Genbank accession numbers are indicated in parentheses. Sequences were aligned using ClustalW, and phylogenetic inferences obtained using the maximum-likelihood method within the MEGA software package. Numbers at the nodes are bootstrap values obtained by repeating 500 times the analysis to generate a majority consensus tree. Cellulosimicrobium cellulans was used as an outgroup. The scale bar represents a 2% nucleotide sequence divergence.
FTIR analyzers An FTIR analyzer records a spectrum over a broad IR range (1800-5000 cm -1 ), thereby offering the possibility to measure a large number of species simultaneously. Spectra are stored and can be analyzed at a later date with a different method to get data with a higher accuracy or study new species. In the FTIR, the infrared signal passes first through a Michelson interferometer, then this modulated beam traverses the sample cell. The resulting time-modulated signal is then converted into an infrared spectrum through Fourier transform. The FTIR analyzer operated by the LSCE is a commercially available Ecotech instrument. The instrument operated by the Bremen University was built at the University of Wollongong, Australia. Both instruments are functionally identical. A detailed description is found in  Griffith et al.
(2002). Briefly, the activity of the short-lived 222 Rn decay products, attached to aerosols, is accumulated on a quartz aerosol filter and assayed on line by α-spectroscopy. This analyzer can measure  222  Rn activities every thirty minutes down to 0. 5 Bq m -3 with an uncertainty below ±20 %. The radon analyzer was installed on top of a building located between the measured ponds. The sampling height was approximately 7 m and next to the FTIR sampling outlet operated by LSCE. Sampling began on 17 September at 17:00 GMT and finished on 21 September at 06:00 GMT. Due to instrument malfunction, no data were recorded between the 19 September from 08:30 GMT to the 20 September at 00:30 GMT and from the 20 September at 06:30 GMT to the 20 September at 17:30 GMT. 9190.
Discussion We can compare the results at three different scales, at the process scale with the chamber measurements, at the plant scale with the tracer release measurements and at the regional scale with the Radon tracer method. The results are summarized in Table  4. With only one period of good correlation, we can not estimate the uncertainties attached to the regional emissions. The European Database for Global Atmospheric Research (EDGAR,  Olivier et al. , 1996)  gives for France the same value of 0. 02 g m -2 d -1. For the city of Valence, the mean value over the pixel containing the city (approximately 10 × 10 km) is slightly higher at 0. 03 g m -2 d -1. If we consider this area then the city emissions are about 1500 kg d -1 and the waste water treatment plant emissions represent approximately 3 % of the city emissions.
Acknowledgements. We thank  Veolia Eau  and especially  Julien Malandain , head of the waste water treatment network from  Drôme-Ardèche , and all the staff including  Sandy Chabanel ,  Cédric Comte ,  Yvan Blache  and  Nicolas Drut  in the stations of  Valence and Romans-sur-Isere  for their technical help and their great involvement and interest in the measurement campaign. We also thank the city of  Valence  for supporting this project. Thank you to  Benoit Wastine  and  Cyrille Vuillemin  from  LSCE  for their help in organizing the campaign. This campaign was funded through the  BridGES  project (supported by the  Versailles Saint-Quentin-en-Yvelines University , the  French Alternative Energies and Atomic Energy Commission (CEA) , the  French National Centre for Scientific Research (CNRS) ,  Thales Alenia Space and Veolia Water).
Pharmacokinetics and biodistribution Tumor-bearing mice (4-6 per group) were sacrificed at predetermined time points after the liposomal mTHPC injection. Tissue samples included in the analysis were tumor, skin, muscle, spleen, liver, kidneys, heart, lungs, and plasma. Tissue samples were rinsed thrice in 0. 9% NaCl and kept at -80°C prior to analysis. The mTHPC concentration in the samples was determined by high-performance liquid chromatography, as previously described. 19 The plasma pharmacokinetics were analyzed using compartmental and noncompartmental methods. 20 Elimination rate constants from the tissues were calculated from semi-logarithmic plots of the three last data points (15 hours, 24 hours, and 48 hours).
The amplitude of the effect is significantly dependent on the mTHPC concentration in liposomes. 17 s described,  17  the amplitude of photoinduced fluorescence quenching in the plasma samples allows the local mTHPC concentration in liposomes to be determined using a method described in Table  S1. These data are used to calculate the percentage of the drug released from intact liposomes at a given time after injection when compared to the initial drug formulation (Foslip ® or Fospeg ® ). Samples of plasma containing mTHPC were irradiated by a 20 mW 650 nm laser (Global Laser Limited, Gwent, UK) for 30 seconds under continuous stirring at 21°C. Fluorescence spectra were recorded on a PerkinElmer LS55B spectrofluorimeter (PerkinElmer, Waltham, MA, USA).
At least 1,200 tracks were completed during the video data analysis using Nanoparticle Tracking Analysis version 2. 3 software (NanoSight, Ltd). Three different samples were used for each formulation, and each aliquot was run in quadruplicate. No Fospeg ® aggregation was registered in serum, while the Foslip ® peak diameter increased by 30 nm after 24 hours.
5, and exposure 200 ms. A quantitative analysis of the colocalization of mTHPC and the vessel marker was performed using the colocalization tool in the Leica Application Suite -Advanced Fluorescence v. 2. 7 software (Leica Microsystems) with 20% background subtraction and 30% threshold for both channels. Three adjacent images from the three-dimensional stack were merged into max projection with no signal saturation. Three different projections were used for each tumor, and three regions of interest, 1,300 µm × 1,300 µm each, with blood vessels and surrounding tumor tissue, were selected for each projection. The selection was based on the sufficient fluorescent microspheres signal delineating the blood vessels. Colocalization was defined as co-compartmentalization of mTHPC and fluorescent microspheres in tumor blood vessels.
as a standard, 5,10,15,20-Tetrakis(Nmethyl-4-pyridyl)-21H,23H-porphine was used, with a quantum yield of singlet oxygen generation in the aqueous solution of 0. 77. The total amount of excitation pulses was 3 × 10 5. To minimize sample photodegradation, continuous magnetic stirring was used. sample photostability was verified with a photodiode during experiments by the intensity of laser radiation transmitted through a sample and with a spectrophotometer before and after the experiment. Within the signal accumulation time (300 seconds) the optical density of the samples decreased by less than 5%. Signal intensities were extrapolated from the fitting of data with the difference of two exponential decay functions. After that, signal intensities were corrected for absorption of the samples at the laser wavelength, and the singlet oxygen quantum yield was calculated according to: where QY sample (QYst) is the singlet oxygen quantum yield of the sample (standard), I sample (Ist) is the sample (standard) extrapolated signal intensity, and OD sample (ODst) is the optical density of the sample (standard) at 531 nm.
Acknowledgments This work was supported by the  Lorraine Cancer Institute research funds ,  PICS  ( International Program for Scientific Cooperation ) grant  6032  ( Belarusian grant  No  M12SP002 ), and  Belarusian Republican Foundation for Fundamental Research  grants ( B11M-004 ,  B11F-004 ,  B13M-013 ). VR acknowledges the fellowship of the  Ministry of Foreign and European Affairs of France. The authors thank  Dr Alexander Stasheuski  and  Dr V Galievsky  of the  BI Stepanov Institute of Physics (Minsk, Belarus)  for help with the singlet oxygen measurements.
Distributed systems are hard to design, and formal methods help to find bugs early. Yet, there may still remain a semantic gap between a formal model and the actual distributed implementation, which is generally hand-written. Automated generation of distributed implementations requires an elaborate multiway synchronization protocol. In this paper, we explore how to verify correctness of such protocols. We generate formal models, written in the LNT language, of synchronization scenarios for three protocols and we use the CADP toolbox for automated formal verifications. We expose a bug leading to a deadlock in one protocol, and we discuss protocol extensions.
As a particular specification language, we have in mind LOTOS NT (LNT for short)  [5] , a successor of LOTOS  [16]  and a variant of the E-LOTOS standard  [17]. LNT has a general parallel composition operator  [12]  that enables multiway synchronization (also available in LOTOS), where a set of two or more tasks can synchronize altogether, and m-among-n synchronization (not available in LOTOS), where any subset of m tasks among a set of n tasks can synchronize altogether. LNT is already equipped with formal verification tools packaged in the CADP toolbox  [11]. In a close future, we would also like to automatically generate from an LNT specification a distributed implementation consisting of one sequential process per task plus a synchronization protocol, as much distributed as possible to avoid the obvious bottleneck that a centralized synchronizer would represent in large distributed systems.
Regarding correctness, we verify not only the absence of livelocks and deadlocks, but we also compare the protocols' behavior with the expected reference behavior, which is obtained using reliable verification tools of CADP. High-level languages allow concurrent systems to be modeled as processes running asynchronously and communicating by rendezvous synchronization on communication actions. Historically, LOTOS  [16]  was the main language of CADP. It combines algebraic abstract data types to model types and functions in an equational style, and a process algebra inheriting from CCS  [21]  and CSP  [15]  to model processes. In recent years, LNT  [5]  was developed, providing an easier syntax closer to mainstream imperative and functional programming languages. Models written in LNT can be verified using CADP, via an automated translation into LOTOS.
The semantics of a LOTOS or an LNT program are defined as an LTS (Labeled Transition System)  [21] , that is a graph whose transitions between states are labeled by actions denoting value-passing communications. Intermediate-level models are representations of systems between high-level languages and low-level models. As such, the EXP. OPEN 2. 0  [18]  language for networks of communicating LTSs consists of LTSs composed using various operators, including LOTOS and LNT parallel composition. EXP. OPEN 2. 0 is a key component of CADP for compositional verification. Low-level models are representations of LTSs. CADP provides the BCG (Binary Coded Graph) format to represent an LTS explicitly (as a set of states and transitions), and the OPEN/CAESAR environment  [9]  to represent an LTS implicitly (as a set of types and functions, including functions for enumerating the successor transitions of a given state), for on-the-fly verification.
Temporal logics allow behavioral properties to be defined. The MCL language  [20]  combines the alternation-free µ-calculus together with regular formulas, primitives to handle data, and useful fairness operators of alternation 2. Model checkers and equivalence checkers are also available in CADP. The EVALUATOR 4. 0 model checker  [20]  allows an MCL formula to be checked on the fly on a system modeled in any language or format available in CADP, through the OPEN/CAESAR interface. The BISIMULATOR 2. 0 equivalence checker  [19]  allows the equivalence of two systems to be checked on the fly, modulo several equivalence relations, including strong  [22] , branching  [29] , safety  [3]  or weak trace  [4]  equivalences. At last, CADP allows complex verification scenarios to be described succinctly using the intuitive language SVL (Script Verification Language)  [10].
For instance in Figure  1 , t2 may synchronize on B with either t3 or t4, but cannot synchronize with both at the same time. Once t2 has synchronized on A, it will never be ready to synchronize on B again, so no other synchronization on B may occur. In the sequel we briefly describe how the three protocols under study fulfill these requirements. For a complete and detailed explanation of their internals, we refer to original publications of the protocols  [27, 23, 26]. Note that LNT also enables data exchange during rendezvous on gates, and guards on data values. We leave those aspects for future work, focusing here on synchronization. In addition, we assume that the composition of tasks is static, i. e. , we do not consider the dynamic creation and deletion of tasks. Sjödin's protocol overview.
This is our input to generate the low-level specification of the scenario, i. e. , the model of the implementation of protocol processes, which manage synchronizations, and of tasks, which interact with protocol processes 2. We write the low-level specification in LNT. We generate an LNT module for each task and for each protocol process. Moreover, a main module will compose tasks and protocol processes, along with LNT processes modeling the underlying network used in communications between tasks and protocol processes. Note that gates of high-level specification become data of message exchanges in low-level specification, and LNT gates in the low-level specification represent communication channels between low-level processes. Low-level Tasks. When a task is ready to synchronize on one or more gates, it must exchange messages with some protocol processes until it receives the confirmation of a successful synchronization.
Now both LTSs have the same labels for task synchronizations, and the low-level one also contains internal actions representing protocol messages. Several equivalence relations correspond to different ways of abstracting away internal actions. We use, in decreasing order of strength, the branching  [29] , safety  [3]  and weak trace  [4]  equivalence relations. The SVL script calls the BISIMULATOR tool, which compares LTSs and, in case of differences, provides a diagnostic showing a behavior possible in an LTS and not in the other.
We obtain low-level LTSs with up to 500, 000 states and 1, 200, 000 transitions. Identification of Deadlocks in Parrow's Protocol. The test suite raised a design error that can lead to deadlocks. Figure  7  illustrates a scenario with two tasks Task1 and Task2 which synchronize two times on gate G. Figure  7  also exposes a negotiation leading to a deadlock after a first synchronization on G, whereas two synchronizations must occur. In the negotiation, we use the original notations of  [23]  for communication channels and data set names. Each task notifies its own mediator with a request message to declare that it is ready on G. Mediators send ready messages to port G, which populates the set, called T , of tasks that are ready. When the port detects that both tasks are ready, it begins a negotiation by sending a query message to the mediator of Task1, called Med1.
These extended versions can handle the generality of LNT synchronization, and we verified that no new bugs were introduced. The formal models of protocols will help us to decide which protocol to use for implementation. Nevertheless, before making our final decision, this work should be continued in several directions. First, we will study how data exchanges can be added to the protocols. Second, we could use the protocol models to precisely measure how many messages are required in each protocol to agree on a synchronization, depending on the number of tasks and the possible synchronizations between them. Moreover, we could use the performance evaluation features of CADP  [7]  to simulate communication latency between remote sites, and measure protocol performances directly on the formal models.
To address the lost of follow up at M36, we looked for parameters that differentiate the patients who were lost between M14 and M36 and these who were followed. Statistical analyses were performed using the STATA software (Stata Corporation, College Station, Texas, USA); all significance tests were two-sided and p-values,0. 05 were considered significant. Luminex data were mined using the Omniwiz software (Biowisdom) and Mann-Whitney analysis is reported. False discovery rate (FDR or q-values) were calculated as correction for multiple analyte testing. To characterize the spatiotemporal evolution of arthralgia, we considered different types of arthralgia. For a given site at a given time point, arthralgia was defined either as a ''persistent symptom'' if the affected site was the same as that reported during the previous time point, as ''a relapse of an the acute symptom'' if the site affected was the same as that at the acute phase, or as ''a new symptom'' if this site was not affected at the acute phase, or as a ''migrating symptom'' if the arthralgia was localized at a site distinct from that previously reported.
Arthralgia status, systemic inflammation and autoimmunity markers At M36, 22 patients with arthralgia (ART+) and 20 patients without arthralgia (ART2) were randomly selected from the cohort to participate to the CHIK IMMUNOPATH study. Its aim was to titrate anti-CHIKV antibodies and identify a serum inflammatory or autoimmune signature associated with the arthralgia phenotype. All patients were negative for CHIKV RT-PCR, and exhibited anti-CHIKV IgGs in serum, while a minority (9. 5%) harbored measurable levels of anti-CHIKV IgM. The activity of CHIKV IgG (GMAT) was significantly higher in ART+ patients  (30)  than in ART2 patients  (20) , but antibody avidity was comparable in both groups (mean 6SD: 31,6620,4 in ART + patients and 33,7619,8 in ART2 patients). Although lymphopenia is a defining feature of acute CHIKV disease  [11] , it was a rare finding at M36 (data not shown).
Discussion Our study is the first prospective cohort study on CHIKV longterm arthralgia that is based on the follow-up of patients who presented with acute CHIKV infection as the inclusion criterion. This study is also the first to define the evolution of CHIKVinduced arthralgia, mapping the frequency and location of arthralgic sites during a three year time period. We have also investigated the impact of CHIKV-chronic arthralgia on daily life of patients, identified clinical signs associated with arthralgia, and analyze biologic markers. Moreover we have evaluated associated risk factors and estimated the economic burden of this disease. Together, these data allow us to define the features of CHIKVinduced chronic arthralgia (Table  5 ), as compared to other viral arthritis  [16] , and to establish a detailed understanding of the public health problem resulting from CHIKV-chronic arthralgia.
This study was funded by the  CRVOI (Centre de Recherche et de Veille sur les maladies emergentes dans l'Ocean Indien) ,  Institut Pasteur ,  Inserm  ( Institut National de la Santeet de la Recherche Medicale ),  Labex IBEID, Fondation pour la Recherche Medicale, BNP-Paribas Foundation and Mairie de Paris. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
These steps provided a robust dataset of high-quality coding variants, considerably reducing the false-positive rate. To identify SNPs and small indels differentiating the C57BL/6J and C57BL/6N strains, we used the paired-end reads generated from the 17 Mouse Genomes Project  [11]. We called variants using the Genome Analysis Toolkit (GATK)  [12] , and found 681,220 variants that distinguish the C57BL/6J and C57BL/6N strains. Using the short-read genome sequence of C57BL/6J generated by the Broad Institute  [13] , we were able to filter out prospective sequencing errors by removing variants common to the Broad C57BL/6J sequence, thus counteracting discrepancies in the reference while improving the false-negative rate. The remaining reads were filtered with an allele ratio of less than 0. 8 (heterozygous) and covered by less than 3 or greater than 150 reads.
Comprehensive phenotypic assessment of C57BL/6N and C57BL/6J mice In parallel to the genomic analyses, the European Mouse Disease Clinic (EUMODIC) consortium has carried out a comprehensive phenotypic comparison of the C57BL/ 6NTac and C57BL/6J strains. EUMODIC comprises four mouse centers  [19]   S1 ). The methods for performing each screen are detailed in the standard operating procedures (SOPs) that can be found in the EMPReSS database  [20]. Data were acquired on 413 phenotype parameters along with 146 metadata parameters, and entered into the Euro-Phenome database  [19]. As part of this work, we have been capturing extensive control data on the baseline phenotype of C57BL/6NTac. We have also taken this T C B6J Guca1a Isoleucine Valine X C A B6N Armcx4 Alanine Aspartic acid Splice site polymorphism 5 A G B6J Tbc1d19 - - Frameshift 1 bp deletions 1 G - B6N Crb1 - - 9 G - B6J Cilp - - opportunity to investigate the phenotype of C57BL/6J mice and to compare this with C57BL/6NTac (henceforth referred to as J and N respectively).
For each line, N and J, age-matched mice have been analyzed through both EMPReSSslim pipelines. Data were acquired from all four centers in the consortium for 19 of the 20 platforms from the pipeline, excluding fluorescence-activated cell sorting (FACS) analyses (see Additional file 2, Figure  S1 ). The EMPReSSslim protocols have been rigorously standardized in the EUMODIC consortium; however, there remain some differences in, for example, equipment and diet, and this is captured in the metadata sets within EuroPhenome. There will of course be other unrecognized environmental differences between centers. Collectively, these may contribute to gene-environment differences and phenotype outcome, but we did not seek to systematically define these effects, instead focusing on phenotypes that are concordant between centers and are clearly robust to unrecognized environmental perturbations.
In addition to the analysis of N and J through the EMPReSSslim primary phenotyping pipeline at the four centers, other partners within the EUMODIC consortium have applied a wider range of often more sophisticated phenotyping tests to gather additional information, some of which explores further and aims to substantiate the phenotypic differences revealed through EMPReSSslim. In analyzing the data, we focused first on identifying phenotype parameters that showed a consistent and significant difference between N and J in three or more centers. We identified 27 phenotype parameters in this class (Figure  1a ; see Additional file 3, Figure  S2a, e ). In several cases, these differences were supported by data from secondary analysis, and we discuss these instances below. We also uncovered a second class of parameters for which similar trends were seen in two centers, but no evidence of trends was seen in the other two centers (Figure  1b ; see Additional file 3, Figure  S2b, f ).