dataesr/scientific-paragraphs-categorization huggingface dataset:
Predicted Molecular Functions of Ruminal and Cecal Bacteria Using PICRUSt as a predictive exploratory tool, the present study revealed no diet-induced shifts, but a strong effect of the digestive compartment on metagenome contents. Of the 41 KEGG modules (data not shown), the majority of the functional genes belonged to cellular processes and signaling, carbohydrate metabolism, amino acid metabolism, replication and repair, translation, energy metabolism and membrane transport (Table  S6 ). Most were not influenced by diet; moreover, comparing ruminal and cecal samples, irrespective of the diet (Figure  2 ), further revealed no changes in carbohydrate, energy, terpenoids, polyketides, and xenobiotics metabolism. On the other hand, lipids, nucleotide, and amino acid metabolism were higher in ruminal compared to cecal contents.
ACKNOWLEDGMENTS Animals were managed in the experimental facilities of  INRA Experimental Unit Herbipôle , of which the personnel is sincerely acknowledged, especially P. Faure as well as staff involved in sampling and data collection ( D. Graviou ,  C. Lascoux ,  Y. Rochette ,  INRA, UMRH, Saint-Genès-Champanelle, France ). We are grateful to the  INRA MIGALE bioinformatics platform  (http://migale. jouy. inra. fr)for providing computational resources.
Morphometric analysis All quantifications in this study were performed on six equally spaced serial stained perpendicular cross-sections throughout the entire length of the vessel, as described previously  [30]. Using image analysis software (Qwin, Leica), total cross-sectional medial area (between both elastic laminae), neointimal area (between internal elastic lamina and lumen) and luminal area was measured. These values were used to calculate the intima / media ratio and percentage luminal stenosis. In the short term study (3 days) the number of leukocytes, macrophages and CCL2 positive cells and after 21 days in PCAF KO vs. WT macrophages were counted manually and expressed as a percentage of the total number of cells (stained with hematoxylin). CCL2 and vSMC content after 21 days is analyzed using Qwin and CCL2 is expressed as the percentage of total medial and intimal area stained positive for CCL2.
Numerous publications address the petrogenesis of the partially dolomitized Latemar carbonate platform, Italy. A common factor is interpretation of geochemical data in terms of heating via regional igneous activity that provided kinetically favorable conditions for replacement dolomitization. New field, petrographic, XRD, and geochemical data demonstrate a spatial, temporal, and geochemical link between replacement dolomite and local mafic igneous dikes that pervasively intrude the platform. Dikes are dominated by strongly altered plagioclase and clinopyroxene. Significantly, where ferroan dolomite is present, it borders dikes. We hypothesize that seawater interacted with mafic minerals, causing Fe enrichment in the fluid that subsequently participated in dolomitization. This hypothesis was tested numerically through thermodynamic (MELTS, Arxim-GEM) and reactive flow (Arxim-LMA) simulations.
This study presents new field, petrographic, XRD, and geochemical data associated with Latemar platform dikecarbonate contacts. From an observational perspective, patterns of ferroan and nonferroan dolomite distribution related to individual dikes and compositional characterization of the igneous rocks are of novel interest. The data are used to define spatial, temporal, and geochemical relationships between the dikes and replacement dolomite. Based on the data, an original dolomitization conceptual model is proposed and then quantitatively evaluated using a multisoftware numerical approach. In the model, Latemar dolomite petrogenesis entails alteration of a limestone host rock by fluid modified compositionally via initial interaction with warm mafic dikes. Model evaluation involves first calculating the thermodynamic properties and compositions of major dike mineral phases upon solidification.
Dike modes were determined via point counting and Jmicrovision software, which allows random point counting on photomicrographs to determine relative phase abundances. The mineralogy of carbonate and dike samples was determined by X-ray diffraction (XRD) using a Philips PW1830 diffractometer with a Bragg/Brentano theta-2 theta setup and CuK radiation at 45 kV and 30 mA. The machine was equipped with a graphite monochromator. The receiving and divergence slit width was 1 mm and the antiscatter width was 0. 1 mm. The resulting XRD diffractograms were analyzed qualitatively with the DiffracPlus (Eva) software. Whole-rock dike oxide data were collected via Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES) at KU Leuven, Belgium, with a Varian ES ICP-OES Spectrometer. Samples were fused using the LiBO 2 method.
Detailed semiqualitative chemical point analyses were performed using a scanning electron microscope (SEM) equipped with an energy dispersive spectrometer (EDS). The beam voltage was set at 12 kV. Resulting spectra were analyzed using AZtecEnergy software (Oxford Instruments). These analyses are supported by electron microprobe analyses (EMPA) that provide quantitative mineral compositional data on carbonate phases. Measurements were carried out with a Cameca SX50 electron microprobe at the Department of Geology and Applied Geology, University of Mons, Belgium. The microprobe is equipped with four wavelengthdispersive spectrometers. Analyzing conditions for the carbon-coated samples were fixed at 15 kV and 20 nA. 2. 2. Modeling Software. Simulations were performed with the MELTS  [26, 27]  and Arxim  [28]  software packages.
MELTS is thermodynamically based software that performs phase equilibria calculations in magmatic systems (i. e. , where silicate melt is present). A system may also contain crystals (including solid solutions) and supercritical fluid (H 2 O). Arxim simulates reactions between multicomponent fluids and minerals based on either global Gibbs free energy minimization (Arxim-GEM) or a Law of Mass Action approach (Arxim-LMA). Arxim-GEM can predict compositions of stable solid solution phases and their thermodynamic properties for chemical systems defined in terms of elementary composition, temperature, and pressure. Arxim-LMA is restricted to fixed-composition phases that are identified a priori. Arxim-LMA has the advantage of allowing kinetic reactions and simple nondimensional reactive flow simulation, where a single host rock cell may react with fluid of specified composition injected at constant velocity.
Calculated solid solution compositions and associated thermodynamic data served to enhance the database used for subsequent Arxim-LMA simulations. Arxim-LMA simulations used new fixedcomposition species corresponding to the pristine dike assemblage. Arxim-LMA modeling aims (Figure  6 ) involved predicting the evolution of fluid composition during interaction between dikes and seawater and then calculating the amounts of ferroan and nonferroan dolomite formed by reaction of modified seawater with limestone (dike-host rock). In particular, the modified seawater composition was calculated by buffering seawater with major dike phases (clinopyroxene and plagioclase solid solutions), meaning that the fluid was allowed to achieve equilibrium with the dike phases. Formation of secondary dike minerals was not considered in this initial study; this is addressed, however, in separate work on dike petrogenesis as mentioned in Section 2.
1 GPa. The Arxim-GEM input composition also corresponds to dike whole-rock oxide data from ICP-OES analysis. Arxim-GEM was not used to model the process of dike solidification. A dike stable mineral assemblage was calculated at 1105 ∘ C and 0. 1 GPa and then compared with MELTS results for validation. The thermodynamic properties of the calculated phases were then evaluated at 40, 60, and 80 ∘ C, spanning the most recently published range of dolomitization temperature for the Latemar platform  [20]. The Arxim-GEM thermodynamic database is based on  [31]  and solid solution activity models for dike minerals come from  [32, 33]. All Arxim-LMA simulations use the Debye-Hückel activity model for seawater  [21, 34]  and thermodynamic data from the SLOP98 database for aqueous species, nonferroan dolomite, and calcite  [35, 36].
Baseline Simulation Results. MELTS simulations, performed under the conditions described above, confirm that clinopyroxene and plagioclase were the dominant stable mineral phases following dike solidification (Table  3 ). Calculated clinopyroxene compositions are diopside (Di 80 Hd 20 to Di 68 Hd 32 ). They encompass measured SEM-EDS data (Di 62 Hd 38 ) reported in Section 3. 3. Predicted plagioclase compositions are anorthitic (An 92 Ab 07 Or 01 to An 83 Ab 15 Or 02 ). Minor phases (<10% of the predicted phase assemblage) include sanidine, spinel, apatite, and olivine. There is no predicted exsolved supercritical fluid phase. Table  3  presents the solid solution compositions predicted by Arxim-GEM for clinopyroxene and plagioclase at solidification conditions, along with the associated thermodynamic equilibrium constants calculated at the three investigated dolomitization temperatures.
Overall, the predicted compositions for the two major dike minerals are comparable to the MELTS results: the clinopyroxene calculated formula unit falls well within the range estimated by MELTS. While a small discrepancy is observed on the feldspar solid solution, we deem it to be acceptable due to the inherent differences between the two codes, such as in their thermodynamic databases. Table  4  shows the effect of seawater-dike interaction on the concentrations of species that are relevant to our concept. Following seawater-dike interaction, the amount of Fe in solution is higher than before interaction by an amount that depends on temperature. At 40 ∘ C, Fe shows an increase of 0. 28 mmol/kg, while at 80 ∘ C, it is limited to 0. 13 mmol/kg. This pattern is consistent with the decreasing solubility of most minerals with increasing temperature.
Conclusion A variety of observations (field, petrographic, XRD, and geochemical data) demonstrate spatial and geochemical relationships between igneous dikes and ferroan and nonferroan dolomite at the Latemar carbonate platform, Italy. Significantly, ferroan dolomite, where present, is restricted to dikehost rock contact areas. Nonferroan dolomite occurs further away from the dikes. SEM-EDS data reveal postsolidification alteration of clinopyroxene in dikes that released Fe and Mg, providing a source for ferroan dolomite. Crosscutting relationships show that ferroan dolomite was the first dolomite phase to precipitate. Based on these observations, we propose a new conceptual model for Latemar dolomitization. The key point is that the Fe component necessary for ferroan dolomite formation was sourced locally by seawater interaction with dike minerals.
We tested the validity of our conceptual model through numerical simulations. The first modeling step consisted of incorporating solid solution models for the two major dike minerals, clinopyroxene and plagioclase. Their compositions and thermodynamic properties at solidification conditions were constrained using MELTS and Arxim-GEM software. Equilibration of seawater with these minerals was achieved using the Arxim-LMA code. The simulations show that seawater-dike interaction resulted in an increase of Fe in solution up to 0. 28 mmol/kg. This enrichment is sufficient to cause the formation of ferroan dolomite during injection of the modified seawater into carbonate host rock. Ferroan and nonferroan dolomite quantities obtained during dolomitization are strongly dependent on temperature, a relationship that helps to constrain dolomitization conditions at the Latemar platform.
These results demonstrate advantages of comparing quantitative predictions from thermodynamically oriented reactive flow simulations with spatially constrained geochemical data from a natural system. For example, parameters such as temperature and the controls of reaction kinetics can be constrained more narrowly. Furthermore, model results provide predictions regarding dolomitization timescales and the nature of dolomitizing fluid. Ferroan dolomite provides a physical link between Latemar platform dikes and dolomitization. This concept may have wider implications related to association of replacement dolomite with igneous intrusions in rifting environments.
Methods Mice. All animals were maintained in specific pathogen free facility at Dijon Zootechnical Centre. Animal use and care were approved by the Burgundy University Animal Experiments Ethics Committee (approved protocol #2212), in accordance with the Federation of Laboratory Animal Science Associations (FELASA). Only females between 6 and 10 weeks of age were used for the experiments. Female C57BL/6 J were purchased from Charles River Laboratories (Saint-Germain sur l'Arbresle, France). IL-9-eGFP mice were provided by R. Flavell. OT-II mice and Irf8 f/f Cd4 cre mice were provided by the CDTA (Cryopréservation, Distribution, Typage et Archivage animal) and distributed by EMMA (European Mouse Mutant Archive, a service funded by the EC FP7 Capacities Specific Programme). In vitro T-cell differentiation.
Acknowledgements We thank  Philipe Bastable  for correcting the manuscript. The authors are supported by grants from the  Ligue Nationale contre le Cancer  ( EL2013. LNCC/FG ) (F. G. , C. R. , F. V. , E. H. ), the  Institut National du Cancer  ( INCan°2012-14 ) (F. G. ), the  Conseil Régional Bourgogne  (M. T. , H. B. ), the  FEDER, INSERM  (M. T. , H. B. ), the  French National Research Agency  ( ANR-13-JSV3-0001 ) (L. A. ) and ( ANR-11-LABX-0021 ), the  Ligue Régionale contre le Cancer Comité Grand-Est  (L. A. ), the  Fondation ARC  (20151203218) and ( PJA2031200106 ) (L. A. , F. C. ) and the  European Community  ( Marie Curie Fellowship   PCIG10-GA-2011-303719 ) (L. A. ).
A smart environment is equipped with numerous devices (i. e. , sensors, actuators) that are possibly distributed over different locations (e. g. , rooms of a smart building). These devices are automatically controlled to achieve different objectives related, for instance, to comfort, security and energy savings. Controlling smart environment devices is not an easy task. This is due to: the heterogeneity of devices, the inconsistencies that can result from communication errors or devices failure, and the conflicting decisions including those caused by environment dependencies. This paper proposes a design framework for the reliable and environment aware management of smart environment devices. The framework is based on the combination of the rule based middleware LINC and the automata based language Heptagon/BZR (H/BZR).
The framework is based on the combination of a rule based transactional middleware (LINC  [17] ) and a reactive language (Heptagon/BZR  [18] ). As shown in Figure  1 , the proposed framework enables the autonomic management of devices through a variant of the MAPE-K loop  [19]  called MIADIE-K (Monitoring, Interpretation, Analysis, Decision, Interpretation, Execution and Knowledge) and consists of: • An abstraction layer: To deal with the heterogeneity of smart environment devices; • A transactional execution mechanism: To prevent from the occurrence of inconsistencies; • An environment aware controller: To make appropriate decisions and prevent from both explicit and implicit conflicts. The controller relies on a generic model of the environment. The generic aspect of this model allows to use the same controller for other environments that have the same types of devices (e.
Background The proposed framework relies on a transactional middleware and a reactive language that supports the synthesis of controllers. The transactional middleware enables the communication with devices and avoids inconsistencies. The reactive language enables the declarative management of devices while preventing from conflicts and objectives violations. In this paper, the transactional middleware LINC  [17]  and the reactive language Heptagon/BZR  [18]  are used. 2. 1 LINC middleware LINC  [17]  is a rule based middleware used to develop and deploy distributed applications. It has been used in several domains such as building automation  [20, 21, 22]  and wireless sensor networks  [23, 24, 25].
Autonomic management of devices The proposed framework enables the autonomic management of smart environment devices, through a variant of the MAPE-K loop (MIADIE-K loop). As shown in Figure  1 , the devices are monitored and the collected data are interpreted (MI). Then, an analysis is done and appropriate commands are computed (AD), based on knowledge (K). Finally, the commands are interpreted and sent and the knowledge is updated (IE). • Monitoring and Interpretation (MI): Provide the data required to make decisions. Sensor data are first collected through the abstraction layer. Then, the data may be interpreted. The aim is to aggregate them, to transform them or to use them to estimate other data. For instance if a temperature data is needed and there are two temperature sensors, their average may be used as the temperature.
This is done by applying a rd on the Sensors bag of a PUTUTU object. Then, at line 2, the precondition reads the value measured by an outdoor luminosity sensor in the variable o_lum_id_val. At line 3, the precondition stores the read sensor values in one variable called inputs using the LINC operation IC (i. e. , INLINE_COMPUTE). In LINC, the IC operation executes any python code and returns a tuple of string (here containing only one value). Finally, the precondition invokes the step function (line 4) and stores the computed commands in the variables shutter_cmd and lamp_cmd (line 5). These variables will be used in the first transaction of the performance. The first transaction verifies if the sensors data did not change (lines 8, 9). Then, it sends the computed shutter command to a shutter actuator.
Study Design Standard protocol approvals, registrations, and patient consents. For this retrospective cohort study we identified patients from the database of our neurophthalmology-neuroimmunology team from 2003-2015. The study was approved by the institutional review board at the Rabin Medical Center. We conducted the following selection process: We included all patients following AQP4positive NMOSD-ON and MOG-positive ON seen between 2003-2015. NMOSD was defined according to the Wingerchuk et al. 2015 criteria  [10]. Serum anti-MOG IgG antibodies were tested in patients with atypical ON using the live cell-based assays at the Institut d'Investigacio Biomedica August Pi I Sunyer, Barcelona, Spain. Anti-AQP-IgG antibodies were initially tested in six patients using the indirect immunofluorescence assay at the Hadassah Medical Center, Israel.
Postacquisition analysis was performed using software version 7. 0. 1. 290 in an automated and masked fashion. Analysis was performed on eyes and the main recorded OCT outcome was the average retinal nerve fiber layer (RNFL) thickness, measured in microns (μ). Patients with insufficient documentation or without highquality spectral-domain optical coherence data (other OCT scans; such as TD-OCT) were excluded from the analysis. Outcome data collection. The following longitudinal clinical data was recorded for each patient: age, gender, inaugural event at presentation, length of follow-up (months), number of ON attacks, whether a bilateral ON attack occurred, presence of disc edema during ON, the worst visual acuity during the most severe ON attack (nadir visual acuity), final visual acuity at last follow-up, final mean visual field defect on Humphrey perimeter (in decibels (dB)), 30-2 spot size 3, (mean deviation) for each eye, the final average RNFL measurement for each eye on OCT and the ICD-10/WHO Visual Disability scale for each patient at last follow-up.
Statistical analysis. The generalized estimating equation (GEE) models with a Gamma distribution and log link function, using SPSS for IBM (2013) for Windows, Version 22. 0. Armonk, NY: IBM Corp were used to compare the final average RNFL (as a dependent variable) between ON eyes of MOG-IgG and AQP4-IgG-positive patients, with the number of ON events as covariate. The absolute mean visual field defect (dB) and the last visual acuity (log-MAR) was compared in AQP4-positive ON eyes and in MOG-positive ON eyes using GEE models. Among all study eyes, the average RNFL was correlated with mean visual field defect and with visual acuity. GEE was performed using both eyes of patients for bilateral cases controlling for within subject inter-e ye correlations.
We report here the draft genome of Kingella negevensis strain SW7208426, isolated from the oropharynx of a healthy 6-year-old boy in Geneva, Switzerland. To our knowledge, this is the first genome report of the newly described K. negevensis species from Europe. K ingella negevensis is a newly described Gram-negative bacterium belonging to the genus Kingella in the Neisseriaceae family (1). Asymptomatically carried in the oropharynx of young children, most K. negevensis strains have been isolated in southern Israel to date (1). To the best of our knowledge, no genome sequence from European strains of this novel Kingella sp. is available in international databases. Herein, we briefly present the genome sequence of K. negevensis strain SW7208426, which was isolated from the oropharynx of a healthy 6-year-old boy at the Hôpital des Enfants in Geneva, Switzerland.
The study received institutional review board approval (09-029R, Mat-Ped 09-008R). Using genotyping by pulsed-field gel electrophoresis and 16S rRNA typing, strain SW7208426 was classified as belonging to genotype , which is the only of the six originally reported K. negevensis genotypes to have been detected in the United States as well (1). Strain SW7208426 was subcultured in blood agar medium at 37°C in a 5% CO 2 -enriched atmosphere. Genomic DNA was extracted as previously described (1) and then sequenced by using an Illumina MiSeq sequencer (Illumina Inc, San Diego, CA, USA) and the mate-pair strategy with the Nextera Mate Pair Sample prep kit (Illumina). The genome assembly was performed with a pipeline utilizing different softwarenamely, Velvet (2), Spades (3), and SOAPdenovo2 (4)-on trimmed (MiSeq and Trimmomatic [5] software) or untrimmed data (only MiSeq software).
For each of the six assemblies performed, GapCloser (4) was used to reduce gaps. This yielded a draft genome consisting of 12 scaffolds (50 contigs) for a total of 2,187,490 bp and a GϩC content of 45. 27%. Open reading frames (ORFs) were predicted using Prodigal (6) with default parameters and the predicted bacterial protein sequences were searched against the Clusters of Orthologous Groups (COG) database using BLASTp. The tRNAS-canSE (7) tool was used to find tRNA genes, whereas rRNA genes were found using RNAmmer (8). Lipoprotein signal peptides and the number of transmembrane helices were predicted using Phobius (9). ORFans were identified if the BLASTp search did not give positive results (E value smaller than 1e Ϫ03 for ORFs with a sequence size superior to 80 amino acids [aa] or E value smaller than 1e Ϫ05 for ORFs with sequence length smaller than 80 aa).
4 complete rRNA operons and 53 tRNAs. Of the 2,253 predicted genes, a total of 1,607 genes (73. 25%) were assigned a putative function; 75 genes were identified as ORFans (3. 42%); and 36 genes (19. 87%) were annotated as coding hypothetical proteins. Further genomic analyses are required in order to elucidate the molecular characteristics involved in the human carriage and dissemination of K. negevensis worldwide. Strain SW7208426 is deposited in the CSUR collection under number P3485. Accession number(s). This whole-genome shotgun project has been deposited in GenBank under the accession number FXBH00000000. The version described in this paper is the first version, FXBH01000000.