dataesr/scientific-paragraphs-categorization huggingface dataset:
The region near the AO control radius, which is dominated by quasi-static speckles, was also masked. SDI images were created by scaling and subtracting each continuum frame from its Hα counterpart, and these were then processed with pyK-LIP. SDI processing serves to remove both PSF artifacts common to both channels and any scattered light structures present in the image; excess emission in the Hα channel is thus preserved. Contrast curves were also generated. The SDI residual image was convolved with an estimate of the PSF, the noise was measured in annuli of 1 FWHM in width and corrected for small sample statistics  (Mawet et al. 2014). Algorithm throughput at the location of any accreting source was computed by injecting fake planets into the raw Hα datasets and processing through SDI and pyK-LIP.
In our data, this is most easily seen in the spiral arm resolved on the East side of the disk that extends nearly up to the location of HD 100546 b, clearly seen in the left panel of Figure  1. The very end of this spiral is broken into blobs by aggressive data processing, and just like our source, these blobs are not robust across algorithms and algorithmic parameters. Quantitatively, we measured the FWHM of the source as a function of the filter size from 16 to 4 pixels, i. e. , from 4. 5 to higher than 1 GPI H band FWHM, for all three ADI algorithms. A Gaussian function was fit to the position, size, and flux of the source in each residual image in a wedge of 3 × 3 GPI H band FWHM centered on the position of the source. Errors were estimated by varying the size of the wedge. To make sure that the fitting process does not subtract both source and disk, the procedure adopted by  Quanz et al.
J. R. , R. D. and D. L. acknowledge support from the  Fonds de Recherche du Québec. KBF and JRM's work was performed in part under contract with the  California Institute of Technology (Caltech) / Jet Propulsion Laboratory (JPL)  funded by  NASA  through the  Sagan Fellowship Program  executed by the  NASA Exoplanet Science Institute. Supported by  NSF  grants  AST-1411868  (K. B. F. and B. M. ),  AST-141378  (G. D. ), and  AST-1411868  (JP). Supported by  NASA  grants  NNX14AJ80G  (E. L. N. , S. C. B. , B. M. , F. M. , and M. P. ),  NNX15AD95G  ( BM, JEW, TME, RDR, GD, JRG, PGK ) and  NNX16AD44G  (K. M. M, T. S. B. , and L. M. C. ). KWD is supported by an  NRAO   Student Observing Support Award   SOSPA3-007. Portions of this work were performed under the auspices of the  U. S. Department of Energy  by  Lawrence Livermore National Laboratory  under Contract  DE-AC52-07NA27344.
Western blotting for cytochrome c (Cytc), uncoupling protein 1 (Ucp1), and Cytc oxidase subunit 4 (Cox4) was performed using rabbit anti-mouse antibodies (catalog number 4272, Cell Signaling; catalog number GTX10983, Acris, Herford, Germany; and catalog number 4844, Cell Signaling, respectively). SDS-PAGE of pure mitochondrial extracts was performed using 6 g protein per sample for BAT and 15 g protein per sample for WAT. Semiquantitative determination of the relative protein amounts was performed by Image J free software.
Electron microscopy analysis of HDL particles Electron microscopy analysis of purified HDL particles was performed at the Laboratory of Electron Microscopy and Microanalysis of the University of Patras. Briefly, APOC3-HDL particles were visualized by transmission electron microscopy (TEM) at 200,000× magnification, then photographed and analyzed as described previously  (34, 46). HDL from AdGFP-infected mice was used as control. By visual inspection, the distributions of HDL particle diameter and area for the samples were evidently skewed. Image analysis of TEM pictures was followed by unpaired t-test statistical analysis for comparison of particle diameters between APOC3-HDL and control-HDL samples. Image analysis of TEM pictures was performed with Image J software.
Metabolomic analysis of serum isolated from C57BL/6 mice infected with AdGFP-APOC3 Two hundred microliters of each serum sample were mixed with 60 l of D 2 O and 340 l of phosphate buffer [1. 5 M Na 2 HPO 4 in water containing 4% NaN 3 and 1 mM 4,4-dimethyl-4-silapentane-1-sulfonic acid (pH 7. 4)]. Then, 550 l of the resulting supernatant were transferred in a 5 mm NMR tube (Bruker BioSpin srl) for analysis. NMR serum metabolomic profiling was performed using a Bruker Avance III HD 700 MHz NMR spectrometer equipped with a TCI cryogenically cooled probe. Three 1 H-NMR spectra were acquired for each sample applying standardized protocols (NOESY, CPMG, and J-resolved pulse sequences). Partial least squares discriminant analysis (PLS-DA) was performed as a supervised method for data reduction and pattern recognition.
The quality of the PLS-DA model was determined by the goodness-of-fit parameter (R 2 ) and the goodness-of-prediction parameter (Q 2 ) following a 10-fold cross validation procedure. Univariate analysis was conducted by calculating the relative concentration through signal's area integration of all successfully assigned metabolites across the 21 acquired spectra. P values <0. 05 were determined using the nonparametric Wilcoxon rank-sum test. NMR data were acquired and processed using TopSpin 3. 6 (Bruker BioSpin srl). Serum metabolite identification was performed using Chenomx NMR software (Profiler 8. 1; Chenomx Inc. , Canada), the public database (HMDB), and literature data  (50, 51). Glucose levels were further assessed spectrophotometrically in serum samples using the DiaSys Glucose GOD FS kit (reference number 10021; Diagnostic Systems, GmbH).
Clinical study We then compared infarct size and the extent of reperfusion injury in a subset of patients from a contemporary study of anterior myocardial infarction who underwent successful percutaneous coronary intervention (PCI) (ClinicalTrials. govNCT01572909). The rationale and methods for the EMBRACE STEMI trial have been previously reported  [9]. This study was approved by the local institutional review committees, and all subjects provided informed consent. The clinical study determined myocardial infarct size by both biomarkers (creatine kinase-myocardial band, CK-MB area under the curve over 72 h (258 non-diabetic patients and 34 diabetic patients) and serum troponin I under the curve over 72 h), and using cardiac magnetic resonance imaging (MRI) at 4 days. MRI contrast injection at 4 days was utilized to assess size and the zone of micro vascular obstruction (MVO), which previously was shown to be similar to the no reflow zones assessed in experimental studies using thioflavin S  [10].
Statistical analysis Statistical analyses for the rat study were performed with Sigma Plot 12 software. Data are reported as mean ± SEM, if the parameter is normally distributed; median, if the parameter is not normally distributed. The Shapiro-Wilk test was performed on each measured parameter in order to evaluate distribution. When the data had a normal distribution then Student's T test was used in order to compare the treated group with the control group. When there was not a normal distribution then Mann-Whitney Rank Sum Test was used in order to compare the treated group with the control group. Statistically significant differences were established at p<0. 05. Statistics for the clinical study were previously described  [9].
In this study, the part of animal study was funded by a grant from  Servier  (grant number N/A). The part of clinical study was funded by  Stealth BioTherapeutics  (grant number  N/A ). Conflict of interest: Author  Robert A. Kloner  has received research grants from  Company Stealth BioTherapeutics and Servier. Author Michael C. Gibson  has received research grants from  Company Stealth BioTherapeutics. Author Muriel Bouly  is an employee of company Servier.
Overall sugar composition of crude cell walls extracts (AIR) Monosaccharide composition of AIR isolated from cell walls of non infiltrated N. benthamiana leaves (Ni) was in agreement with previous data (Figure  2a )  (Nguema-Ona et al. , 2012)  showing that glucose, galacturonic acid and rhamnose are the most abundant monosaccharides. Similar monosaccharide compositions were measured in AIR isolated from water, p19-and H1infiltrated leaves collected from day 1 to day 7 (Table  S1 ). Sugar linkage composition of neutral monosaccharides, identified by gas chromatography coupled to an electron ionization mass spectrometer (GC-EIMS) after permethylation of the sample, was first determined in the Ni group. In the cell wall AIR fraction of these plants, the most abundant partially methylated alditol acetate derivatives were assigned to 4-linked glucose, 4,6-linked glucose, 2-and/or 4-linked xylose and terminal xylose derived from cellulose and xyloglucans (Figure  2b ).
The presence of xylans was investigated by digesting hemicellulose extracts with an endoxylanase and analysing the resulting xylan fragments by MALDI-TOF MS. No fragment corresponding to the xylan fingerprint was detected indicating that these hemicelluloses are in trace amounts in N. benthamiana cell wall (data not shown). In contrast, as illustrated in Figure  4b , digestion of the hemicellulose fractions with an endoglucanase, that cleaves xyloglucan after non substituted glucose residues, released three main fragments for which masses were assigned to XXG, XSG and XSGG/GXSG, in agreement with data reported in the literature  (Chevalier et al. , 2010; Sims et al. , 1996; York et al. , 1996). These fragments were named according to the nomenclature suggested by  Fry et al. (1993)  where G refers to glucose units of the backbone, X and S refer to a-D-Xylp-(1?6) and to a-L-Araf-(1?2)-a-D-Xylp-(1?6) xyloglucan side chains, respectively (Figure  4d ).
Sugar linkage analysis Five milligrams of AIR was permethylated with iodomethane in a suspension of NaOH in dry DMSO, and the resulting partially methylated alditol acetate derivatives were prepared according to protocols described by the glycotechnology core resource of SanDiego (http://glycotech. ucsd. edu/protocols/07_Comp_Analy-sis_by_Alditol_Rev2. pdf). Monosaccharide derivatives were injected in a gas chromatograph (GC) (Hewlett-Packard 6890 series) coupled to an Autospec mass spectrometer (MS) (Micromass, Manchester, UK) equipped with an electron ionization (EI) source and the Opus 3. 1 data system. Separations were obtained using a Zebron Z5-MSi (30 m, 0. 25 mm id, 0. 25 lm film thickness, Phenomenex) capillary column. Helium was the carrier gas, and the flow-rate was 0. 8 mL/min. The temperature programming started at 100 °C for 1 min, ramped up to 160 °C at 10 °C/min, then ramped up to 220 °C at 2 °C/min and finally ramped up to 270 °C at 15 °C/min (maintained at 270 °C for 1 min).
The temperature of the injector, the interface and the lines was 250 °C. Injections of 0. 5 lL were performed in splitless mode. EI mass spectra were recorded using an electron energy of 70 eV, an acceleration voltage of 8 kV and a resolving power of 1000. The trap current was 200 lA, and the magnet scan rate was 1s/decade over a 600-38 m/z range. The temperature of ion source was 250 °C. Sugar linkage analysis was deduced from the EI-MS fragmentation patterns of partially methylated alditol acetate derivatives according to http:// www. ccrc. uga. edu/databases/PMAA. 0. 1M HCl and then used for Klavons titration. Five hundred microlitres of samples was diluted to 1 mL with 0. 1M potassium phosphate pH 7. 5. One millilitre of alcohol oxidase 1U/mL was then added, and the solution was incubated for 15 min at 25 °C.
Quantification of phenolic compounds In the xyloglucan-enriched fractions obtained after endoglucanase digestion precipitated with absolute ethanol, a silylation was performed through a treatment with BSTFA/TMCS (Supelco, Sigma Aldrich) in dry pyridine solvent for 30 min at 80 °C. Samples were then dried and resuspended in cyclohexane before being analysed by GC-MS Hewlett-Packard 6890 series gas chromatograph coupled to the Autospec mass spectrometer (Micromass, Manchester, UK) equipped with the Opus 3. 1 data system. Separations were performed using a Zebron Z5-MSi (30 m, 0. 25 mm id, 0. 25 lm film thickness, Phenomenex) capillary column. Helium was the carrier gas, and the flow rate was 0. 8 mL/min. The temperature programming started at 100 °C for 1 min, ramped up to 160 °C at 10 °C/min, then ramped up to 220 °C at 2 °C/min and finally ramped up to 270 °C at 15 °C/min (maintained at 270 °C for 1 min).
Sleep monitoring and preprocessing During naps EEG was recorded from 26 scalp sites (FP1, FP2, AFz, F7, Fz, F8, FC5, FC1, FC2, FC6, C3, Cz, C4, T7, T8, CP5, CP1, CP2, CP6, P7, P3, Pz, P4, P8, O1, and O2) using Ag-AgCl ring electrodes placed according to the extended 10 -20 international EEG system. FCz was used as ground site. Data were recorded with the BrainAmp amplifier system (Brain Products) at a sample rate of 500 Hz and band-pass filtered between 0. 05 and 127 Hz. All electrode recordings were referenced to an electrode attached to the nose. Impedances were Ͻ5 k⍀. In addition, EMGs at the chin as well as horizontal and vertical EOGs were recorded according to standard sleep monitoring. Following the application of a notch filter (centered at 50 Hz with a bandwidth of 5 Hz), a combined semiautomated and visual rejection of raw data was applied to eliminate epochs contaminated by artifacts.
We additionally considered the slow spindle frequency band slow spindle (8 -12 Hz) to compare with previous results  (Marshall et al. , 2006). Topographic regions of interest (ROIs) for these frequency bands were selected according to previous research  (Mo ¨lle et al. , 2011; Ladenbauer et al. , 2016). The electrode sites FC1, Fz, and FC2 and CP1, Cz, and CP2 were pooled into two ROIs, frontal and centroparietal, respectively. Thus, mean spectral power reflects an average over the electrodes of each ROI. BrainVision Analyzer software was used to perform spectral power analyses. Phase-amplitude coupling (PAC). To assess the temporal relationships between memory-relevant oscillations in the EEG signal, we used an event-locked analysis based on  Staresina et al. (2015)  using the FieldTrip toolbox (RRID:SCR_004849;  Oostenveld et al.
, 2011)  for MATLAB (The MathWorks, RRID:SCR_001622) as well as custom MATLAB functions. Specifically, we characterized SO-to-spindle PAC for the frontal ROI (Fz, FC1, and FC2) and the centroparietal ROI (Cz, CP1, and CP2) by applying the following procedures: (i) SO events were identified for each subject, condition, and electrode based on an established detection algorithm  (Mo ¨lle et al. , 2011 (Mo ¨lle et al. , , 2002)). First, EEG data were filtered between 0. 16 and 1. 25 Hz (two-pass FIR band-pass filter, order ϭ 3 cycles of the low cutoff frequency). Only artifact-free data were used and periods of wake state and REM sleep were also excluded for event detection. Second, periods of SO candidates were determined as the time between two successive positive-to-negative zero crossings in the filtered signal.
The interval for computing the SI was Ϫ1 s to ϩ1 s around the SO center. The distribution of SIs per condition and ROI are visualized in Figure  3d. For statistical analyses (see below), SIs were averaged per subject, condition, and ROI. Sleep architecture. The sleep structure, including the time and proportion spent in different sleep stages, was determined based on polysomnographic criteria according to  Rechtschaffen and Kales (1968). For this purpose, EEG data were down-sampled to 250 Hz and 30 s epochs were scored manually by means of Schlafaus software (Steffen Gais) in sleep stages 1, 2, 3, and 4 and REM sleep, epochs of wakefulness, or movement artifacts. Epochs during so-tDCS were not scored due to strong artifacts from so-tDCS in the EEG signal. Likewise, corresponding epochs in the sham session were not scored to obtain comparable time and proportions of sleep stages.
The Greenhouse-Geisser correction of degrees of freedom was applied when appropriate. Statistical analyses for memory tasks, power measures, and sleep structure were performed using SPSS (version 22. 0; RRID:SCR_002865; IBM); PAC behavior was statistically analyzed using the FieldTrip  (Oostenveld et al. , 2011)  and CircStat  (Berens, 2009)  MATLAB toolboxes as well as custom MATLAB functions. A two-sided significance level ␣ was set to 0. 05 in all analyses. Given multiple testing for the primary parameters of interest [n ϭ 5; visual recognition performance (percentage correct score) and the four memory-related EEG spectral power measures, i. e. , frontal and centroparietal SO and fast spindle power], we applied the Benjamini-Hochberg correction  (Benjamini and Hochberg, 1995)  to control for the false discovery rate at q ϭ 0.
Results We analyzed EEG data and memory performance of 16 MCI patients (7 female, mean age 70. 6 years Ϯ 8. 9 SD) who were tested on memory tasks before and after a 90 min nap with either so-tDCS or sham stimulation (two sessions, balanced crossover design). A schematic diagram of the experiment is shown in Figure  1. We focused on visual recognition memory, whose im-pairment occurs early in the course of AD  (Barbeau et al. , 2004)  and thus constitutes a sensitive target for interventional approaches in MCI (for details, see Materials and Methods). For comparison with previous studies, we additionally assessed word pair recall. We first asked whether so-tDCS beneficially affects memoryrelevant sleep measures. For this purpose, we focused on the immediate effects on SO and fast spindles measured by EEG during 1 min lasting stimulation-free intervals after up to 5 so-tDCS/ sham blocks.
DNA sequencing and molecular phylogenetic analysis The nucleotide sequences of the cloned 16S rRNA gene were determined on both strands using BigDye Terminator Cycle Sequencing Ready Reaction kits and the automated DNA sequencer ABI PRISM ® 3100-Avant Genetic Analyser (Applied Biosystems, Foster City, CA, USA. The RapidSeq36_POP6 run module was used, and the samples were analyzed using the ABI sequencing analysis software v. 3. 7 NT. The sequences obtained were compared to those present in the public sequence databases and with the EzTaxon-e server (http://eztaxon-e. ezbiocloud. net/), a web-based tool for the identification of prokaryotes based on 16S rRNA gene sequences from type strains  (Kim et al. 2012). Phylogenetic and molecular evolutionary genetic analyses were conducted via the the molecular evolutionary genetics analysis (MEGA) software version 5 (http:// www.
Screening of xylanase-producing bacteria from Algerain soil and molecular characterization of the target microorganism In the current study, ten candidates were obtained from the first screening as xylanase producers. Among them, a bacterium called SJ3, displayed the highest extracellular xylanase activity after 2 days incubation in an initial medium (data not shown) and was, therefore, retained for all subsequent studies. The physiological and biochemical characteristics of the SJ3 isolate presented in this study were investigated according to well-established protocols and criteria described in the Bergey's Manual of Systematic Bacteriology as well as the API 50 CHB/E and the API 20E galleries for representative strains. The findings indicated that the SJ3 isolate was Gram-stain-positive, motile, rod-shaped, catalase-positive, aerobic, and endospore forming microorganism.
Optimal growth temperature was 37 °C; optimal pH was 7. 0. According to the results obtained using the API 50 CHB/E medium and the API 20E strips, the characteristics strongly confirmed that the isolate belongs to Bacillaceae order and Bacillus genus. The physiological and some biochemical properties of the isolate SJ3 are given in Table  1. The 16S rRNA gene sequence (KT222887) obtained was submitted to GenBank BLAST search analyses, which yielded a strong homology of up to 99% with those of several cultivated strains of Bacillus. From the analysis of the almost-complete 16S rRNA gene sequence, this strain was found to be similar to B. oceanisediminis strain H 2 T (99. 16% sequence identity). Through the alignment of homologous nucleotide sequence of known bacteria, phylogenetic relationships could be inferred, and the phylogenetic position of the strain and related strains based on the 16S rDNA sequence is shown in Fig.
Results and Discussion In what follows, K -2 V states are investigated experimentally for the C and S K-edges of CS 2 and for the S and F K-edges of SF 6 , and are directly compared to theoretical modelling. The binding energies of these states are calculated using a Hartree-Fock and a post Hartree Fock (Møller Plesset at second order level of perturbation, MP2) code from the GAMESS package. In addition, Density Functional Theory (DFT) calculations are performed for core-hole formation on C and F atoms. In Table  1  the experimental binding energies are reported and compared with the theoretical results obtained with the different methods. In the following, we will discuss the two molecules separately and the two edges for each of them. Carbon K -2 V states of CS 2. An electron spectrum of CS 2 was obtained at the lowest possible photon energy of the GALAXIES beam line, i.
Acknowledgements This work has been financially supported by the  Swedish Research Council (VR)  and the  Knut and Alice Wallenberg Foundation ,  Sweden. D. K. acknowledges financial support of  Labex Michem, France. B. C. M. acknowledges financial support by  ANR SUMMIT, France. A. F. L. thanks  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brazil  for their support. We thank the synchrotron radiation facility SOLEIL for the allocation of beam time (project number  99140034 ) and the staff of SOLEIL for particularly smooth running of the storage ring in top-up mode.
For all samples, the EvtGen v1. 2. 0 program  [18]  is used for properties of the bottom and charm hadron decays. The effect of in-time as well as out-of-time pile-up is simulated using minimum-bias events generated with Pythia8. 186 to reflect the pile-up conditions during the 2015 data-taking period, using the A2 tune  [19]  and the MSTW2008LO  [20]  PDF set. All generated events are processed with a detailed simulation of the ATLAS detector response  [21]  based on Geant4  [22]  and subsequently reconstructed and analysed in the same way as the data.
Event reconstruction The raw data collected by the ATLAS detector is reconstructed in the form of particle candidates and jets using various pattern recognition algorithms. The reconstruction used in this analysis are detailed in Ref. [1] , while an overview is presented in this section. Inputs to jet reconstruction Jets in ATLAS are reconstructed from clusters of energy deposits in the calorimeters. Two methods of combining calorimeter cell information are considered in this paper: topological clusters and towers. Topological clusters (topo-clusters)  [23]  are built from neighbouring calorimeter cells. The algorithm uses as seeds calorimeter cells with energy significance  3  |E cell |/σ noise > 4, combines all neighbouring cells with |E cell |/σ noise > 2 and finally adds neighbouring cells without any significance requirement.
2 rad) -N HS (|∆φ(Z, jet)| < 1. 2 rad)] • (π -2. 8 rad)/1. 2 rad, (7) where N control j (|∆φ(Z, jet)| < 1. 2 rad) is the number of jets in the data control region and N HS (|∆φ(Z, jet)| < 1. 2 rad) is the expected number of hard-scatter jets in the control region, as predicted in simulation. The hard-scatter efficiency is therefore measured in the signal region as ε = N pass j -N pass PU N signal j -N signal PU , (8) where N represent the overall number of pile-up jets in the signal region and the number of pile-up jets satisfying the fJVT requirements, respectively, and are both estimated from simulation. Figure  18  shows the hard-scatter efficiency evaluated in data and simulation. The uncertainties correspond to a 30% uncertainty in the number of pile-up jets and a 10% uncertainty in the number of hard-scatter jets in the signal region.