dataesr/scientific-paragraphs-categorization huggingface dataset:
1. 3). To identify the correlated samples and the correlated metabolites, hierarchical clustering was done on both rows and columns. Columns and rows were then reordered, putting similar observations close to each other, illustrating the clustering process and structure by dendrograms. We used generalized Least Squares, a linear model allowing adjustment for group variances when needed, in package nlme in R  (Zuur et al. , 2009) , to compare the amounts of metabolites in the different mutants to amounts in the Col-0 control, taking day as the other factor. For the ProDH and OAT activities, the three mutants were compared to each other using the same approach and one-sample t-tests were performed to compare them to a normalized control value at 100.
The data matrix consisted of 116 metabolites characterized GC-MS and derived from DIS time-course samples in Col-0 (wild-type), and prodh1prodh2, p5cdh and oat mutants. Metabolite concentrations were determined as the peak area in GC-MS analyses normalized to an internal standard and sample fresh weight. Numerical labels on the lines of each genotype indicates data points for each day (Days 0-5) of the DIS experiments. The variance explained by each principal components (PC) is given along each axis. Data are mean values of four biological replicates per genotype and per day. amino acids were transiently higher in prodh1prodh2. When the amounts in glutamate and proline were compared, Col-0 and oat mutant followed the same trend of glutamate consumption during DIS with no proline accumulation (Figure  6a ).
, 2019). A F I G U R E 7 Transcript levels of ProDH1, P5CDH, OAT and SAG12 during DIS. Transcript levels were quantified by droplet digital PCR after 0-3 days of DIS in WT and the prodh1prodh2, p5cdh, oat mutants. SAG12 is a senescence-induced marker. Data from one representative experiment is presented as means ± SD of normalized number of copies for a given gene. OAT,ornithine δ-aminotransferase; ProDH, proline dehydrogenase; P5CDH, P5C dehydrogenase; SAG, senescence-associated genes. F I G U R E 8 ProDH1, P5CDH, OAT and SAG12 protein levels during DIS in Arabidopsis. Western blots of total proteins extracted from 100 mg leaves 7-9 of WT, as well as prodh1prodh2, p5cdh, oat mutants after 0-5 days of DIS. Proteins from different genotypes, indicated in the left, were separated by Bio-Rad TGX Stain-Free PAGE gel and blots were probed with antibodies raised against ProDH1, P5CDH, OAT and SAG12, as indicated at the top of each blot.
For OAT western blots, a stain-free fluorescent signal was observed because of a low OAT signal. Arrows indicate the corresponding protein bands with the expected molecular sizes, 54 kDa for ProDH1, 61 kDa for P5CDH, 52 kDa for OAT and 38 kDa for SAG12. OAT, ornithine δ-aminotransferase; P5CDH, P5C dehydrogenase; ProDH, proline dehydrogenase; SAG, senescence-associated genes. Crude mitochondria extracted from 4-day DIS treated leaves of 4-week-old WT, prodh1prodh2, oat and p5cdh plants. Proteins (5 μg) were loaded for each genotype on SDS-PAGE. Western blots were probed with antibodies raised against ProDH1, P5CDH or OAT, respectively (a) (one representative set of western blots is shown). (b) Box-plots displaying median (horizontal lines), interquartile range (boxes) where 50% of the data is found, maximum and minimum values (whiskers) and outliers (black dots) of the quantified western blots (n ≥ 6 biological repetitions).
I G U R E 3 Heat map and hierarchical clustering analysis of metabolic changes in excised leaves during 5 days of dark-induced senescence in Arabidopsis. Metabolite concentrations were determined as the peak area in GC-MS analyses normalized to an internal standard and sample fresh weight. Metabolite abundance was normalized to the maximum abundance (set to 1, yellow) and minimum abundance (set to 0, blue). Dendrograms (left and top) show the hierarchical clustering of the metabolites into 14 clusters (right) and the hierarchical clustering of genotypes at each day of DIS (bottom, boxes outline clusters). Data are mean values of four biological replicates per genotype and per day. [Color figure can be viewed at wileyonlinelibrary. com] PYRROLINE-5-CARBOXYLATE METABOLISM AND LEAF SENESCENCE | 907 F I G U R E 4 (See caption on next page) F I G U R E 4 Details of hierarchical clustering of metabolic changes during 5 days of dark-induced senescence in Arabidopsis.
F I G U R E 3 Heat map and hierarchical clustering analysis of metabolic changes in excised leaves during 5 days of dark-induced senescence in Arabidopsis. Metabolite concentrations were determined as the peak area in GC-MS analyses normalized to an internal standard and sample fresh weight. Metabolite abundance was normalized to the maximum abundance (set to 1, yellow) and minimum abundance (set to 0, blue). Dendrograms (left and top) show the hierarchical clustering of the metabolites into 14 clusters (right) and the hierarchical clustering of genotypes at each day of DIS (bottom, boxes outline clusters). Data are mean values of four biological replicates per genotype and per day. [Color figure can be viewed at wileyonlinelibrary. com] PYRROLINE-5-CARBOXYLATE METABOLISM AND LEAF SENESCENCE | 907 F I G U R E 4 (See caption on next page) F I G U R E 4 Details of hierarchical clustering of metabolic changes during 5 days of dark-induced senescence in Arabidopsis.
16, 17 or integrin α v subunit immunostaining, rabbit polyclonal antibodies against integrin α v subunit, biotinylated goat anti-rabbit immunoglobulins, HRP-conjugated streptavidin, and 3,3′-diaminobenzidine (DAB) substrate (see Supplementary material online, Tables  S7  and S8 ) were used. Composition of the arterial wall and the media cross-sectional area (MCSA) were determined using a Nikon NIS-Elements Basic Research microscope imaging software as described previously. 18 mmunofluorescence staining on 8 µm cryo-sections fixed with 4% paraformaldehyde or cooled acetone for 5 min was performed with specific antibodies as described previously. 14 Briefly, sections were incubated with primary antibodies at 4°C overnight after permeabilization with 0. 2% Triton X100 for 10 min and blocking with 5% bovine serum albumin (BSA) for 1 h.
Native images acquired with Leica LAS-AF software were converted in 32 bit RGB images. The quantitative analyses of collagen fibre directionality and region of interest (ROI) in carotid media were manually selected for each image using ImageJ® software. Second harmonic generation signal was quantified in the grey level by the following equation: grey = (0. 299×red) + (0. 587×green) + (0. 114×blue). Finally, measured signals were normalized accordingly to the number of pixels contained in each ROI for obtaining the mean grey level per pixel. 19.
Microarray analysis Total RNA was isolated from the aorta of three control and three α v SMKO mice with the RNeasy fibrous tissue kit (Qiagen). Microarray analysis for each genotype and treatment was performed with three samples, each hybridized to one Affymetrix GeneChip® Mouse Exon 1. 0 ST Array (Affymetrix, Santa Clara, CA) by the genomic facility of Cochin Institute (Paris, France) according to Affymetrix guidelines. The differentially regulated transcripts in microarray experiments were analysed using A Gene Annotation & Analysis Resource Metascape (https://metascape. org) for Gene Oncology (GO) term and pathways enrichment and the Ingenuity Pathways Analysis (IPA) (current version 51963813, release date 11 March 2020, Ingenuity Systems).
As CD109, which is known to attenuate TGF-β1 signalling, was found to be upregulated in response to Ang II, we performed immunofluorescence analysis of CD109 in carotid arteries and ELISA quantitation both in aorta proteins and plasmas. CD109 expression was clearly elevated in the medial sections of Ang II-treated α v SMKO mice as compared with Ang II-treated Ctrl mice at 28 days (Figure  3D ). This result contrasts with the observed upregulation of Cd109 gene in both strains (Table  1 ). To more specifically assess early and late upregulation of CD109 in response to Ang II, CD109 gene and protein expression were assessed at 4 days after administration of Ang II. The Cd109 gene was expressed 2. 6-fold and 2. 3-fold higher at 4 days following Ang II infusion in Ctrl and α v SMKO mice, respectively (data not shown).
Acknowledgements We wish to thank  Zhor Ramdane-Cherif ,  Kenza Benkirane , and  Cindy Lerognon  for their expert technical assistance. We thank the  Imaging Core Facility (PTIBC)  of  UMS2008/US40  IBSLor ( Université de Lorraine-CNRS-INSERM ) for their SHG microscopy and the IBPS platform ( Sorbonne University ) for their confocal microscopy analysis and housing of mice.
Funding This work was supported by  Future program  the  Agence Nationale de la Recherche  (No  ANR-15-RHU-0004  and  ANR-13-BSV1-0026 ); the  Contrat Plan État Région ,  Innovations Technologiques ,  Modélisation & Médecine Personnalisée , and  FEDER  ( Fonds Européen de Développement Régional) ; the  'Federation Française de Cardiologie '.
CE-MS analysis and proteomic data processing CE-MS sample acquisition was performed using a P/ACE MDQ capillary electrophoresis system (Beckman Coulter, Fullerton, USA) on-line coupled to a Micro-TOF MS (Bruker Daltonic, Bremen, Germany). Prerequisite for coupling of the CE with the MS was that the ESI sprayer (Agilent Technologies, Palo Alto, USA) was grounded, the ion spray interface potential set between -4. 0 and -4. 5 kV and contactclose-relays were used to control MS data acquisition automatically by the CE. Spectra were accumulated every 3 s over a m/z range of 350-3000. For raw proteomic data processing, the in-house software MosaiquesVisu  [29]  was used to deconvolute mass spectral ion peaks representing identical molecules at different charge into single masses. MosaiquesVisu uses both isotopic distribution as well as conjugated masses for charge-state determination of peptides with charge states > 1 and signal-to-noise ratios > 4 observed in a minimum of three consecutive spectra.
This remains true, even if some of the peptides used for calibration are affected by group differences. The normalized peptide profiles were deposited, matched, and annotated in a Microsoft SQL database, allowing further analysis and comparison of multiple samples. Peptides were considered identical within different samples, when mass deviation was lower than 50 ppm for peptides less than 2000 Da or 75 ppm for larger peptides. After calibration, deviation of migration time was controlled to be < 0. 45 min.
In silico protease mapping In silico protease maps were generated using Proteasix (available online at http://proteasix. org/), which allows retrieving protease/cleavage sites (CS) associations based on the MEROPS (https:// www. ebi. ac. uk/merops/) and the SWISS-PROT database (http://www. uniprot. org/)  [36]. N-and C-terminal cleavage sites were retrieved for the sequenced BR-associated peptides, based on an alignment to the respective substrate sequence information. The association of proteases with their cleavage sites is based on octapeptide consensus sequences. A list of 6000 random CS sequences was used as reference to determine the specificity of prediction against false-positive associations. Only proteases/CS associations which were previously observed or predicted with high/medium confidence level and having at least two cleavage site associations were considered.
Statistical analysis and definition of peptide markers Statistical analyses were carried out using the statistical programming language R and the statistical software MedCalc version 12. 7. 5. 0 (MedCalc Software; Mariakerke, Belgium). Signal intensities of each of the 3555 annotated peptides were correlated with serological laboratory markers using Spearman's rank correlation for univariate analysis. To reduce the risk of false discovery, the patient cohorts from Strasbourg and Paris were studied independently from each other. Statistical adjustment of p-values for multiple testing was performed by the method of Benjamini and Hochberg  [37]  and by taking into account the zero-inflated distribution of the peptide's intensity values  [38]. For the selection of age-and gender-matched normal individuals, as well as age-, gender-, and eGFR-matched CKD patients as reference groups to compare signal intensities of selected peptides, the R-script "MatchIt"  [39]  was used, which conducts nearest neighbor matching with logistic regression to estimate the propensity score.
Urinary peptides with association to CTX, BAP, and osteocalcin In a first step, association of urinary peptides associated with bone metabolism in the setting of kidney transplantation was investigated. 1. As indicated by this table, both cohorts are comparable to the specific presented clinical and demographic characteristics. For most of the patients, samples were taken within the first 2 years after transplantation. The only differences were higher prevalence of steroid treatment in the Paris group and serum PTH being higher in the Strasbourg group. Applying a minimum frequency threshold of 30%, 3555 urinary peptides were selected and evaluated in the 85 kidney transplantation patients. The 30% lower limit was selected as this was determined in previous studies using CE-MS  [43] [44] [45]  to be an adequate data foundation and optimal tradeoff to obtain statistically meaningful results in multivariate analysis and to compensate for biological variability between individuals in respect to protein turnover and proteolytic environment, for example, distinct protease profiles and enzyme activities.
Identification of the CTX-associated marker peptides in plasma From the 59 out of the 82 urinary bone remodeling markers for which sequence information is available, 36 were also detectable in human plasma  [26]. as presented in Table  S2. Except for the Gelsolin peptide 605-WVGTGASEAEKTGAQEL-621, all of these peptides are derived from collagen chains and here especially from COL1A1 (n = 30). In contrast, when comparing all 3648 peptide sequences in our urinary sequence database with the 3035 peptide sequences identified so far in plasma samples, an overlap of only 143 peptides were detected which corresponds to 3. 9% agreement (143 out of 3648). This limited overlap between peptides in human urine and plasma was reported previously by Magalhães et al. [26]  and Parker et al. [46]. Comparing this proportion to the 61% overlap (36 out of 59) in case of the bone remodeling markers, this amounts to a 15-fold enrichment for the bone-specific clearance process that was highly significant (p < 0.
Involvement of CTX-associated urinary peptides in CKD To investigate if the 82 CTX-associated urinary peptides are also associated with CKD, these peptides were compared with the peptides (N = 273) contained in the CKD273 urinary classifier, which was developed on a large set of CKD patients and disease-matched controls using the same CE-MS peptide annotation database  [23]. By  First and last amino acid positions within the protein's linear sequence, according to UniProt Knowledge Base numbering. In cases where peptides only differ in the extent of their proline hydroxylation, the number of hydroxyprolines in the peptide's sequence is also indicated, for example, '*O(2)' means two hydroxyprolines. overlap was calculated to be 2. 3%, since 82 differentially regulated peptides were identified in a data space of 3555 urinary peptides.
Therefore, we searched for putative proteases in the MEROPS proteolytic database and discovered that the most frequent cleavage sites for the CTX-associated urinary peptides in the COL1A1 linear sequence match to the consensus sequence of Cathepsin K (CTSK) and MMP9. Involvement of CTSK is further supported by the finding that for the gelsolin-derived peptide 605-WVGTGASEAEKTGAQEL-621, as one non-COL1A1 CTX-associated urinary peptide we identified in our study, cleavage by CTSK between leucin in position 604 and tryptophane in position 605 is reported by Vizovišek et al. [70]. Cathepsin K is a cysteine protease which is known to play a pivotal role in osteoclastmediated bone resorption  [71]. It degrades collagen I in the organic matrix of bone  [72]  and has become a drug target in the treatment of osteoporosis  [73].
amino acid sequence identification, CTX-associated urinary peptides derived from the CE-MS spectra, based on their MS-detected molecular mass and characteristic migration time in the CE, were searched against Mosaiques' in-house human urinary peptide database containing sequences from both LC-and CE-MS/MS using a Dionex Ultimate 3000 RSLS nano flow system (Dionex, Camberly, UK) for LC-and a Beckman CE/Orbitrap Q Exactive plus combination for CE-MS/MS (Thermo Scientific, Waltham, MA) [35].
For amino acid sequence identification, CTX-associated urinary peptides derived from the CE-MS spectra, based on their MS-detected molecular mass and characteristic migration time in the CE, were searched against Mosaiques' in-house human urinary peptide database containing sequences from both LC-and CE-MS/MS using a Dionex Ultimate 3000 RSLS nano flow system (Dionex, Camberly, UK) for LC-and a Beckman CE/Orbitrap Q Exactive plus combination for CE-MS/MS (Thermo Scientific, Waltham, MA) [35].
Introduction The primary focus of most studies conducted on critically ill patients has been to ascertain the diagnosis, occurrence rates, and clinical outcomes among individuals who acquired ventilator-associated pneumonia (VAP)  [1]. However, recent data have shown that not all patients with lower respiratory tract infections (LRTI) admitted to the intensive care unit (ICU) are alike and may have different clinical characteristics and clinical outcomes  [2] [3] [4]. Still, other less-known entities are frequent in the ICU, such as ventilator-associated tracheobronchitis (VAT) and hospital-acquired pneumonia (HAP) happening on the ward or in the ICU patients with spontaneous breathing but needing mechanical ventilation after diagnosis-(so-called ventilated HAP [VHAP])  [5, 6]. Most of the cumulative incidence of these entities is extracted from clinical trials and administrative observational datasets that allow us to determine the effect of medical interventions, often of new antibiotics  [7].
Data collection Recorded data included demographic characteristics, comorbidities, the time course of illness, treatments administered, laboratory and microbiologic data, complications during ICU stay, and outcomes. We determined disease severity by Simplified Acute Physiology Score (SAPS) II  [16]  and assessed organ failure using the Sequential Organ Failure Assessment (SOFA) score  [17]  calculating both within the first day of ICU admission. The study protocol of this project has been previously published and was shared with all the local investigators before beginning the enrolment  [5, 15]. Ventilatory management strategies, treatments and microbiological assessments were not standardised among centres. They were left to the discretion of the attending clinician, based on local guidelines and recommendations and supported by international guidelines  [1, 4].
Context. Several new ultrahigh-energy (UHE) γ-ray sources have recently been discovered by the Large High Altitude Air Shower Observatory (LHAASO) collaboration. These represent a step forward in the search for the so-called Galactic PeVatrons, the enigmatic sources of the Galactic cosmic rays up to PeV energies. However, it has been shown that multi-TeV γ-ray emission does not necessarily prove the existence of a hadronic accelerator in the source; indeed this emission could also be explained as inverse Compton scattering from electrons in a radiation-dominated environment. A clear distinction between the two major emission mechanisms would only be made possible by taking into account multi-wavelength data and detailed morphology of the source. Aims. We aim to understand the nature of the unidentified source LHAASO J2108+5157, which is one of the few known UHE sources with no very high-energy (VHE) counterpart.
Methods. We observed LHAASO J2108+5157 in the X-ray band with XMM-Newton in 2021 for a total of 3. 8 hours and at TeV energies with the Large-Sized Telescope prototype (LST-1), yielding 49 hours of good-quality data. In addition, we analyzed 12 years of Fermi-LAT data, to better constrain emission of its high-energy (HE) counterpart 4FGL J2108. 0+5155. We used naima and jetset software packages to examine the leptonic and hadronic scenario of the multi-wavelength emission of the source. Results. We found an excess (3. 7σ) in the LST-1 data at energies E > 3 TeV. Further analysis of the whole LST-1 energy range, assuming a point-like source, resulted in a hint (2. 2σ) of hard emission, which can be described with a single power law with a photon index of Γ = 1. 6 ± 0. 2 the range of 0. 3 -100 TeV.
We did not find any significant extended emission that could be related to a supernova remnant (SNR) or pulsar wind nebula (PWN) in the XMM-Newton data, which puts strong constraints on possible synchrotron emission of relativistic electrons. We revealed a new potential hard source in Fermi-LAT data with a significance of 4σ and a photon index of Γ = 1. 9 ± 0. 2, which is not spatially correlated with LHAASO J2108+5157, but including it in the source model we were able to improve spectral representation of the HE counterpart 4FGL J2108. 0+5155. Conclusions. The LST-1 and LHAASO observations can be explained as inverse Compton-dominated leptonic emission of relativistic electrons with a cutoff energy of 100 +70 -30 TeV. The low magnetic field in the source imposed by the X-ray upper limits on synchrotron emission is compatible with a hypothesis of a PWN or a TeV halo.
2019). Even though IC γ-ray emission beyond 100 TeV is suppressed because of the Klein-Nishina effect, it has been shown that IC can still dominate UHE emission in radiation-dominated environments (e. g. ,  Vannoni et al. 2009; Breuhaus et al. 2021). This mechanism was used to explain UHE γ-ray emission of extended sources detected by HAWC, and three LHAASO sources associated with pulsars  (Breuhaus et al. 2021 (Breuhaus et al. , 2022)). In addition, the study of  Albert et al. (2021)  suggests that UHE γ-ray emission may be a generic feature in the vicinity of pulsars with high spin-down powers Ė > 10 36 ergs - 1. The same limit on Ė was also derived from first principles, showing that only very energetic pulsars can power PeV γ-ray emission (de Oña  Wilhelmi et al. 2022). According to the ATNF database 1  (Manchester et al.
67σ  (Li & Ma 1983)  and a signal-to-noise ratio (S/N) of 46% under the point-like source assumption. This signal is insufficient to claim a detection of the source in the TeV energy range. However, if the excess above 3 TeV proves to be significant in future observations, then no excess seen at lower energies may suggest a hard spectral index of the potential VHE source. We performed a 1D spectral analysis using the Gammapy  (Deil et al. 2017 ) package, adopting the source coordinates reported by  Cao et al. (2021a). As LST-1 is in the commissioning phase and the analysis software and methods are still under intensive development, we only performed a preliminary 2D analysis using an acceptance model taken from real LST data, which was then used to correct for radial dependencies in the background models in Gammapy.