dataesr/scientific-paragraphs-categorization huggingface dataset:
Our study shows that Chironomids are indeed a quite diverse group with different ecological requirements and characteristics, and if used at the genus or species level, they have the potential to improve the signals provided by ecological assessment tools, either in taxonomic-based structural assessments or in indirect functional assessments using multiple-traits-based approaches. To further prove these insights, tests comparing both types of assessments based on sub-family level and genus/species level are needed. Our database, which is the first comprehensive European database for Chironomidae traits at the genus level to the best of our knowledge, can be used for that purpose, as well as in ecological studies on functional patterns of freshwater systems, especially those including habitats that are traditionally considered less diverse.
Acknowledgments This work was made possible by funding of the  Portuguese Foundation for Science and Technology (FCT)  through a  PhD scholarship  ( SFRH/BD/80188/2011 ), the cotutelage between the  University of Coimbra  and the  University of Lyon 1 , and the cooperation between the  MARE, University of Coimbra, Portugal , and the  LEHNA -Laboratoire d'Ecologie des Hydrosystèmes Naturels et Anthropisés, University of Lyon, France. The research partly benefited from the  European Community 's  Seventh Framework Programme , Grant Agreement No. 603629-ENV-2013-6. 2. 1-Globaqua.
Lappodiamesa sp. (Diamesinae), Stackelbergina sp. (Orthocladiinae), Gillotia sp. and Neostempellina sp. (Chironominae). Prodiamesinae were relatively well characterised; only Monodiamesa sp. had less information than the other 2 genera included in the subfamily (Prodiamesa and Odontomesa). A total of 16 European genera of the Diamesinae, Orthocladiinae and genera with less information such as Meropelopia sp. (Tany- podinae), Chironominae subfamilies lacked trait information resulting in the absence of entries in the database: Arctodiamesa, Pagastia, Arctosmittia, Bavarismittia, Boreosmittia, Corynoneurella, Lappoki- efferiella, Molleriella, Neobrillia, Prosmittia, Tavastia, Baeotendipes, Carbochironomus, Nilomyia, Olecryptotendipes, Synendotendipes. Buchonomyiinae 1 1 100 1 1 100 Chironominae 64 59 92 474 300 64 Diamesinae 11 9 82 69 47 68 Orthocladiinae 78 69 88 588 304 52 Podonominae 5 5 100 9 3 33 Prodiamesinae 3 3 100 10 7 70 Tanypodinae 30 30 100 106 81 76 Telmatogetoninae 2 2 100 4 1 25 Total 194 178 1261 744.
8a, b ), in contrast to the BLI experiments. To explore whether the discrepancy between the data using the FLuc reporter and GFP reporter had a dose-related component, we injected mice with three doses (5×10 10 , 3×10 11 , and 1×10 12 g. c. ) of either AAV8-CBA-GFP or exo-AAV8-CBA-GFP. We also used a single-stranded (ss) GFP genome, which matches the ss FLuc genome. This reduces the efficacy of detection compared to sc vector so we used anti-GFP immunoblots to detect GFP in the brain of mice. Two weeks post-injection, mice were sacrificed. First we performed qPCR analysis on genomic DNA isolated from brain homogenates. We observed an increase (1. 8-fold) in AAV genome copies in the brain of mice injected with exo-AAV8 at the lower doses, and no increase at the highest dose (Fig. 8c ). We next performed western blotting for GFP at the lowest dose of vector (5×10 10 g.
Epifluorescence microscopy Tile-scan images were collected on a Zeiss Axio Imager Z epifluorescence microscope equipped with AxioVision software and modified for automated acquisition of entire brain slices, using either 5×, 10× or 20× objectives. Exposure times for each specific immunostaining were maintained unchanged between each slice imaged, in order to document differences in the transduction efficiency between AAV and exo-AAV.
Bioluminescence imaging All imaging experiments were performed using the IVIS Spectrum imager outfitted with an XGI-8 gas anesthesia system (PerkinElmer, Waltham, MA). Mice which had previously been i. v. injected with either ss-AAV8-CBA-FLuc or ss-exo-AAV-FLuc were anesthetized and then injected intraperitoneally with 4. 5 mg of D-luciferin substrate resuspended in 150 µl of PBS. Five minutes post-substrate injection, mice were imaged for luciferase expression using auto-acquisition. Region of interest were selected and imaging were analyzed using LivingImage software (v4. 17,PerkinElmer).
Statistics Statistical analysis of data was performed using GraphPad Prism software (version 5. 01). As indicated in the text, unparametric Mann Whitney tests were performed to compare the transduction efficacy of AAV9 with exo-AAV9. A value of p< 0. 05 was considered to be statistically significant. For statistical analysis of bioluminescence assays, comparison between two groups was performed using an unpaired t test. genome copies) were suspended in media and dispensed into the upper chamber (apical side of cell culture inserts), while vector-free media was added to the lower chamber (below the inserts). After 24 hours, media was collected from the lower chamber to determine the number of AAV genomes present in the media by qPCR. (a) Data are presented as the mean of total AAV genome copies below the cell culture insert ± standard deviation.
Acknowledgments We thank  Miguel Sena-Esteves  for providing the AAV plasmids as well as  Dr. David P. Corey  for the expertise and resources of his laboratory on the cochlear transduction analysis. This work was supported by  NIH/NINDS   R21 NS081374-01  (CM), an  American Brain Tumor Association Discovery grant  ( CM ) and the  Alzheimer's Drug Discovery Foundation (EH)  and  JPB Foundation (BTH). In addition, this work was also partially supported by  NIH/ NINDS   R01 NS086570-01  (SHR). We would like to acknowledge the  Nucleic Acid Quantitation Core at MGH Neuroscience Center  for quantitative PCR analysis.
Sample Preparation for Raman and ROA. Monosaccharides were prepared to 40 mg/mL in 0. 10 M NaCl, pH 6. 0. MUC5B and MUC5B subdomains were dialyzed into 0. 10 M NaCl, pH 6. 0 and concentrated to 5-15 mg/mL in Sartorius Vivaspin 5-10 kDa molecular weight cut-off (MWCO) columns. Raman and ROA Data Acquisition. Raman and ROA spectra were measured using a BioTools ChiralRaman spectrometer. The instrument was set up in a backscattering geometry and operated with a Nd:VO4 laser with an excitation wavelength of 532 nm and spectral resolution of 7 cm -1. Samples were measured in quartz microfluorescence cells. Mucin samples were measured with a laser power of 600 mW at the sample, laser illumination period of 1. 24 s and data were gathered over a 36-48 h time period, whereas monosaccharide and amino acid spectra were acquired for 4-12 h.
Raman spectral baselines were corrected based on an approach reported elsewhere,  21  averaged and smoothed with a 15 point Savitzky-Golay filter. Sample and reference Raman spectra were then normalized to an invariant band (270 cm -1 ), and then the reference Raman spectrum of 0. 10 M NaCl was subtracted from the sample Raman spectrum. ROA spectra were averaged and smoothed with a 15 point Savitzky-Golay filter. CD Data Acquisition. MUC5B subdomains (NT5B, CT5B, and Cys-domain) were prepared to 1 mg/mL for CD. CD measurements were made using an Applied Photophysics Chirascan qCD spectropolarimeter, step size 0. 5 nm, 5 s per time point, and 4 repeats. Measurements were made in a 0. 5 mm quartz cell at 12 °C in 10 mM Tris-HCl, 50 mM NaCl, pH 7. 4. TERS Sample Preparation. Glass coverslips were washed in 100% ethanol overnight and air-dried for 2 h before addition of the sample.
Purified MUC5B at 10 μg/mL was extensively dialyzed against PBS, pH 7. 4, and was prepared on a glass substrate by drop-deposition of 100 μL onto precleaned glass for 30 s, followed by washing with 6 × 300 μL of ddH 2 O and air-dried for at least 24 h before being imaged. Monosaccharides for TERS analysis were prepared to 1 mM in ddH 2 O, drop-deposited onto precleaned glass substrates, and dried under argon, based on established protocols. 22 ERS Data Acquisition. TER spectra and corresponding AFM images were collected with a Nanowizard II atomic force microscope (JPK Instrument AG, Germany) mounted on an inverted microscope (Olympus IX70, Japan) with a confocal Raman spectrometer (Acton Advanced SP2750 A, SI GmbH, Germany) and a 400 pixel charge-coupled device (CCD) camera. Tap190Al-G (Budget Sensors) AFM-tips were evaporated with 25 nm silver, as described previously.
23 An oil immersion objective (40×, 1. 35 NA, Olympus) was used and a 532 nm laser as the excitation source. Each TER spectrum was acquired for 5 s. TER spectra were truncated before 560 cm -1 to remove the silicon peak from the AFM-tip. Spectral baselines were corrected and cosmic ray strikes were removed using an in-house Raman toolbox in Matlab software, developed by Dr. Ben Gardner. 24 Data sets of mucin TERS spectra were normalized to the silicon band at ∼940 cm -1 and then normalized between 0 and 1. Color-coded heatmaps were generated using the "msheatmap" function in Matlab.
3] [14] [15]  The data presented here may indicate that the β-sheet is important for the functions of these globular regions of mucins, such as in forming intramolecular and intermolecular interactions that are paramount for mucin cross-linking and polymerization and thus the integrity of saliva and mucus gels. Following the Raman/ROA/CD analyses of the various mucin subdomains, polymeric MUC5B was analyzed by a novel spectroscopic technique, TERS. TERS combines the spatial resolution of AFM with the chemical specificity of Raman spectroscopy, enabling structure features to be examined along the length of individual molecules. Therefore, we aimed to generate the first TERS spectra along the length of an individual mucin molecule and identify signatures of glycosylation and secondary structure.
By collection of the TER spectra of the major monosaccharide building blocks of mucins (Figure  S4 ), we generated a library of monosaccharide marker bands (Tables  S3  and S4 ) in order to examine signatures of glycosylation along the length of the molecule. All sugar marker bands were identified within the MUC5B TERS data set and show distinct distribution patterns along the length of the molecule (Figure  6a ). This may reflect the glycosylation  of the molecule (i. e. , the presence of particular monosaccharides, Table  S4 ), or different orientation of sugars on the glass substrate, which affects their detection by TERS. At this stage it is not possible to unequivocally distinguish between these possibilities; this would be a fruitful area for longer term study. Despite this, it is striking that monosaccharide marker bands are displayed throughout the MUC5B TERS data set, suggesting that the area imaged includes at least part of a highly glycosylated mucin domain.
In recent years TERS has proven to be an effective tool for investigating the secondary structure distribution along the lengths of protein structures. Therefore, we examined the amide I band positions along the TERS data set of MUC5B. The position of the amide I band was assigned as α-helix/ unordered (1640-1664 cm -1 ) or β-sheet (1665-1678 cm -1 ), based on previous TERS analysis of proteins,  17  providing a map of secondary structure signatures along the MUC5B molecule (Figure  6b ). The plot in Figure  6b  shows that α-helix/ unordered conformation was identified in the majority of spectra all along the area imaged. Since our Raman, ROA, and CD analyses showed that unordered structure is present in MUC5B O-glycan-rich regions and there is no evidence of αhelical structure in any of the MUC5B subdomains, this suggests that these TERS signals originate from unordered conformation rather than α-helix.
However, it must be considered that there are differences between these techniques and TERS; far field Raman/ROA/CD detects the core structures of biomolecules, while TERS is extremely sensitive to structures present on the surface of molecules. Therefore, although it is likely that the secondary structure signatures identified in the TERS data set represent the locations of the mucin subdomains (by  correlation with Raman/ROA/CD analysis), it cannot be ruled out that they may have another local origin, thus an absolute statement regarding the conformation on the surface from the presented experiments alone cannot be made at this time. Here, we present the first demonstration of TER spectra acquisition with high spatial resolution along a gel-forming mucin where different secondary structures and glycosylation signatures can be discerned.
PLR non-invasively delivers a fluid challenge of around 300 mL to the central circulation, allowing assessment of fluid responsiveness without fluid administration  [4, 5]. An increase in cardiac output of >10-15 % in response to PLR predicts fluid responsiveness and this technique has been validated in spontaneously breathing patients so may be of use to emergency physicians  [6, 7]. The inferior vena cava (IVC) may be measured by ultrasound. The proportion that the IVC collapses with respiration is termed the inferior vena cava collapse index (IVCCI = (IVC max -IVC min )/IVC max ). This has been shown to indicate fluid status in children  [8] , ventilated patients  [9, 10]  and healthy volunteers  [11, 12]. Previous studies have demonstrated that IVCCI predicts fluid responsiveness in mechanically ventilated patients  [9, 13]  but data in spontaneously ventilating patients is less robust  [14, 15].
Analysis All Gaussian distributed variables are expressed as mean and standard deviation (SD), and non-normally distributed variables were analysed as paired data and expressed as median and interquartile range (IQR). Means drawn from normally distributed data were compared with paired Student T test and non-parametric data were analysed with the paired Wilcoxon rank. Normality was assessed using Kolgomorov-Smirnov test. Correlation between stroke volume with carotid VTI and cardiac output with carotid flow was sought using Pearson test. A p-value of <0. 05 was required for statistical significance, and all analyses were two-tailed. Data were analysed using SPSS (IBM SPSS Statistics 21. 0, Chicago, Il, USA). No power calculation was possible for IVCCI and carotid Doppler changes with PLR as no previous work was available to base this upon, however, we estimated that a sample size of >30 subjects may be sufficient.
Results Thirty-four participants were recruited into the study. Data for one participant was not recorded correctly and was excluded from analysis leaving 33 (17 male, 16 female) data sets. The physiological changes observed in this study are summarised in Table  1. PLR was associated with significant reduction in mean inferior vena cava collapsibility index from 0. 24 to 0. 17 (p < 0. 01)mean difference of -0. 06 (95 % CI -0. 10; -0. 02). PLR was associated with a 23. 6 % increase in mean stroke volume from 56. 0 to 69. 2 mL (p < 0. 01), 18. 2 % increase in cardiac output from 3. 3 to 4. 1 L/min (p < 0. 001)with a mean difference of 0. 75 L/min (95 % CI 0. 55; 0. 96). Mean sniff collapsibility index reduced significantly from 0. 57 to 0. 42 (p < 0. 01)with a mean difference of -0. 15 (95 % CI -0.
3e, f ). These data suggest that XACT induction and Xi reactivation are temporally uncoupled in this reprogramming scenario. Partial reactivation of the human Xi upon reprogramming. To examine reactivation of Xi-specific alleles at the molecular level, we used single-nucleotide polymorphisms (SNPs) to distinguish transcripts originating from each of the two X chromosomes. To do this a human fibroblast cell line carrying heterozygous SNPs in ATRX and PHDA1 genes was used to derive clonal populations where cells expressed the same X chromosome (X 1 or X 2 ; illustrated in the schematic representation shown in Fig. 4a ). Both alleles of ATRX and PDHA1 were expressed in the original human fibroblast line (hF complementary DNA), whereas only one of the two alleles was detected (X 1 a or X 2 a) in single-cell-derived clones (clone a1 and clone b1; Supplementary Fig.
7NA CS HC PL APO objective by using Leica SP5 confocal microscope and LAS AF Matrix Screener software. Images were then analysed by ImageJ to find the coordinates of heterokaryons that showed partially overlapping signals for mouse-specific Cot-1 repeats and human-specific X-linked gene probes. Selected positions were then acquired at higher resolution by Â 63/1. 4NA HCX PL APO lambda blue objective (z ¼ 0. 2 mm). Images were processed and analysed by ImageJ. Volumetric analysis was performed by NEMO/3D-FISH plugin  59. RNA extraction and RT-PCR. Total RNA extraction and SYBR-Green RT-qPCR were performed as previously described in ref. 37. Primers used in this study are reported in Supplementary Table  2. XIST allele-specific expression (Supplementary Fig. 4f ) was analysed by Taqman PCR performed with SNP genotyping assay (rs1620574; assay ID C-7626536_10 Applied Biosystems) and Taqman Universal Master Mix No AmpEraseUNG (Applied Biosystems) according to the manufacturer instructions.
Acknowledgements We thank  J. B. Lawrence  for providing G1A XIST plasmid,  K. E. Brown  and  H. Bacgi  for communicating unpublished data, and  P. Hajkova ,  T. Lavagnolli  and  A. Malinowski  for critical reading of the manuscript. These studies were supported by  EMBO  (I. C. ), the  Human Frontiers in Science Programme (I. C. ) , the  European Research Council.
Surface Plasmon Resonance Spectroscopy-Analyses were carried out on a BIAcore 3000 (BIAcore, GE Healthcare). For analyses on PR3-coated surface, PR3 was diluted to 50 g/ml in 10 mM sodium acetate, pH 4. 5, and immobilized onto a CM5 sensor chip (GE Healthcare) using the BIAcore amine coupling kit. The running buffer for PR3 immobilization was 145 mM NaCl, 5 mM EDTA, and 10 mM HEPES, pH 7. 4. Binding of 06:0 PS (Avanti Polar Lipids, Inc. ) to immobilized PR3 (3500 response units) was measured at a flow rate of 20 l/min in the running buffer (PBS, pH 7. 4). Surfaces were regenerated by one injection of 10 l of 0. 05% SDS. The specific binding signal shown was obtained by subtracting the background signal, which was obtained by injection of the sample over an activated-deactivated surface. Data were analyzed by global fitting to a 1:1 Langmuir binding model of the association and dissociation phases for several concentrations of 06:0 PS using BIAevaluation 3.
Briefly, a 1-palmitoyl-2oleoyl-sn-glycero-3-phosphocholine (POPS) bilayer neutralized with Na ϩ counterions but lacking ionic strength was created at an initial surface area of 58. 5 Å 2 using the membrane builder module of CHARMM-GUI  (28). This bilayer alone was then equilibrated for 150 ns using NAMD2. 9 (29) and the CHARMM36 force field  (30)  including the non-bonded corrections for sodium ions  (31). During this phase, the surface area remained stable, averaging 59. 4 Ϯ 0. 8 Å 2. Order parameters and hydrogen bond analyses also revealed that both ordering of the aliphatic chains and interactions between headgroups reached satisfying convergence. The protein was inserted into the bilayer using the previously reported orientation  (10). To account for the difference of width between the POPS and the implicit bilayer, the protein was translated by 2.
Acknowledgments-We greatly acknowledge Prof. Luc Mouthon from the department of Internal Medicine for providing the sera from patients with GPA, Muriel Andrieu and Karine Labroquère of the Cochin Cytometry and Immunobiology facility, the staff of the Animal Care Facilities at the Cochin Institute, the excellent technical assistance of Valérie Gausson for the overlay experiments, and the Etablissement Français du Sang (Saint-Antoine Hospital, Paris, France).
* This work was supported in part by the  Investissements d'Avenir Program  (Grant  ANR-11-IDEX-0005-02 ),  Sorbonne Paris Cite ,  Labex Inflamex, Départements Hospitalo Universitaires Autoimmune and Hormonal Diseases (DHU-AUTHORS) ,  Labex LipSTIC  (Grant  ANR-11-LABX-0021 ), and the  Chancellerie des Universités de Paris  ( Legs Poix  to V. W. -S. ). All authors declare that they have no significant competing financial, professional or personal interests that might have influenced the performance or presentation of the work described in this manuscript. 1Supported by funding from the  Labex Inflamex and National Health and Medical Research Council   Early Career Research Fellowship   1092602. 2 Supported by a doctoral fellowship from  Vaincre la Mucoviscidose. 3 Supported by funding from the  Labex Inflamex.
Research networks provide a framework for review, synthesis and systematic testing of theories by multiple scientists across international borders critical for addressing global-scale issues. In 2012, a GHG research network referred to as MAGGnet (Managing Agricultural Greenhouse Gases Network) was established within the Croplands Research Group of the Global Research Alliance on Agricultural Greenhouse Gases (GRA). With involvement from 46 alliance member countries, MAGGnet seeks to provide a platform for the inventory and analysis of agricultural GHG mitigation research throughout the world. To date, metadata from 315 experimental studies in 20 countries have been compiled using a standardized spreadsheet. Most studies.
Development and description MAGGnet arose from member discussions during the GRA Croplands Research Group annual meeting in San Antonio, Texas, USA, October 20, 2011. A key action item identified during the meeting was the "development of an inventory database on long-term measurement experiments," which was distilled from a broader goal to create a global network of reputable GHG emission and soil C sequestration data from specific management practices for synthesis evaluations and model testing/validation across a diversity of environments  [4]. Following discussions with Croplands Research Group co-chairs in late 2011, the action item was focused to compile key metadata from cropland experimental sites throughout the world where biomass yields, GHG flux and soil C dynamics were monitored.
Among GHG flux measurements, nitrous oxide (N 2 O) was measured most frequently across studies (82%), followed by carbon dioxide (CO 2 ; 45%) and methane (CH 4 ; 29%). Grain, stover and roots were assessed in 56, 35 and 9% of the studies. Treatments most commonly measured in MAGGnet included fertilizer rate, manure/ soil amendment and tillage type, included in 68, 52 and 43 studies, respectively (Table  4 ). Emphasis on such management practices was unsurprising, as they corresponded to an overarching goal of generating recommendations to mitigate agricultural GHG emissions. Despite the explicit cropland focus within MAGGnet, 29 sites included a pasture/grazing system (Table  4 ). Data submissions from this land use were encouraged given the clear engagement by researchers focused on grazed systems and the strong linkages between grazed lands and imported feed from cropland.
Recent applications Despite its recent emergence as a GHG network, MAGGnet has served to leverage limited resource investments within individual countries to produce an inclusive, shared meta-database for use by all GRAmember countries. While its potential as a functioning network has yet to be fully realized, MAGGnet occupies a unique niche among GHG networks given its geographical domain (global) and intended focus (cropland)  [7]. With time and continued effort, MAGGnet can serve to further GHG mitigation science through new collaborations among contributing members. MAGGnet has contributed to modeling efforts since its inception, and has spurred other research groups in the GRA to collect experimental site metadata. MAGGnet was used in January 2014 to help identify experimental sites for potential inclusion in an international model intercomparison exercise coordinated by the GRA Soil CarbonÀNitrogen Modeling cross-cutting team  [11].