dataesr/scientific-paragraphs-categorization huggingface dataset:
The threshold was set to quantify only synaptic terminals: threshold level was increased until all fibers disappeared from thresholded images. This approach was selected as more appropriate to reveal changes in the sparse VAChT signal than relative fluorescent density. Glutamatergic density was assessed through the intensity of fluorescence after subtraction of background fluorescence taken from the surrounding corpus callosum. This relative fluorescence approach was selected for assessing VGluT1 changes because VGluT1-positive terminal dense packing and the absence of fiber staining precludes a thresholding approach similar to that used for cholinergic terminals. Synaptic cholinergic and glutamatergic data were expressed as a ratio of the respective sham density (for each delay, genotype, and sex) to extract and compare specifically lesion-related synaptic reorganizations throughout groups.
9 mm from dura; perforant path stimulation: AP-0. 43 mm; ML Ϯ0. 24 mm; P Ϫ0. 15 from dura). Optogenetic stimulations were done with an LED stimulation light (Prizmatix) at 470 nm through a 250 m optic fiber (10 ms pulses at 0. 2, 5, or 20 Hz; 15-20 mW output power). Recordings were done with a linear 16-channel silicon probe (Neuronexus, A1x16-2 mm-50-177) connected to an AlphaLab recording system (Alpha-Omega). Raw signal was amplified (200ϫ), filtered between 0 and 9 kHz, and digitalized at 22 kHz. Silicon probes, optical fiber, and stimulating electrodes were painted with 2% Dil solution (Sigma-Aldrich) for localization. Analyses were performed using custom-made scripts in MATLAB (MathWorks, RRID:SCR_001622). When present, slow drift and electrical noise were removed using the Chronux signal processing toolbox  (Bokil et al.
, 2010; RRID:SCR_005547). LFP data were down-sampled to 2200 Hz and filtered between 0. 1 and 500 Hz. Spectral analysis were carried with the Chronux toolbox with a time-frequency product of three and five tapers. Time-frequency analyses were done on 4 s window moved across the data in 1 s increments. Due to the high occurrence of dentate LFP spikes in lesioned mice, power in the theta band (3-9 Hz in anesthetized mice) was measured as peak power, a measure removing contamination caused by high-amplitude events. Urethane anesthesia is characterized by alternating theta and nontheta states  (Pagliardini et al. , 2013). Only periods associated with theta (theta/delta power ratio Ͼ5) were analyzed. Electrophysiological changes induced by optogenetic cholinergic stimulations were extracted from the comparison between 60 s of baseline with 60 s of stimulation followed by 60 s of recovery.
Cardiorespiratory Fitness Measurement A validated combined heart rate and movement sensor (Actiheart, CamNtech, Cambridge, UK) was fitted on the chest of the participating women  [23]. The Actiheart is a small (33 mm diameter × 7 mm thick), light weight (<8 g), waterproof monitor which simultaneously measures heart rate (128 Hz) and uniaxial acceleration (32 Hz) by a piezoelectric element  [23]. During the step test, subjects also wore a separate heart rate monitor with a display (Polar FS2C, POL90027148). An 8 min sub-maximal ramped step test (200 mm step; Reebok, Lancaster, UK) was performed, followed by a 2 min recovery phase. Heart rate was recorded throughout the exercise test and recovery phase. Based on the data on heart rate and work load, CRF (VO 2max (mL/kg/min)) was estimated by extrapolating the individually established relationship between oxygen consumption and heart rate  [24]  to age-predicted maximal heart rate  [25].
Results Table  1  shows the characteristics of the women studied, and the CRF and PA data obtained from combined sensing and self-report. PAEE was 37. 1 kJ/kg/day by the objective measure and 33. 2 kJ/kg/day by self-reported measure. From the 6-and 10-point scale for home and work PA-related environments, respectively, participants reported a mean score of 1. 9 for home and 4. 2 for work. For some items (e. g. , "Bicycles provided" at work), the number of positive responses was low). Table  2  shows the relationships between the ALPHA PA-related home and work scores with CRF and PA data obtained from both objective and self-report measures. Both home and work PA-related environment scores were positively correlated with CRF (r = 0. 18 and r = 0. 28, respectively). Table  3  shows mean values of CRF, PAEE, and time spent sedentary, according to items related to the home and work environments from the ALPHA questionnaire.
Discussion In this study, we assessed the relationships between characteristics of the self-reported home and work environments related to PA and actual physical fitness and PA levels in French women. The home and work environments were assessed by the ALPHA self-reported questionnaire, specifically designed for European studies. Usual PA was measured by both objective and subjective methods. A main result was that some home, and, to a lesser extent, some workplace characteristics were significantly related with CRF in these women. Although data on the population distribution of fitness levels are limited, CRF levels in our population appear to be similar to previously-reported descriptive data. In the 1999-2004 NHANES survey, the median maximal oxygen uptake estimated from a submaximal treadmill test was 33.
3 mL/kg/min in women aged 40-49 years  [31]  compared to 31. 7 mL/kg/min in our women aged 54 years on average. We report significant relationships between attributes of the self-reported indoor PA-related environment and a measure of CRF in adult women. The data suggest the importance of the availability of equipment in the home environment. To have a personal bicycle or a garden at home was positively associated with CRF and usual PA-related energy expenditure. CRF is a predictor of CVD risk and all-cause mortality in women as in men  [32]. There are very few published data based on objective fitness assessment with which to compare our findings. Previous studies have documented (in adults) associations between the availability of PA/sporting equipment at home and usual PA-either self-reported  [7] [8] [9] [10] [11] [12] [13] [14] [15]  or recorded by accelerometry  [14] -in general agreement with our findings.
The size of our sample is, however, too small to draw solid conclusions. Among the strengths of the study, the women underwent a submaximal exercise test allowing the assessment of CRF-a recognized predictor of CVD risk and all-cause mortality  [17, 32]. Another strength is the use of both accelerometry plus heart rate-based and questionnaire-based measures of usual PA, in an attempt to overcome the limitations of both subjective and objective assessments of PA. To measure home and work PA-related environmental factors of interest, we used the ALPHA questionnaire-an instrument specifically designed to assess an individual's perceptions of the neighborhood-related physical environment in European settings-and it included some indoor characteristics. Among the study limitations, the data were cross-sectional, therefore preventing the possibility of drawing conclusions about causality or temporality.
Acknowledgments: The InterAct study received funding from the  European Union  ( Integrated  Project  LSHM-CT-2006-037197  in the Framework Programme 6 of the European Community). The work of SB was supported by the  UK Medical Research Council  ( MC_UU_12015/3 ). No funds were received for covering the costs to publish in open access. Gaël Dos Santos ,  Elodie Gibert ,  Vanessa Lizon  and  Toni Afiate  are gratefully acknowledged for their assistance in data collection and the first steps of data analysis. We thank the  Physical Activity Technical Team (MRC Epidemiology Unit, Cambridge, UK)  for processing the Actiheart data.
Search Strategy Two independent investigators (M. C. , Z. B. ) conducted 2-step literature searches according to a specific protocol. First, the Web of Knowledge database was searched, incorporating both the Web of Science and MEDLINE. The search was extended until June 30, 2015, including abstracts in English language only. The electronic research adopted several combinations of the following keywords: "First episode psychosis," "Diagnostic accuracy," "Sensitivity," "Specificity," "Psychosis prediction," "Psychosis onset," "Diagnostic stability," "Prediction," "DSM-IV," "ICD-10," and "Follow-up". Second, we used Scopus to investigate citations of possible previous reviews/ meta-analyses on diagnostic stability of first episode psychosis diagnoses, and a manual search of the reference lists of retrieved articles.
Selection Criteria Studies were eligible for inclusion if the following criteria were fulfilled: (a) were original articles, written in English; (b) included a group of ICD-10 or DSM-IV/ DSM-IV-TR first episode psychosis patients (defined as first-ever admission to mental health services), diagnostically assessed at baseline and follow-up; and (c) reported the baseline and follow-up number of specific psychotic diagnoses: schizophrenia, schizoaffective disorders, schizophreniform disorder, affective spectrum psychoses, delusional disorder, substance-induced psychotic disorder, psychosis not otherwise specified, acute and transient psychotic disorder/brief psychotic disorder. Further details on the specific ICD-10/DSM-IV diagnostic codes are appended in supplementary methods. When data were not directly presented they were indirectly extracted from associated data.
Corresponding authors were contacted to retrieve additional data when possible. Exclusion criteria were: (a) abstracts, pilot datasets, and papers in languages other than English; (b) articles not employing the internationally ICD/DSM validated diagnoses for psychosis; (c) articles not providing enough meta-analytical data; (d) articles reporting on organic psychoses, and (e) articles with overlapping datasets. Specifically, in case of multiple publications deriving from the same study population, we selected the articles reporting the largest and most recent data set. Literature search was summarized according to the PRISMA guidelines. 15.
The secondary outcome is the retrospective diagnostic stability/instability across each diagnosis, defined as the proportion of follow-up patients that receives the same diagnosis that they had at baseline, or another diagnosis as compared to that received at baseline respectively. 12 Meta-analysis was conducted with the "metaprop" package 17 of Stata 13. 1. This package is specifically developed for pooling proportions in a metaanalysis of multiple studies. The CIs are based on score (Wilson) procedures. 18 As proportions were expected to be often small we used Freeman-Tukey Double Arcsine transformation  19  to stabilize the variances and then we performed a random effect meta-analysis implementing the Der Simonian-Laird method. 20 The influence of moderators (age, gender, time to follow-up, comorbid substance abuse, ICD-10 vs DSM-IV diagnostic criteria, publication year, quality assessment, clinical setting of initial diagnosis, baseline functional level measured with the Global Assessment of Functioning [GAF] scale, duration of untreated psychosis) on the diagnostic instability of each diagnostic category was tested using meta-regression analyses with the "metareg" function.
Database Literature search (PRISMA flow-chart figure  1 ) uncovered 42 independent articles. The list of excluded studies is detailed in the supplementary table 2. There were 23 studies employing ICD-10 and 22 employing DSM-IV, with 3 studies contributing 2 samples (ICD-10 and DSM-IV) each,  13, 29, 30  for an overall of 45 independent samples. The final database comprised 14 484 first episode patients, 10 510 diagnosed with ICD-10 and 3974 diagnosed with DSM-IV. The mean age of the patients was 29 years (median 27 years, age range 16-75) and the mean proportion of females was 0. 49. Age, gender, diagnostic instrument employed to assign the psychotic diagnosis, quality assessment, baseline sample size are detailed in table 1, while duration of untreated psychosis and operationalization of first episode of psychosis are detailed in supplementary table  3.
Discussion We present here the first meta-analytical estimate of diagnostic stability of first episode psychosis diagnoses. Our database was large, including 42 studies and 45 samples for a total of 14 484 subjects, followed up for a mean period of 4. 5 years. There was significant construct variability  68  across prospective diagnostic stability of different firstepisode psychotic diagnoses: schizophrenia 0. 90, affective spectrum psychoses 0. 84, schizoaffective disorder 0. 72, substance-induced psychotic disorder 0. 66, delusional disorder 0. 59, acute and transient psychotic disorder/brief psychotic disorder 0. 56, psychosis not otherwise specified 0. 36, and schizophreniform disorder 0. 29. This is the first robust meta-analytical evidence documenting a very high diagnostic stability of schizophrenia, which is comparable to other clinical diagnoses in medicine.
, 2011). Methane emissions from the Arctic Ocean are expected to dominantly come from microbial activity in the ESAS seabed and thawing carbon-rich permafrost as suggested by  Shakhova et al. (2010) , and less from hydrate methane destabilization. Isotopic compositions measured at ZEP during September 2008 and September-October 2009 are compared to CHIMERE simulations in Sect. 3. 3 for assessing methane emission processes in ESAS.
, 2011)  for each year for the simulation of dynamic forest and peatland vegetation growth. The GFED v3 database is built from the 500 m collection 5. 1 MODIS DB burned-area mapping algorithm  (Giglio et al. , 2009). Methane emissions at monthly and daily scales are deduced from the burnt areas using Carnegie-Ames-Stanford approach (CASA model;  Potter et al. , 1993)  and emission factors (van der  Werf et al. , 2010). EDGAR v4. 2 FT2010 reports emissions for the year 2010, and not 2012. Anthropogenic emissions are reported on an annual basis in this inventory and have been found to only change slightly for the Arctic in the fast track recent release for 2012 (http://edgar. jrc. ec. europa. eu/). Moreover, as we analyse synoptic signals here, our results are not very sensitive to small annual changes.
We thus directly transpose 2010 anthropogenic emissions to the year 2012. GFED v3 database and LPJ model also do not provide emission fluxes for years later than 2010. We thus take a climatology of biomass burning and wetland emissions computed over the years 2000-2010 to represent fire and wetland emissions in 2012. In the absence of the actual year, this is a conservative approach in order to represent all potential emitting areas for these two sources. The first three types of emissions are projected from their original grids of 0. 1 • × 0. 1 • (EDGAR) and 0. 5 • × 0. 5 • (LPJ, GFED) to CHIMERE grid. The ESAS emissions are directly built on CHIMERE grid from  Shakhova et al. (2010)  as they provide a detailed description of the emission areas and emission strengths per period (winter and summer).
Statistical assessment of the ESAS emissions The magnitude of the ESAS emissions can be derived by adding scaled ESAS emissions to the basic scenario (see Sect. 2. 3), so that simulated time series best fit with observed time series (see Eq. (  1 ) below for agreement score definition). However, the emission databases used in the transport model, as well as the lateral boundary conditions and the transport representation itself, suffer from uncertainties. A tolerance interval for magnitude of the ESAS emissions as seen by atmospheric sites is computed through Monte Carlo experiments to account for these uncertainties. The Monte Carlo ensemble (20 000 samples hereafter) is generated by randomly scaling the anthropogenic emissions, the wetland emissions, and the lateral boundary conditions, for each month of the year.
3. For all samples i of the Monte Carlo ensemble, we define a minimum agreement score S min , which corresponds to the points of the emission trajectories closest to the reference point (perfect correlation and no bias; black star in Fig. 3 ). Tolerance intervals TI i for the ESAS emissions are deduced for all samples of the Monte Carlo ensemble, so that all ESAS emissions with associated scores within [S min , S min +10 %] are considered compatible with the atmospheric signal. These tolerance intervals are computed for every month of the year 2012. In the end, for each month of the year 2012, we compute aggregated tolerance intervals for the ESAS methane emissions such that 95 % (equivalent to 2σ interval for Gaussian distributions) of the Monte Carlo ensemble is within the interval. This statistical analysis is not performed on the whole available data set, but on afternoon averaged mixing ratios.
Model-observation comparisons at four Arctic sites At PAL, BRW, ZEP, and ALT, as shown in Fig. 4 , the continuous methane observations exhibit similar seasonal variations with a minimum during summer (June-July) and a maximum during winter (December-January). At PAL, ZEP, and less evidently at ALT, the synoptic variations appear larger in winter than in summer. At TIK, the seasonal maximum is observed in August, associated with large synoptic variations and a less pronounced seasonal cycle, suggesting an influence of local to regional emissions during summer months. At PAL, a site scarcely influenced by the ESAS emissions, most of the atmospheric signal is explained by the lateral boundary conditions (i. e. by air masses coming from outside the CHIMERE domain), especially the large synoptic variations during winter months.
Polar CHIMERE computed with the basic emission scenario demonstrates a very good skill in winter in representing the atmospheric methane mole fraction variability at high-latitude sites; the performance is slightly less good in summer. As shown in Fig. 4a , the variability of the daily averages' observed methane mole frac-tions is indeed well captured by CHIMERE (annual temporal correlation of r = 0. 87, winter r = 0. 89, summer r = 0. 63; Table  1 ). Discrepancies between the observed signal and the simulated one at other sites can then be reasonably interpreted in terms of misspecified regional emissions. At ZEP,  ALT, and BRW (Fig 4b, c, and d) , three sites remote from ESAS but influenced by long-range transport from ESAS across the Arctic Ocean (see Figs. S2  and S3 ), nonsummer mole fractions (i.
We assume that the estimation of the ESAS fluxes for late summer 2012 as computed in Sect. 3. 2 can be transposed to the periods of isotope measurements in 2008 and 2009. Emis- sions are expected to vary from a year to the other, but this variability is unlikely to impact the qualitative isotopic calculations. During the observation campaigns, episodes with identified air origin from Ob River and eastern Siberia exhibited a mean signature of -65 ± 3 ‰ in September 2008  (Fisher et al. , 2011)  and of -62 ± 3 ‰ in 2009 (see Fig. 6 ). These values point toward a dominant biogenic origin of emitted methane. More precisely, in these air masses, the contribution of the different methane sources can be estimated as they are run separately in the CHIMERE model. The ESAS emissions are found to contribute 35-45 % to the observed signals (with ESAS emission strengths of 6 TgCH 4 yr -1 as computed in Sect.
Acknowledgements. We thank the principal investigator from the observation sites we used for maintaining methane measurements at high latitudes and sharing their data. We are grateful to the anonymous referees for their valuable comments which led to a substantial improvement in the presentation of the paper. We also thank  Patrick Crill  ( Department of Geological Sciences, Stockholm University ) for his kind advice and remarks on the manuscript and  F. Marabelle  (LSCE) and the  LSCE IT team  for the computer resources. This study was supported by the  CEA ,  ANR - CLIMSLIP  project, and iZomet  Franco-Swedish  project. Edited by:  M. Heimann.
Clinical Endpoints We assessed early kidney dysfunction and the development of clinical pancreatitis. Kidney dysfunction was defined as a decrease in creatinine of less than 70% of basal creatinine by day 7 after transplantation or the need for hemodialysis during the first week. Clinical pancreatitis was defined as a clinical status of abdominal distension, abdominal pain, pancreas graft swelling, and the need for pancreatic rest with total parenteral nutrition. This diagnosis was performed independent of this study by the clinicians who were in charge of the patient and was recorded in the patient posttransplant database. We also assessed early posttransplant graft and patient survival at 3 months to determine the impact of these early events on hard endpoints.
Hyg R , hygromycin resistance gene serving as selectable marker for eukaryotic cells. (C) Design of the experimental approach to test for trans-mobilization of the mneoI-tagged LAVA C element by the human L1 protein machinery. LAVA or SVA retrotransposition reporter plasmids, or pCEPneo were each co-transfected with L1 protein donor plasmid pJM101/L1 RP Áneo or pJM101/ L1 RP ÁneoÁORF1 (blue) into HeLa-HA cells that were subsequently selected for hygromycin resistance for 12 days. Hyg R cells were assayed for retrotransposition events by selecting for 9-12 days for G418 R HeLa colonies. S2 , Supplementary Material online), respectively. Each of the four different PCR products was cloned into the pGEM-T Easy vector (Promega), sequenced, and the obtained sequences were compared with the LAVA C sequence of pDRIVE-LAVA C in order to check for PCR artifacts (data not shown).
24 h later, cells were selected for L1 retrotransposition events in medium containing 400 mg/ml G418. Statistical evaluation was performed by means of an Analysis of Variance (ANOVA). To control the overall type I error (a = 0. 05), P-values were adjusted according to Dunnett for multiple comparisons. The statistical analysis was performed with SAS Õ /STAT software, version 9. 2 SAS system for Windows.
0 available from UCSC's Table Browser, to select 200 full-length L1PA insertions randomly. Next, 100-bp marginal sequences consisting of ±50 bp upstream and downstream of the start and end position of each element were extracted. These pairs of marginal sequences were then aligned using Clustal Omega  (Sievers et al. 2011 ) and the alignments were visualized in Geneious version 5. 6. 5 (http://www. geneious. com; last accessed September 21, 2016)  (Kearse et al. 2012)   To quantify mneoI-transcripts expressed from the various reporter plasmids in our assay system, plated HeLa cells were cotransfected with 3 mg of each of the four LAVA C reporter plasmids, the SVA reporter plasmid, or pCEPneo, and 3 mg of the L1 driver pJM101/L1 RP Áneo, or with 3 mg pCEP4 and 3 mg of pJM101/L1 RP. Hygromycin selection started 24 h post-transfection.
Acknowledgments We thank  John Moran  for providing plasmids pJM101/L1. 3,pJM101/L1 RP , and pJM101/L1 RP Áneo and the cell lines HeLa-HA. We are grateful to  John Goodier  and  Haig Kazazian  who provided us with the reliable aL1ORF2p-N antibody. We are indebted to  Kay-Martin Hanschmann  for statistical analyses. This work was supported by the  Deutsche Forschungsgemeinschaft  [ DA 545/2-1  and  SCHU 1014/8-1  to G. G. S. ] and the  National Institutes of Health  [ NCRR P51 ,  RR000163  to LC]. Moreover, T. J. M. was supported by the  National Library of Medicine of the National Institutes of Health  under Award Number  T15LM007088. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.