dataesr/scientific-paragraphs-categorization huggingface dataset:
The rate of follow-up could not be computed in 38 studies (37%) because data were not reported; one-quarter had a follow-up rate of 80% or higher, while 15 studies (15%) had a rate below 50%. The cognitive test was performed using full (59%) or short (24%) versions of an intelligence test. Children with severe conditions/impairment were excluded in 58 studies (56%), and among the other 39 studies, 16 reported that untestable children were assigned a score (usually the lowest score possible). Most studies did not provide information on inclusion or proportion of multiple births (61%), and information on socioeconomic characteristics was lacking in 12% of studies. In 54 cohorts, social characteristics (e. g. parental education, occupational status and social class) were balanced between VPT and term groups, whereas one reported IQ scores after adjustment for social factors and four others provided estimates of the association between VPT and IQ adjusted for social characteristics (Table  2 ).
9-kV needle voltage. Data were acquired using Xcalibur 4. 3 software in a data-dependent mode. MS scans (m/z 375-1500) were recorded at a resolution of R = 120,000 (@ m/z 200) and an AGC target of 4 × 10 5 ions collected within 50 ms. Dynamic exclusion was set to 60 s and top speed fragmentation in HCD (Higher-energy Collisional Dissociation) mode was performed over a 3 s cycle. MS/MS scans with a target value of 3 × 10 3 ions were collected in the ion trap with a maximum fill time of 300 ms. Only +2 to +7 charged ions were selected for fragmentation. Other settings were as follows: heated capillary temperature, 275 • C; normalized HCD collision energy of 30%, and an isolation width of 1. 6 m/z. Monoisotopic precursor selection (MIPS) was set to "Peptide" and an intensity threshold was set to 5 × 10 3.
Database Search and Results Processing Data were searched by SEQUEST using Proteome Discoverer 2. 4 (Thermo Fisher Scientific Inc. ) against a Pseudotsuga menziesii predicted proteins database from PineRefSeq project (54,830 entries, https://treegenesdb. org/FTP/Genomes/Psme/v0. 5/ (accessed on 10 September 2023)). Spectra from peptides higher than 5000 Daltons (Da) or lower than 350 Da were rejected for quality reasons. The search parameters were as follows: mass accuracy of the monoisotopic peptide precursor and peptide fragments was set to 10 ppm and 0. 6 Da, respectively. Only b-and y-ions were considered for mass calculation. Oxidation of methionines (+16 Da) and protein N-terminal acetylation (+42Da) were considered variable modifications, while carbamidomethylation of cysteines (+57 Da) was considered a fixed modification.
Two missed trypsin cleavages were allowed. Peptide validation was performed using the Percolator algorithm  [42] , and only "high confidence" peptides were retained, corresponding to a 1% False Positive Rate at the peptide level. Peaks were detected, and Area Under the Curve was integrated using the Minora algorithm embedded in Proteome Discoverer. Proteins were quantified based on unique peptide intensities. Normalization was performed based on the total Douglas fir protein amount. The raw data are available in Table  S2 , containing all the biological replicates and MS information. A selection phase of the 11,563 proteins thus identified and quantified (Table  S2 ) was carried out according to the following two criteria: being of the Master protein type and being present in the three biological repeats of an organ.
Gene Ontology (GO) Analysis GO analysis was conducted using the Sma3s software  [43]  version 2. Sma3s is a tool for massive annotation of new protein sequences. It is composed of three integrative modules that annotate unknown sequences. These modules use a preliminary exhaustive BLAST search as their starting point and together generate annotations. We used the reference database UniRef90 based on the UniProt database (https://www. uniprot. org (accessed on 10 September 2023))  [44]  and the "goslim" parameter that gives slim GO terms, which represent more generic annotations. Relocation of some classes of GO terms was carried out with custom scripts to simplify interpretation (see Table  S4  for details). The presence of each protein in each organ was counted under the "Molecular Function" and "Biological Process" GO terms, allowing us to obtain the frequency of each GO term for each organ.
Discussion We compared the proteomes of 12 organs of Douglas fir, a tree of economic importance for timber production in North America and Europe. To our knowledge, this is the first study to analyze proteomic data with gene ontology annotation for multiple organs and characterization of the proteome profile of any tree species. Our analysis identified 3975 proteins across all Douglas fir organs, comparable to the 4,000 proteins identified in the rice proteome  [46]  and the 2830 proteins identified in Quercus ilex  [36]. Nevertheless, it is doubtful that this number captures all proteins, given that 36,000 coding genes are present in plants  [47] , whereas only 931 proteins are referenced in the Uniprot database for Douglas fir. This number of coding genes encompassing counterparts must be overestimated.
For this purpose, we used both random forest methods  43  and Lasso regularization for imputations. No correlation was found between income and risk-tolerance in our sample (Table  S. 9, column 1), although this had been previously found elsewhere  44. After imputing the missing values with either of these methods and controlling for income, the effect of the 7R allele on risk-tolerance remained significant (Table  S. 9, columns 3 and 4). A second potential confounding factor is the experience with commerce or financial activities. Prior work has suggested that market integration of a community can impact decision-making in behavioral economics paradigms  45. It is possible that the two populations differ in this aspect, but no information is available on this point in our dataset. Table  2.
Population genetics. DRD4 locus was tested for conformity with Hardy-Weinberg (HW) equilibrium using the exact probability test  47. Deviations from HW equilibrium were measured using the F IS estimator  48. DRD4 and microsatellite loci genotypic differentiation between populations was tested for by calculating an unbiased estimate of the P-value of a log-likelihood (G) based exact test  49  , a global test over loci was calculated using Fisher's method. Population differentiation was measured using the F ST estimator  48. Calculations were performed using Genepop R package (V. 1. 1. 7), based on  50.
Acknowledgements The authors gratefully acknowledge financial support from  GENES , the  Key initiatives MUSE Sea & Coast and Investissements d' Avenir  ( ANR-11-IDEX-0003/Labex Ecodec/ANR-11-LABX-0047 ). The authors also acknowledge the help in data collection from field research assistants. Microsatellite design and analysis were performed at the PGTB (doi: 10. 15454/1. 5572396583599417E12 )with the help of Zoe Delporte.
MATERIALS AND METHODS Participants We included patient data from the NeuroPharm clinical trial, which investigated a set of prognostic biomarkers for recovery following standard treatment for depression in a naturalistic setting (ClinicalTrials. gov, identifier: NCT02869035)  [31]. All participants gave written informed consent, and the study was approved by the Committees on Health Research Ethics in the Capital Region of Denmark (Protocol ID: H-15017713). Data were available from the Center for Integrated Molecular Brain Imaging (Cimbi) database  [32]. Upon inclusion, patients were required to meet the DSM-5 criteria for single or recurrent unipolar depression and to rate more than 17 points on the 17 item Hamilton Depression Rating Scale (HDRS-17)  [33]. Some highlighted exclusion criteria were a) use of pharmacological antidepressant treatment within two months prior to enrolment, b) more than one attempt with an antidepressant treatment in the current episode, c) use of medical treatment affecting the central nervous system (e.
Neuroimaging PET neuroimaging was obtained using a high-resolution research tomography Siemens PET-scanner (CTI/Siemens, Knoxville, TN, USA). Participants underwent a 6 min transmission scan for correction purposes. Following this, an intravenous bolus of approximately 600 MBq of the PET tracer ligand [ 11 C]SB207145 was administered over 20 s succeeded by a 120-min dynamic PET data acquisition  [31]. MRI (Magnetic Resonance Imaging) scans were acquired using a Siemens 3-Tesla Prisma scanner (Erlangen, Germany). The high-resolution structural T1 weighted images were segmented into grey matter, white matter and cerebrospinal fluid and aligned and co-registered to PET images using Statistical Parametric Mapping (SPM8). PET scans were motion corrected using Air 5. 2. 5  [39]. The pallidostriatum was selected as the region of interest since striatal density of 5-HT 4 Rs is high and the striatum is a key hub in the reward circuity  [21, 40].
The Pvelab software package was used to extract the region of interest  [41]. To estimate the non-displaceable binding potential (BP ND ) of the 5-HT 4 R, mean tissue time activity curves for grey matter volumes were extracted for kinetic modelling, where the simplified reference tissue model (SRTM) was used. Here, the cerebellum (excluding vermis) was used as a reference region since it has a negligible 5-HT 4 R specific binding  [21]. BP ND is proportional to the density of 5-HT 4 R or the number of receptors available for binding. In this paper, "binding" is used synonymously with BP ND.
ACKNOWLEDGEMENTS We gratefully acknowledge personnel from the NeuroPharm-NP1 study as well as collaboration general practitioners and the  Center for Referral and Diagnostics, Mental Health Services, Capital Region of Copenhagen  for helping with recruitment of patients. We wish to thank  Lone Freyr ,  Anders Jørgensen ,  Peter Jensen ,  Dorthe Givard ,  Birgit Tang ,  Arafat Nasser ,  Ida Marie Brandt ,  Elizabeth Landman ,  Asbjørn Poulsen ,  Anna Maria Florescu ,  Agnete Dyssegaard ,  Jacob Madsen ,  Sune Keller ,  Svitlana Olsen , and  Gerda Thomas  and the technical staff at the  Dept. of Nuclear Medicine, Rigshospitalet. Further, we would like to thank the following funds:  Innovation Fund Denmark ,  Novo Nordisk Foundation ,  Research Fund of the Mental Health Services-Capital Region of DK , The  Augustinus Foundation , H.
argos-system. org/manual/3-location/34_location_classes. htm). The transmitter was programmed to collect GPS locations every hour during daylight hours. The GPS data collection rates and transmission could be affected by low solar charging, resulting in data collection gaps. GPS data gaps were rare in the early years of the study; however, they became more frequent with time. Moreover, the volume of collected data also declined with transmitter age. Therefore, the data collected during the winter 2014-2015 were excluded from between-year range comparisons due to the small sample size. We tracked this eagle until the PTT device failed on 12 January 2016. We cleaned the GPS and Argos location datasets to remove outliers and implausible location estimates. The arrival times at and departure from the wintering grounds were objectively determined by applying piecewise regression to a plot of GPS-determined latitudes against the respective date  (Limiñana et al.
2007 ). Arrivals and departures were recorded as the dates and times where the regression "broke," indicating a change in the bird's flight behaviour between migration and wintering. Subsequently, we directly examined the Argos location data to improve the precision of arrival and departure estimates when there were gaps in the GPS data. The non-breeding season spans from approximately late September to early April. Therefore, the locations were categorised by the year in which the non-breeding season began. For instance, the locations collected in January 2006 were considered part of the 2005 data because the bird arrived at the non-breeding grounds in the autumn of 2005. Additionally, because the bird generally split its time between two sub-areas within its nonbreeding range (west and east), we conducted analyses to understand the characteristics of those sub-areas and elucidate any differences between them.
Specifically, we visually inspected the locations in ArcMap (ESRI 2019) and categorised them into either sub-area or as instances of travelling between them (although the bird typically spent only one day or less travelling between sub-areas; see Figs. 2  and 3 ). We estimated separately the non-breeding home ranges and core ranges of the bird for each sub-area using 95% and 50% utilisation distributions (UD) from the kernel density estimator (KDE;  Worton 1989 ). We estimated the ranges using all non-breeding data combined for each year and each sub-area by year. We used the data of at least 30 locations in the dataset (year, sub-area, or sub-area by year) to estimate the home range and the reference bandwidth (i. e. kernel smoothing parameter) to estimate the overall non-breeding home range.
To investigate whether the bird's movements were related to the onset of drier conditions during winter, we examined the monthly averages of enhanced vegetation indices (Africa Soil Information Service: http://www. africasoils. net/data/datasets?page=1) within annual KDEs. However, no obvious relationship could be discerned as our data were from a single individual. Therefore, we did not pursue further habitat analyses. All analyses were performed using R statistical software  (version 3. 4. 4; R Core Team, 2019). We estimated the bird's home ranges using the "adehabitat" package  (Calenge 2006 ) and calculated step lengths using the "adehabitatLT" package in R  (Calenge 2006). All statistical tests were performed using base R functions.
Discussion In this study, a breeding adult female greater spotted eagle was tracked during ten consecutive boreal winters. Although these data are from a single individual, they are unique as no other greater spotted eagle has been followed during so many consecutive winters using a GPS tag. Nor, as far as we know, has any other eagle species, apart from the lesser spotted eagle  (Meyburg 2021) , been tracked for such a long duration. The data presented in this study provide novel insights regarding the wintering ecology of the greater spotted eagle, providing a theoretical basis for further investigations with larger sample sizes. greater spotted eagles that use this route along the eastern Mediterranean coast may go unrecorded by observers, as they are mistaken for the much more common lesser spotted eagle  (Bijlsma 1983).
Results This section starts with an evaluation of the ECHAM6 model performance in reproducing reconstructed Antarctic accumulation and the SAM (Section 3. 1). This evaluation ensures we have confidence in the application of the simulation to investigate source regions (Section 3. 2) and source properties (Section 3. 3) of Antarctic precipitation. Moisture sources of HP and LP are investigated in Section 3. 4. Relationships between SAM and the evaporative sources of precipitation are explored in Section 3. 5. reconstruction. For the comparison, both datasets are regridded to 1°× 1°grids using a bilinear method. Accumulation in the simulation is defined as differences between precipitation and evaporation, while post-depositional effects are not considered. Black empty circles represent ten sites whose names are given in Fig.
The simulation of Antarctic precipitation and the SAM The overall spatial patterns of accumulation (precipitation minus evaporation) are captured in the ECHAM6 model results (Fig. 1 ). Though it is on the low side over the Antarctic plateau and the Antarctic Peninsula (AP) compared to the Medley and Thomas (2018) reconstruction, and is high across some coastal areas. This could be partly due to the relatively coarse (T63) 155 spatial resolution of our simulation, though it could also be related to uncertainties that afflict all reconstructions of Antarctic accumulation  (Monaghan et al. , 2006). Interannual variability, measured as the percentage of annual standard deviation to the annual mean, is in the same order for both datasets: ∼20% for the ECHAM6 simulation and ∼10% for the  Medley and Thomas (2018)  dataset.
The annual cycle of Antarctic precipitation in our simulation is similar to ERA5 (Fig. B3 ), with precipitation averaged over Antarctica exceeding 15 mm month -1 from March to August; a peak in May; and a minimum in December-January. Spatial patterns of HP contributions to total precipitation in our simulation are likewise very similar to those in  Turner et al. (2019) , with high values around major ice shelves (Fig. B4 ). We evaluated our modelled SAM index against the SAM index based on station observations between 1971-2000  (Marshall, 2003). Due to the SAM definition, both datasets have similar mean values and standard deviations. We therefore look at monthly zonal mean sea level pressure (MSLP) at 40°S and 65°S, and their differences, to check whether our simulation features a realistic SAM.
Acknowledgements. This publication was generated in the frame of  DEEPICE  project. The project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  955750. QG and MW acknowledge the technical support and computing resources provided by the  AWI Computer and Data Center  in setting up and running the ECHAM6 simulations. AM and LCS were supported by: the  European Union 's  Horizon 2020 research and innovation programme  under Grant agreement no. 820970  ( TiPES  project; this paper is TiPES contribution number 224) and LCS also from the  NERC National.
Code and data availability. The ERA5 reanalysis can be obtained from the Climate Data Store (https://cds. climate. copernicus. eu). The Antarctic accumulation reconstruction from Medley and Thomas (2018)  is available here: https://earth. gsfc. nasa. gov. The SAM index compiled by  Marshall (2003)  is available here: https://legacy. bas. ac. uk/met/gjma/sam. html. The Bedmap2 product created by Fretwell et al. (2013)  is available here: https://www. bas. ac. uk/project/bedmap-2. The division of Antarctica is available here: http://imbie. org/imbie-2016/ drainage-basins. The AMIP SST and SIC dataset is available here: https://esgf-node. llnl. gov/search/input4mips. The ECHAM6 simulation output and data analysis scripts are available from the authors upon reasonable request.
Marker and primer design Two sets of markers were developed in this study, one was designed using nuclear DNA regions to allow differentiation of C. fimbriata, C. eucalypticola and C. manginecans from one another, while the second targeted their mitochondrial genomes. For this purpose, the genome sequences for the respective species were obtained from the database (https://www. ncbi. nlm. nih. gov/genome/) of the National Centre for Biotechnology Information (NCBI) using the accession numbers SGIO00000000, APWK03000000 and LJOA00000000 for, respectively, C. manginecans CMW46461, C. fimbriata CMW14799 and C. eucalypticola CMW9998. The C. fimbriata genome had been previously annotated  [41]  and was used to design three sets of nuclear markers to use as a diagnostic for identifying the individual species.
The analytical run is isocratic with 15% of an eluent of sodium hydroxide (200 mM) and sodium acetate (4 mM) and 85 % water, at 1 mL min -1  (Samake et al. , 2018). The blank value was subtracted from the concentrations for both ions and sugars. Detection and quantification limits are presented in Table  S1  of the supplementary material. Further chemical species were determined with IC or HPLC-PAD. However, acetate, pyruvate, NO2 -and sorbitol were excluded from the database due to experimental uncertainty and possible artifacts. From 2015 onwards, aerosol concentrations of inositol, glycerol, erythritol, xylitol, threalose and rhamnose were also obtained, but results are not included here due to the incompleteness of the data series and because for sorbitol, xylitol and rhamnose, most of the values were below the quantification limit.
, 2021). Two different meteorological inputs were used to drive the backtrajectory analysis: 1) High resolution dataset from January 2012 to September 2016: based on WRF model nested in three domains (Figures S5, S6 and Table  S5 ) down to 1 km resolution, with ERA-interim data as boundary conditions. Nine starting points at 500 m a. g. l. were used in a 3x3 grid over Chacaltaya, with the station coordinates as the central point. This dataset has a topography better represented than the coarse resolution dataset described below, and represents well the transport over the complex terrain of the region (e. g. , uplift of Amazonian air masses through valleys). It was used in previous works  (Chauvigné et al. , 2019; Koenig et al. , 2021)  and we use it for both individual sample-to-sample and grouped analysis.
2) Coarse resolution dataset from January 2012 to April 2021: based on data from ERA-5, which has 30 km resolution and a more smoothed topography. This dataset cover the entire sampling period. The starting point of the backtrajectory was set at 1500 m a. g. l (of the ERA-5 defined topography) which corresponds to 528 hPa in the pixel containing the station This choice, however, produces a long-range biased backtrajectory due to the higher wind speed at upper levels. This dataset covers all samples, but sample to sample comparison with the nested-WRF backtrajectories shows only 43% of coincidences in transport patterns, although for long-range grouped back trajectories, there is overall agreement (Figure  S8 ). We thus use this dataset for the interpretation of the entire record, using grouped samples (section 3.
This hints to a predominant concentration of most species in PM2. 5 fraction, long-range transport of small particles, and in some cases co-emitting or co-located sources. For the three PM10 groups, there was variability from group to group and season to season (Table  S4 ), indicating highly variable environmental controls. Table  2. Bulk mass concentration (ng m -3 ) of EC, OC, and species measured in Mt. Chacaltaya aerosol. Mean (x ̅ ), median (x ), standard deviation (σ) and number of samples above the quantification limit (N) reported. Volume taken to standard cubic meters (1013hPa, 0 o C). Data segregated by seasons and also at ambient conditions can be found in Tables  S2  and S3.
Mass and mass closure Aerosol mass for PM10 is presented on Table  3 , and it ranges from 2. 4 to 22. 6 μg m -3 (at ambient conditions: 1. 1-12. 0 μg m -3 ). Even though this dataset is limited in temporal extent, it is consistent with measurements made on mountain sites, like Pûy de Everest in Nepal (10 μg m -3 ,  Decesari et al. , 2010) ; Montsec in Spain (2. 9-20. 6 μg m -3 ;  Ripoll et al. , 2015) , and Izaña at the Canary Islands (10. 9-50. 5 μg m -3 ;  García et al. , 2017a, b). The highest measured mass coincides with a dry month and the start of the eruptions of Sabancaya volcano in November 2016  (Moussallam et al. , 2017). For mass closure estimation, organic matter (OM) was calculated based on OC concentrations. As source contributions vary with the seasons, it is not possible to assign a single conversion factor between OC and OM.
This similarity points to a preferential long-range transport for those species that overwhelms local emissions. This is interesting because even if the Chacaltaya observatory is under stable atmospheric conditions between 02:00 and 09:00 BOT, low free tropospheric intrusions are difficult to observe with high-volume sampling because they are not long enough  (Scholz et. al 2023 reported free tropospheric episodes lasting less than 8 hours) to leave a marked fingerprint in our dataset. In this regard, high frequency (on-line) measurements of the aerosol chemical composition at the station prove their value as complementary to the long-term observations  (Bianchi et al. , 2021; Scholz et al. , 2023; Zha et al. , 2023).