dataesr/scientific-paragraphs-categorization huggingface dataset:
05, **P , 0. 01, ***P , 0. 001). botanical surveys provide the very substantial quantity of data required to enhance our understanding of species distribution  (Guitet et al. 2014). The complementarity of these datasets provides information on both the spatial and the ecological distribution of taxa and an assessment of g-diversity. The dataset is unbalanced with regard to the number of occurrences found on UM substrates (two-thirds of the dataset). This pattern results from a huge dataset collected over decades on UM substrates to assess and anticipate the environmental impacts of mining activities  (McCoy et al. 1999). In a cruel irony, our knowledge of tree species distribution on UM has increased in direct proportion to the decline in rainforest areas. In addition, the mismatch between the number of occurrences and available rainforest areas along the elevation and rainfall gradients reveals substantially unknown rainforest areas (notably at low elevation and with rainfall).
Geographical and environmental distributions of species Most of the tree species selected from rainforest plots occurred beyond the rainforest boundaries ( 60 %). This habitat transgression may be explained by a high tolerance of some species to open habitats such as 'maquis'. However, it could also be partially due to the scale of the digitalization of the vegetation map, likely to be inappropriate for exhaustively delineating rainforests which are highly fragmented from low to mid-elevation  (Morat et al. 1999). This fragmentation results from a dramatic decrease in rainforest areas, which now occur on only 50 % of the pre-human surfaces  (Jaffreet al. 1998). As a consequence, occurrences recorded in small relict patches were not included in the rainforest dataset but they did contribute in identifying the range of species tolerance with respect to substrate, elevation and rainfall.
Surprisingly, few species are substrate-specialists and our findings depart markedly from a balanced distribution in the three edaphic compartments (UM, non-UM and generalist). The adjective 'affiliated' should be used rather than 'specialist' since some edaphic transgressions could be explained by soil properties that could mitigate deficiencies and toxicity arising on UM substrates rather than by the substrate itself  (Read et al. 2006; Ibanez et al. 2014). This tolerance to such contrasting habitats leads to an imbalance in species frequency; few were very abundant (124 species that account for 50 % of our dataset) while the vast majority remained rare in our dataset. Hyperdominance or oligarchy has previously been reported in other locations  (Pitman et al. 2001 (Pitman et al. , 2013;; ter Steege et al.
Conclusions This study is the first to attempt to describe tree diversity in New Caledonian rainforests through extensive datasets collected over several decades. The collection effort is clearly critical if a realistic view of the observed g-diversity is to be obtained, while the complementarity of several data collection methods provides both a comprehensive and dense coverage of tree species distribution. The wide ranges of tree species distribution with respect to substrates, elevation and rainfall contrast (i) with their spatial aggregation, (ii) with the small extent of rainforest core and (iii) with the lowest g-diversity observed at high elevation or rainfall. Our results suggest a uniform g-diversity from low to mid-elevations (,800 m) and rainfalls (,2. 5 m year 21 ), mostly dependent of occurrences and availability of rainforest areas.
Sources of Funding Our work was partially funded by the DDEE-SIEC, 'La direction du developpement economique et de l'environnement-Service impact environmental et conservation' of the Northern Province of New Caledonia. Contributions by the Authors P. B. conceived the study, managed the database, compiled the datasets, proceeded with the GIS-analysis and led the writing of this article. T. I. conceived the study, led the statistical analyses and actively contributed to the writing. R. P. actively contributed to the data compilation, the statistical analyses and the writing. H. V. was involved in the collection of many tree species occurrences in all the datasets used in this article. V. H. was also involved in the collection of many tree species occurrences in all the datasets used in this article.
Acknowledgements The authors wish to thank all the contributors who have collected data over lengthy periods, without whose considerable efforts this study would not have been possible. This dedication includes botanists who have collected many samples as well as technicians and students who have participated in the overall effort of observation of New Caledonia trees. We also thank the two anonymous referees for their invaluable help in improving the manuscript. Finally, we express our sincere thanks to  J. J. Cassan  and the  SIEC ('Service Impact environmental et conservation') of the Northern Province of New Caledonia  for their unwavering commitment to our work.
Enzyme-Linked Immunosorbent Assay (ELISA) for Insulin Measurement Human insulin assay-Insulin concentrations in each specimen (culture medium and cell extracts) were measured using Ultra Sensitive Human Insulin ELISA Kit (Milllipore, EZHI-14K) according to the vendor's instructions and insulin concentrations were calculated using KC Junior ® microplate reader software (Bio-Tek Instruments, Inc. )  [31]. Briefly, insulin standards and appropriately diluted samples were added to an insulin antibodycoated 96-well microplate and, incubated for 1 hour at room temperature after adding the detection antibody. After washing five times, anti-human insulin enzyme conjugate was added to the well and incubated for 30 min at room temperature. After washing five times, an enzyme substrate solution was added and then incubated 10 to 15 min at RT in the dark.
Acknowledgments This publication was made possible, in part by  Roger Williams Hospital Research Funding  and  NIH   Research Grant award Number   1R01DK097380-01A1  from the  NIDDK  and  NIGMS  for  Dr. Luo. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the  NIH. Funding Resources: This publication was made possible, in part, by  Roger Williams Hospital Research Funding  for  Dr. Luo  and  NIH   Research Grant award Number   1R01DK097380-01A1  from the  NIDDK  and  NIGMS.
This neurophysiological mechanism generates and modulates the evaluation of the ethical and scientific background of all requests by the PharmaCog Steering Committee. Funding: The activity leading to the present study has received funding from the Seventh Framework Programme of European Community (FP7/2007-2013) for the Innovative Medicine Initiative under Grant Agreement No. 115009 (www. pharmacog. org) (title of the project: "Prediction of cognitive properties of new drug candidates for neurodegenerative diseases in early clinical development"; acronym: PharmaCog). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: None of the authors has conflicts of interest or financial disclosures in the development of the present precompetitive basic neurophysiological research, which was based on EEG recordings in freely behaving mouse lemurs receiving no (pharmacological or nonpharmacological) intervention.
This is true also for the following co-Authors: Dr. Yves Lamberty, Jill Richardson and Wilhelmus Drinkenburg, who are affiliated to UCB Pharma, Glaxo SmithKline and Janssen Pharmaceutica, respectively. Their position to the mentioned companies does not alter the authors' adherence to the PLOS ONE policies on sharing data and materials. so-called cortical EEG rhythms, and is considered as an important neural substrate of vigilance, spatial orientation, consciousness, and planning of the behavior  [23, 24, 25]. Are cortical EEG rhythms a useful translational model of the mentioned neurophysiological mechanisms in human and sub-human primates? In humans, resting state eyes-closed EEG oscillations at 8-12 Hz are dominant in posterior sensory (i. e. 8-10 Hz, alpha rhythms) and frontal motor areas (i.
Paul, MN, USA). To do so, a small transmitter with a volume of 1. 9 cc and weighing 3. 9 g (model PhysioTel F20-EET, DataScience Co. Ltd, Minnesota, USA) was implanted into the peritoneal cavity of the lemurs under a ketamine anaesthesia (Imalgene, 100 mg/kg intraperitoneal) empowered by administration of Metacam (meloxicam, 0. 2 mg/kg). Silicone elastomer insulated stainless-steel electrode wires (Ø 0. 3 mm) were then subcutaneously led, passing between the scapula, from the abdomen to the skull, and there sealed using dental cement. The EEG implanted electrodes were located in premotor cortex (Brodmann area 6, BA 6) in the anterior position of 7. 00 mm according to the transversal section, which corresponded to the plate 34 of the "Stereotaxic atlas of the brain grey mouse lemur"  [33]. Electrode referencing was done by bipolar subtraction using Data Science International implantable bipolar electrodes.
Physiological data recording and analysis During the present experiments, the lemurs were housed in their individual cages provided with branches and a wooden nest in which temperature and humidity were controlled and maintained constant (ambient temperature = 24-26°C, relative humidity = 55%). Food and water were available ad libitum (availability of food and water was kept constant according to the general conditions of captivity in the colony;  [2]. Normal circadian rhythms were artificially set as follows: 14 h lights on and 10 h lights off. Electrophysiological and behavioral data were acquired by telemetry during 5 consecutive days (Dataquest Lab Pro v. 3. 0; Data Science International, St. Paul, MN, USA). The data for the present study were taken from the first experimental day. EEG and EMG signals were acquired and exported by Neuroscore software v.
2. 1 (Data Sciences International, St. Paul, MN, USA). As mentioned above, the EEG recording was performed from one EEG bipolar channel located in the premotor frontal cortex (500 Hz sampling rate; 1-100 Hz bandpass). The EMG signals were collected from one EMG channel using bipolar electrodes sutured to the neck muscles with non-absorbable polyamide suture (500 Hz sampling rate; 1-100 Hz bandpass). The temperature was also recorded (250 Hz sampling rate, range 34-41°C) using a thermistor integral to the electronics module inside the implanted transmitter. Locomotor activity was indexed by the variation of electromagnetic signal strength, recorded on a dedicated channel of the telemetry system (1 Hz sampling rate). Electrophysiological recordings were performed for 1 wake-sleep cycle. As mentioned above, this cycle was divided into 14 h of daytime (It was the rest phase as lemurs are nocturnal animals) and 10 h of nighttime (The stage of most animal activities).
Data analysis focused on a recording segment of 1 h during nighttime, namely the light-off period. It also focused on a recording segment of 1 h during the daytime, namely the light-on period. These 1-h recording segments were taken starting from 1 h after the light-off/on instant. This instant switched from daytime to nighttime and vice versa. The recording segments started 1 h after this instant to minimize the effects of stress due to the changes of environmental lightening. In these recording segments, behavioral and EEG data were analyzed to identify the first 2-5 min of artifactfree EEG data in the passive behavioral state and the first 2-5 min of artifact-free EEG data in the active behavioral state. The general procedure of this preliminary analysis is described in the following paragraphs.
Continuous EEG recordings were segmented into contiguous periods of 10 s for the analysis of animal behavior. Any period of 10 s was rated based on the prevalent behavior of the animal ("behavioral periods"). The classification of the behavioral periods was performed in two steps. In the first step, these periods were automatically classified by expert experimenters of Brunoy group (Dr. Anisur Rahman, Ph. D. , or Dr. Fabien Pifferi, Ph. D. ) using Neuroscore software v. 2. 1 (Data Sciences International, St. Paul, MN, USA). This software classified the periods into mutually exclusive behavioral states such as active wake (i. e. active state) and passive wake (i. e. passive state) of interest for the present study and other states. The classification of Neuroscore software was based on the collected EEG, EMG, temperature, and locomotion variables (Brunoy group used this procedure successfully in previous studies  [2, 34] ).
The results showed a higher amount of artifact-free EEG epochs in the active state compared to the passive state in both daytimes (t = 2. 99, p<0. 01) and nighttime (t = 3. 13, p<0. 05). EEG power spectrum analysis of the artifact-free EEG epochs was performed by a standard (Matlab; MathWorks, Natick, MA, USA) FFT algorithm using Welch technique and Hanning windowing function (no phase shift) with 1 Hz frequency resolution. A normalization of the EEG data was obtained by dividing the EEG power density at each frequency bin with respect to the EEG power density averaged across all frequencies from 0 to 100 Hz (excluding power density peak due to the utility frequency line frequency at 50 Hz). After such normalization, the solutions lost the original physical dimension. They were represented by an arbitrary unit scale in which "1" means absolute power density equal to the average of absolute EEG power density from 0 to 100 Hz while "2" means the double of that mean etc.
At this early stage of research, narrow EEG frequency bands with shared frequency bins were used to avoid any a priori hypothesis about EEG banding. These bands were the following: 1-2 Hz, 2-4 Hz, 4-6 Hz, 6-8 Hz, 8-10 Hz, 10-12 Hz, 12-20 Hz, and 20-30 Hz. Sharing of a frequency bin by two contiguous frequency bands has the theoretical advantage that one cannot assume that these frequency bands reflect distinct underlying neurophysiological mechanisms  [26, 35, 36]. EMG data of artifact-free EEG epochs were rectified using a proprietary home-made routine written with MatLab programming language (MathWorks, Natick, MA, USA)  [37]. The mean rectified EMG was used as an index of muscular activity for the correlation with spectral EEG markers.
Analysis of lemurs' behavioral states An important step of the data analysis was the classification of animal behavioral state during the EEG periods of interest (see previous section) in terms of active state vs. awake passive state. To this purpose, we used instrumental markers of movement and EMG activity. Neuro-Score software (Data Science International, St. Paul, MN, USA) was used to classify periods lasting 10 s each into the following behavioral classes: 1. ACTIVE STATE. Overt exploratory movements. 2. AWAKE PASSIVE STATE. Immobility or small movements but absolutely no sleep. To be sure that the animal was not sleeping, experimenter classified as "awake passive state" a 10 s period in which the animal was mostly in a passive behavioral state and he/she was reasonably sure that the animal was not sleeping.
Acknowledgments The activity leading to the present study has received funding from the  Seventh Framework Programme of European Community  ( FP7/2007-2013 ) for the  Innovative Medicine Initiative  under Grant Agreement No. 115009  (title of the project: " Prediction of cognitive properties of new drug candidates for neurodegenerative diseases in early clinical development "; acronym: PharmaCog). All electroencephalographic (EEG) data of this study were collected in the framework of a pre-competitive research in the Work Package 2 ( WP2 ) of the  PharmaCog  project. Furthermore, the results are presented on behalf of the  PharmaCog Consortium. Therefore , the  PharmaCog Consortium  is the legal owner of these data and outcomes. In particular, this Consortium is formed by 31 Official Partners including 15 academic institutions, 12 global pharmaceutical companies, 5 small and medium-sized enterprises (SMEs), Patient representation from  Alzheimer Europe , and  European Medicines Agency  (i.
Two of the longest running great tit studies have been ongoing since the 1950s and are located in the Hoge Veluwe National Park  (Netherlands, NL; van Balen 1973)  and Wytham Woods (United Kingdom, UK; see, for example,  McCleery et al. 2004  and references therein). In the late 1980s, a subpopulation study was established adjoining Hoge Veluwe at Westerheide, near Arnhem, NL  (Dingemanse et al. 2002). In both NL and UK populations, trait data and social pedigree relationships have been recorded for most individuals. Maternal, morphological and personality traits have been the focus of pedigreebased quantitative genetic studies, and in both populations these traits have moderate heritabilities (see, for example,  Garnett 1981; van Noordwijk et al. 1988; Dingemanse et al. 2002; McCleery et al.
Genetic analyses Pedigree-and marker-based heritabilities. Trait heritabilities were determined by two approaches, first (i) using pedigree information from each population and second (ii) using the genomic marker relatedness between individuals. For each trait in each population, heritabilities were estimated using a linear mixed model (the 'animal model',  Henderson 1984; Kruuk 2004) , where the relatedness between individuals was estimated from the pedigree (A matrix; approach i) or where the relatedness between individuals was estimated from the marker data (G matrix; approach ii). For (i), for consistency with the quantitative trait locus analysis (see below), the pedigree was restricted to genotyped related individuals. By including all firstto fourth-degree family links, 666 NL individuals and 1733 UK individuals  (Santure et al.
Finally, linear regressions were fitted between the variance explained by each chromosome and the number of genes they contained, to test whether traits were influenced by a large number of loci distributed throughout the genome. Quantitative trait locus analysis. A two-step variance components analysis  (George et al. 2000; Slate et al. 2002)  was performed to map loci contributing to variance in the quantitative traits. Using the 1524 markers in the framework without LD map, the identical-by-descent (IBD) coefficients between all pairs of individuals in the two QTL pedigrees (see above) were derived at 5-cM intervals across the genome using the software LOKI v2. 4. 5  (Heath 1997; Heath et al. 1997) , with 100 000 iterations for each position. The statistical significance of the QTL effects was then tested using a likelihood ratio test comparing the log likelihood of a polygenic model with the log likelihood of a polygenic plus QTL model, fitting full mixed models to account for the effects of important random and fixed effects on the raw phenotypes (see Appendix S1, Supporting information).
QTL scans were performed in the two populations for each trait. To account for the multiple tests performed genome-wide, the approach of  Lander & Kruglyak (1995)  was used to adjust the significance thresholds based on the length of the linkage map (19. 16 morgans) and number of mapped chromosomes (31). For these data sets, a logarithm of odds (LOD) score (where LOD = LRT/2ln(10)) of 1. 620, corresponding to a nominal P value (P) of 0. 003, is expected to occur once by chance in every genome scan and is termed 'genome-wide suggestive linkage', while a LOD score of 3. 062 (P = 9 9 10 À5 ) is expected with probability 0. 05 every time a genome scan is performed and is termed 'genome-wide significant linkage'  (Lander & Kruglyak 1995; Nyholt 2000). Nominal significance (P < 0. 05) requires a LOD score of ≥0.
2007b) , adjusting for population stratification due to the presence of related individuals by fitting the internally calculated genomewide kinship matrix as a random effect  (Amin et al. 2007). The significance of association was assessed using GENABEL P values, adjusted for multiple testing using a Bonferroni correction based on the effective number of independent tests. Taking into account LD between markers using the package Keffective (Moskvina & Schmidt 2008), the panel of chromosome-assigned SNPs was determined to yield 5573 effective tests, giving a genome-wide significant threshold of P = 9. 0 9 10 À6. The between-population correlation of absolute estimated SNP effect sizes from the GWAS analyses was calculated in R (R Development Core Team 2012) for each trait. A significant correlation in SNP effect sizes between a NL and UK trait would indicate that many shared loci contribute to variation in the two populations.
The significance of each correlation was tested by permuting effect sizes across SNPs 10 000 times. Finally, for each trait, standardized EPVs and genotypes from each population were merged to test for association across both data sets. SNP association was tested by running a 'polygenic' mixed model in GENABEL, where the genome-wide kinship matrix is fitted as a random effect, population fitted as a fixed effect and the first 10 principle components of the kinship matrix fitted as fixed effects to account for population-level SNP relatedness differences. The significance of association was assessed from 'mmscore' P values after Bonferroni correction. Concordance between GWAS, QTL mapping and chromosome partitioning. The QTL mapping and GWAS approaches described above exploit different marker and phenotype information, with different numbers of individuals and markers used.
At the suggestive threshold, 21% and 26% of QTL explaining all additive genetic variance were detected in the fledgling weight (of offspring) and exploratory behaviour simulations. There is unlikely to be power to detect QTL of more reasonable effect sizes in either the NL or UK data sets; QTL simulated to explain 5% of phenotypic variation were never detected in the NL simulations and were rarely detected in the UK simulations. As expected, the estimated effect sizes for QTL detected in the simulations were highly inflated  (Beavis 1994; Slate 2013 ). Power to detect QTL was dependent on QTL effect size and the number of individuals (P < 0. 001 in both cases; tested using an analysis of variance with the lm function in R (R Development Core Team 2012); other effects including trait heritability, population, the number of trait records and whether traits were maternal or individual were not significant.
Acknowledgements We thank the many people who have collected data from  Hoge Veluwe, Westerheide  and  Wytham Woods  great tit populations over the past decades and the many funding sources, too numerous to list here, that supported the collection of the data. We extend a special thanks to  Conor Scott  for his diligent work resolving the NL pedigree during an  On CampUS Placement  from the  University of Sheffield , awarded to AWS. This work was funded by the  European Research Council (ERC) , project  AvianEGG , number  202487 , awarded to JS, a  Natural Environment Research Council (NERC) UK  fellowship to MRR number  NE/G013535/1 , by  Natural Sciences and Engineering Council of Canada (NSERC)  and  Marie Curie  fellowships to JP and by the  Netherlands Genomics Initiative (NGI-HORIZON)  to MAMG, MEV  & KvO  and with an  NWO-VICI  grant to MEV.
The TSPO versus CBR (central benzodiazepine receptor) selectivity of compounds 11, 13-19 and 27-33, was also assessed, using membrane homogenates from rat cerebral cortex and screening against [ 3 H]flunitrazepam, and compared to SSR180575  (1). All compounds previously reported for having nanomolar to subnanomolar affinities to the TSPO, showed no inhibition of [ 3 H]flunitrazepam binding to the CBR at 1 µM, attesting the excellent selectivity over the CBR for these compounds. Evaluation of the lipophilic properties of all target compounds was investigated by determination of their logD 7. 4 (noctanol/buffer pH 7. 4 partition coefficients) values, using a validated and standardized HPLC method. For compounds targeting the CNS, these data give a first indication of potential brain penetration and possible in vivo non-specific binding.