dataesr/scientific-paragraphs-categorization huggingface dataset:
Principal component analysis was performed using the SNPRelate R package  (Zheng et al. , 2012)  using only individuals of European ancestry. Rare-Variant association testing for sequencing data was carried out to detect rare mtDNA variants involved in human longevity. For this purpose, the SKAT algorithm was used: a supervised and flexible regression method to test for association between rare variants in a gene or genetic region with a continuous/dichotomous trait. This algorithm was implemented in the SKAT package of R. Compared with other gene-based approach, SKAT is able to deal with variants that have different direction and magnitude of effects, allows for covariate adjustment, and also avoids arbitrary selection of threshold in burden test  (Wu et al. , 2011). Genebased analysis was adjusted for the first four PCs derived from the principal component analysis previously described.
To compute the SKAT P-values, we used a resampling procedure based on 10 000 bootstraps. The function used to measures the genetic similarity between two random subjects i and i' in the analyzed region via the P selected markers was the weighted linear kernel function. As suggested by  Wu et al. (2011) , the diagonal weight matrix was chosen using the beta distribution density function with parameters a1 = 1 and a2 = 25 evaluated at the sample minor-allele frequency or MAF (default option of SKAT package of R).
Furthermore, it is quite troublesome to assess the absence of species from an area while species occurrence data are abundant in museum databases and the literature. As a consequence, most SDMs rely on presence-only techniques  [1]. A crucial assumption in using SDMs to forecast the spread of introduced species or distribution changes in response to environmental change is that the model is transferable to the new conditions  [6]. In the case of introduced species, models trained primarily on distribution data from the species' native range need to be transferred to the region where it has been introduced. This often implies projecting the species response to climatic conditions that are not present in the native (training) range, which is an innately difficult task. For such situations, it is valuable to visualize those areas where extrapolation beyond observed conditions was required and consider those distribution predictions as uncertain  [7, 8].
g. references 46,47 and unpublished data GSB). As C. cylindracea is only found near shipping ports and had not been reported from this area prior to 2003  [48] , it is most likely that this species is a recent introduction. The rapid spread of this species through the European invaded range makes it a suitable case study for the question at hand. A total of 191 distribution records were assembled from the native range in and around Australia (65 records), the invaded range in Europe (111) and the recently invaded areas in southern Australia  (15). The data sources for these records are: Australia Virtual Herbarium (http://chah. gov. au/avh/), new collections from Victoria by GSB deposited in the AD herbarium, the data gathered by FM for the ERC FP5 ALIENS project, and the literature  [31, 41, 44, [48] [49] [50] [51] [52] [53] [54] [55] [56] [57] [58] [59] [60].
The absence of the species in various DNA bar coding surveys of Caulerpa from some other parts of the Indo-Pacific (Philippines, Japan, Tanzania, Red Sea) suggests that the native range may be limited to Australia and some closeby locations (unpublished data: Stefano Draisma, Thomas Sauvage, Heroen Verbruggen). We used the Bio-ORACLE dataset  [61]  as a source of marine environmental grids (90 ° N-90ºS, real values). To make the distribution records compatible with the grids, occurrence coordinates situated on land according to the Bio-ORACLE grids were moved to the closest cell in the ocean. When multiple records were situated in the same Bio-ORACLE grid cell, a single record was retained and as a result, the dataset reduced to 95 distribution records.
Occurrence Thinning Geographical biases in the occurrence records were dampened by thinning the distribution points with OccurrenceThinner 1. 03  [62]. We developed this program to filter occurrence records using a probability-based procedure. The probability that any specific occurrence record is removed is proportional to the density of occurrence records in the area as defined by a kernel density grid. The two-dimensional binned kernel density grid used in this procedure was computed from the occurrence records with the bkde2D function in the R package KernSmooth v. 2. 23-7  [63, 64] , with a bandwidth of 3. 0. The thinning procedure with thresholds t 1 =0. 5 and t 2 =1. 0 was repeated 10 times, resulting in 10 occurrencethinned datasets. These datasets had on average 25 records from the native range, 46 from the European invaded range, and three from the southern Australian invasive populations.
Predictor Sets The predictor variables were chosen in two steps. The first step consisted of a priori selection of a set of 8 predictors. This selection was based on knowledge of the physiological determinants of seaweed distributions  [65] , and takes the structure of the Bio-ORACLE dataset into account by not using multiple closely correlated predictors. The eight resulting predictors were mean sea surface temperature (SSTmean), the range in sea surface temperature (SSTrange) as a measure of seasonality, mean photosynthetically active radiation (PARmean), salinity, pH, mean diffuse attenuation (DAmean) as a measure of water transparency, dissolved oxygen (dissox) and the phosphate concentration. Nitrate concentration was not included because it is correlated with the phosphate concentration  [61].
It worked from the thinned set of occurrences and restricted the model complexity to linear and quadratic features. Each of the five runs listed above was repeated ten times (i. e. , on each of the ten replicate sets of thinned occurrences). The training and test data were randomly drawn from the occurrence records and do not represent a subdivision into the native vs. invaded ranges. As a consequence, the model performance used to evaluate predictor combinations does not represent transferability between regions. From the MMS results, a consensus was derived as to which variables are most important across the different runs. We retained only those variables that were present in more than 60% of the top-scoring models for at least two out of three regions (native, Europe, combined, i. e. conditions 1, 2 and 3 described above).
Niche Model Inference Niche models were inferred with Maxent 3. 3. 3f [24, 68, 69]. The analyses were automated via a Perl script and carried out on a multicore linux server. All analyses were run with 10,000 random background points as specified above. The training, test and background points, were provided as SWD files and the resulting models were projected onto the Bio-ORACLE grid  [61]. Maxent's jackknife function was activated and samples were not added to the background to avoid complicating model comparisons. The models resulting from the ten replicate occurrence-thinned training sets were averaged for visualization but other interpretations were based on the individual models.
Downstream Analyses Models were compared to identify which choices lead to better-performing models. In order to evaluate the transferability of models, we compared models built on the native and invaded ranges in a pairwise fashion, using the Schoener's D niche similarity measure  [70]  and reciprocal test AUC (i. e. native training samples with test samples in invaded range and vice versa). The overall predictive power of models was compared with the test AUC, taking care to only compare models built with identical geographic background datasets.
However, we do acknowledge that such predictor selection methods are no substitute for physiological knowledge of the organism  [78] , and here they were used to further refine a set of predictors that was already reduced from the full Bio-ORACLE dataset based on what we know are important factors determining algal growth. Previous studies have also shown that reducing the complexity of models to fit smoother responses yields the best correspondence to physiological knowledge and as such, the models achieve better overall performance and have higher transferability  [7, 10, 11, 79]. For these reasons, the use of simple environmental response surfaces to avoid overfitting has been recommended for SDMs of invasive species  [4, 7, 11]. Generally, the complexity of maximum entropy models is adjusted by using L 1 regularization  [68] , which varies along a continuous scale and has been used in other studies aimed at improving the performance of Maxent SDMs  [10].
This is especially true if the number of occurrence points used to build models is large, because this increases the potential for overfitting. Since the identification of suitable predictors and an appropriate level of model complexity are related to one another, it may be advisable to integrate these two into a single procedure as commonly done in classical model selection procedures  [75]. The use of thinned occurrences generally resulted in SDMs with better transferability, but the effect was not significant in a Wilcoxon signed-rank test and small compared to that obtained from predictor selection. Nonetheless, we anticipate that this approach may be useful in situations where the geographical bias is stronger than in our dataset and/or in situations with stronger spatial autocorrelation in the environmental grids.
Similarly, variables that are directly relevant to the distribution may differ systematically between regions, decreasing the transferability of the SDMs built from them  [74]. Even though it can be expected that the distance-based thinning will improve most models, this may not always be the case. In fact, this procedure may discard useful data when regions of dense sampling coincide with steep ecological gradients over short geographic distances. Also, if sampling reflects population densities, geographic autocorrelation of records can add a potentially desirable quantitative aspect to the model. This will, of course depend on the specific goal and the dataset being studied. Finally, our evaluation of methods is based on a single case study, and there are no guarantees that our results will extrapolate to other introduced species.
Acknowledgements We thank  Thomas Sauvage  and  Claude Payri  for sharing their work on New Caledonian Caulerpa and providing New Caledonian occurrence records of C. cylindracea. We thank  Stefano Draisma  and  Willem Prud'Homme van Reine  for sharing information about their DNA bar codes survey of Caulerpa collections and  John Huisman  for providing many of the occurrence records of C. cylindracea from  NW Australia. Niels Raes  and  Dirk Maes  provided valuable feedback on a previous version of this manuscript.
Funding was provided by the  Australian Research Council  ( FT110100585  to HV,  LP0882109  and  LP0991083  to CFDG),  Research Foundation-Flanders , the  AXA Research Fund  (to FM), the  Adelaide & Mt Lofty Ranges Natural Resource Management Board , the  Australian Census of Coral Reef Life  and the  Australia Biological Research Study  ( ABRS 209-62  to CFDG). LT is a doctoral fellow of the  Agency for Innovation by Science and Technology (IWT). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data The AeroCom initiative asks models to simulate the direct aerosol radiative effect under as similar conditions as possible. All models run single year simulations using the same base years for aerosol emissions (2000 and 1850), with unchanged meteorology to exclude indirect effects. Both circulation models and transport models have participated. Two sets of model intercomparisons have been performed, here labeled AeroCom Phase 1  (Dentener et al. , 2006; Textor et al. , 2006; Schulz et al. , 2006)  and AeroCom Phase 2  (Myhre et al. , 2013). See the references or the main AeroCom website (aerocom. met. no) for details. For the present study, 4-D (spatial and temporal) BC concentration fields from 12 global aerosol models participating in AeroCom Phase 2 are used. For six of the models, concentration fields of BC from fossil and biofuel burning only (BCFF) are also used.
This gives confidence that the variability due to vertical profiles can indeed be regarded as independent of that due to the present day emissions dataset. Results for the P1 and P2 submissions of the IMPACT model, where no major aerosol microphysical changes have been performed, are quite similar. For OsloCTM2, however, where both an ageing scheme and modifications to the washout of BC were added, the forcing efficiency changes between P1 and P2 by as much as half of the full range observed. Hence, the transport scheme and model treatment of BC are crucial factors in determining the modeled value of forcing efficiency, and both of these factors are closely linked to the vertical distribution.
Acknowledgements. S. Ghan ,  X. Liu  and  R. Easter  were funded by the  US Department of Energy, Office of Science, Scientific Discovery through Advanced Computing (SciDAC) Program  and by the  Office of Science Earth System Modeling Program. Computing resources were provided by the  Climate Simulation Laboratory at NCAR's Computational and Information Systems Laboratory (CISL) , sponsored by the  National Science Foundation  and other agencies. The  Pacific Northwest National Laboratory  is operated for DOE by  Battelle Memorial Institute  under contract  DE-AC06-76RLO 1830. Simulations of the ECHAM5-HAM,  INCA ,  CAM4-Oslo  and  HadGEM2  models have been supported with funds from the  FP6  project  EUCAARI  (Contract  34684 ). A. Kirkevag ,  T. Iversen  and  Ø. Seland  ( CAM4-Oslo ) were supported by the  Research Council of Norway  through the  EarthClim  ( 207711/E10 ) and NOTUR/NorStore projects, by the  Norwegian Space Centre through PM-VRAE , and through the  EU  projects PEGASOS and ACCESS.
G. Myhre and B. Samset were funded by the  Research Council of Norway  through the EarthClim and SLAC projects. K. Tsigaridis  and  S. Bauer  were supported by  NASA-MAP  ( NASA award   NNX09AK32G ). Resources supporting this work were provided by the  NASA   High-End Computing (HEC) Program  through the  NASA Center for Climate Simulation (NCCS)  at  Goddard Space Flight Center. N. Bellouin  was supported by the  Joint DECC/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ). We also thank the two anonymous reviewers for their efforts. Edited by:  E. Highwood.
The reference simulation will hereafter be referred to as REF05 (the full name is PERIANT05-GCDMP5 in the Drakkar model archive). REF05 is forced at the sea surface by Drakkar Forcing Set 3 (DFS3)  (Brodeau et al. 2010)  [a composite of 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and ECMWF products] and at 308S by the output of the simulation ORCA05-G70. 112 (Drakkar-Group 2007;  Barnier et al. 2006). Simulations are initialized from rest and with the National Oceanographic Data Center World Ocean Atlas  (Levitus and Boyer 1994)  and run from 1980 to 2004. To avoid spurious trends in ACC transport due to the misrepresentation of bottom water in this specific configuration [a ubiquitous problem in eddy-permitting z-coordinate models as discussed by  Lee et al.
36 Sv while the along-f/H stress drives SD(a 2 t f/H ) 5 1. 8 Sv of variability. The ACC transport in DFS025-L46 is less well correlated with that predicted using (8) (r 5 0. 78) than it is with the along-f/H stress alone (r 5 0. 81). A linear fit of (8) to the interannual data from CORE025-1 yields a 1 5 70 Sv (N m 22 ) 21 and a 2 5 539 Sv (N m 22 ) 21 while for DFS025-L46, a 1 5 25 Sv (N m 22 ) 21 and a 2 5 894 Sv (N m 22 ) 21. We have found that the ACC transport is significantly correlated with both along-ACC and along-f/H stress in a range of forced simulations. Given the perturbation experiments carried out and the wind-forced variability from both DFS and CORE, the along-f/H stress is likely to drive between 3. 5 and 5 times as much variability in the ACC as the stress along the ACC [i.
Acknowledgments. We thank  Jean-Marc Molines  for his modeling support and  Andrew Meijers ,  Daffyd Gwyn Evans ,  Andrew McC Hogg ,  Andrew F. Thompson  and  Bernard Barnier  for comments on versions of this manuscript. This work was supported by the  National Environment Research Council  through a  NERC  Fellowship grant and by the  Agence Nationale de la Recherche FIG. 7. ACC transport anomaly predicted using (8) with coefficients derived from perturbations experiments  NY-WIND025  and  NY-ANT025  (black) and the model value (gray): (a) CORE2-forced simulation CORE025-1 and (b) DFS3-forced simulations  DFS025-L46. The contribution to the predictions from along ACC stress (t ACC , red) and along-f/H stress (t f/H , blue) are also shown along with 6 one std dev of each term (vertical bars at right).
Experimental Crystal data  Hydrogen-bond geometry (A ˚, ). D-HÁ Á ÁA D -H HÁ Á ÁA D Á Á ÁA D -HÁ Á ÁA C12-H12Á Á ÁO1 i 0. 93 2. 36 3. 258 (3) 161 C6-H6Á Á ÁN3 ii 0. 93 2. 62 3. 507 (3) 161 Symmetry codes: (i) Àx þ 2; Ày þ 1; Àz; (ii) x À 1 2 ; Ày þ 3 2 ; Àz. Data collection: APEX2  (Bruker, 2009) ; cell refinement: SAINT  (Bruker, 2009) ; data reduction: SAINT; program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012) ; software used to prepare material for publication: PLATON  (Spek, 2009)  and publCIF  (Westrip, 2010).
Computing details Data collection: APEX2  (Bruker, 2009) ; cell refinement: SAINT  (Bruker, 2009) ; data reduction: SAINT  (Bruker, 2009) ; program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012) ; software used to prepare material for publication: PLATON  (Spek, 2009)  and publCIF  (Westrip, 2010).
Population and outcome data The malaria research programme conducted in Dielmo village in Senegal has been ongoing since 1990 as described elsewhere. 27 In brief, between 1990 and 2008, a longitudinal study involving the inhabitants of the village of Dielmo, Senegal, was carried out to identify all episodes of fever. The study design included daily medical surveillance with systematic blood testing of individuals with fever and examination of 200 oil-immersion fields on a thick blood film for malaria parasites (about 0. 5 μL of blood). Each individual was given a unique identification code and details of family ties, occupation and precise place of residence were recorded on detailed maps of each household with the location of each bedroom. All households were visited daily, absenteeism recorded and the presence of fever or other symptoms assessed.
Statistical analysis Statistical analyses were performed using R V. 2. 12. 0 (The R Foundation for Statistical Computing, Vienna, Austria). To address the effect of allergic status on the risk of clinical P falciparum episodes, we performed Generalised Linear Mixed Models (GLMM) extended to pedigree data using the pedigreemm package for R to account for the non-independence of individuals because of family relationships, shared house and for repeated measures from the same individual (see online supplementary technical appendix). Correlated individual effects due to familial relationships were taken into account by using the pedigree-based genetic relatedness matrix that contains the genetic covariance among all pairs of individuals in the study cohort and is calculated using the pedigree information.
Covariables included sickle cell trait  33  gender, number of days present on site during the trimester, trimestrial incidence of P falciparum and age. Age was initially analysed as a continuous covariate. To assess the age-specific effect of allergy, age was categorised into two levels (<3. 5 years of age and ≥3. 5 years of age, based on the age of peak clinical incidence) and allergy class was nested within age class. The age threshold was varied from 1. 5 to 5. 5 years of age and the data reanalysed to assess at which age there was the strongest effect. The association of allergy classes with IgE levels was analysed by Box-Cox transforming the data and fitting a GLMM with a normal distribution.
22 The only other epidemiological study that has previously examined the link between malaria and atopy 38 also interpreted the result from the perspective of the impact of malaria on atopy. They examined the reinfection rate with P falciparum over a 5-year period in 91 children who were subsequently classified as atopic or not using skin prick tests (SPT) with house dust mite antigen. Their conclusion was that, as with measles  13  and tuberculosis  15  malaria infection reduces atopy. However, the study lacked previous infection data since birth of the participating individuals and focused on atopy as determined by SPT against a single allergen. The case-control study of atopic dermatitis risk factors cited above found no overall association between allergen skin sensitisation and atopic dermatitis.
The central nervous system is especially vulnerable to perinatal injuries, including hypoxia, systemic inflammation and traumatic brain injury. Maternal exposure to neurotoxic compounds and drugs can have dramatic consequences for neurogenesis, leading to irreparable long-term neurodevelopmental disorders  [28]. The purpose of the present study was to expand our knowledge of neuroprotective mechanisms at the blood-CSF barrier in the developing brain and our understanding of the factors setting the cerebral bioavailability of drugs in the context of pediatric treatments. We have established the developmental expression profiles of genes involved in drug transport and detoxification in the choroidal tissue. We have combined data obtained from Illumina RNA-Seq (high throughput RNA sequencing) and Affymetrix microarray technologies developed in two independent laboratories and analyzed by quantitative realtime PCR (qRT-PCR) additional transcripts not represented on the array.
Briefly, 10 μg of labelled cRNA was fragmented and denaturated in hybridization buffer, then hybridized on the chip for 6 h at 45°C with constant mixing by rotation at 60 rpm in an Genechip hybridization oven 640 (Affymetrix). After hybridization, arrays were washed and stained with streptavidin-phycoerythrin (GeneChip® Hybridization Wash and Stain Kit) in a fluidic 450 (Affymetrix) according to the manufacturer's instruction. The arrays were read with a confocal laser (Genechip scanner 3000, Affymetrix). CEL files summarizing the probe cell intensity data were generated using the Affymetrix GeneChip Command Console software 3. 0. Data were normalized with Affymetrix Expression Console software using MAS5 statistical algorithm. Data have been deposited into the Gene Expression Omnibus repository (http://www.
Refer to  [34]  for full methods and bioanalysis. Details of the analysis of the output from the high throughput RNA-Seq are published separately  [34]. Briefly, reads were trimmed to remove ambiguous bases from the start and segments with low quality scores from the end. Trimmed reads were mapped with Bowtie version 0. 12. 7 to the Ensembl rat genome, release 61, and reads that did not map uniquely were discarded. The number of reads mapped to nuclear genes was determined with HTSeq version 0. 4. 7p4, using the default "union" counting option. Data have been deposited into the Gene Expression Omnibus repository under accession code GSE44072.