dataesr/scientific-paragraphs-categorization huggingface dataset:
The quadratic diameter was defined as the quadratic mean tree size in a plot  50  : ∑ = Quadratic diameter D BH n ( )/ i 2 where DBH i is the mean diameter at breast height (mm) of the i th tree for a given census period, and n is the number of trees in the plot. Stem density was estimated by counting the number of living trees with DBH > 10 cm in each plot (trees ha -1 ). The biomass of each tree was estimated using a pantropical allometric equation 51 : ρ =. Biomass D BH H 0 0673( ) 2 0976 where ρ is wood density (g cm -3 ), DBH is diameter at breast height (cm), and H is tree height (m). Data for wood density were obtained from the global wood-density database  52, 53  for each species (58. 9% of the trees). We assigned the mean genus value (11. 3%) to incompletely identified species and the plot-level mean (29.
Statistical analyses. We first conducted a principal component analysis (PCA) using the 'FactoMineR' package  56  as implemented in R 3. 2  57  to identify the relationships between variables of forest structure and dynamics amongst sites. All variables were then analysed using linear mixed models with the 'lme4' package  58. We first designed a null model that only included the random-site effect, which accounted for the spatial dependencies in our sampling design (several plots per site). The initial screening was based on univariate analyses to identify the potential predictors, and we then built a full model, where all potential predictors were added to the null model. We only used the predictors that were poorly correlated with each other to avoid problems of collinearity (Table  S5 ).
Only a few of the soil and litter variables analysed were thus retained. We used an automated model selection based on the BIC to identify the best models using the 'MuMin' package  59. The BIC values were always much lower for the best model than for the second-best models (∆ BIC > 2), so we did not perform model averaging and directly reported the best model estimates. The p-values of the best model were calculated with the 'nlme' package  60. Conditional R 2 was estimated following the method described in ref. 61. If more than one predictor was selected in the final model, we calculated the partial residuals of each predictor with the 'effects' package  62  to determine the effect of each predictor on a given variable by excluding the effects of the other predictors. We used the predictors that became significant in the mixed-model analyses to infer a flow diagram, converted into a directed acyclic graph (DAG) using Bayesian networks with the 'abn' package  63  , which allows accounting for dependencies amongst plots at each site.
Acknowledgements This research was supported by the  European Research Council Synergy  grant  ERC-2013-SyG 610028-IMBALANCE-P ,  Spanish Government  projects  CGL2013-48074  and  Consolider Ingenio Montes  ( CSD2008-00040 ), and  Catalan Government  project  SGR 2014-274. CIRAD, CNRS and ONF jointly managed the Guyafor permanent plot network and provide basic annual funding and stipends. Fieldwork has benefited from European structural funding (Guyafor and  GFclim  project). This work has also benefited from " Investissement d'Avenir " grants managed by the  French Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01  and  TULIP, ref. ANR-10-LABX-0041 ).
Conclusion The purpose of this study was to examine the intra-and inter-day reliability of body composition measurements provided by a multifrequency bioelectrical impedance meter, the Z-Metrix (Biopar-Hom, Bourget du Lac, France). Our three main results were 1) the absence of systematic bias within and between days; 2) a high to very high relative reliability of measurements; and 3) an absolute reliability that varies substantially according to the measurements or the time of day. These data underscore the importance of high standardization and also the need to adjust the cut-off value for ▶Table 5 Intra-and inter-day reliability of ionic indexes. each parameter in order to make sure that a real change has occurred. Future studies should focus on implementing these measures in real-life conditions with different sports or training loads in order to select the most sensitive ones in the follow-up of athletes.
Study design This report includes prespecified and post-hoc analyses of data from a 28-week, open-label, randomized trial at 71 centers in 10 countries. Detailed study design and primary study outcomes were reported previously  (Naber et al. , 2015). Patients were randomized to treatment with AOM 400 (dose reduction to 300 mg permitted for reasons of tolerability) or PP [78-234 mg/month as PP (USA), equivalent to 50-150 mg/month as paliperidone (European Union and Canada)]. After randomization, patients converted from their previous antipsychotic to oral aripiprazole (5-30 mg/day) or paliperidone (3-12 mg/ day) over 3 weeks and then received the respective LAI formulation  (Naber et al. , 2015). The study was carried out in accordance with Good Clinical Practice and the Declaration of Helsinki, and the national coordinating investigator for each country (European Union) or the principal investigator at each site obtained protocol approval from their respective ethics committees (ClinicalTrials.
Results Community analyses. To examine the relative importance of the elevation gradient compared to spatial features such as watersheds and volcanoes in structuring communities, we performed a non-parametric multivariate analysis of variance using distance matrices computed from the transformed species presence/absence matrix (Supplementary Table  S1 ). The test revealed a highly significant effect (P = 0. 0001) of elevation on the community structure of mayflies, explaining 15. 6% of the variance in species composition across the 31 sampled sites (Supplementary Table  S2 ). To test for spatial autocorrelation in the dataset, we performed a Mantel test between the Euclidean distance matrix among sites and the Bray-Curtis transformed species presence/ absence matrix, which uncovered a weak spatial autocorrelation (R 2 = 0.
RADseq analysis. Sequencing of the RADseq library following Mastretta-Yanes et al. 24  produced a total of 295 million reads from 448 individuals in the four-ubiquitous-species spanning the elevation gradient. After de novo assembly and quality filtering, including minimum coverage cutoffs and discarding populations represented by single individuals with pyRAD  29  , the number of samples recovered was 84 for Baetis cf. sabahensis, 62 for Bungona sp. , 91 for Liebebiella cf. vera, and 62 for Thalerosphyrus sinuosus. Reproducibility in the presence/ absence of markers was >0. 99 in all replicate pairs. Our final dataset comprised between 549 and 1,523 loci, including 4,007 Single Nucleotide Polymorphisms (SNPs) for T. sinuosus, 6,052 SNPs for L. cf. vera, 8,527 SNPs for Bungona sp. , and 8,665 SNPs for B.
cf. sabahensis, characterized by a mean coverage (or depth sensu Eaton  29  ) of 76, 73, 52, and 26, respectively (pyRAD output files are available on Zenodo, doi:10. 5281/zenodo. 1045809). In order to produce phylogenetic analyses based on RADseq loci, we distinguished between putative exon and intron loci. In total, we assigned for all four species ≥70% of the loci as introns (Bungona sp. : 84%; L. cf. vera: 71%; B. cf. sabahensis: 82%; T. sinuosus: 85%) and ≤20% as exons (Bungona sp. : 11%; L. cf. vera: 20%; B. cf. sabahensis: 12%; T. sinuosus: 9%). Spatial genetic structure. To investigate the spatial structure of populations in each of the four investigated species we ran Markov chain Monte Carlo (MCMC) analyses, which grouped Bungona sp. into three clusters, L. cf. vera into five, B.
sinuosus. Overall, a relatively high proportion of genetic variation was explained by the among and within population levels (Supplementary Table  S7 ). When computing species-specific regional differentiation (i. e. F RT ) between lowland and highland elevation classes, cutoffs at 843, 697, 903 and 898 meters maximized F RT values for Bungona sp. , L. cf. vera, B. cf. sabahensis, and T. sinuosus, respectively (Supplementary Fig. S8 ). The four values average at 835 meters, an elevation close to the 850 meters cutoff based on community composition. Isolation by distance. Three Isolation by distance (IBD) analyses per species were performed to determine the relative isolation of populations: (i) for the whole dataset, as well as for (ii) lowland and (iii) highland populations separately. Isolation by distance analyses on combined lowland and highland populations were significant for all species (Table  2 ).
Analyses considering populations in the two elevation categories separately were significant for two species, i. e. Bungona sp. and L. cf. vera, for both highland and lowland elevation datasets. In these two species, regression slopes were on average 2. 9 times steeper for highland than lowland populations. For the two other species, i. e. , B. cf. sabahensis and T. sinuosus, IBD was only significant at high elevation after multiple-test correction.
e. community composition and within-species genetic variation in Bungona sp. , L. cf. vera, and B. cf. sabahensis (see Supplementary Table  S9 ). Only for one species, T. sinuosus, none of the measured factors were found significantly correlated to the ordination axes (Table  3 )-this dataset being incidentally characterized by the highest level of missing measurements. In addition, to visualize clustering of species within communities and individuals within species along the elevational gradient and to determine how well the ordination fits the elevation factor, we fitted and plotted a Figure  1. Plotted alpha (α), beta (β) and gamma (ɣ) diversity against elevation. The diversity indexes are estimated by an additive partitioning of species diversity (ɣ = β + α) with gamma being the total diversity, beta the among-site diversity (average amount of diversity not found in a single site), and alpha the average withinsite diversity (as described in Lu et al.
Discussion In this study, we tested the effect of three spatial factors, i. e. , elevation, volcanoes and watersheds on the spatial structuring of species within communities and genes within species in tropical freshwater organisms. Based on a based on the Bray-Curtis dissimilarity index of the species presence/absence matrix as well as the species genetic distance matrix. Analyses were performed through 1000 permutations with the vegan package  82. survey of 31 mayfly communities, and RADseq genetic analysis of four ubiquitous mayfly species, we found that elevation is the main factor molding biodiversity at both levels. At the community level, results revealed that elevation is a major factor explaining community composition of mayflies on tropical volcanoes and that similarity in species composition between communities decreases as elevational difference increases (Supplementary Fig.
Our results are therefore in agreement with the niche theory  41  , in the sense that similar environments should be associated with similar species composition  42. Congruent with our results, studies focusing on the macro-zoobenthos community composition found similar patterns with elevation acting as an important factor in determining taxonomical composition  11, [43] [44] [45] [46]. S10 ). We fitted generalized additive models (GAM) using a 2D smooth surface onto the NMDS site scores. The NMDS analyses were performed with the metaMDS function implemented in the vegan package and isoclines were fitted to the plots with the ordisurf function. Furthermore, to visually inspect the accuracy of the models, we added "spider" diagrams connecting communities or individuals from highlands or lowlands to their group centroid.
Community data analyses. Because time allocated for sampling was not equal among all sites, we used the species presence/absence matrix as a measure of community composition. To examine the relative importance of elevation, watersheds and volcanoes in structuring communities, we performed a non-parametric multivariate analysis of variance using distance matrices [i. e. , permutational manova  80  ]. We used the adonis function in the R CRAN  81  vegan package  82  , which we parameterized with 10,000 permutations using the Bray-Curtis dissimilarity measure applied to the species presence/absence matrix. Dissimilarity matrices for all spatial features investigated were produced using (i) Euclidean pairwise metric distances among communities for elevation and (ii) binary matrices for sharing/not sharing of watershed or volcano among communities.
We used a derived method that quantifies the species and sites distinctiveness and attributes the additive beta diversity component to each site  83. The diversity indexes are estimated by an additive partitioning of species diversity (ɣ = β + α) with gamma being the total diversity, beta the among-site diversity (average amount of diversity not found in a single site), and alpha the average within-site diversity (for computational details, see Lu et al. 83  ). Indexes were computed by the contribdiv function of the vegan package using the species richness index. To test for elevation gradients in diversity, we regressed the observed alpha, beta and gamma diversities against elevation using linear regressions. In order to identify the elevation cutoff between lowlands and highlands that best predicts changes in community composition, we performed a non-metric multidimensional scaling (NMDS)-based on the Bray-Curtis similarity index-of the species presence/absence matrix  84  , using the vegan package.
Elevation was split each 50 meters into binary vectors, encoding for communities sampled below a given threshold and for communities sampled above or exactly at the threshold. By fitting all binary vectors onto the ordination, we retrieved R 2 values for each threshold and considered the value with the highest R 2 score as the most meaningful cutoff between lowlands and highlands. Because two elevation cutoffs were associated with nearly identical maximum R 2 values (see Results section), we performed the same analytical procedure using the species abundance matrix to discriminate between the two. RAD sequencing. Based on their ubiquitous distribution at low and high elevation in our dataset, four species (Bungona sp. , Liebebiella. cf. vera, Baetis cf. sabahensis and Thalerosphyrus sinuosus) were selected for genetic analysis and prepared with a double digest RAD library.
In addition, each plate included two blanks and six randomly chosen replicates for inter-plate comparisons and reproducibility rate estimation. The library was sequenced twice using a single-read protocol on an Illumina 2500 HiSeq (Lausanne Genomic Technologies Facility, University of Lausanne, Switzerland). RADseq output treatment. We used the pyRAD v. 3. 0 29 pipeline to demultiplex, filter and assemble the RADseq outputs. The software pyRAD allows de novo clustering of reads from different individuals into loci and further SNP calling. It has the advantage over other pipelines [e. g. , Catchen et al. 81  ] of better dealing with indel containing loci and incompletely overlapping reads  29. Minimum sequencing depth (Mindepth) was fixed to six and the clustering threshold value for Vsearch 85 set to 0.
88. Additionally, we filtered out reads with more than four sites per read with an error rate >1% and set the maximum proportion of shared polymorphic sites in a locus (MaxSH) to three. Other parameters were left to default values. After retrieving filtered sequences from each individual, we applied a species-specific cutoff to discard individuals and loci with low coverage (i. e. , less than 10% representation). For the time-calibrated phylogenetic analysis (see below), we filtered out the RADloci being assigned as intron regions. Therefore, we used the longest sequence of each RAD locus for blastx searches  86  against the insect protein database ("txid50557 [Organism:exp]") and used an e-value threshold of 1e-2. Spatial genetic structure. We used STRUCTURE v. 2. 3. 4  87  to investigate the spatial structure of populations in each of the four species.
The cutoff points were computed as the median values between two successive elevational sampling stations. Environmental factors. Three recorded environmental factors (i. e pH, stream velocity and temperature) were fitted onto the ordination (NMDS) of the species presence/absence matrix as well as the genetic distance matrix of the four sequenced species using the vegan package. Community and genetic distance matrices were transformed using the Bray-Curtis dissimilarity index and fitting of the environmental factors was done through 1,000 permutations with the function envfit of the vegan package. Because the temperature variable could not be measured in 17 of 31 sampling points, fitting this variable considerably decreased the data size and thus increased possible biases. Therefore, for each data set, we performed the analyses with and without temperature, and accounted for multiple tests correction as described above.
A Bayesian relaxed (uncorrelated lognormal) clock with a constant-size coalescent was used. We ran multiple independent runs with 10 9 generations, sampling trees every 10 4 generations. For subsequent analyses, we selected among all ten runs, the one with the best convergence (ESS >100) assessed using Tracer v. 1. 6  90. Maximum clade credibility trees were obtained using TreeAnnotator v. 2. 4  89  , whereby we discarded 10% as burnin. The phylogenetic relationships within each species were additionally estimated using a quartet-based coalescent-approach. For this we used the program SVDquartets 91 within PAUP* v. 4. 0a149  92. We evaluated all quartets and ran 1,000 bootstrap replicates. All trees were plotted with the R package ggtree  93.
Acknowledgements We are grateful to  Bob Smoked ,  Wayhu Angraini  and  Ditta Amran Balke  for their precious help in organizing and performing fieldwork, as well to  Jean-Luc Sequeira  for helpful advice in processing the RAD library. We also thank  Russell Naisbit  of  Albion Science Editing , as well as three reviewers for meaningful corrections and suggestions on the manuscript. This work was funded by a  Swiss National Science Foundation  grant ( PP00P3_144870 ) awarded to  Nadir Alvarez. SR was supported by the  Swiss National Science Foundation  ( Early PostDoc. Mobility  fellowship  P2SKP3_158698 ) and a fellowship from the  Janggen-Pöhn-Stiftung.
Data collection For each participant, study teams collected information on gender, age, household size and composition, history of oral antibiotic treatment and respiratory symptoms (cough, runny nose/ sneezing, difficulties breathing, sore throat, otitis) in the two weeks preceding the study. One NP sample was obtained by experienced nurses from the posterior pharynx of each study participant using a flexible, pediatric-size or adult-size flocked nylon swab (Copan Diagnostics, USA) in accordance with World Health Organization (WHO) guidelines  [22].
Data analysis Data were double-entered using Voozanoo (www. voozanoo. net, Epiconcept, Paris, France). To adjust for clustering, standard errors were computed by using the linearized variance estimator. To account for the unequal intra-household sampling probabilities, analyses were weighted on the household size. Reported proportions are weighted unless otherwise specified. Results combining all age strata were also weighted to account for the age-stratified design. The prevalence of NP carriage with S. pneumoniae is presented with their associated 95% confidence intervals (95%CI). Associations between S. pneumoniae NP carriage and participant characteristics (sex, history of oral antibiotic treatment and respiratory symptoms) were analyzed by logistic regression, adjusting for age group (<2 years; 2-4 years; 5-14 years; !15 years).
That is, as the dynamic protocol more images are used, but in monitoring mode, images are captured over longer interval on the same type of examination, for instance every 6 or 12 months, in order to monitor suspicious region, for early detection of a disease, to verify the progress of a disease and treatment after an therapy  [27, 29]. Of course, the fourth aspect can be considered as a repetition of the third. For this, the protocols which deserve more analysis and experimentations are the static and the dynamic (Passive or Active) acquisition protocol grouped in the third aspect commented before. In order to see what can be considered the best one for the interested community on promote the union of effort for better use of IR images on diagnosis, data from these two types of acquisitions, obtained by our group, are available in a database of breast thermal images, named Database for Mastology Research with Infrared Image-DMR-IR  [27] , and can be used by researchers on breast infrared images.
Storage and Retrieval The Database for Mastology Research with Infrared Image-DMR-IR contains infrared images and temperature matrixes, digitalized mammograms and clinical data acquired in the university hospital (Hospital Universitário Antônio Pedro-HUAP) of Federal Fluminense University, Rio de Janeiro, Brazil. The data are from patients of the gynecologic department of HUAP. There are data from healthy patients and from patients with a number of breast diseases, including cancer. This research is approved by the Ethical Committee of the HUAP and registered at the Brazilian Ministry of Health under number CAAE: 01042812. 0. 0000. 5243. The DMR-IR is accessible through a user-friendly interface (http://visual. ic. uff. br/dmi) for managing and retrieving information. All data are from those patients that agree on signing a term of understanding and knowledge about the research details and consent on the use of their data for the research.
They are computed from each breast separately without combination or subtraction of images or features. The same 28 patients are considered. These characteristics are grouped in an array of data processed by the WEKA (http://www. cs. waikato. ac. nz/ml/weka/) classifier software, as the previous one. The principal component analysis (PCA) is performed in two ways: with the base divided into four groups of features and with the entire amount of data. The Support Vector Machine (SVM) was used for patient classification by the PCA best group. The best accuracy is 82. 14 and 91. 70% is the best sensitivity obtained  [21]. In a third work  [4] , the used features were from fractal geometry (Higuchi fractal dimension and Succolarity), basic statistics (average, standard deviation, and thermal amplitude), histograms from the thermal matrix, geostatistics (Moran index and Geary coefficient), and Ripley Diversity Index, using a total of 40 features computed from 51 patient's ROIs.
Conclusions In this chapter, we present several approaches that have been implemented related to the use of infrared images to breast modeling and diseases diagnosis; they consider all aspects of the process of diagnostic tool implementation: capture thermal matrix of patient body, storage and retrieval of images from a database, segmentation, 3D reconstruction, feature extraction and classification. The experiments and implementations were done by the Visual Lab group of the Fluminense Federal University in Niteroi, Rio de Janeiro, Brazil. The results are promising and it promotes diseases classification with very high accuracy. Each aspect considered in this chapter is an important application of infrared images for breast modeling and diagnosis. Test showed that the proposed methodology is able to detect breast anomalies, thus contributing to adopted thermography for breast cancer screening programs.
Acknowledgements This work was supported by  Brazilian CAPES ,  CNPq ,  FAPERJ  and  FAPEMA  agencies. This research has also been partially supported by project " Pro-Engenharia 021-2008 ,"  INCT-MACC and SiADE. We thank very much all collaborators of Visual Lab Group. Fig. 7 Accuracy by group of features using active dynamic thermography [30].