dataesr/scientific-paragraphs-categorization huggingface dataset:
Soil type diversity and implications for MAAT-pH reconstructions The 27 soil types represented in the 358-component composite global soil dataset denote its high diversity (Fig. 3  and Supplementary Fig. S1 , Supplementary Table  S1 ). Most of the soil samples were taken from surficial and/or A horizons (Supplementary Table  S1 ). Thus, we may assume that soil depth has no significant effect on the global brGDGT distributions considered in this study. Pedogenetic processes depend on multiple factors such as climate, vegetation type, topography and parent material (e. g. , IUSS   2014 ). Only soil types comprising at least 10 samples from the global datasets are considered. When outliers are identified for a given soil type, their number is indicated as (initial number of samples -number of outliers) below the number of samples actually considered.
BrGDGT-derived proxies BIT values in Fan Si Pan soils (Table  3 ) are close to the reported mean soil BIT value of 0. 90 ± 0. 14  (Schouten et al. , 2013)  and are consistent with those observed in wet and acidic soils (e. g. ,  Weijers et al. , 2006b; Loomis et al. , 2011; Dirghangi et al. , 2013). For MAAT, only one Fan Si Pan soil fell outside the global MAAT-MBT 0 /CBT RMSE of  Peterse et al. (2012)  of 5 °C (Table  3 ). For pH, half of the Fan Si Pan soils fell outside the global pH-CBT RMSE of  Peterse et al. (2012)  of 0. 8 pH units (Table  3 ). As RSME means that globally 66% of the soils will fall within this error range, Fan Si Pan soils adhere quite well to the variabilities of the global soil calibration dataset of  Peterse et al. (2012) , especially for MAAT-MBT 0 /CBT. BrGDGT-derived proxies and MBT 0 /CBT-derived mean annual air temperature (MAAT) and pH based on the extended soil calibration  (Peterse et al.
Conclusions and perspectives Soil types were assigned to a composite global dataset of 358 soils to investigate MAAT-pH/brGDGT relationships as well as MAAT (and pH) residuals per soil type. Distributions of brGDGTs were also investigated in five soil profiles excavated along an altitudinal transect in the Mount Fan Si Pan National Park. Surrounding vegetation, geological setting, as well as soil sequence had previously been intensively characterized, providing an opportunity to investigate their impact on proxies intensively used in paleo-studies. The soil type diversity of the global dataset was high, traditional MAAT-pH/brGDGT relationships per soil type were not all similar and different soil types (e. g. , Alisols and Podzols) did not all provide the same contribution to global MAAT-pH/brGDGT scatters.
varied between À7. 0 and 3. 5 °C (Figs. 4A and 5A and B). Mean pH residuals per soil type based on the pH-CBT global soil calibration of Peterse et al. (2012)  varied between À1. 3 and 0. 4 pH units (data not shown). Using the global soil dataset of De Jonge et al. (2014), mean MAAT residuals per soil type based on the MAAT-MBT 0 /CBT global soil calibration of Peterse et al. (2012) varied between À7. 5 and 3. 5 °C (Figs. 4B and 5C and E). Mean MAAT residuals per soil type based on the MAAT-MBT 0 5ME global soil calibration of De Jonge et al. (2014) varied between À3. 5 and 2. 5 °C (Figs. 4C, 5D and F). Mean pH residuals per soil type based on the pH-CBT global soil calibration of Peterse et al. (2012) varied between À1. 4 and 0. 2 pH units (data not shown). Mean pH residuals per soil type based on the pH-CBT 0 global soil calibration of De Jonge et al.
(Huguet et al. , 2010;Peterse et al. , 2012;Dirghangi et al. , 2013;Anderson et al. , 2014;De Jonge et al. , 2014)and newest mean annual air temperature (MAAT)-pH/brGDGT linear regressions per soil type using the samples of the composite global soil dataset(Huguet et al. , 2010;Peterse et al. , 2012;Dirghangi et al. , 2013;Anderson et al. , 2014;De Jonge et al. , 2014)reconsidered by De Jonge et al. (2014). Only soil types comprising at least 10 samples from the global dataset are considered for the linear regressions. When outliers are identified for a given MAAT-pH/brGDGT relationship per soil type, their number is indicated as (initial number of samples -number of outliers) next to the number of samples actually considered. Statistical significance is reached for p-values < 0. 05.
Acknowledgements G. M. thanks  Anneke de Rouw  and the  HYDRARIDE consortium  for making this study and the connection with pedologists possible through discussions in the field in Cameroon. Work at CEREGE is supported by  ECCOREV FR3098  (project  ECCOTEMP ) and the  Collège de France. N. D. thanks  Ecole Normale Supérieure de Lyon  for providing salary support. We are grateful to two anonymous reviewers for their constructive suggestions that greatly helped us to considerably improve this manuscript.
Museum collection numbers are provided in the text when more than one individual is known for a single taxon. In view of recent proteomic evidence for the close relationship of litopterns and notoungulates to Perissodactyla  (Welker et al. , 2015; Buckley, 2015) , we make specific comparisons to living horses, tapirs, and rhinos where warranted. However, our primary purpose here is to investigate the relationships of Huayqueriana within the context of Litopterna, not to test the likelihood of Panperissodactyla  (Welker et al. , 2015)  from the standpoint of cranial morphology. Segmental data on Huayqueriana cf. H. cristata IANIGLA-PV 29 and Tetramerorhinus lucarius AMNH 9245 were collected with the GE Phoenix vtomex s240 μCT scanner housed at AMNH. The skull of Huayqueriana was scanned at 180 kV, 220 mA, with a voxel size of 124.
06 μm. The scan resulted in a final stack of 3585 slices of 1619 × 1166 pxl. The program 3DSlicer (open source http://www. slicer. org/; Fedorov et al. , 2012)  was used for 3D reconstruction and segmentation of the skull, endocast, and associated structures. Mimics 18. 0 (Materialise NV, Leuven, Belgium) was used for 3D reconstruction of the petrosal and VGStudio MAX (Volume Graphics GmbH, Heidelberg, Germany) for the osseous inner ear. Linear and volumetric measurements of the endocast were calculated with 3D Slicer following standard dimensions proposed by  Macrini et al. (2007b) ; angular measurements were calculated with the program ImageJ; linear measurements of the bony labyrinth were calculated with Mimics following the protocole of  Schmelzle et al. (2007)  for the dimensions of the semicircular canals.
Phylogenetic relationships were explored via a cladistic analysis employing TNT  (Goloboff et al. , 2008). The data matrix is a modified version of that published by  Schmidt and Ferrero (2014). The body mass of IANIGLA-PV 29 was estimated using a variety of allometric equations in the literature that are in turn based on dental and cranial traits of extant euungulates (Artiodactyla and Perissodactyla) (see Body Size). Of general interest is our finding that body mass estimators based on cranial measurements were more plausible than ones based on teeth (cf. Cassini et al. , 2012b  Anatomical: Abbreviations used in individual figures, most of which are set in lowercase, are indicted in captions. In the main text, we use additional conventions. Upper teeth are capitalized while mandibular teeth are in lowercase, as follows: C/c, canine, I/i incisor; M/m, molar; and P/p, premolar; D/d, deciduous dentition.
This point can be resolved only by the discovery of additional specimens and further taxonomic studies. Phylogenetic analysis: A parsimony analysis was performed based on 19 taxa scored for 34 morphological cranial and dental characters, using a modified version of the matrix of  Schmidt and Ferrero (2014)  and eight additional characters (appendix 2). Six characters on which a logical sequence could be imposed to their states were treated as additive  (chars. 1, 4, 25-27, 29). The The data matrix (appendix 2) was analyzed using TNT 1. 1  (Goloboff et al. , 2008)  under maximum parsimony. The analysis included a heuristic search of Wagner trees with 1000 random addition sequences, followed by TBR and retention of 10 trees per round. The first analysis (all taxa included, Proadiantus excavatus used as "strict outgroup" to give plesiomorphic states of characters) yielded 27 most parsimonious topologies (length, 70; CI, 0.
Based on the data of  Klevezal (1996)  for the M1 and M2 of ungulates, a correction of 0 to 1 year must be added to the cementum count. Therefore, we suggest that IANIGLA-PV 29 died at an age of almost 10 or 11 years. Longevity is known to be positively correlated with body size in mammals  (Schmidt-Nielsen, 1984). We compared the estimated age of IANI-GLA-PV 29 to the longevity of wild extant ungulates approximating a body mass of 250 kg (longevity data from AnAge database,  Tacutu et al. , 2013 ; body mass ranges from  Nowak and Paradisio, 1983). Longevity is notably greater in the latter. Examples are: ~15 years for the topi, Damaliscus lunatus; 24 years for the muskox, Ovibos moschatus; and 22 years, or possibly more, for the elk, Cervus canadensis, or moose, Alces alces. By contrast, ungulates with a longevity in the wild of roughly 10-12 years are conspicuously smaller, with a mean body mass below 100 kg (e.
On his analysis the two fermentation styles were found to form two distinct groups  (Madden, 2015). In order to test Madden's hypothesis statistically, we measured SL and TS in 43 foregut and hindgut fermenters (distributed across 11 taxa; appendix 3). In our analysis, foregut and hindgut fermenters did not form distinct groups (fig. 29 ). As the data do not follow a normal distribution as judged by the Shapiro-Wilk and Anderson-Darling tests (p-value <0. 01 for the foregut fermenter group), either a nonparametric test or a data transformation is needed to compare the two groups. Using the nonparametric Mann-Whitney U test, we found that there is no signifi-cant difference between the two groups (p-value >0. 14). Similarly, after log-transforming the data and checking for both normality of the distribution (Shapiro-Wilk and Anderson-Darling tests, p-value >0.
CONCLUSIONS The various families of South American native ungulates grouped as Litopterna thrived on that continent from at least the late Paleogene to the beginning of the Holocene. The terminal taxa of Litopterna included the Macraucheniinae, characterized inter alia by a highly derived cranial structure, in which the nasal aperture was rotated to a position between the orbits, near the summit of the skull. This contribution presents a detailed study of the morphology of a newly discovered and nearly complete macraucheniine skull (IANIGLA-PV 29) from the Huayquerías Formation (Late Miocene, Huayquerian SALMA) in west-central Argentina  (Mendoza Province). Referral of IANIGLA-PV 29 to Huayqueriana cf. H. cristata  (Rovereto, 1914)   (Litopterna,. Box plot depicting ratio of temporalis canal width to skull length, with data pooled by family (appendix 3).
ACKNOWLEDGMENTS We acknowledge  José Ginart  for permission to allow our fieldwork on his land, " Las Aguadas del Sur. " Field trips were made possible by the joint effort of  F. J. Prevosti ,  A. C. Garrido ,  R. Bonini ,  S. Echarri ,  G. Turazzini  (who found the IANIGLA-PV 29),  C. Suarez ,  M. de la Fuente ,  F. Pujos ,  G. Re ,  M. Bourguet ,  B. Vera , and  S. Kay. We especially acknowledge one anonymous reviewer and  J. R. Wible  for their enriching comments and suggestions. Early versions of the manuscript were improved by fruitful exchanges with  E. Cerdeño ,  M. Bond ,  A. Kramarz ,  M. R. Sánchez-Villagra ,  C. Kolb ,  F. Bibi , and  A. Martinelli. We acknowledge  A. C. Garrido  for the stratigraphic column of figure care. We kindly thank  M. Bourguet  for the fossil preparation,  Sergio Mosconi  for scanning at FUESMEN and advice on digital files, and  R.
Study design The SURMetaGIT study is an observational, longitudinal, prospective study testing the general hypothesis that RYGBinduced anatomic alterations modify GI function as an adaptive mechanism that affects systemic metabolism. The study protocol comprises several steps to conjunctly investigate associations between potential RYGB-induced changes in GI gene expression and postoperative biomarkers related to improvement or worsening of obesity-associated comorbidities, mainly T2DM remission. The SURMetaGIT study was approved by the local ethics committee (CAPPesq1011/09) and registered at Plataforma Brasil (19339913. 0. 0000. 0068) and www. clinicalTrials. gov (NCT01251016). The protocol is currently being implemented; the steps completed and those still in the process of completion are shown in Figure  1.
Array quality was checked using boxplots, correlation analysis, and principal component analysis (PCA). Expression values were obtained using the robust multiarray average data pre-processing method. 21 he combat method (http://jlab. byu. edu/ ComBat/Abstract. html) was used to remove batch effects. 22 The significance of microarrays  23  and rank products  24  methods were analysed to select differentially expressed genes using the criterion of P < 0. 05 (corrected for the false discovery rate). 25 All analytical tools are available in the R Bioconductor program (http://www. bioconductor. org/). Real-time reverse-transcription quantitative polymerase chain reaction was performed to validate the significance of changes in some genes of interest identified by microarray analysis using TaqMan gene expression assays (Life Technologies, Carlsbad, CA, USA).
Plasma proteomics and lipidomics. We planned to correlate the activity of differentially expressed GI genes with plasma proteomic and lipidomic data obtained by mass spectrometry (MS) at the systemic level. Global plasma lipid profiles were assessed (shotgun lipidomics) before and after RYGB by matrix-assisted laser desorption/ ionization MS. Shotgun proteomic analyses were performed for protein profiling using nano-scale LC tandem MS (LC-MS/MS), a well-established technique for peptide identification from complex mixtures (i. e. , protein digests). We planned to further validate the identification of proteins of interest by shotgun analysis using MS, with a targeted approach when necessary. 26 asma and urine metabolomics. Plasma obtained from EDTA-treated blood samples was aliquoted in polypropylene microtubes and stored at À80 C until metabolomic analysis.
The web-based analytical pipeline Metabo Analyst 2. 0 (www. metaboanalyst. ca) and the ROC Curve Explorer & Tester (http:// www. roccet. ca/ROCCET/) were used for data upload and importation, respectively. The MetaboAnalyst protocol was used to normalize data for univariate and multivariate analyses. 29 etagenomics. The patients performed selfcollection of faecal samples at home using a specimen collection system (Fisherbrand Commode Specimen Collection System; Fisher Scientific, Hampton, NH, USA), as described previously. 17 The samples were frozen immediately at À20 C and transported to our laboratory by a motorcycle courier service specializing in the transport of biological samples under temperature control. At the laboratory, the faecal samples were aliquoted immediately (without thawing) and stored at À80 C until DNA extraction and storage at À20 C, as described previously  30  and as detailed in International Microbiome Standard IHMS-SOP06 (http://www.
microbiome-standards. org). Library preparation for the MiSeq Sequencing System (Illumina, San Diego, CA, USA) involved amplification of the V3 -V4 region of 16 S ribosomal DNA genes, as described previously. 17, 31 Amplification relied on MolTaq 16 S and the corresponding master mix. The polymerase chain reaction mix contained 10 ng of DNA, 1 mL of dNTPs (10 mM), 1. 25 mL each of forward and reverse primer (20 mM), and 0. 5 mL of Taq in a total volume of 50 mL. The program was set to 94 C for 60 s, followed by 30 cycles at 94 C for 60 s, 65 C for 60 s, 72 C for 60 s, and final extension at 72 C for 10 min. Sequencing was performed using MiSeq technology and the Genopole Toulouse Midi-Pyrenees genomics platform (http://get. genotoul. fr/). The resulting sequences were assigned to taxonomic levels ranging from phylum to genus using the Ribosomal Database Project (http://rdp.
1 Â 50 mm, 1. 8 u; Agilent Technologies). Spectra were accumulated and processed using mass hunter qualitative analysis. To assess faecal metabolomic changes, we chose to perform PCA (3:11 Pirouette software; Infometrix, Bothell, WA, USA) with data from a database matrix based on the relative intensity of ions (m/z values) in the profiles obtained by LC-MS using Lab Solutions software (Shimadzu, Kyoto, Japan) and Excel (Microsoft, Redmond, WA, USA). 34 ther discriminant techniques available for the analysis of metabolomic datasets, including partial least-squares discriminant analysis  35  and orthogonal partial least-squares discriminant analysis  36  , may be used.
Secondary endpoint analyses. Nutritional intake. Nutritional intake was assessed several weeks before, 3, 12 and 24 months after RYGB. Two trained dietitian oriented the patients as described previously. 37 The types and amounts of food and beverages consumed were assessed using three nutritional tools: 24-h food records, 7-day food records, and food frequency questionnaires. The amount of food consumed was recorded in terms of cooking units (e. g. , tablespoons, cups) as guided by the Consumo Alimentar: Visualizando Porc¸o˜es (Food Consumption Book: Viewing Portions). 38 ata were used to calculate total calories and macronutrient and micronutrient consumption with Virtual Nutri Plus software (www. virtualnutriplus. com. br). 39 The following data sources were chosen to determine the food nutritional composition: the food chemical composition table developed by Philippi  40  and the Brazilian food chemical composition table.
50 Before integration, we planned pre-processing of each omics dataset by procedures such as normalization or scaling, missing value imputation, variable selection, and dimension reduction. 51 or microarray gene expression analysis, we planned to primarily use the software Ingenuity Pathway Analysis (Qiagen, Redwood City, CA, USA). This tool performs a functional analysis (in silico), resulting in networks of gene interactions. 52 or ''omics'' integrated analysis, we planned to perform initial exploratory data analyses. We then planned to apply multivariate methods such as co-inertia analysis, PCA, or correspondence analysis to examine single datasets and explore relationships between two or among more than two datasets. We also considered the application of approaches that allow the visualization of different omics data types on pathway maps; the use of classification trees to identify variables predicting the expression of associated genes  53  ; and the application of other advanced statistical methodologies that have been used in multi-omics analysis, such as the construction of artificial or Bayesian neural networks  54  and the application of dynamic control system theory.
These approaches would allow for the creation of a global network of pathway interactions from genetic, proteomic, and metabolomic data. Additional statistical analysis of each study variable and its temporal relationship, as well as analyses integrating all omics data, were also planned. The R statistical software (http://www. r-project. org), including the R Bioconductor repository, was chosen to conduct the statistical tests. For integrative analyses of diverse omics data, we planned to use Galaxy (https://usegalaxy. org) and STATegra (http://www. stategra. eu) software.
Initial results The first 20 obese patients selected for omics analysis were included in this study (demographic data are provided in Table  2 ); the 5 patients to be used for validation remain under selection. The 20 initial patients completed all GI tissue, blood, urine, and faecal sample collections. In addition, all omics (GI gene expression, plasma protein, plasma metabolite, plasma lipid, faecal metagenomic, and faecal metabolite), nutritional, anthropometric, body composition, and REE data were obtained preoperatively and 3 months postoperatively, and statistical analyses are underway. Urinary metabolites remain to be assessed, and data from 24 months after RYGB are under collection. The protocol steps completed and those in the process of completion for these 20 patients are shown in Figure  1.
Discussion Ideally, research questions should be simple and involve only a few key variables. However, the multifactorial nature of some contemporary diseases has hindered the achievement of this premise. Modern researchers increasingly face the need to develop studies involving a wide range of variables to answer simple questions. Aiming to contribute to the resolution of this issue, our article describes the protocol for a pan-omics clinical study of obese patients, a useful research approach that is rarely applied because of its great complexity. Obesity is a multifactorial disease that involves several metabolic disturbances and chronic inflammation associated with the development of a wide range of comorbidities. A major challenge of our study was to correlate a large number of GI findings with several systemic datasets to answer the main question of whether potential RYGBinduced changes in GI genes affect T2DM remission and long-term T2DM recurrence.
AGRADECIMIENTOS El autor desea agradecer la colaboración de todos los socios dentro del proyecto Centro de Excelencia y Apropiación en Internet de las Cosas -CEA-IoT. El autor también desea agradecer a todas las instituciones que apoyaron este trabajo: el Ministerio de Tecnología de la Información y Comunicaciones -MinTIC de Colombia y al Departamento Administrativo de Ciencia, Tecnología e Innovación -Colciencias, a través del Fondo Nacional de Financiamiento para la Ciencia, Tecnología y la Innovación Francisco José de Caldas -ID Proyecto: FP44842-502-2015.
(IoT Developer Survey, 2016)  Figura 20. Lenguajes de Programación utilizados para IoT. Fuente (IoT Developer Survey, 2016) Tabla 13. Comparativo de programas de automatización. Fuente Autor. Al escoger los cuatro con características deseables (que utilizan Python), se realizó la prueba de cada uno de ellos donde se escoge Domoticz debido a que contiene un módulo de servicios web interno (dentro de la red local) y externo (internet), compatibilidad con los controladores (drivers) y librerías de los diferentes sensores que se van a utilizar para la implementación. Sumado a lo anterior, se incluye en la escogencia del software Domoticz por la sección de complementos (plugins) en el cual se encuentra Homebridge, que proporciona un puente entre la API de Domoticz y la API HomeKit de iOS (Homekit Siri -Domoticz, 2016).
echo "create database radius;" | mysql -u root -p raspbian Luego, se crea el esquema de la base de datos. mysql -u root -p raspbian radius < /etc/freeradius/sql/mysql/schema. sql Inmediatamente, se crea un perfil administrativo para la base de datos. mysql -u root -p raspbian radius < /etc/freeradius/sql/mysql/admin. sql Adicionalmente, se crea las tablas de almacenamiento NAS mysql -u root -p raspbian radius < /etc/freeradius/sql/mysql/nas. sql Luego en la línea 700 se debe remover el comentario del archivo de configuración se /etc/freeradius/radiusd. conf, debido a que se debe iniciar el módulo de SQL. Para habilitar la autenticación de la base de datos se debe remover el comentario de las líneas 177, 406 y 454. Para iniciar y probar la configuración realizada se debe detener el modulo con el siguiente comando.
apt-get install-y php5-mysql php-pear php5-gd php-db php5-fpm libgd2-xpm-dev libpcrecpp0 libxpm4 php5-xcache Luego se descarga el daloRADIUS, y se instala en la carpeta de apache www/html git clone https://github. com/lirantal/daloradius. git Se agrega la información utilizada por daloRADIUS en la base de datos de FreeRadius mysql -u root -p raspbian radius </www/daloradius/contrib/db/fr2-mysqldaloradius-and-freeradius. sql mysql -u root -p raspbian GRANT ALL ON radius. * to 'radius'@'localhost'; GRANT ALL ON radius. * to 'radius'@'127. 0. 0. 1'; exit; En el archivo www/daloradius/library/daloradius. conf. php se define los permisos de acceso a la base de datos.