dataesr/scientific-paragraphs-categorization huggingface dataset:
If they were endotherms, then their body temperature would most likely fall within the 36°C-40°C range, as in most modern endotherms. Thus, the distribution of data in the predefined fields of body temperatures will offer a basis for discussing the thermophysiological status of vertebrate taxa studied. To infer the thermometabolic condition of Goniopholididae and Dyrosauridae, both bone histology and isotopic geochemistry were used. We describe the bone cross sections of two specimens of these groups and use them to infer RMR, RBC w , and RBC a using quantitative histology. We use already published isotopic data for Goniopholididae and present here new isotopic data for Dyrosauridae, with the aim of estimating their T b. Finally, we propose a putative state of their thermometabolism using all these parameters.
Material Quantitative Histology. -Two extinct species were studied: †Goniopholis simus Owen, 1879 from the Berriasian (Early Cretaceous) of Cherves-de-Cognac, France (Muséum national d'Histoire naturelle/Paris Museum of Natural History: MNHN CHE. 02. 046) and †Dyrosaurus sp. Pomel, 1894 from the Ypresian (Eocene) of North Africa. For all species, both extinct and extant, we studied the femur. Their data were incorporated into two datasets comprising histological variables and the variables of interest (RMR or RBC width/ area) of a set of extant species. Both datasets are from  Cubo et al. (2020). The first one, used in RMR inferences, comprises 18 extant species. The second one, used in RBC inferences, comprises 14 extant species. The complete list of included species is given in the Supplementary Material and Methods.
Methods Preparations of Sections. -Thin sections of †Goniopholis simus and †Dyrosaurus sp. were prepared at the closest possible level to the middle of the diaphysis. For each species, one cross section from the mid-shaft of a femur was prepared and consequently analyzed. They were mounted on a glass slide following the method described in Lamm (  2013 ). These sections were deposited at the vertebrate hard tissues histological collection of the MNHN, where they are available upon request to the curator, with the respective access numbers MNHN-F-Histos-2751 ( †Goniopholis) and MNHN-F-Histos-2752 ( †Dyrosaurus). The histological terminology follows  Francillon-Vieillot et al. (1990)  with addenda and revision from  Prondvai et al. (2014). Cross sections were observed under a Nikon Eclipse E600 POL microscope, either in linearly polarized light (LPL) or in cross-polarized light (XPL) with a lambda wave plate, in order to determine the nature of bone tissue in each topologic region using patterns of isotropy/ anisotropy  (Bromage et al.
2003; Faure-Brac et al. 2019). Variations in isotropy allowed us to assess orientation and organization of collagen fibers. Anisotropic fibers, which appear blue/yellow under XPL with a lambda compensator, are highly organized and parallel thus and perpendicular to the direction of propagation of XPL. For isotropic fibers, which appear red under XPL with a lambda compensator, two possibilities exist: either fibers lie in parallel and are parallel to the direction of propagation of XPL (i. e. , cut transversally) or woven bone (for a review, see  Faure-Brac et al. 2019). Histological Variables. -Pictures were taken at different magnifications and analyzed with the software ImageJ v. 1. 8. 0_172  (Rasband 1997)  to quantify the different histological variables. Three were quantified: relative primary osteon area (RPOA) for RMR inferences (described as primary osteon density by  Fleischle et al.
HMC and can min of vascular canals were quantified in the outer cortex, as our sample of extant animals is composed of adults only. In accordance with  Huttenlocker and Farmer (2017) , up to fifty canals were quantified in each section (see Fig. 2B ). We delineated canals and fit the largest ellipse in them. Then, we measured the minimal axis. HMC was calculated using the following equation: HMC = n n i=1 (1/x i ) (7) where n is the number of measured vascular cavities, and x i the value of the minimal axis for the i th cavity. can min is simply the smallest minimal axis in our sample. The dataset of extant species used to infer RMR is from  Cubo et al. (2020) , and the dataset used to infer RBC variables is from  Huttenlocker and Farmer (2017). All values for the extinct species were quantified in this study.
Phylogeny. -Two phylogenies were used, one for each set of taxa. The phylogeny used to infer RMR is an amended tree taken from Cubo et al. (  2020 ), and the one used to infer RBC variables is amended from  Huttenlocker and Farmer (2017). In both cases, extinct species were removed and replaced with †Dyrosaurus sp. and †Goniopholis simus. Their placement follows the consensual phylogeny presented by  Puértolas-Pascual et al. (2020). As the phylogenetic comparative method necessitates branch lengths, we dated the trees using the minimal age of apparition of each node. Branch lengths reflect, then, the time between the apparition of two successive nodes, or between a node and a leaf. Age of a given node was computed as the minimal age of the oldest fossil included in it and was taken from the Paleobiology Database (https://paleobiodb.
We used the package MPSEM  (Guénard et al. 2013) , with the software R v. 3. 5. 1 (R Development Core Team 2008). The script, data, and phylogenies used are given in the Supplementary Files. Isotopic Geochemistry. -Chemical preparation and measure of the oxygen isotope composition of tooth and bone apatite phosphate were realized following the protocol published in  Lécuyer et al. (1993)  and detailed in the Supplementary Material and Methods. Phosphate from tooth enamel and bone was isolated in the form of silver phosphate crystals that were pyrolyzed at 1450°C using an elemental analyzer and then analyzed with an isotopic ratio mass spectrometer. The measurements were calibrated by performing a two-point calibration using the international reference materials NBS120c (δ 18 O VSMOW = 21. 7‰; Lécuyer et al.
Acknowledgments We thank  L. Cavin ,  R. Allain , and  V. de Buffrénil  for providing access to the studied material. We thank  S. Morel  for the preparation of cross sections and  D. Germain  for the access to the hard tissue collection. The histology part of the study was designed by J. C. ; the isotopic geochemistry part was designed by R. A. and C. L. All authors contributed equally in the analyses. M. G. F. -B. wrote the first version of the article, and all authors contributed equally to its improvement. We thank  Roger Seymour , another anonymous reviewer, and our editor  Matt Friedman  for their constructive comments.
, 2020). A box of  5 × 5 MODIS pixels and a time slot of ±30 min around the satellite overpass is considered to be a good compromise; however the window-size dependence is small, and this compromise is more dictated by statistics rather than physics  (Ichoku et al. , 2002). Gridded daily or monthly mean MODIS level 3 AODs have also been demonstrated to fit the AERONET retrievals  (Ruiz-Arias et al. , 2013; Wei et al. , 2019). As the objective of the paper is to address the ability of MODIS data to reflect aerosol changes in a specific area rather than the validation of the retrieval algorithm, hereinafter we rely on the gridded level 3 MODIS product of the AQUA satellite (namely MYD08_D3) from 2003 to 2019. MODIS AQUA has been selected following the recommendations of  Wei et al. (2019)  for long-term trend analysis.
For the purpose of satellite validation, the satellite AOD and AE of the nearest cell to the photometer location are extracted. We have adopted the evaluation metrics proposed by  Sayer et al. (2014) , including the linear correlation coefficient, the median bias, the root mean square error, the mean absolute percentage error and the fraction of data falling within the MODIS expected error (EE) given by EE = ±(0. 05 + 0. 15 × AOD).
Daily statistics A total of 2323 handheld-sun-photometer observations (including data collected during the 2006 campaign) have been acquired. Starting and ending dates are reported in Table  1  along with the number of observations, median and interquartile range (IQR) of AOD and AE distributions. We select the AERONET data until the end of the CALITOO observation period, i. e. , March 2017, for a total number of 1248 daily observations. There is an excellent time coverage for the stations of Lamto and Cotonou by the CALITOO observations. Observations were performed for 66 % of the time in Cotonou and 68 % in Lamto. As a comparison, this rate is 68 % for the automatic sun photometer in Koforidua, indicating that handheld measurements can be as representative as the automatic ones. This rate drops to 24 % in Abidjan and 39 % in Savè due to operating issues leading to gaps in the time series.
It can be noticed from Fig. 7  that there is no clear definition of AOD and AE thresholds for each aerosol category, and the scatterplots of Fig. 7  reflect the high mixing of different aerosol types. The absolute error in AE is a function of the relative error in AODs and depends on the spectral range investigated  (Hamonou et al. , 1999; Wagner et al. , 2008). Typical error in AE is ±0. 3 for an AOD of 0. 2, and there is a risk of overinterpreting AE variations. In this paper we classify the daily observations according to the AE values using a simple statistical analysis and a threshold on AOD. The whole sun photometer data set is divided into three quantiles. The first third corresponds to AE ≤ 0. 45, and observations having an AOD ≥ 0. 8 are labeled "coarse dust", while observations having an AOD < 0.
8 are labeled as "mixed". The threshold on AOD corresponds to the third quantile of AOD distribution and is used to better identify dust events. The last third corresponds to AE ≥ 0. 80 and is labeled "urban-like". The data having 0. 45 < AE < 0. 80 fall into a "mixed" category, being more populated than the two others. This rather crude classification enables us to identify the main aerosol influence with a significant number of observations in each category. Table  3  presents the typology of the sites according to the aforementioned classification. Comoé is the most influenced by coarse dust aerosol (20 %), followed by Ilorin (12. 7 %), Savè (12. 2 %), Cotonou (10. 2 %) and Lamto (8. 6 %). The southwestern sites, Abidjan (3. 7 %) and Koforidua (4. 9 %), are less influenced by dust events than the eastern sites.
5 -to-AOD conversion factors to the daily MODIS AOD observations between 2003 and 2019. As the MODIS AE cannot be used to classified the daily aerosol observations, we have applied the mean seasonaly adjusted PM 2. 5 /AOD ratios. The database of daily AOD observations consists of 2675 observations for the area of Abidjan and 3018 for the area of Cotonou, corresponding to about 160 observations per year on average. To increase the number of observations per year, we introduce a new area named SWA, located between 7 • W and 5 • E and between 6 • N and 4 • N. There is at least one MODIS level 3 observation per day in the largest coastal area that encompasses both cities. Mean retrieved PM 2. 5 is 28. 3 ± 22. 2 and 30. 5 ± 24 µg m -3 in Abidjan and Cotonou, respectively. Almost all the years have an annual average above the EU target value of 25 µg m -3 , except 2003, 2013, 2014 and 2019.
The trend of monthly means is retrieved after a seasonal decomposition using a procedure based on loess  (Cleveland et al. , 1990). The trend does not have an obvious pattern; however one can observe a pseudocycle of 4 to 5 years. We can notice a decrease in the mean concentrations after 2017. The drop in 2018 and 2019 is due to lower AODs for those 2 years. The annual mean AOD decreases by 20 % between 2017 and 2019; however we did not investigated further a possible explanation for the decrease. The Mann-Kendall seasonal trend test  (Hirsch et al. , 1982)  applied to monthly means is not significant over the whole 2003-2019 period. To further investigate a possible trend in the urban-like aerosol, we have selected the data acquired during the short dry period (August-September), during which no dust events have been detected in our sun photometer data set.
Conclusions An increase in the anthropogenic emission of atmospheric pollutants is expected as a result of the massive urbanization of the Gulf of Guinea. The scarcity of ground-based observations in SWA is still a limiting factor for a comprehensive understanding of the short-time trend over growing African cities. Moreover, the large influence of natural aerosol emission in SWA produces a complex mixing of particles in the urban atmosphere of SWA cities. In this paper, sun photometer and satellite observations have been used to characterize the magnitude and seasonal behavior of the aerosol optical depth in SWA. We have set up a small network of lightweight handheld sun photometers that provides an unprecedented data set on the AOD over SWA between 2015 and 2017. This data set was complemented by additional measurements from AERONET data and observations obtained during a previous campaign in 2006 in Côte d'Ivoire.
Data availability. Handheld sun photometer and PM 2. 5 data are available at http://baobab. sedoo. fr/ (last access: 5 February 2021). AERONET sun photometer data are available at https://aeronet. gsfc. nasa. gov/ (last access: 5 February 2021). MODIS aerosol data can be downloaded from https://ladsweb. modaps. eosdis. nasa. gov/ (last access: 5 February 2021) Author contributions. JFL, CL, ABA and VY designed the research and organized the field experiment. MoB, JD and MaB carried out the measurements and the analysis. JFL finalized the analysis and prepared the manuscript with contributions from all co-authors. Competing interests. The authors declare that they have no conflict of interest. Special issue statement. This article is part of the special issue "Results of the project "Dynamics-aerosol-chemistry-cloud interactions in West Africa" (DACCIWA) (ACP/AMT inter-journal SI)".
Acknowledgements. The authors greatly thank all the operators who contributed to the acquisition of handheld-sun-photometer observations at the  Lamto geophysical station, CEG Dantokpa and CEG Savè. We acknowledge the AERONET and PHOTONS sunphotometer networks, their staff, and the PI of the sites for their work to produce the data set used in this study (http://aeronet. gsfc. nasa. gov/, last access:  5 February 2021 ). Handheld CIMEL data were processed by  Isabelle Jankowiak  ( Laboratoire d'Optique Atmosphérique, Université Lille 1 ). Financial support. This research has been supported by the  FP7 Environment  (DACCIWA; grant no. 603502 ). Review statement. This paper was edited by  Evangelos Gerasopoulos  and reviewed by two anonymous referees.
Bacterial diversity: DNA extraction, sequencing, and data analysis Total DNA (metagenomic DNA) from untreated and treated OS (mesocosms) was extracted using the PowerSoil R ⃝ DNA isolation Kit (MoBio Laboratories). Extractions were performed following manufacturer's instructions with slight modification in incubation times as described  (Terrisse et al. , 2017). The DNA quality and size were verified by electrophoresis on a 1% w/v agarose gel in Tris-Borate-EDTA buffer. The DNA extracts were aliquoted and stored at -80 • C until use. The bacterial diversity was analysed by Illumina MiSeq 16S rRNA gene sequencing. MiSeq sequencing was performed at the PREMICE platform (Pau, France). The variable V1-V3 regions of the 16S rRNA gene were amplified with 27F (5 ′ -AGRGTTTGATCMTGGCTCAG-3 ′ ) and 519R (5 ′ -GTNTTACNGCGGCKGCTG-3 ′ ) primers as described  (Jeanbille et al.
, 2016). The 16S rRNA gene sequence reads were analysed using the QIIME package  (Kuczynski et al. , 2012)  as previously described  (Terrisse et al. , 2017). Briefly, after removing low-quality and chimera sequences, the high-quality sequences were clustered into Operational Taxonomic Unit (OTU) using a 97% sequence identity threshold with UCLUST  (Edgar, 2010). The OTU table was filtered for low abundance OTUs followed by a subsampling-based normalisation implemented in QIIME. The full taxonomic classification was performed by UCLUST querying the Greengenes database  (McDonald et al. , 2012) , a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB. The bioconductor Phyloseq package  (McMurdie and Holmes, 2013)  was used for alpha and beta diversity analysis, as well as graphic representation.
Statistical analyses Statistical analysis for comparing the effect of incubation time (90, 120, 150, and 180 days) and/or treatment (HO_only, C1, C1x, C1_C1x, NP, NP_C1, NP_C1x) on: (1) Aliphatic hydrocarbons alkanes, PAHs, TPH, metals and seeds germination was performed using PROC MIXED procedure in SAS (SAS version 9. 4; Institute. , 2014). Incubation time and treatment were modelled and along with the interactions were considered as fixed effects, and blocks (replications) along with the interactions were considered as random effects in the mixed model to fit this split split-plot design. The mean comparisons were done using Tukey's Honest Significant Difference (HSD, SAS Institute, 2014) with an alpha (α) of ≤ 0. 05. Multivariate analyses for comparing bacterial communities (clustering, CA, and CCA) were performed with MVSP v3.
12d software (Kovach Computing Service, Wales). The two-ways repeated measures Analysis of Variance (ANOVA) was used to test the significance (p ≤ 0. 05) of incubation time (90, 120, 150, and 180 days) and treatment (HO_only, C1, C1x, C1_C1x, NP, NP_C1, NP_C1x) on: (1) Aliphatic hydrocarbons alkanes, PAHs, TPH and metals changes in concentration, (2) microbiological response (cultivable oil-degrading and non-cultivable detected bacteria), and (3) seeds germination. ANOVA was performed in R Studio using the ''aov'' function of the vegan library  (Oksanen et al. , 2013). Significance levels of each relationship were determined from the p value and recognised as significant where p < 0. 05. Linear Spearman regression analysis was performed to determine significant (r 2 > 0. 75) correlations between chemical data, and microbial or ecotoxicological response.
Objective To improve classification of movement behaviours in free-living accelerometer data using machine-learning methods, and to investigate the association between machine-learned movement behaviours and risk of incident cardiovascular disease (CVD) in adults. Methods Using free-living data from 152 participants, we developed a machine-learning model to classify movement behaviours (moderate-to-vigorous physical activity behaviours (MVPA), light physical activity behaviours, sedentary behaviour, sleep) in wrist-worn accelerometer data. Participants in UK Biobank, a prospective cohort, were asked to wear an accelerometer for 7 days, and we applied our machinelearning model to classify their movement behaviours. Using compositional data analysis Cox regression, we investigated how reallocating time between movement behaviours was associated with CVD incidence.
Device-based measures of movement behaviours in UK Biobank Between June 2013 and December 2015, participants with a valid email address (excluding North West region due to participant burden concerns) were invited to wear an accelerometer. A total of 106 053 consenting participants were sent an Axivity AX3 wrist-worn triaxial accelerometer to be worn on the dominant wrist for 7 days. 24 A readable accelerometer dataset was obtained from 103 683 participants. Initial data processing followed established methods:  24  participants were excluded if the device could not be calibrated, if more than 1% of readings were 'clipped' (fell outside the device's dynamic range of ±8 g) before or after calibration, if they had less than 3 days of data or did not have data in each 1-hour period of the 24 hour cycle (with non-wear time defined as unbroken episodes of at least 60 min during which SD of each axis of acceleration was less than 13.
CAPTURE-24 CAPTURE-24, an accelerometer validation study of 152 adults aged 18-91 recruited by advertisements in Oxford, UK, in 2014-2015, 11 was used to develop machine-learning classification methods. Participants were asked to wear an Axivity AX3 wrist-worn accelerometer for 24 hours, wear a Vicon Autographer wearable camera while awake during that period, and keep a time use diary. 10 Using camera images and time use diaries, trained annotators annotated accelerometer data with labels from the Compendium of Physical Activities. 25  Finegrained labels were mapped to sleep, sedentary behaviour (eg, sitting working at a computer, watching television), light physical activity behaviours (eg, cooking, self-care) and moderateto-vigorous physical activity behaviours (MVPA; eg, walking the dog, cycling) (see online supplemental methods and online supplemental table 1).
Machine-learning for behaviour classification Using this labelled data from the CAPTURE-24 study, a balanced Random Forest with 100 decision trees was trained to classify the behaviour in 30 s time windows using 50 rotation-invariant time and frequency domain features of the accelerometer signal (online supplemental table 2). As the Random Forest did not use time sequence information, the behaviour sequence was smoothed using a Hidden Markov model. This model treated the Random-Forest-predicted behaviours as 'emissions' from an underlying true behaviour sequence, and used the Viterbi algorithm to identify the most likely underlying true sequence given the observed sequence. 27 Transition probabilities between different behaviours were determined using camera validation data, and probability of the Random Forest predicting each behaviour conditional on the true behaviour was estimated using out-of-bag estimates from the Random Forest.
To assess the performance of our model in the age group of interest, we also calculated mean per-participant accuracy and Cohen's kappa in participants aged 38 years or older (age group as in a release version of this dataset). We also report a model trained in this age group only in online supplemental methods. Although overall comparison is precluded by the different behaviours classified, we compared precision and recall for MVPA using our model compared with using the standard cut-point of 100 mg. 28.
Compositional data analysis for movement behaviour data A compositional data analysis approach was used in the statistical analyses. This approach uses log-ratios (log-transformed ratios between movement behaviours) to describe and adjust for the movement behaviour composition. By using ratios between behaviours, the relative time in different behaviours, rather than the absolute time in any given behaviour, is modelled. For this analysis, we used isometric log-ratio pivot coordinates, a particular set of log-ratios which is widely used in movement behaviour research (see online supplemental methods for more detail). 19 29 ur results are described by pairwise time reallocation plots, which show the HR associated with reallocating time from one behaviour to another behaviour, and by a plot showing the HR associated with particular reallocations of time between behaviours (eg, reallocating 1 hour/day to sedentary behaviour from all other behaviours proportionally).
Separate analyses were also performed in women and men, and in those aged under 65 vs over 65 at the time of accelerometer wear. All adjustment variables were measured at baseline assessment (online supplemental table 3 gives more details on all variables used in the analysis), and variables were not adjusted for if they were likely mediators of the association between movement behaviours and CVD. Participants with missing data in any adjustment variable were excluded. The proportional hazards assumption was tested component-wise and globally using the Grambsch-Therneau test with the Kaplan-Meier transformation,  29  and there was no evidence (at the 5% level) that it was violated in the main analysis. Plots of the Schoenfeld residuals were also examined. Results were reported according to Strengthening the Reporting of Observational Studies in Epidemiology guidelines (STROBE; see online supplemental material),  33  and all CIs are 95% CIs.