dataesr/scientific-paragraphs-categorization huggingface dataset:
Satellite-Derived Rainfall Estimates From the Modern Period Even in the 21st century, there is a severe lack of in-situ rain gauge data over Africa; South Africa is probably the best populated in terms of rainfall measurements, but in other countries such as Angola or Namibia rain gauge data are sparse or non-existent (e. g. ,  Williams et al. , 2007 Williams et al. , , 2008 Williams et al. , , 2010)). The CenTrends precipitation data set  (Funk et al. , 2015)  contains measurements going back to 1900, but only for a small number of countries in East Africa. Likewise, although the Global Historical Climate Network database  (Durre et al. , 2010 (Durre et al. , , 2008;; Menne et al. , 2012)  does contain temperature measurements going back to 1861, precipitation measurements do not begin until the 1950s and are again relatively sparse in Africa.
Therefore, a possible solution to the problem of data unavailability is to use satellite-derived rainfall estimates (SREs), which offer near-uniform coverage at relatively high spatial resolution from the 1980s onwards. Here, TAMSAT is used as a comparative tool for evaluating the PI simulations of the DeepMIP models. A caveat here is that the models are showing precipitation simulated under PI boundary conditions, whereas TAMSAT is showing precipitation from the late 20th and early 21st century (referred to here as modern) and will therefore contain an anthropogenic signal; this, however, is unavoidable given the lack of PI precipitation observations. It is expected that the biases between comparing the models to PI precipitation vs. comparing them to modern precipitation will be less than the biases between the models themselves (i.
, 2019). Geodetic coordinates of occurrences were obtained for the NLR of each plant group from the Global Biodiversity Information Facility (see Table  S2  in Supporting Information S1). These occurrence data sets were then filtered for uncertain, exotic, and superfluous occurrences, as well as subjected to a random resampling to avoid regional overrepresentation of densely sampled areas. A climatic envelope for each plant group (see Table  S2  in Supporting Information S1) was then generated by extracting precipitation data (MAP, wettest month (WMP), driest month (DMP), warmest and coldest quarter precipitation (WQP and CQP, respectively) and the precipitation seasonality coefficient [PS]) using the DISMO package in R  (Hijmans et al. , 2005). A probability density function was then generated for each co-occurring plant group by testing the likelihood of the plant group occurring at 100,000 unique extant combinations of MAP, WMP, DMP, PS, WQP, and CQP.
comm. ). The MME precipitation anomaly for this experiment is shown in Figure  6a ; it should be noted that, although six models conducted this experiment (CESM1. 2_CAM5, COSMOS-landveg_r2413, GFDL_CM2. 1, HadCM3B_ M2. 1aN, HadCM3BL_M2. 1aN, and MIROC4m), only the latter four are included here in the MME, to be consistent with the analysis of the CO 2 component (Section 3. 2. 2). From the available DeepMIP results, it is impossible to disentangle the boundary conditions and ascertain which is dominant in causing the precipitation response; in an ideal world, sensitivity experiments would be conducted to introduce each boundary condition individually, but this is not possible with the results currently available on the DeepMIP database. Nevertheless, based on the results it is possible to theorize which of these boundary conditions might be causing this MME precipitation response.
DeepMIP Models' Eocene Simulations vs. Proxy Data In this final section, the focus is on comparing precipitation from selected DeepMIP early Eocene simulations (using the MME from the same two samples as discussed above) with newly available precipitation reconstructions (described in Section 2. 3. 2). Before the results are presented, however, several sources of uncertainty in the proxies and models must be noted, aside from analytical uncertainty that is expressed in the reconstructed confidence intervals. First, the fossil plant assemblages analyzed here have broad age constraints. Paleofloral assemblages may capture a snapshot within those age constraints that deviated climatically from the average climatic conditions of a specific age that the model was calibrated on. In addition, fossil plant assemblages tend to preserve better in wetter climates, with drier climates lacking the water bodies needed to preserve plant fossils.
The uncertainties discussed above are likely contributing to the lack of consistency presented in some of these model-data comparisons, such as the MME showing better agreement with the reconstructions at lower and higher levels of CO 2 , but not in between (e. g. , the 3x simulation), but this is, at present and given the data sparsity, unavoidable. In conclusion, therefore, this study has shown that the DeepMIP models are able to approximately reproduce the modern African precipitation and, in response to rising CO 2 , suggest an enhancement of precipitation in this region associated with increasing temperatures and changes to low-level circulation. At very high levels of CO 2 the models may be too wet, relative to proxy reconstructions. However, this might be because the NLR proxy approach has difficulty generating MAP values above modern, or connected to the relatively few early Eoceneaged data points within the reconstructions (meaning some of the comparisons here were necessarily made with data from the middle or late Eocene).
Acknowledgments CJRW acknowledges the financial support of the  UK Natural Environment Research Council  funded  SWEET  project ( Super-Warm Early Eocene Temperatures ), and that of the  European Research Council. WLC and AAO acknowledge funding from  JSPS KAKENHI  and  MEXT KAKENHI , and are grateful to  JAMSTEC  for use of the Earth Simulator. The numerical simulations performed by DKH and AMB used resources provided by the  Swedish National Infrastructure for Computing (SNIC) at the National Supercomputer Centre (NSC) , partially funded by the  Swedish Research Council. YD and JBL thank  GENCI  for providing access to the HPC resources of TGCC. PAM thanks  Evgeny Volodin  and  INM RAS  for the help with INMCM simulations. GK acknowledges financial support by  PACES  through the Helmholtz association and the computing center of the  Alfred Wegener Institute in Bremerhaven  and the  DKRZ in Hamburg (Germany)  for computational resources, infrastructure, and support.
Data Availability Statement TAMSAT data are publicly available to download at https://www. tamsat. org. uk/; please see  Maidment et al. (2014 Maidment et al. ( , 2017) )  and  Tarnavsky et al. (2014). The paleobotanical precipitation estimates compiled here are available as a spreadsheet, available to download at  Williams (2022). The DeepMIP PI and Eocene simulations are available by following the instructions at https://www. deepmip. org/data-eocene/; please see  Hollis et al. (2019).
Data and code availability In situ hybridization experiments were carried out using HPLC-purified 5 0 digoxygenin labelled DNA-Oligoprobes designed to specifically bind 18S rRNAs of notothens (AGAGCATCGAGGAGGCGCCGAG AGGC) or Notoxcellia (TAGGAATTCCTCGTTCAAGACG) (Genewiz, Cambridge, MA, USA). The in situ hybridization protocol was extensively adapted from the protocol described by  Freeman (2009) , which started with sections of formaldehyde fixed samples embedded in paraffin. In detail, samples fixed with 4% paraformaldehyde (PFA) and stored at À20⁰C in 100% methanol were rehydrated and processed for cryosectioning at 15 mm. Serial sections were deposited on alternate slides, air dried, and stored at À20 ⁰C until in situ hybridization as follows. First, the sections were rehydrated in TBS pH 8.
041 mm. MicroMRI and microCT images are available in MorphoSource Project 000405843. Resulting microMRI and microCT images were analyzed with Amira 5. 6 (FEI, Visualization Sciences Group) for segmentation and interactive pdf models (available in USAP-DC Project p0010221) were generated using Adobe Acrobat 3D v8 Toolkit. Volume fraction of tumors relative to the total body volume of the specimen was based on the microCT data using stereological principles as described by  Mu ¨hlfeld et al. (2010). In short, volume fraction was estimated by choosing 15 equally spaced parallel transversal sections spanning the entire body and applying a systematic uniform point grid on each section plane. Volume fraction was calculated by counting the number of test points intersecting either tumors or body using the Cavalieri estimator.
Morphometric analyses Using the standard length (SL, cm) and total weight (W, g) of each specimen (available in USAP-DC Project p0010221), we analyzed the length-weight relationship and Fulton's Condition factor (K) of healthy and diseased fish. We first tested the effect of SL, sex, and tumor as a binary trait (presence/absence) and all their interactions on W. This full model was pruned in a step-wise manner until only factors significantly affecting the weight were retained. The quality of the resulting model was validated by the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) (Data S4A). Only SL and the tumor status variables were significant factors in the length-weight relationship. Linear regressions were then applied separately to the length-weight relationships of visually healthy and diseased specimens.
Finally, we compared K of healthy and diseased fish using a one-sided t-test after verifying applicability using Levene's and Shapiro tests. These analyses were repeated using the tumor factor as a continuous variable defined by percentages of body surface affected by the parasites (see Surface analysis) (Datas S4B and S4C). All statistical analyses were performed using R software v. 4. 0. 3 (R Core Team, 2017) through RStudio v. 1. 3. 1093 (R Studio Team, 2020) and graphs were generated using ggplot2 package v. 3. 3. 3  (Wickham, 2011).
QUANTIFICATION AND STATISTICAL ANALYSIS Details of statistical analysis are provided within the relevant figure legends, their legends, and associated detailed methods. All statistical analyses were performed using R v4. 0. 3 (R Core Team, 2017) through RStudio v. 1. 3. 1093 (R Studio Team, 2020). Metagenomic analyses (n = 6 ''healthy'' and n = 6 ''tumor'') were performed with Kaiju v. 1. 7. 4  (Menzel et al. , 2016)  using a custom database. Taxonomic levels were obtained using the function kaiju2table and taxonomically classified contigs were analyzed using Bray-Curtis dissimilarity in the vegan v. 2. 5-7 package  (Oksanen et al. , 2020)  and plotted in a non-scaled principal coordinates analysis (PCoA) using the ape v. 5. 4-1  (Paradis and Schliep, 2019 ) and ade4 v. 1. 7-16 (Dray and Dufour, 2007)  packages.
ACKNOWLEDGMENTS We thank the captain and crew of the  ARSV Laurence M. Gould , the personnel of the  U. S. Antarctic Program  for assistance in Chile, at sea, and at  Palmer Station , as well as the  logistics in Denver, CO  for their support related to the Antarctic fieldwork required for this study. We also thank the  University of Oregon Fish Histology Group. We are grateful for our Antarctic fish research colleagues who responded with surprise, interest, and concerns after we contacted them about this pathologic outbreak. Contribution [423] from the  Marine Science Center at Northeastern University. The work behind this scientific publication inspired a short scientific graphic novel A mysterious disease in Antarctic fish publicly available at https://blogs. uoregon. edu/antarcticxcell/.
Software and algorithms Trimmomatic v0. 39  (Bolger et al. , 2014)  http://www. usadellab. org/cms/? page=trimmomatic metaSPAdes 3. 14. 0 (Nurk et al. , 2017) http://cab. spbu. ru/software/spades/ Kaiju v. 1. 7. 4 (Menzel et al. , 2016) https://kaiju. binf. ku. dk/ vegan v. 2. 5-7 R package (Oksanen et al. , 2020) https://cran. r-project. org/web/packages/ vegan/index. html ape v. 5. 4-1 R package (Paradis and Schliep, 2019) https://cran. r-project. org/web/packages/ape/ index. html ade4 v. 1. 7-16 R package (Dray and Dufour, 2007) https://cran. r-project. org/web/packages/ ade4/index. html ggplot2 package v. 3. 3. 3 (Wickham, 2011) https://cran. r-project. org/web/packages/ ggplot2/index. html MAFFT v. 7. 407 (Katoh et al. , 2019) https://mafft. cbrc. jp/alignment/software/ BMGE v. 1. 12 (Criscuolo and Gribaldo, 2010) https://bioweb.
The 2 3 150 bp libraries were sequenced on an Illumina NovaSeq6000 sequencer at Psomagen Inc. (USA). Raw reads were deposited in NCBI SRA under BioProject PRJNA789574. Demultiplexed reads were trimmed using Trimmomatic v0. 39  (Bolger et al. , 2014)  and de novo assembled using metaSPAdes  (k = 33, 55, 77) 3. 14. 0 (Nurk et al. , 2017). Resulting contigs >750 nts were analyzed against the NCBI RefSeq viral protein database to identify viral-like sequences using blastx. Each library was further analyzed with Kaiju v. 1. 7. 4  (Menzel et al. , 2016)  using a custom database generated from the NCBI NR database retrieved on July 24 th , 2021, and containing all viruses, archaea, and bacteria sequences, as well as microbial eukaryotes and Trematomus spp. fish sequences (available in the United States Antarctic Program Data Center USAP-DC Project p0010221).
PCR amplifications DNA was extracted from the same sample sets used for the metagenomic analysis using the Qiagen DNeasy Blood and tissue kit (Hilden, Germany). DNA concentrations were measured with a Qubit 3 fluorometer, and 2 ng of DNA was used as input for each PCR reaction. PCR amplification of X-cell 18S rRNA small subunit (18S SSU rRNA) fragments were performed at the annealing temperature of 55⁰C, 40 cycles of PCR using the primers presented in Figure  2  and Data S2. Amplification of the notothenioid fish mitochondrial marker cytochrome c oxidase I, mitochondrial (mt-co1) was performed as previously described  (Desvignes et al. , 2019). PCR amplification accuracy was verified by sequencing PCR products in both directions at GENEWIZ (Cambridge, MA, USA) followed by BLAST searches in the NCBI nucleotide database.
Phylogenetic tree reconstruction For phylogenetic tree reconstruction, we gathered sequences previously used to decipher the phylogenetic position of the Xcellidae family in the superphylum Alveolata  (Freeman et al. , 2017). The NCBI nucleotide database was searched for newly added, representative sequences from Xcellidae and from all sister lineages of the phylum Perkinsozoa, which includes Perkinsus spp. , Parvilucifera spp. , Snorkelia sp. , Tuberlatum coatsi, and Dinovorax pyriformis. Several undescribed alveolate lineages not belonging to Xcellidae were excluded from subsequent analyses. Combined sequences were aligned with MAFFT v. 7. 407 using the E-INS-i algorithm and otherwise default parameters  (Katoh et al. , 2019). The resulting alignment was visually corrected and further curated with BMGE v.
Convective, variable rain Figure1 is a plot of the time height MRR vertical air velocity, attenuation corrected rainfall rates using observations which were collected by one of the MRR radars as part of a National Science Foundation project and operated by the College of Charleston located near Charleston, South Carolina. It is located on property owned by the College of Charleston Foundation that is used for a variety of ecological, educational, and research purposes (e. g. , see  [15] ). The methodology for the correction of the data for vertical air velocity and attenuation is as explained in  [7]. As one would expect for this time of year in South Carolina, the rain is associated with convection having a wide-range of rainfall rates. The most noticeable feature overall is the vertical structure of the rain that, of course, is not surprising in convective rain.
The results are illustrated in Figure2a for the temporal dimension and in Figure2b for the vertical direction. Obviously, there are significant differences between the two. The grey horizontal lines largely correspond to symmetrical regions in the fluctuations so that  should be relatively constant. These regions are then used in the analyses. The blue line in Figure2b highlight the ever changing values of the fluctuations so that  would also be ever changing. Hence the entire region is selected as one block of data as indicated by the extended grey line. The more symmetric region of fluctuations is identified by the second grey line. In each of these sections, the Bayesian analysis is performed to determine the numbers of contributing mean value components (Nb). In addition, the  analyses were performed separately to yield the  relative dispersion RD  = || /   where || is the absolute value of  and   is the standard deviation of  given by ( ) where n is the number of measurements in the string of observations (from Jameson et al.
3. This latter requirement is designed to satisfy the directional independence of statistical homogeneity. The results are illustrated in Figure  4  where the contours of shading indicate where statistical homogeneity is possible (brighter areas) and where it is not (darker areas) are over laid on the rainfall rates. The first obvious feature is that with the exception of a tiny narrow region at the top-left, these data are all statistically heterogeneous. To see whether these results also apply to other data, we next consider three more sets of data all from June 3, 2019 and measured using a NASA MRR-Pro radar located at the Wallop's Island Flight Facility. The rainfall rates have already been previously determined as explained in  [7] ). These observations were broken into three segments denoted as early, middle and later pieces.
On the other hand the general similarity of the various fits suggest that it might be useful to combine the data in the two dimensions. This is done next by computing the one-dimensional radial spectra regardless of time or altitude as illustrated in Figure  9. This is accomplished here first by converting the temporal axis into a spatial dimension assuming a mean advection speed of 1 m s -1. The 2D horizontal-vertical coordinate system of the original 2D power spectrum by first using the fft2 routine in Matlab  and then multiplying by its complex conjugate. This 2D power spectrum of values at (Δx,Δy)is then converted into 2D polar coordinate system of (Δr,θ) values of the power spectrum so that the radial spectra can then be computed by integrating over all the angles θ for each Δr. The intercept at k = 1 provides a good measure of the total variability of the data at the different times.
Concluding Remarks In this paper, fine scale rainfall rates were calculated using Doppler radar measurements properly corrected for vertical air velocity and attenuation. This produced timeheight profiles of rainfall rates which could be statistically analyzed over periods of varying length up to 1. 28 km height at 10 m/ 10 s resolution for convective types of rain and for one case of less intense, steadier rain. With the exception of one period of steadier rain, the data were all found to be statistically heterogeneous with only very localized pockets of statistically homogeneous rainfall. Consequently, in general it was not possible to construct meaningful correlation functions or to use the Khintchine-Wiener theorem to transform such relations into radial power spectra. Instead it was necessary to directly compute 2-D power spectra using a Fourier transform after first assuming a fixed advection velocity of 1 ms -1 so that the temporal axis could be transformed into a spatial axis These, in turn, could be used to compute 1D radial power spectra applicable to each case only.
The injector and the detector were maintained at 250 • C. FAMEs were identified by comparison with commercial and synthetic standards. The data were processed using the EZChrom Elite software (Agilent Technologies, Massy, France) and reported as a percentage of the total FAs. For analysis, 25 plasma FAs (saturated, monounsaturated and polyunsaturated) levels were considered. Long-chain FAs were synthesized through desaturation and elongation; then, enzymatic activities indexes were estimated through the calculation of the precursor to product ratios of individuals FAs as follows: ∆5 desaturase (C20:4 n-6 to C20:3 n-6) and ELOVL2/5 (C22:4 n-6 to C20:4 n-6 and C22:5 n-3 to C20:5 n-3).
13 Data on survival and subsequent hospital admissions were obtained either at a follow-up visit or by a telephone call at 12 months. Detailed methodology of the ESC-HFA EORP HF Long-Term Registry was previously described,  5, 12, 13  and the protocol is available as online supplementary Appendix S2. The registry was approved by local ethical review boards according to the regulations of each participating country. All patients gave written, informed consent, unless exempt by the local ethics committee. The current study was a retrospective analysis of data from the ESC-HFA EORP HF Long-Term Registry, and included patients hospitalized for AHF who had an echocardiogram with EF assessment performed during hospitalization. Patients with acute coronary syndromes (ACS), as well as patients with significant (moderate to severe) aortic stenosis were excluded.
Statistical analysis Baseline characteristics were at time of hospital admission. Categorical data were presented as percentages and compared with the chi-square test. Continuous variables were reported as median and interquartile range (IQR) or as mean ± standard deviation (SD) as appropriate, and tested with Kruskal-Wallis test. We report hospital duration and crude in-hospital mortality. Due to the low absolute number of in-hospital deaths, we did not perform an analysis of predictors of in-hospital death in EF subgroups. Post-discharge outcome analyses were performed excluding patients who died in hospital or were lost to follow-up and the time was calculated from date of discharge. For all post-discharge deaths (except all-cause death) competing causes of death were censored unless stated otherwise.
For patients with missing information on the date of rehospitalization (10% of all rehospitalizations), the time to hospitalization was imputed with half the time to last follow-up. This was based on the observation that median time to rehospitalization in patients with available dates of rehospitalization, was 0. 5 of the time to last follow-up. To avoid bias due to data not missing at random, we performed multiple imputation for the covariates included in the models using Multivariate Imputation by Chained Equations (MICE) for 10 datasets and 10 iterations. 14 Variables included in the imputation model were the same as included in the Cox regression analysis. The death outcome was included as the Nelson-Aalen estimator in the imputation model. The proportional hazards assumption was investigated using the scaled Schoenfeld residuals and possible outliers were visually inspected by plotting the dfbetas.
For all tests, the level of significance was set to 0. 05, two-sided. No adjustment for multiple comparisons were made and therefore the results should be viewed with some care. All statistical analyses were conducted using R version 4. 0. 2 (2020-06-22) (R Core Team 2019). All code used to perform the data handling and statistical analysis can be found at https://github. com/ KIHeartFailure/characteristicsOutcomesHeartFailureESC.
p is for difference between HFpEF, HFmrEF and HFrEF. Analysis included patients who survived to hospital discharge (n = 5781), and only those with both admission and discharge values available. For change in weight data were missing in 5%; sodium: 24%; estimated glomerular filtration rate (eGFR): 24%; haemoglobin: 28%; N-etrminal pro-B-type natriuretic peptide (NT-proBNP) or B-type natriuretic peptide (BNP): 88% of 5781 patients. Standard deviation (SD) for weight was 4. 6%, 6. 6% and 4. 3% for HFpEF, HFmrEF and HFrEF, respectively; for sodium 3. 3%, 3. 4% and 3. 4%, for eGFR 39%, 46% and 420%, for haemoglobin 42%, 14% and 28%, for NT-proBNP 55%, 111% and 114%, and for BNP 72%, 48% and 96% for HFpEF, HFmrEF and HFrEF, respectively. AF, atrial fibrillation; CABG, coronary artery bypass grafting; PCI, percutaneous coronary intervention.