dataesr/scientific-paragraphs-categorization huggingface dataset:
Depot → ktr Abs. → ka Central → CL (2) PK/PD Modeling Analysis for Urinary Na + /K + Ratios PK/PD modeling analysis was performed for urinary Na + /K + ratios by Nuventra Inc. (Durham, NC, United States). Data from each of the five groups in studies A and B were used to evaluate the PK/PD relationship between urinary Na + /K + ratios and average plasma concentrations of eplerenone or KBP-5074. Data from each study were analyzed independently according to a sigmoid E max model using the equations below. The maximum effect of eplerenone or KBP-5074 on Na + /K + ratio (E max ) was calculated with the assumption that the maximum possible response was the Na + /K + ratio from group 1 (Vehicle Sham group). Baseline (BSL) was defined as the Na + /K + ratio for group 2 (Vehicle Aldosterone group) on Day 25 and was estimated by multiplying E max by the effect of aldosterone (ALD).
Statistical Analyses Body weight, urine volume, food and water intake, and biomarker values were expressed as means ± the standard error of the mean. Differences among and within groups were evaluated by two-way ANOVA Tukey's multiple comparisons tests and the Sirius Red and Masson Trichrome Stain results were evaluated by unpaired t test using GraphPad Prism software v6. 0 (Irvine, CA, United States). A difference was statistically significant when p < 0. 05.
PK of Eplerenone and KBP-5074 Plasma concentrations for eplerenone and KBP-5074 were determined for all three dose groups on Day 1 and Day 26 at 1, 2, 4, and 7 h post first dose, except for the 4-h sample for one rat in the eplerenone study, which was inadvertently missed. The C max and AUC 0-last were calculated for eplerenone and KBP-5074 and are shown in Table  1. The data show a positive correlation between exposure and dose in vivo for both eplerenone and KBP-5074, with values increasing from the low to high doses and between Day 1 and Day 26. The PK parameters calculated in the PK/PD modeling analysis are shown in Table  2. Clearance (CL) estimates were 6. 17 and 0. 734 L/h with volume (V) estimates of 0. 034 L and 11. 6 L for eplerenone and KBP-5074, respectively. All percent relative standard errors (%RSE) were less than 50%, with the exception of the %RSE of V for eplerenone, which was 68.
(2013). It has also been investigated which parts of the client interact with and are important for binding to the chaperones. Regarding the transmembrane part of the clients, e. g. , the ADP/ATP carrier (Aac) or the GDP/GTP carrier of the inner membrane, the abovementioned integrated NMR study has provided only limited information on a residue-by-residue basis. The millisecond FIGURE 3 | charged residues, lower electrostatic potential, compared to the rest of the protein and compared to TIM9. 10 (panel E). Mapped conserved hydrophobic residues on TIM8. 13 model (purple, lower panel). Yellow: polar residues in the conserved hydrophobic positions leading to weaker binding of all hydrophobic clients. (D) Properties of TIM9. 10 chaperone (PDB: 3dxr). Upper panel: positive (blue), negative (red) charges and polar (green) residues.
Spy The ATP-independent periplasmic chaperone Spy (spheroplast protein y) can bind to both native (folded) and non-native proteins, but with higher affinity for the latter. Ability to bind both unfolded and folded clients enables client folding while bound to the chaperone  Mitra et al. (2021). It can be imagined that this ability could disrupt normal cell function by interacting with folded proteins. However, in the cell, levels of Spy are well controlled and Spy is only up-regulated in the stress conditions induced by protein aggregation  Quan et al. (2011). This stress-induced dimeric chaperone forms a cradle-like structure through an anti-parallel coiled-coil interaction of two 16 kDa monomers  [Quan et al. (2011) , PDB ID: 3o39]. Spy binds unfolded periplasmic or outer-membrane proteins with its cradle-shaped binding site, formed mostly by positively charged residues, and allows for their full folding while they are bound  Stull et al.
(2016). The structural and dynamical properties of Spy with a client, the small helical protein Im7, have been investigated independently by several groups, using NMR spectroscopy, MD simulations, crystallography and other biophysical methods  [Salmon et al. (2016 ), Horowitz et al. (2016) ,  He et al. (2016) ]. Im7 is an interesting case because in the absence of its cognate binding partner (colicin E7), several residues at its binding interface are in energetic conflicts, i. e. , they are restrained such that they cannot engage in the energetically favorable interactions with other residues, a situation termed local frustration  Ferreiro et al. (2014). The Spy-Im7 complex is an instructive example for understanding chaperone-client interactions. It is also an interesting case that highlights the difficulty of studying such complexes by crystallography: the high flexibility of the client and accordingly low electron density renders interpretation of the data difficult, possibly leading to mis-interpretation  Wang (2018) , which might have challenged one such study  Horowitz et al.
(1998)]. Huang et al. (2016)  reported affinities for binding of unfolded MBP and phosphotase A (PhoA), which are of the order of 0. 05 µM (MBP) to 0. 5 µM (PhoA); for shorter fragments of the clients (of the order of 30-80 residues) the K d are in the 1-70 µM range. SecB forms homo-tetramers (dimer of dimers) of ∼68 kDa [PDB: 1qyn,  Dekker et al. (2003) ]. Huang et al. (2016)  have used two different client proteins, the periplasmic maltose binding protein (MBP, 396 amino acids) and alkaline phosphatase A (PhoA, 471 amino acids) as client proteins, and investigated the formed complexes by NMR spectroscopy. MBP and PhoA were chemically unfolded (urea), and while they fold upon removal of urea in the absence of chaperone, SecB keeps them in an unfolded state. NMR spectroscopy experiments showed that both MBP and PhoA remain in an unfolded conformation upon binding SecB  Huang et al.
Citric acid was determined by MS-Omics (Vedbaek, Denmark) using a gas chromatography-mass spectrometry (GC-MS) method. Samples were derivatized as described by Smart et al. [15]  and the derivatization reaction was stopped by the addition of chloroform. Subsequently, the chloroform phase with derivatives was injected into the GC. All samples were analyzed in a randomized order. Analysis was performed using gas chromatography (7890B, Agilent, Santa Clara, CA, USA) coupled with a quadrupole mass spectrometry detector (5977B, Agilent). The system was controlled by ChemStation (Agilent). Raw data were converted to netCDF format using Chemstation. Then, the data were imported and processed in MATLAB R2018b (Mathworks, Inc. , Natick, MA, USA) using the PARADISe software described by  Johnsen et al.
[16]. Sample analysis for contents of short-chain fatty acids was carried out by MS-Omics as follows. Samples were acidified using hydrochloride acid, and deuterium-labeled internal standards were added. All samples were analyzed in a randomized order. Analysis was performed using a high polarity column (Zebron™ ZB-FFAP, GC Cap. Column 30 m × 0. 25 mm × 0. 25 µm) installed in a GC coupled with a quadropole detector and controlled by ChemStation, similar to the analysis for citric acid. Raw data was converted to netCDF format using Chemstation before the data was imported and processed in Matlab R2014b (Mathworks, Inc. ) using the PARADISe software [16].
Bioinformatic Analyses for Genome Annotation The presence of genes included in citrate metabolism pathways 1 and 2 were examined for a number of Carnobacterium spp. whole-genome sequences. A single nucleotide polymorphism (SNP) cladogram was constructed using Carnobacterium spp. sequences downloaded from NCBI GenBank (n = 21), RAST annotation server (n = 1), and the C. maltaromaticum isolates from the present study (n = 6). The annotations of the pathways were predicted via the RAST annotation server  [17] [18] [19]. The SNP tree was constructed by CSI Phylogeny 1. 4 (https://cge. cbs. dtu. dk, accessed on 17 June 2021) with ATCC 35586 as the reference genome. The select minimum depth at SNP positions was set at 10x. The select minimum relative depth at SNP positions was 10%. The select minimum distance between SNPs (prune) was 10 bp, while the select minimum SNP quality was 30.
Finally, the select minimum read mapping quality was 25 and the select minimum Z-score was set at 1. 96. Then, the phylogenetic tree was imported to iTOL (Interactive Tree of Life) for visualization (https://itol. embl. de, accessed on 17 June 2021)  [20]. Leaf sorting was set as the default and the tree was re-rooted at midpoint with ATCC 35586 as the reference genome. Detailed genome annotations were carried out using a combined bioinformatical approach including the RAST annotation server  [17] [18] [19] , SMART  [21] , Pfam 32. 0  [22] , Uniprot database  [23] , KEGG database  [24] , and local Blast within the CLC Genomics Workbench 10. 1. 1. Briefly, whole-genome assemblies were uploaded to the seed-based RAST annotation server for genome annotation and in MicroScope  [25]. To improve the annotation accuracy for genes found to be associated with citrate metabolism, based on the KEGG pathway website, sequence alignments for specific genes were downloaded from the Pfam and Uniprot databases.
Data Availability Statement: The genome sequences of Cm 4-1, Cm 6-1, Cm 1-2, Cm 3-1, and Cm 5-1 strains (BioProject ID: PRJEB40278) and of JIP 2891 and 10040100629 strains (BioProject ID: PRJEB40429) were uploaded to GenBank. The Carnobacterium divergens Cd 1-1 sequence was from the RAST annotation server (job ID #692064). Sequences of ATCC 35586, LMA 28, and the remaining strains were downloaded from GenBank. Accession numbers are listed in Figure  S1.
POU5F3 and SOX2 regulate constitutive expression of cNANOG in PGCs To further examine PGC-speci c cNANOG promoter activity and binding to the proximal enhancer, we generated four constructs harboring fragments of the -250/+70 bp region of the cNANOG promoter via deletion of the 5' upstream region. Among the four constructs, the -210/+70 bp, -170/+70 bp, and -130/+70 bp fragments still showed promoter activity in PGCs, while the -69/+70 bp fragment did not (Fig. 3a ). These results suggest that a positive transcriptional cis-regulatory element is located between -130 and -69 bp. Based on the ndings regarding cNANOG promoter activity described above, we predicted TFs with binding sites located between -130 and -69 bp of the cNANOG promoter using two software programs (PROMO and MatInspector).
TP53 suppresses cNANOG gene expression in PGCs Luciferase activity was at least 3-fold higher in PGCs transfected with the -210/+70 bp, -170/+70 bp, and -130/+70 bp fragments than in PGCs transfected with the -250/+70 bp fragment (Fig. 3a ). These results suggest that a negative transcriptional cis-regulatory element is located between -250 and -210 bp. To investigate suppression of cNANOG promoter activity, we predicted TFs that have binding sites within this region using two software programs (PROMO and MatInspector) (Fig. 4a ). Among the predicted TFs, TP53 is a suppressor of NANOG transcription, while ZIC2/3 and CEBP are positive regulators of NANOG transcription  (21, 33, 34). We further examined whether TP53 affects cNANOG promoter activity in PGCs by performing site-directed mutagenesis and comparing the mutant with the wild-type -250/+70 bp fragment (Fig.
CEBP transactivates the cNANOG promoter in ESCs To further investigate the potential transcriptional cis-regulatory elements in ESCs, we generated four constructs harboring fragments of the -442/+70 bp region of the cNANOG promoter via deletion of the 5' upstream region. Among the four constructs, the -407/+70 bp, -377/+70 bp, and -312/+70 bp fragments exhibited signi cantly reduced cNANOG promoter activity in ESCs (Fig. 5a ). These results suggest that a positive transcriptional cis-regulatory element is located between -442 and -407 bp. We analyzed the -442/+70 bp fragment using two software programs (PROMO and MatInspector) to identify important TF-binding sites that maintain the basal activity of the cNANOG gene in ESCs. Only a CEBP-binding site was identi ed between -442 and -407 bp (Fig.
To advance research on this topic, we built a large dataset that we shared as one of the challenges of the LifeCLEF 2020  (Goëau et al. 2020 ) and 2021 evaluation campaigns  ( Goëau et al. 2021). It includes more than 320K herbarium specimens collected mostly from the Guiana Shield and the Northern Amazon Rainforest, focusing on about 1K plant species of the French Guiana flora. A valuable asset of this collection is that some of the specimens are accompanied by a few photos of the same specimen, allowing for more precise machine learning. In addition to this training data, we also built a test set for model evaluation, composed of 3,186 field photos collected by two of the best experts on Guyanese flora. Based on this dataset, about ten research teams have developed deep learning methods to address the challenge (including the authors of this abstract as the organizing team).
e. , species traits (plant life form, woodiness and plant growth form). The use of the species traits allowed slight performance improvement of the best adversarial adaptation method (with a MRR equal to 0. 198). In conclusion, the results of the experiments conducted are promising and demonstrate the potential interest of digitized herbarium data for automated plant identification. However, Table  1. Synthesis of the obtained results. AI-based Identification of Plant Photographs from Herbarium Specimens.
,. Green hatched areas represent zones where P. brutia (light green) forests are present according to our own surveys but whose extent has not been mapped in the publicly available cartography, probably because they are the result of plantations, have been affected by fires and/or present very low tree densities 2012) and following the PCR conditions described in  Arribas et al. (2016). The PCR products were purified and bidirectionally Sanger sequenced (Macrogen). These "voucher" sequences were used for constructing a local reference database, which allowed us (i) to improve the higher-rank taxonomic assignment of the metabarcode reads and thus maximize the number of ASVs retained for each of the three taxonomic groups and (ii) to apply a recently developed method for read filtering (metamate,  Andújar et al.
9. 1  (Rognes et al. , 2016). Once denoising was performed, reads from all metabarcoding samples were pooled and again dereplicated (discarding no sequences) to generate a catalogue of unique putative haplotypes (ASVs). Subsequently, we ran blast to compare all ASVs against a combined database composed of the NCBI nt collection (accessed November 2020) and a curated reference catalogue including the 344 Sanger sequences of the "voucher" specimens plus 561 previously available sequences corresponding to soil lineages of Acari, Collembola and Coleoptera  (Arribas et al. , 2016; Arribas, Andújar, Salces-Castellano, et al. , 2021). Based on the blast output we assigned the ASVs to high-rank taxonomic levels, by applying the weighted lowest common ancestor algorithm in megan6  (Huson et al. , 2016 ; see also  Hleap et al.
, 2021). Only ASVs assigned to Acari, Collembola or Coleoptera were retained and used for downstream analyses. We further filtered the ASVs using metamate version 0. 1b18  (Andújar et al. , 2021) , a novel approach aiming at removing putative nuclear copies of mitochondrial DNA (NUMTs;  Lopez et al. , 1994)  and other types of low-frequency erroneous sequences from denoised metabarcoding data sets. This software allows the application of multiple read-abundance filtering strategies and posterior evaluation of their effects on the prevalence of known authentic mitochondrial haplotypes and presumed nonmitochondrial copies (e. g. , those violating the reading frame or expected length, as expected for NUMTs and erroneous sequences) in the final filtered data set  (Andújar et al. , 2021). The designation of ASVs as either known mitochondrial haplotypes or presumed nonauthentic sequences was performed using both global (GenBank, NCBI nt collection) and local ("voucher" specimens) reference databases.
We selected the most stringent filtering solution to ensure the removal of most erroneous sequences (see Appendix S1 for details on ASV designation and filtering in metamate). Subsequently, we used vsearch to generate a read-count community table of the metamate-filtered ASVs by matching them with a 100% identity value against the raw read data set before dereplicating, length filtering and denoising. We further filtered these community tables by removing ASVs showing abundances of two or fewer reads and also those whose contribution to the total number of reads per taxonomic group and library was <1%. Finally, filtered read-count community tables were converted to presence/absence tables (see  Jurburg et al. , 2021). Negative controls were processed alongside actual samples throughout the filtering workflow.
| Characterization of sampling sites using spatial, environmental and landscape variables A number of spatial, environmental and landscape variables were calculated to characterize the sampling sites and quantify the distances among localities, taking into account the high topographic complexity and environmental/landscape heterogeneity of the study area. We calculated pairwise weighted topographic distances (SPA TWD ) based on a digital elevation model (DEM) at 90-m resolution using the topoDistance r package  (Wang, 2020)  as described in the Appendix S1. We also generated a set of high-resolution environmental variables (at 90-m resolution) for Cyprus, by spatial interpolation of temperature and precipitation layers at lower resolution These variables are known to affect the water-energy dynamics and to explain patterns of diversity in several arthropod groups, including Coleoptera  (Hawkins et al.
, 2003). We extracted values of each interpolated variable along with the elevation for all sampling sites and for 500 randomly distributed points throughout Cyprus, to avoid potential biases resulting from only considering conditions at focal sites. We then applied a principal component analysis (PCA; Figure  S1 ) to reduce the dimensionality of the data set and eliminate covariance among variables and we retained the two first principal components (PCs). PC1 (accounting for 81. 3% of the variation) was positively correlated with altitude and precipitation variables and negatively with temperature variables, and PC2 (8. 0%) was positively correlated with the topographic wetness index (Table  S3 ). The PC1 and PC2 scores for each sampling site were considered as topoclimatic predictors (ENV PC1 and ENV PC2 ) for downstream analyses.
The best-fit model was selected after ensuring there were no issues of multicollinearity (variance inflation factors, VIF <10; e. g. ,  Tonkin et al. , 2016). Finally, the best-fit models for the across-habitat analyses were used to partition the variance explained exclusively by each variable group (forest habitat type, space and topoclimate) and their intersections using the adjusted coefficient of determination (R 2 ADJ )  (Zinger et al. , 2019). These analyses were performed using the BiodiversityR  (Kindt & Coe, 2005)  and vegan r packages. at To validate the dbRDA inferences  (Jupke & Schäfer, 2020) , we also applied multivariate generalized linear models (mvGLMs) as implemented in the mvabund r package  (Wang et al. , 2012). Unlike dbRDA, which is a distance-based approach, this model-based method fits a separate GLM per species and performs resamplingbased hypothesis testing for community-level effects of predictors.
Here, incidence (presence/absence) data sets were used as input and tested against the same sets of predictors as in dbRDA. Models were fitted using a binomial error distribution and a log link function. We first assessed the predictor significance using single-term models and only those predictors showing a significant effect were used to assemble a full model. The best-fit model was built following a backward stepwise selection approach  (How et al. , 2020). Term significance was assessed using likelihood ratio tests, PIT-trap resampling and 999 bootstrapping iterations  (Wang et al. , 2012). Finally, we assessed the effect of habitat connectivity on the community composition of the Quercus alnifolia (Qa) sampling sites by applying matrix regressions with randomization (MRR;  Wang, 2013).
part r package  (Walsh & Mac Nally, 2003). Once the best-fit model was selected, we visualized the relationship between community similarity (1β SIM ) and the explanatory distance/ resistance matrices and fitted a distance-decay of community similarity curve  (Gómez-Rodríguez & Baselga, 2018; Nekola & White, 1999). Specifically, we fitted a negative exponential function to univariate GLMs assuming a Gaussian error distribution and a log link function as implemented in the betapart r package.
| Richness and uniqueness of soil microarthropod communities After denoising and chimera filtering, the removal of putative spurious sequences by metamate, followed by additional filtering of community tables, generated a fully filtered data set comprising 907 ASVs which clustered into 386 OTUs (putative species) across the three taxonomic groups. Specifically, we retrieved a total of 353 ASVs and 154 OTUs of Coleoptera, which was the most diverse group at both across-and within-habitat scales. The Acari and Collembola data sets comprised 237 and 317 ASVs which clustered into 139 and 93 OTUs, respectively (Figure  2 ). The average richness of haplotypes per sampling site differed among habitat types, with the Cedrus brevifolia (Cb) habitat showing significantly higher richness than the other four forest types (Tukey's test: p <.
, 2011). However, such differences between species and haplotype diversity were not reflected in patterns of community uniqueness, as our ASV-and OTU-based estimates of LCBD (local contribution to β diversity) were correlated and similarly explained by topoclimatic variation (Table  1 ; Figure  S2 ), without any clear signature of historical contingencies, as those probably affecting longitudinal haplotypic richness patterns across Troodos. Our results therefore highlight the complementarity of OTUs and ASVs for community metabarcoding, as such side-by-side comparisons can help to detect processes that produce uncoupled patterns between the two levels of diversity  (Reisch & Schmid, 2019). Our ASV-level analyses were facilitated by the application of the metamate tool that utilized local and public reference sequence databases to discard nonauthentic ASVs and retain only true biological sequence variants  (Andújar et al.
, 2021). Although applying the most stringent filtering in metamate might have caused the removal of valuable rare biological haplotypes, appreciable intraspecific genetic variation (on average 2. 35 ASVs per OTU) was still retrieved and produced reasonable haplotype diversity patterns as explained above. Based on our results, we advocate stringent ASV filtering, as it can provide informative data sets without compromising the required reliability for haplotype-level metabarcoding. Even if we cannot be fully confident that all erroneous haplotypes were filtered out, as the performance of the approach depends on the completeness of the reference sequence catalogue  (Andújar et al. , 2021) , the future incorporation of intraspecific genetic data in local reference databases will provide further confidence and the opportunities for intra-OTU analyses of ASV variation  (Elbrecht et al.