dataesr/scientific-paragraphs-categorization huggingface dataset:
Annotation of the enriched reactions and pathways. Reactions expanded or overexpressed in at least 50% of the plant species per vegetation belt (i. e. at least 50% of the species from Steppe, Prepuna or all Atacama plants when compared to related Sisters) were annotated using the MetaCyc database  (Caspi et al. , 2020). Besides, metabolism, sub-pathways and biochemical pathways were determined using KEGG and HMDB databases  (Kanehisa et al. , 2014; Wishart et al. , 2018). Testing for positively selected genes (PSG) within vegetation belts. Due to reactions enriched in specific vegetation belts, we ran PSG analysis to unveil proteins positively selected for the unique environments of Atacama vegetation belts, an addition to the published PSGs of Atacama vs Sisters  (Eshel et al. , 2021) (Tab.
S11 and S12). We processed previous transcriptomics data through the previously established PhyloGenious pipeline  (Eshel et al. , 2021)  to determine the ortholog groups and their best parsimonious tree structure for 32 Atacama transcriptomes and 6 reference transcriptomes (Arabidopsis thaliana, Brachypodium distachyon, Glycine max, Physcomitrium patens, Solanum lycopersicum, and Zea mays). To find PSGs adapted to Prepuna or Steppe environments, two methods from the HyPhy package (v. 2. 5. 24) were implemented to cross-validate positively selected orthologs groups associated with adaptation to a vegetation belt  (Pond et al. , 2005; Murrell et al. , 2015; Smith et al. , 2015). First, BUSTED (v. 2. 0) tested a collection of branches of a test-group (i. e. plants from a specific vegetation belt) against background branches (i.
g. N1,N5,N10-Tricoumaroyl spermidine), the best reaction to spectra ID correlation was chosen. A reaction was considered correlated to a metabolite if Pearson r ≥ 0. 7 and P < 0. 05. We additionally noted how often a correlated reaction was Atacama versus Sister enriched, either by isoform number or expression enrichment. This included the highly stringent convergent enrichment requirement of 50% of relevant species, as well as a less stringent requirement that reactions should be enriched in 3 or more Atacama species than in Sisters (Tab. S13 and S14). The tree of 16 species with metabolomics and transcriptome annotation was adapted from the total evidence species tree created by Eshel et al. , 2021 using the keep. tip function from the ape package (v. 5. 7) to retain species with metabolomics data and was displayed using the R package ggtree (v.
Results Atacama-exclusive reactions undergo species and environment specificity. As a first step towards understanding the metabolic capacity expressed by Atacama plant species, we annotated and compared reactions and pathways in 32 Atacama plant species to their related Sister species. Reactions and pathways were defined using previously acquired sequencing data from 32 Atacama species and publicly available transcriptomic data from their 32 Sisters  (Eshel et al. , 2021). A total of 5,006 annotated reactions (Fig. 1 ) were extracted from the transcriptome data available in the 64 species using PathwayTools. To assess the presence of chemical mechanisms unique to Atacama plants, we categorized this set of annotated reactions into exclusive or common reactions. A set of 4,260 reactions were shared, while 463 were exclusive to the Atacama species data set.
Acknowledgements. We acknowledge the  Comunidad de Talabre  for providing us access to the Talabre-Lejía transect. We also thank  Gil Eshel  and  Fabien Jourdan  for their help and analytical advice. We thank  Fondo Nacional de Desarrollo Científico y Tecnológico  ( FONDECYT 1180759 ),  DOI   EVONET Proyect  and  ANID -Millennium Science Initiative.
Data collection and analysis We used a cross-sectional study design to describe the case-area targeted interventions, their coverage and potential effects. Data sources included: (i) the patient list for those admitted to healthcare facilities; (ii) the household contact list held by the intervention team; and (iii) the data collected within the target area by the intervention team, including coverage of interventions. Intervention coverage was estimated as the proportion of people and households who received the interventions within a given target-area radius. The main variables and outcomes were: promptness of the response; the proportion of people or households who received the interventions (health education, vaccination, antibiotic chemoprophylaxis and point-of-use water treatment); and the incidence of cholera in the target areas over time.
A trained epidemiologist supervised the data collection. Data were entered into tablets using Kobo software (Kobo Inc. , Toronto, Canada) to reduce the potential for errors. For the analysis, we expressed continuous variables as means and simple ranges and summarized categorical variables (coverage) as counts and percentages. We constructed an epidemic curve to visualize the incidence of cases and timeline of interventions. We compiled the direct and indirect costs of the intervention (human resources, equipment and vaccines). We used Microsoft Excel.
Introduction Clinical trials evaluating the efficacy of disease modifying osteoarthritis drug (DMOAD) candidates seek to enroll participants with high likelihood of structural progression and persistent pain over the course of the trial in order to demonstrate efficacy of the DMOAD candidates. Structural progression is, however, only observed in a minority of knee osteoarthritis (OA) patients  1. In order to enrich clinical trial cohorts with knees likely to show structural progression, recent clinical trials used the presence of baseline radiographic joint space narrowing (JSN) as criterion  2, 3. Pain severity has been reported to be e on average e stable in knee OA patients enrolled in long-term observational studies over periods typically used for clinical trials 4e6. Still, recent data suggested that OA patients may be more motivated to enroll in interventional trials when experiencing pain flares and a significant proportion of these OA patients therefore may show improvement that is not due to treatment intervention but due to regression to the mean  7, 8.
The selection of knee OA patients based on data available at the time of enrollment therefore remains a challenge. Recent studies reported machine-learning techniques to be capable of predicting symptomatic and/or structural OA progression 9e14 and such techniques may eventually provide reliable predictions of the subsequent development of symptomatic and structural OA status. In addition, machine-learning may allow to identify progression phenotypes  14  , which could be of value for recruitment in future DMOAD trials as the efficacy of DMOADs may depend on OA phenotypes. IMI-APPROACH (Applied Public-Private Research enabling OsteoArthritis Clinical Headway, https://www. approachproject. eu/, clinicaltrials. gov identifier: NCT03883568) is an exploratory, European, 5-center, 2-year prospective follow-up cohort project.
The index knee was required to have predominantly femorotibial OA and had to fulfill the clinical American College of Rheumatology (ACR) criteria  24  ; a detailed description of the inclusion and exclusion criteria has been published  16. Demographic and clinical data, blood and urine samples, and imaging data (weight-bearing X-ray, MRI of the index knee) were collected from the participants at enrollment and at the month 6, 12, and 24 follow-up visits, CT images were collected at enrollment and 24 month follow-up only. IMI-APPROACH was approved by the respective Institutional Review Boards (Netherlands: NL61405. 041. 17, Spain: 2015-651, France: 2017-A02469-44; Norway: 2017-1051) and was conducted in compliance with the study protocol, the Declaration of Helsinki, and the applicable ethical and legal regulatory requirements.
Statistical analysis The root-mean square (RMS) SD and coefficient of variation (CV %) were computed from baseline (or month 6) MRI to estimate the inter-site variability and the testeretest variability (Appendix). Mean change, SD of the change, and 95% confidence intervals over the full 2-year observation period (baseline / month 24: n ¼ 226 knees) and the intermediate observation periods (baseline / month 6: n ¼ 264 knees, baseline / month 12: n ¼ 248 knees) were reported for the various location-based and location-independent measures. The SDC thresholds computed from the longitudinal testeretest data were used to determine the percentage of knees exceeding the SDC thresholds for the different observation periods and measures. The association between the predicted probability of structural progression (s-score) over the course of the study period and the observed 24-month structural progression exceeding the SDC threshold was analyzed using binary logistic regression with adjustment for site, age, sex, and body mass index (BMI) comparing the quartile with the highest s-scores against the quartile with the lowest s-scores in order to compare the knees with the highest vs the lowest progression probability.
As comparator(s) for the s-score quartiles, these analyses were repeated using the presence of definite radiographic OA (ROA; KLG 2e4 vs KLG 0e1) as predictor. Progression in the entire FTJ was chosen as primary outcome measure, because IMI-APPROACH did not enroll participants with predominantly medial or lateral disease. The compartment-specific cartilage thickness measures (MFTC and LFTC) and the locationindependent ThinningScore were selected as secondary outcome measures for these analyses. All analyses were performed using SPSS 27 (IBM Corporation, Armonk, NY).
Another potential limitation of the study is that the SDC thresholds depend on the length of the observation period and that the number of knees with progression was therefore only determined for the full (2-year) observation period and not for the intermediate observation periods. SDC thresholds for a 1-year observation period have, however, been previously reported based on data from the OAI pilot study  41, 42. Another limitation is that the analysis comprised the weight-bearing femorotibial joint only and did not include the patellofemoral joint or the posterior aspects of the femoral condyles. The analyzed region was chosen because of the focus on femorotibial OA in IMI-APPROACH and to match the region of interest analyzed in clinical trials  3, 43. It also is the only region for which eligibility assessments are possible from standing, weightbearing radiographs.
Finally, the inter-site analysis included only few knees. For this reason, this study was not able to investigate the impact of site-or scanner-specific factors on the observed cartilage thickness measurements. In conclusion, IMI-APPROACH successfully enrolled participants with substantial cartilage thickness loss and a considerable proportion of knees with structural progression exceeding the SDC thresholds over the 24-month observation period. These data will be used in subsequent analyses to evaluate the impact of the numerous clinical, imaging, biomechanical and biochemical biomarkers on cartilage thickness loss and will also be used to refine the machine-learning model-based structural progression probability score, which was not observed to be associated with cartilage thickness loss in the current study.
Acknowledgments The research leading to these results have received support from the  Innovative Medicines Initiative Undertaking  under Grant Agreement no  115770 , resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies' in kind contribution. See www. imi. europa. euand www. approachproject. eu. The authors would like to thank the IMI-APPROACH participants and the staff at each of the clinical centers.
Only those that were different were subsequently sequenced. For conservation, bacterial isolates were grown in R2A Broth (Acumedia, Neogen Culture Media) before the addition (1:1 vol/ vol) of glycerol (70% v/v) and 5% dimethyl sulfoxide. The cells were stored at -80 °C and added to the Banyuls Bacterial Culture Collection (https:// banyu ls-bacte rial-cultu re-colle ction. fr/). Sequences of the unique strains depicted in Fig. 1  have been submitted to GenBank under accession numbers OP985055-OP985077, except for Gordonia Fig. 1  Maximum likelihood phylogenetic tree of isolated strains and their degradation capability (right). Green and three stars represent strains that degraded the selected UV filter over 70% after 20 days, light green and two stars represents between 30 and 70% degradation, pale green and one star represents between 10 and 29% degradation, and red represents strains that degraded the targeted UV filter less than 10% after 30 days OC_13I, which is 100% identical to Gordonia sp.
UV filter extractions and HPLC analysis Extractions of organic UV filters for the biodegradation assay were performed as previously described  (Fagervold et al. 2021; Fagervold and Lebaron 2022). Briefly, whole tubes were extracted directly in DCM, shaken overnight and injected into an Ultimate 3000TM HPLC system equipped with a DAD detector (Thermo Fisher Scientific). The injection volume was 5 µL, and a Phenomenex Kinetex Biphenyl column with 2. 6 µm particle size and dimensions 150 × 4. 6 mm was used. The data acquisition software was Chromeleon™ 7. 2 (Thermo Fisher Scientific). Calibration curves and retention times of all the different UV filters were determined as described previously  (Fagervold et al. 2019).
Acknowledgements We would like to thank the company  Greencell  for providing the commercial microbial consortia evaluated here. Furthermore, thank you to  Cécile Villette  for performing the sequencing of bacterial strains. This work was carried out in conjunction with the  European Marine Biological Resource Centre (EMBRC-ERIC-Banyuls-sur-Mer Oceanographic Observatory , OOB). We are grateful to the  BIO2MAR platform  for providing access to instrumentation. We would like to thank  Christophe Salmeron  of the  Bio-PIC Imaging  and cytometry platform (OOB) for performing the flow cytometry and the technical support of  EMBRC-France , whose French state funds are managed by the  ANR within the Investments of the Future program  under reference  ANR-10-INBS-02.
70 ES_64F Cream OTU 2 Rhodococcus qingshengii strain djl-6-2 (NR_115708) 99. 78 ES_53 Cream Acidovorax wautersii strain NF 1078 (NR_118410) 99. 53 ES_S3 Cream Comamonas terrigena strain NBRC 13299 (NR_113613) 97. 22 a = isolates with prefix "OC" were isolated from OC degrading enrichment cultures, the same for "BM", "HS" and "ES" b = Color and other distinguishing feature of colonies c = the corresponding OTU from enrichment cultures described previously (Fagervold, 2022)   d = Identity to the nearest top hit using Basic Local Alignment Search Tool (BLAST) search of described species (rRNA/ITS databases).
Population and phenotypic data collection Animals (Dezhou donkeys) used in this study were obtained from the Black Donkey Research Institute, Dong-E E-Jiao Co. , Ltd. (Liaocheng, Shandong, China). The dataset of numbers of thoracolumbar vertebrae consisted of two parts. One contained data of 202 male donkeys from several different fattening herds. These animals were slaughtered in the same abattoir, their skeletons were isolated and thoracolumbar vertebrae were counted. The other part contained the data of 145 donkeys from a breeding herd. These animals were scanned in vivo using Digital Radiography (DR, x-ray) and their thoracolumbar vertebrae were counted on the scan pictures. For all animals, blood samples were collected from jugular vein using anticoagulation tubes. All blood samples were stored at -20°C for the extraction of total DNA.
Sequencing and mapping The extracted DNA samples were sheared, end repaired, A-tailed and ligated to Illumina paired end adapters (Illumina, CA, USA) and then constructed 350-bp DNA library. Whole genome sequencing was performed on an Illumina NovaSeq 6000 platform (Illumina) following a standard protocol. The length of the sequence reads was 150 bp. Clean data were obtained after removing poor quality reads, adaptors, and more N bases reads using Trimmomatic (v0. 39)  (Bolger et al. 2014). Read quality was assessed through FastQC Program (v0. 11. 9). Subsequently, clean reads were mapped to donkey reference genome  (Wang et al. 2020)  using BWA (v0. 7. 17)  (Li and Durbin 2009). QualiMap (v. 2. 2. 1, http://qualimap. conesalab. org/)  (Okonechnikov et al. 2016 ) was used to evaluate sequencing alignment data.
Genotype imputation and evaluation of imputation accuracy High-quality reads were used for SNP calling with BaseVar (v0. 8. 0) (https://www. cnpython. com/pypi/ basevar). We used STITCH (v1. 6. 3)  (Davies et al. 2016)  to impute the missing genotypes assuming 10 ancestral haplotypes. Since there were still a high proportion of missing genotypes after this imputation, we performed second imputation using Beagle (v5. 1)  (Browning and Browning 2016 ) following the suggestion of  Teng et al. (2022). The resulted SNP genotype data were qualitycontrolled by removing SNPs with minor allele frequency (MAF)<0. 05, imputation info_score<0. 4, and a Hardy-Weinberg Equilibrium (HWE) P-value<1e-6. We evaluated the imputation accuracy using sequence data of additional 18 donkeys, which were sequenced at high depth of 11.
The GEMMA Software (v0. 98. 1, https://stephenslab. uchicago. edu/software. html)  (Zhou and Stephens 2012)  was employed for the GWAS analysis using this model. The second model is a logistic mixed model which is commonly used for the analysis of binary (type 1 and type 2) phenotypes. The model is as follows: ln =μ+b 1 p 1 +b 2 p 2 +g+a π 1-π ( ) where π is the probability that an animal having type 1 phenotype given its corresponding first and second PC values (p 1 and p 2 ), SNP genotype (g) and polygenetic effect (a). The GMMAT Software (https://github. com/ hanchenphd/GMMAT; Chen et al. 2016) was employed for this analysis. The analysis of LVn was conducted using animals with 18 thoracic vertebrae, including 183 L5 and 67 L6 individuals. The analysis of TVn was conducted using animals with 5 LVn, including 183 T18 and 17 T17 individuals.
Population stratification analysis The population stratification was evaluated on the basis of genome wide SNP data of autosome. PCA indicated that there was indeed stratification in the study population, the first PC divided the population into 2 groups and the second PC further divided one of them into 2 subgroups, with only a few outliers (Fig. 3 ). The first PC explained 20. 42% of the variation and the second PC explained 14. 08% of the variation. Therefore, the first 2 PCs were used as covariables in the GWAS models to account for the population stratification effect.
Conclusion A GWAS using imputed whole sequence data was carried out to detect the associations between genomic variants and TVn and LVn in Dezhou donkey. For TVn, we identified 38 genome-wide significant and 64 suggestive SNPs, which relate to 7 genes (NLGN1, DCC, SLC26A7, TOX, WNT7A, LOC123286078, and LOC123280142). For LVn, we identified 9 genome-wide significant and 38 suggestive SNPs, which relate to 8 genes (GABBR2, FBXO4, LOC123277146, LOC123277359, BMP7, B3GAT1, EML2, and LRP5). The genes (WNT7A, BMP7, and LRP5) involve in the Wnt and TGF-β signaling pathways and may play important role in embryonic development or bone formation and could be good candidate genes for TVn and LVn.
The sections were post-stained with 2% uranyl acetate and lead citrate (5 min each). Samples were observed using a Tecnai G2 Spirit BioTwin microscope (FEI company, Hillsboro, OR, USA) operating at 120 kV with an Orius SC1000B CCD camera (Gatan, Pleasanton, CA, USA). Electron tomography-For electron tomography, 200-350 nm thick sections were used. Tomogram tilt series were collected at ×6500 from -40 • to +40 • or ×9600 from -50 • to +50 • with 2 • intervals on a Tecnai TF20 microscope operating at 200 kV (FEI company, Hillsboro, OR, USA) and using a CCD (4 k × 4 k) camera (Gatan, Pleasanton, CA, USA). Tomography data acquisition, automated centering, focus adjustment, tilt setting, and image capture were performed using the SerialEM software 3. 8. 0 beta  [65]. Typical size pixel at the sample level was 15.
92 nm (×6500) or 10. 68 nm (×9600) with a defocus target set to -5 microns. Tomograms were reconstructed using IMOD software 4. 11. 11 [65, 66]  while contours of cellular features were modelled using the 3dmod software 4. 11. 11. TEM of fungal cells grown in planta-Infection assays were performed using primary French bean leaves (Phaseolus vulgaris var Saxa) inoculated with conidia (10 4 /mL in 1 /4 PDB) and incubated 40 h at 21 • C under 80% relative humidity and dark-light (16 h/8 h) conditions. Infection zones were collected and fixed in 0. 1 M phosphate buffer, pH 6. 8, containing 2. 5% GA and 4% PFA (2 h at RT including the first 30 min under vacuum, and then 4 h at 4 • C). Following 6 washes in 0. 2 M phosphate buffer, pH 6. 8 (5× 30 min and overnight; 4 • C), 2 mm leaves band were cut out, treated with 2% osmium in 0.
005% final) was directly added to the samples from a 10× solution in water. For onion epidermis observations, 1. 5 cm 2 of epidermis was deposited onto 10 µL of CFSE-BcEVs (fraction F5) on a microscope slide and incubated in the dark for 3 h at 21 • C. Before observation, the onion epidermis was rinsed with PBS and mounted with a coverslip. All observations were performed with a Zeiss axio Observer 7 LSM-800 confocal microscope (Zen 2. 6 Blue software) set at 493 nm (excitation) and 817 nm (emission) for CFSE detection and at 353/465 nm for aniline blue detection.
After a 3 min desalting step, peptides were separated on a 50 cm EasySpray column (75 µm ID, 2 µm C18 beads, 100 Å pore size, ES903, Thermo Fisher Scientific, Carlsbad, CA, USA) with a 4-40% linear gradient of 0. 1% formic acid in 80% ACN in 58 min. The separation flow rate was set at 300 nL/min. The mass spectrometer operated in positive ion mode at a 1. 8 kV needle voltage. Data were acquired using Xcalibur 4. 1 software in a data-dependent mode. MS scans (m/z 375-1500) were recorded at a resolution of R = 120,000 (@ m/z 200) and an AGC target of 4 × 105 ions collected within 50 ms, followed by a top speed duty cycle of up to 3 s for MS/MS acquisition. Precursor ions (2 to 7 charge states) were isolated in the quadrupole with a mass window of 1. 6 Th and fragmented with HCD@30% normalized collision energy.
MS/MS data were acquired in the ion trap with rapid scan mode, AGC target of 3 × 103 ions and a maximum injection time of 35 ms. Selected precursors were excluded for 60 s. Protein identification and label-free quantification (LFQ) were carried out using Proteome Discoverer 2. 4. MS Amanda 2. 0, Sequest HT, and Mascot 2. 5 algorithms were used for protein identification in batch mode by searching against a ENSEMBL B. cinerea ASL83294v1 database (13,023 entries, release 47). Two missed enzyme cleavages were allowed for the trypsin digestion. Mass tolerances in MS and MS/MS were set to 10 ppm and 0. 6 Da. Oxidation (M) and acetylation (K) were searched as dynamic modifications and carbamidomethylation © as a static modification. Peptide validation was performed using Percolator algorithm  [68]  and only "high confidence" peptides were retained corresponding to a 1% false discovery rate at peptide level.
Minora feature detector node (LFQ) was used along with the feature mapper and precursor ions quantifier. The quantification parameters were selected as follows: (1) Minimum of 2 unique peptides, (2) precursor abundance based on intensity, (3) normalization mode: total peptide amount, (4) protein abundance calculation: summed abundances, (5) protein ratio calculation: pairwise ratio based, (6) imputation mode: none, and (  7 ) hypothesis test: t-test (background-based; p-value < 0. 05). The mass spectrometry proteomics data were deposited in the ProteomeXchange Consortium via the PRIDE  [69]  partner repository with the dataset identifier PXD035766.