dataesr/scientific-paragraphs-categorization huggingface dataset:
8-5, 5-20, 20-180, 180-2000 µm). This table was subsampled (rarefied) at the minimum number of reads observed for a sample (313,539 reads) for comparison of OTU richness between samples. Based on the rarefied OTU table, an OTU is considered as unique to P2 if it is present in at least one P2 sample and not present in any of the 386 Tara Oceans surface samples. The plankton samples from 'Objectif Plancton' used to compare the preservation methods were processed independently using the same bioinformatic pipeline. Raw data are available under the bioproject XXXXX (https://www. ncbi. nlm. nih. gov/bioprojectXXXXX).
Acknowledgments We first deeply acknowledge the generosity and curiosity of the pioneer planktonauts who have collected plankton samples from the world oceans and made the P2 proof-of-concepts possible:  E. Abadie  on board Manevai, I. Autissier on board Ada 2,  R. Barnaud  on board Kauana,  C. Beaumont  on board Folligou,  E. Brossier on Vagabond ,  K. & K. Brownie  on board Astral Express, V. Chirié on board Dreamweaver,  I. Corrias  on board Attila 3, Q. d'Avout on board  Vitamin Sea ,  B. de Ravignan  on board  La Cardinale ,  M. Denis  on board Isbjørn II,  E. Di Iorio  on board Suhail, B. Dumontet on board Ainez,  E. Dupuis  on board Vaihere,  N. Fabbri  on board Zigomar,  Y. Gladu  on board Tethys,  J. Goulias  on board Gwalarn,  M. Hardy & A. Schnarwiler  on board Taravana, P. Laparre on board Amasia,  A.
Microwave reactions were performed in 20 mL vials using a Discover SP from CEM using an Anton Parr Monowave 300 instrument. All reagents and solvents were purchased commercially and used directly without any further purification. Synthesis. Phidianidine A (1). A sample of phidianidine (1) was prepared for biological testing according to literature procedures. 38, 52 he substituted 1,2,4-oxadiazole ring of 1 was prepared via the route described for the synthesis of phidianidine by Manzo et al. 52  from N-Boc-1-amino-5-[(E)-2-hydroxyguanidino]-pentane and 6-bromo-3indoleacetic acid ethyl ester and was subsequently guanidylated with N,N′-di-Boc-1H-pyrazole-1-carboxamidine according to Lin and Snider. 38 Compound 1 was prepared in 28% yield (2 steps) with spectral data matching those previously reported.
Adhered bacteria were observed with Syto9 nucleic acid stain (5 μM, λ excitation = 488 nm, λ emission = 498-540 nm). Adhered diatoms were observed by their fluorescence (λ excitation = 633 nm, λ emission = 638-720 nm). The extent of growth, biovolume, and overlap percentage was determined with a JAVA program (Universite′ de Bretagne-sud, Lorient, France). 50, 51 ASSOCIATED CONTENT * sı Supporting Information The Supporting Information is available free of charge at https://pubs. acs. org/doi/10. 1021/acs. jnatprod. 0c00881.
Participants The INSIGHT-PreAD cohort has been described previously  [14]. Briefly, 318 participants (and 318 study partners) were included in the study. INSIGHT-PreAD is a prospective ongoing cohort study. When we performed the statistical analyses, the participants were undergoing their M54 or M60 visit. Only data up to M36 were analyzed in this article (7 timepoints), because the data of subsequent visits had not yet been fully collected and/or checked. However, at the time of the analyses,14 participants had been labeled as "decliners" (that is, exhibit at least two of the following changes over two consecutive evaluations 6 months apart: CDR increasing from 0 to 0. 5 and/or an MMSE below 26 and/or a FCSRT total score below 40). The INSIGHT-PreAD protocol stipulated that participants would stop the follow-up as soon as they were classified as "decliners.
79, which was analogous to the threshold found using a method validated by Gael Chetelat in the IMAP study. We also examined cortical glucose metabolism using fluorodeoxyglucose (FDG) PET. A metabolism index was calculated by averaging the FDG-SUVs of four bilateral regions of interest, whose metabolic changes are considered to be a "signature" of AD  [21] : posterior cingulate cortex, inferior parietal lobule, precuneus, and inferior temporal gyrus. The pons was used as a reference region. The FDG-SUV has been included as a continuous variable. More details about imaging data acquisition are available in previous works  [14].
e. , amyloid load, glucose metabolism, age, gender, and educational level) were compared to the class with the largest number of subjects. Normality of residuals and random effects as well as heteroskedasticity were checked visually. For this analysis, subjects with at least two timepoints of ACDI and with no missing baseline data (amyloid load, metabolism, age, gender, and education) were included. We also performed generalized linear mixed effects models (GLMM) to evaluate the effect of the ACDI at baseline on changes in cognitive scores (objective 2) and the effect of longitudinal changes in ACD on the evolution of cognitive scores (objective 3). Link function was chosen for the underlying data generation mechanism with logit for binomial data and identity for continuous data. One GLMM was performed for each score, and then the Benjamini-Hochberg method was used to correct for multiple comparisons.
Cohen's f 2 were calculated, using the marginal R 2  [23] , for each effect to estimate their size. For this analysis, we only included subjects with at least two timepoints for each cognitive score, and with no missing data for ACDI at baseline or class, age at baseline, gender, and education. Baseline characteristics were compared between subjects included and excluded in the analysis using the χ 2 test for categorical variables and Student's t test for continuous variables. A p value < 0. 05 was considered significant. Statistical analyses were performed using R3. 6. 1. The packages lme4 (1. 1-21) and LCMM (1. 8. 1) were used to perform LMM and LCLMM, respectively.
Acknowledgements INSIGHT  Pre-AD study group :  Audrain C, Auffret A, Bakardjian H, Baldacci F, Batrancourt B, Benakki I, Benali H, Bertin H, Bertrand A, Boukadida L, Cacciamani F, Causse V, Cavedo E, Cherif Touil S, Chiesa P A, Colliot O, Dalla Barba G, Depaulis M, Dos Santos A, Dubois B, Dubois M, Epelbaum S, Fontaine B, Francisque H, Gagliardi G, Genin A, Genthon R, Glasman P, Gombert F, Habert M O, Hampel H, Hewa H, Houot M, Jungalee N, Kas A, Kilani M, La Corte V, Le Roy F, Lehericy S, Letondor C, Levy M, Lista S, Lowrey M, Ly J, Makiese O, Masetti I, Mendes A, Metzinger C, Michon A, Mochel F, Nait Arab R, Nyasse F, Perrin C, Poirier F, Poisson C, Potier M C, Ratovohery S, Revillon M, Rojkova K, Santos-Andrade K, Schindler R, Servera M C, Seux L, Simon V, Skovronsky D, Thiebaut M, Uspenskaya O, Vlaincu M.
Funding Supported by  NIH  ( OR 76675882 ). The study was promoted by  INSERM  in collaboration with ICM, and  Pfizer  and has received support within the  "Investissement d'Avenir" (ANR-10-AIHU-06) program. The study was promoted in collaboration with the " CHU de Bordeaux " (coordination CIC  EC7 ), the promoter of Memento cohort, funded by the  Foundation Plan-Alzheimer. The study was further supported by  AVID/Lilly. This project/research has received funding from the  European Union 's  Horizon 2020 Framework Program for Research and Innovation  under the  Specific Grant  Agreement No. 785907  ( Human Brain  Project  SGA2 ). The funding sources had no role in the study design, data collection, data analysis, or data interpretation.
The deflection differential equation is: 2 2 () () d W x EI M x dx  (16) where, E represents the Young's modulus of the branch, and I is the area moment of inertia. Equation (15) and Equation (  16 ) are integrated in the following equation: 3 ( ) 1 () 6 dW x EI q L x C dx     (17) where, C is an arbitrary constant. The constraints at the fixed end are as follows: 0 () 0 x dW x dx   (18) Then C is calculated as Equation (  19 ): 3 6 qL C  (19) Combining (  17 ) with (  19 ), the following equation can be obtained: 33 () [ ( ) ] 6 dW x q L L x dx EI    (20) Finally, the deflection angle can be calculated as: () xL dW x dx    (21) Figure  8  Diagram showing the stress analysis of cantilever beam. Uniformly distributed loads q are applied to the beam. F RO is a reactive force on the fulcrum generated by the wind load q 3 Simulation Results Using the method described above, a dynamic simulation system with 3D tree model was implemented in the Eclipse development environment using Java programming language and JOGL graphics engine.
7 V, the skimmer voltage was at 40 V and the trap drive was at 68. 7. Data analysis was performed with Bruker Data Analysis 3. 2 software (Palaiseau, France). The identification of the compounds was based on chromatographic behavior (retention times, UV-Vis absorption maxima) and mass spectra obtained in a positive mode by using their fragmentation patterns and literature data: trans-and cis-resveratrol  [26, 27] ; p-coumaric acid, quercetin and myricetin  [28] [29] [30].
Homology Modeling of OE33PA and Cinnamic Acid and Quercetin Docking The homology model of the OE33PA receptor-binding protein (RBP) head domain was built using MODELLER in the MPI Bioinformatics Toolkit  [36]  and the phage p2 RBP crystal structure was the template (PDB ID 1ZRU). We obtained the cinnamic acid and the quercetin 3D structures from the protein data bank (PDB) entries 4cq5 and 1h1i, respectively (named TCA and QUE, respectively) and docked them as rigid bodies into the receptor-binding pockets of the OE33PA RBP homology model using Coot  [37, 38].
In contrast, phage OE33PA had a severely reduced adsorption efficiency on cells grown in the presence of quercetin for 24 h and 80% of the phages were not adsorbed after 40 min. In our experimental set up, the adsorption of phage OE33PA on the cell wall was impaired in the presence of quercetin. Similar data were obtained with morin  [40]. tested the idea that OE33PA performs less efficiently in the presence of these molecules due to reduced adsorption to bacterial cells. Bacterial cells were grown for 24 h in MRS broth in the presence (MRS+Q) or absence of quercetin (MRS). Cells were collected and resuspended in the same media (MRS or MRS+Q). Adsorption assays were performed to identify the rate at which phage OE33PA adsorbed to the surface of O. oeni in both conditions (initial concentrations of bacteria and phage were ~2 × 10 8 and 0.
6 × 10 6 PFU mL -1 , respectively). The resulting graph (Figure  3 ) revealed a rapid decrease in free phage concentration over time in the control with around 20% of the phages unbound after 40 min. In contrast, phage OE33PA had a severely reduced adsorption efficiency on cells grown in the presence of quercetin for 24 h and 80% of the phages were not adsorbed after 40 min. In our experimental set up, the adsorption of phage OE33PA on the cell wall was impaired in the presence of quercetin. Similar data were obtained with morin [40].
oeni with OE33PA resulted in the emergence of BIM, which remained sensitive to Vinitor162. Likewise, BIM obtained after challenging the sensitive strain with Vinitor162 were still sensitive to OE33PA. Hence our data suggest that OE33PA and Vinitor162 recognized distinct bacterial cell wall structure components. The comparative analysis of phage genomes is in agreement with these findings  [23, 31]. The vast majority of Siphoviruses infecting LAB possess a large organelle at their tail tip, either a bulky baseplate or an extended tail tip, bearing the RBPs and mediating host adsorption  [41, 42]. These essential genes are located between the tmp (gene coding for the Tape Measure Protein that determines the length of the tail) and the holin/endolysin lysis cassette (Figure  4a ). For OE33PA, HHpred  [36]  identified that Gp16 is an evolved distal tail (Dit) protein (the host-binding device hub docking structure), which contains carbohydrate-binding module (CBM) insertions as compared with classical Dit, related to that of Lactococcus lactis phage p2 (PDB ID 2WZP, Figure  S3 ).
The size of OE33PA Dit (659 amino acid residues) suggests that there should be two insertions of a CBM as previously observed in Lactobacillus casei phage JL1  [45, 46]. However, HHpred identified only one CBM (PDB ID 4XUP) with a low probability, suggesting that the CBMs present in this Dit have folds not represented in the PDB. Gp17 was identified as a short tail-associated lysozyme (Tal) possessing only the structural domain (PDB ID 3GS9, Figure  S4 ). The next Gp18 was identified as the RBP with a fold similar to that of the phage p2 RBP (PDB ID 1ZRU, Figure  S5 ). These analyses strongly support that the phage OE33PA adhesion device is highly similar to that of phage p2, which recognizes saccharide-based receptors in the L. lactis cell wall  [43, 47, 48] ; the only difference being that its Dit is evolved, while that of phage p2 is classical.
We could therefore readily build a homology model of the OE33PA RBP head domain (residues 160-261) based on the 3D structure of phage p2 RBP (PDB ID 1ZRU  [47]. An alternative explanation to our observations could also be that PCs may directly interact with the phage RBP (Gp18). Whether OE33PA and p2 share the same structure, a cavity would be present at the surface of the RBP, which is dedicated to the docking onto the host receptors. In OE33PA, PCs may occupy this cavity and therefore compete with adsorption on the cell wall. To explore this possibility, we first performed three additional killing-curve assays in the presence of quercetin and different concentrations of phages, yielding MOI of 0. 01, 0. 003 and 0. 002 (Figure  S6 ). We observed a dose-response relationship and the lower the phage concentration, the higher the inhibition of its killing effect on the host.
This may indicate that when less phage particles are present, the probability for quercetin to target the cavity in the RBP increases, yielding a higher proportion of phages with reduced adhesive properties. We then made a deep analysis of the RBPs of phages p2 and OE33PA. Both proteins share 21% sequence identity and a high similarity. Moreover, only three amino acid deletions were observed between the head domains of both phages with OE33PA being the shorter (Figure  S5 ). We could therefore readily build a homology model of the OE33PA RBP head domain (residues 160-261) based on the 3D structure of phage p2 RBP (PDB ID 1ZRU  [47]. Phage p2 receptor-binding sites are located at the interface between two monomers of the trimeric RBP head  [48]. Four key residues have been identified in phage p2 RBP for binding to occur: His 232, Asp 234, Trp 244 and Arg 256.
Supplementary Materials: The following materials are available online at http://www. mdpi. com/1999-4915/12/ 11/1316/s1, Table  S1 : LC-MS identification of the major phenolic compounds detected in the active fractions F9 and F11; Figure  S1 : The OE33PA phage lysate was incubated for 72 h without (white) or with PCs (50 µg mL Funding: Support for this project was provided by the French ANR (grants ANR-10-INBS-04-01 to the national infrastructure France BioImaging; ANR-14-CE20-0007 to the Enology group and ANR-11-INSB-0010 to the Bordeaux Metabolome Facility and MetaboHUB).
Thus, the stool broth was further tested for the stx-subtype specific PCR (Istituto Superiore di Sanità, Italy, http://old. iss. it/vtec/) and was positive for the stx2f subtype. Bacterial culture confirmed the presence of an EHEC strain (PMK-45057) harboring the eae, stx2f, and ehxA genes. On CHROMagar™ STEC medium, this strain has a particular color (brownish) characteristic of O80 serogroup while most of STEC belonging to the major serogroups appear purple (Figure  1 ). PMK-45057 whole genome sequencing shows that it belongs to serotype O55:H9, ST-301 and harbors fimH54. Sequencing data confirmed the combined presence of the eae-ξ, ehxA, and stx2f subtype but also unexpectedly, genes characteristic of the pS88 plasmid, a key determinant of extra-intestinal pathogenic E. coli virulence (2).
Application of the ArgCW-LCA Methodology in Practice In order to illustrate the application of the methodology, two exemplary cases were examined. These cases were chosen, in part, because of the potential regional or national scale impacts that could result from decisions that would be guided from their implementation. The first case compares various energy production methods, based on Ecoinvent  [25]  database processes. This comparison is done to illustrate the effectiveness of the ArgCW-LCA methodology utilizing well known and established inventories that represent the assessed alternatives and to illustrate a type of large-scale decision making that might benefit from the inclusion of ArgCW-LCA. In order to illustrate the utility of the ArgCW-LCA methodology in practice, a second test case comparing various polyphenol extraction methods developed as part of the NoAW project was conducted.
Search for articles The search strategy was followed as described in the protocol and details of the combination of search terms are given in Additional file 2. The search for articles was conducted in English language with no restriction in date. The first search was conducted in nine publication databases in July 2017 (17th-21st, depending on the database): Web of Science, Pubmed, Scopus, DOAJ, JSTOR, Agricola, Ingenta Connect, AGRIS FAO and BioOne. Access to Web of Science, PubMed and Ingenta Connect has been allowed through INSERM/ INIST institutional subscription, while that of Scopus thanks to a temporary account as a reviewer. Contrary to statements in protocol, the search in Wiley Online Library was not performed due to time constraints (see "Limitations due to the search strategy" section).
We considered: study scale (lab/field), study period and time length (days/ weeks/months/years), number of ATBR markers measured (one vs several genes or bacteria) and expression of results (absolute vs relative abundance), and finally by considering global susceptibility to bias (low external validity if definitively low internal validity). The whole review team contributed to study validity assessment and all decisions were double-checked by the project manager. Disagreements were discussed during working meetings until an agreement was reached. The study validity assessment was used to consider results from medium/high validity studies in narrative synthesis. The impacts of validity levels on the results of meta-analysis were tested in the quantitative synthesis ("Quantitative synthesis with meta-analysis" in "Data synthesis and presentation" section).
Metadata (all studies) Metadata, i. e. , descriptive data were extracted from all included articles. Extracted data were reference, study type, study location (country), Population, Intervention/ Exposure, study design including Comparator, sampling and Outcomes (Additional file 5). The project manager and experts independently conducted the metadata extraction, which was double-checked by reviewers and the project manager, respectively. The experts did not extract the data of articles they authored. Then, metadata were coded in order to create a database easy to use for descriptive statistics and further perusal (Additional file 5). The project manager coded the metadata, which was double-checked by the experts. Disagreements were discussed during working meetings until an agreement was reached.
Qualitative data for narrative synthesis (subset of studies) To conduct the narrative synthesis, qualitative data from medium/high validity studies were extracted by coding the direction of the effect size as well as its statistical significance when available. A distinction was made between ATBR markers for which the effect size was positive or negative in order to assess how effective the Intervention was depending on the marker. The whole review team contributed to data extraction and all the data was double-checked by the project manager. The experts did not extract the data of articles they authored.
Data for meta-analysis (subset of studies) Both qualitative and quantitative data were extracted from articles selected for the meta-analysis ("Supplementary selection of articles for the final syntheses" section). The list of data is presented in Table  5. Data was extracted by the project manager and one reviewer (neither of them is the author of the articles). Unpublished data collected from an expert was implemented in the dataset by the expert under the supervision of the project manager. Due to resource limitations, data from a subset of 28% of articles randomly chosen were extracted independently by each reviewer in order to check for consistency and minimize mistakes. A unique identifier was created for each article to facilitate reporting. Quantitative data (medians, means) were directly extracted from text, tables and/or from figures by using the image analysis software WebPlotDigitizer (https ://apps.
Descriptive statistics and systematic map All studies were included in the systematic map. The relevant literature was organized as a database (evidence map) according to the three sub-questions S1, S2 and S3. Data was coded in order to make available the range of Interventions and Exposures as well as the quantity of related literature and knowledge gaps. Out of the systematic map, a prioritization exercise was conducted with the experts and the commissioner (as mentioned in "Supplementary selection of articles for the final syntheses" section) to select studies for narrative and quantitative syntheses including study validity assessment.
Quantitative synthesis with meta-analysis The meta-analysis was carried out to assess the effects of organic waste treatments (related to S2) (see selection criteria in "Supplementary selection of articles for the final syntheses" section). The quantitative synthesis was conducted based on the results of the following statistical analyses. Effect size, i. e. , the effectiveness of the treatment in reducing ATBR, was calculated as the ratio between the relative abundance (RA) after and before treatment. The analysis was conducted on logarithms of the ratios in order to normalize the data distribution. The results were back-transformed and expressed in percent (%). The percentage was calculated as followed (Eq. 1): A variable "experimental condition" was built as the concatenation of study identifier, pre-treatment, main treatment, second treatment, any treatment condition, feeding mode, conditions of solid and hydraulic retention times (SRT/HRT), adjustment of pH, treatment phase and temperature.
An average log ratio was calculated for each experimental condition. An empirical variance of the log ratio was then calculated from the log ratio measurements available for each level of the variable "experimental condition". When only one measure of log ratio was available, the variance was imputed by the mean of the other calculated variances. The confidence interval of each log ratio was derived from the empirical standard error. Several random-effects models were fitted to the dataset by restricted maximum likelihood with R (version 3. 5. 1, package lme4). In each model, the study identifier was included as random effect and the empirical variances were used to weight the individual log ratio. Mean effect sizes and their associated 95% confidence intervals (CI) were estimated for the different types of organic treatments defined above.
Review descriptive statistics and systematic map Literature search and screening As detailed on flow chart (Fig. 1 ), the search resulted in the collection of 20,542 articles (18,824 from eight publication databases, seven articles from additional searches and 1711 articles from alert in Web of Science). The search in BioOne was not reported because it was not possible to save the results; moreover, this search only led to three irrelevant results when conducted only on titles, and, when searching on abstracts, to 58 articles that were already retrieved by the search in Web of Science. Similarly, results of the search in Google Scholar were not reported because only duplicates were retrieved (two first pages of results) compared to Web of Science. From the search for grey literature in 23 websites, no relevant study was obtained considering the review question, and results were considered only as sources of information.