dataesr/scientific-paragraphs-categorization huggingface dataset:
IMPLEMENTATION We implement a general library for zero-knowledge proofs on arithmetic circuits combined with algebraic statements, based on our protocol from Figure  4. Our library is written in C/C++17 (compiled using the g++ compiler from GCC) and uses the Relic-Toolkit  [3]  for elliptic curves and the EMP-Toolkit  [43]  for garbled circuits and committing oblivious transfer. Our code is publicly available and open source for reproducibility and verifiability. 6  In this section, we present the implementation details and measurements of our library applied to the construction of an information CP system for various purposes. In particular, we explore the application of selling an ECDSA signature or RSA signature on an agreed message. In Sections 5. 1 and 5. 2 we propose two methods to argue (in zero-knowledge) knowledge of an ECDSA or RSA signature (respectively) by showing the knowledge of a discrete logarithm.
Therefore these methods can be instantiated very efficiently using the celebrated Schnorr sigma-protocol  [40]. In all cases, we assume the seller (the prover) has encrypted the secret  to be sold under some key , producing a ciphertext  ← Enc  (), computed  SHA() and shared both values (, ) with the buyer (the verifier). The seller then committed to the secret  as com =   +  , for some  chosen uniformly at random, where  is an EC group element with unknown dlog (at least, unknown to the seller,  may be chosen by the verifier). Our implementation consists of the computation of the previous values and the execution of the protocol from Figure  4 , so that the verifier gets convinced about the fact that the value the seller committed to in com is the output of circuit  (,) on some input 6 Source code at https://github.
1). In the case of an RSA signature , the prover samples  ← $ Z  (the secret to be encrypted), masks the signature  ℎ  (mod  ) and shares  with the verifier (see Section 5. 2). The prover also encrypts the corresponding secret under a freshly generated 8 With  = com,  = ,  =  ,  ′ =   /ℎ,  ′ = ℎ  and  ′ = 0. 1 : Experimental results of protocol applied to CP for selling an ECDSA/RSA signature on an agreed message. We consider the algebraic and arithmetic computation time of both parties as well as the total time for the protocol execution (which includes the data transmission time over the LAN). We present two rows per scheme, representing whether we have used ( ) compression ( ) of data during the transmission or not (×). key , sending both the ciphertext and SHA() to the verifier. Finally, the prover computes a Pedersen-commitment com of the secret and sends it to the verifier.
Trophic mode predictions from genome and transcriptome analysis Predicted peptides from whole genome  [49, 50]  or transcriptome data  [51]  were downloaded from publicly available databases as detailed in Table  S2. Because computational predictions are based on presence/absence information, predicted peptides from independent transcriptome assemblies of the same strain were concatenated to include as much information about each 8/44 strain as possible. Computational prediction of phagocytotic, photosynthetic, and prototrophic capabilities were completed as in Burns et al. [43]. This involved scoring a set of 14,095 protein profile hidden Markov models (HMMs) that were derived by clustering all proteins in 35 reference eukaryote genomes of known trophic mode against all proteins from each test genome or transcriptome.
Final prediction probabilities for each test genome/transcriptome were calculated against the reference trophic mode models using a probability neural network. To visualize the prediction output, which exists in four dimensions with three degrees of freedom (phagocytosis, photosynthesis, prototrophy, and a fourth dependent dimension for absence of each trophic mode), predictions were normalized such that the sum of the three predictions plus the probability of not fitting each trophic mode equaled 1 using the relation: 1 -(p phago + p proto + p photo )/3. The fractional independent probabilities of each trophic mode and the dependent absence number were mapped to 4-dimensional color space and projected onto a circle using scripts adapted from the R package "pavo"  [52]. Scripts are available at https://github.
22/44 List of Tables  Table 1  List of algal strains used in this study with isolation region, coordinates, depth (m) and growth temperature ( • C). Table  S2  List of strains used for transcriptome analysis. MMETSP corresponds to the Marine Microbial Eukaryote Transcriptome Sequencing Project  [51]. METDB corresponds to the micro-eukaryotic marine species transcriptomes database available from http://metdb. sb-roscoff. fr/metdb/. Table  S3  Summary of experimental conditions and results for all experiment performed with M. Polaris and O. triangulata strains. The percent of cells with preys (Mean±sd) is indicated for each time point after the addition of prey (T 0 , T 20 and T 40 , where the subscript corresponds to minutes). The last four columns correspond to Student and Welsh p-values. Table  S4  Comparison of feeding on three different YG-bead sizes (0.
Acknowledgments This work was supported by  ANR  contract  PhytoPol  ( ANR-15-CE02-0007 ). We thank  Robert Sanders  and  Rebecca Gast  for hosting VJ and training her on techniques to determine mixotrophy in phytoplankton. We also thank  Dominique Marie  for help with flow cytometry,  Christian Jeanthon  for help and advice with fluorescently labelled bacteria,  Sophie Le Panse  from the Merimage.
ISBA: default configuration ISBA uses a so-called composite approach which is defined herein as using a single energy balance for the combined soil-vegetation surface Fig. 1a. The properties of soil and vegetation are aggregated depending on the fraction that vegetation occupies (veg) in the considered grid cell  (Noilhan and Planton, 1989; Noilhan and Mahfouf, 1996). The snow fraction is also used in the aggregation of properties of the surface. Its formulation, which is detailed in Sect. 2. 1. 4 and plotted on Fig. 2 , results from a compromise between an atmospheric and soil point of view and represent one of the significant limitation of the model. Since the inception of ISBA, many developments have been made to improve the representation of physical processes as the knowledge of key processes, the quality and spatial and temporal coverage and resolution of input datasets, and the computing speed have improved.
Model Dev. , 13, 6523-6545, 2020 In order to evaluate the model performance, measurements of turbulent fluxes, upwelling short and longwave radiation, soil temperature, and volumetric water content profiles are also available at a 30 min time step. Snow depth was measured every 30 min during the duration of the ground-based snowpack. In addition, manual measurements of snow water equivalent were made up to six times per year. The observed shortwave radiation being transmitted through the canopy (reaching the ground or snowpack surface: SW g ) was derived from the photosynthetically active radiation (PAR). The data were filtered to account for measurement error due to a direct flux on the sensor around midday, which caused very high peaks. The filter is based on the surrounding three points, using the following threshold:  16 ) and thus we reject the observation at i if δ ≥ 100 W m 2.
Finally, a sensitivity analysis is performed to test several new MEB parameters that are the most likely to influence the snow processes. Model input parameters have been chosen that correspond to site measurements where possible. For the remaining parameters, we use the physiographic database developed for SURFEX (ECOCLIMAP,  Champeaux et al. , 2005)  and the soil parameters from the HWSD dataset (harmonized world soil database,  Nachtergaele and Batjes, 2012). The main parameters are given in Table  2.
In contrast, if the standard p snv parameterization is used in ISBA (similar to the coupled configuration), ISBA tends to produce the effects cited herein. There, MEB has removed this inconsistency. For application of MEB in spatially distributed applications, such as for numerical weather prediction (NWP) or hydrological forecasting, such parameters are referred to as "primary parameters" and are generally fixed or prescribed from look-up tables based upon land use classification or plant functional types (set in the SURFEX physiographic database ECOCLIMAP): thus, care must be taken to define values and explore model sensitivity. This is in contrast to so-called "secondary parameters", which are derived based upon input primary parameters or input physiographic data (such as LAI, soil texture, etc.
Code availability. The MEB code is a part of the ISBA LSM and is available as open source via the surface modeling platform SUR-FEX, which can be downloaded at https://www. umr-cnrm. fr/surfex/ spip. php?article387 (last access: July 2020, CNRM, 2020) The developments presented in this paper are available starting with SURFEX version 8. 1. The input forcing files and evaluation data used in the study are available at https://doi. pangaea. de/10. 1594/ PANGAEA. 897575, last access: March 2019  (Menard and Essery, 2019). Author contributions. AB and AN contributed to the development and improvement of the MEB code. TW and AN performed the simulations and evaluations by comparing the model results with the experimental data. All authors contributed to writing the text.
5 × IQR of the hinge. Data beyond the end of the whiskers are outliers and are plotted individually. Significance is calculated by Mann-Whitney test. *p ≤ 0. 05, **p ≤ 0. 01, ***p ≤ 0. 001. c MDS plot of microbial profiles of all stool samples stratified by time before and after HSCT and disease activity status (active pre-HSCT (n = 15), active post-HSCT (n = 35) and inactive post-HSCT (n = 83)). d Comparison of relative abundance of functional modules between CD patients with active or inactive disease (post-HSCT) using Linear discriminant analysis effect size (LEfSe) analysis generated from Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2). A number of 25 KEGG modules are significantly enriched in patients with active disease and 37 KEGG modules are enriched in patients with inactive disease.
Data show differences in predicted bacterial metabolic function between CD patients with active or inactive disease (post-HSCT). Metabolic pathways enriched in active disease (red) and pathways enriched in patients with inactive disease (blue). e Comparison of relative abundance of bacterial genera between CD patients with active or inactive disease (post-HSCT) using LEfSe analysis. Taxa meeting an LDA significant threshold 2 are shown, active disease-enriched taxa (red) and taxa enriched in patients with inactive disease (blue). f Receiver operating characteristic (ROC) curves for all bacterial taxa to classify CD patients based on disease activity, or disease outcome post-HSCT. For each dataset, we ran random forest (RF) models to classify disease state and treatment response separately.
OTU tables from each dataset were preprocessed and normalized as described in the Methods section. RF implemented in the WEKA software suite  61  was used as a base-classifier and the number of trees was set to 100. The model was evaluated in a 10-fold cross-validation. HSCT  compositions in CD patients during active disease post-HSCT, we performed UHPLC-TOF-MS metabolite profiling using reversedphase ultra-high-performance liquid chromatography (RP-UHPLC) and hydrophilic interaction ultra-high-performance liquid chromatography (HILIC-UHPLC). In combination, these techniques cover polar and nonpolar metabolites and spectral features were detected. The number of features annotated after preprocessing, peak picking, and removal of near-zero variance features is summarized (Supplementary Table  4 ).
3c ). To identify relevant microbiota-metabolite interactions, we performed correlation analysis on the differentially abundant features from both datasets, using canonical correlation analysis and partial least squares regression, as described before  28  (Fig. 3e ). A relevance network analysis highlighted two clusters, both including features from microbiota and metabolome datasets (Fig. 3d ). We identified two hubs of OTU-metabolite interactions enriched with active or inactive disease states. While OTUs corresponding to Enterococcus, Desulfovibrio, Prevotella, Alistipes, Ruminococcaceae, Lachnospiraceae correlated with active disease, an enrichment of Akkermansia, Oscillibacter, Anaeroglobus, and Prevotella was associated with inactive disease. We next set out to identify bacterial taxa associated with metabolic changes in both CD patients and corresponding gnotobiotic mice.
Although CD patients and respective humanized mice appeared to cluster separately (Supplementary Fig. 5c ), the integrated microbiotametabolome dataset allowed a clear separation between active and inactive disease (Supplementary Fig. 5d ). Using correlation analysis as described before, we identified a subset of metabolites and OTUs in the combined and integrated dataset (Fig. 3e ). A full list of interacting OTU and metabolite features is summarized (Supplementary Data 2). Classification of samples based on these selected microbiota features significantly improved the predictive modeling compared to what we have identified previously solely based on taxonomic composition (Fig. 3f ). LEfSe analysis on the OTUs involved in these interactions, identified 7 OTUs to be differentially abundant between microbial communities corresponding to active or inactive disease.
i Representative H&E staining of cecum tissue in ex-germfree Il-10 -/-mice, colonized with microbiota from patients with active or inactive disease. j MDS of microbial profiles showing humanized mice (in halos) reflecting the individual features of their respective human donors (arrows). k 10-fold cross validated classifier identified 10 bacterial taxa differentiating humanized mice based on inflammation. The analysis was carried out in WEKA (Version 3. 8). Random Forest implemented in the WEKA software suite  61   Remarkably, functional analysis of the gut microbiome of humanized mice clearly demonstrated that genes responsible for regulating the anabolism or catabolism of sulfur-containing amino acids (cysteine and methionine) significantly contributed to the separation between mice colonized with microbiota from CD patients with active or inactive disease (Fig.
c Volcano plot of 1252 differentially abundant metabolites selected based on fold change log 2 ≥ 3) and significance (p < 0. 01). d Correlation plot of bile acid concentrations and disease activity. e LEfSe generated from PICRUSt2 identified the most differentially abundant functional modules between mice colonized with microbiota from human donors with active (n = 37) or inactive disease (n = 18). A number of 42 KEGG modules significantly enriched in mice colonized with microbiota from patients during active disease and 42 KEGG modules were enriched patients with inactive disease (significance: log10FC > 2 and p < 0. 01. ). Inactive (post-HSCT) functional modules are indicated with a positive LDA score (blue) and modules enriched in (active post-HSCT) with a negative score (red). Only functional modules meeting an LDA significant threshold of >2 are shown.
Methods Patient cohort and study design. This paper represents a combined analysis of CD patients recruited within the ASTIC clinical trial (NCT00297193) and the observational trial TrIM, (SAF2012-33560). Autologous Stem Cell Transplantation -International Crohn's Disease Trial (ASTIC) is a multicenter, prospective, randomized phase III study conducted by the European Crohn's and Colitis Organization (ECCO), sponsored by the Autoimmune Disease Working Party of the European Group for Blood and Marrow Transplantation (EBMT). The primary and secondary endpoints of ASTIC trial have been published previously  46. In addition to the ASTIC trial, we recruited patients for expanded microbiome sampling from the "Autologous Hematopoietic Stem Cell Transplant in Crohn's Disease: genetic, Immune and Microbiome factors involved in disease control (TrIM)" study.
Crohn's disease activity index (CDAI) and laboratory markers including CRP, albumin, hemoglobin, ESR, and leukocyte counts were assessed weekly during the first 30 days, and every 6 weeks thereafter. Colonoscopy and/or magnetic resonance were performed at baseline and at weeks 26 and 52 after transplant. Simple endoscopic score for Crohn's disease (SES-CD) index was used at baseline and during follow-up to assess endoscopic activity. Mucosal healing was defined as SES-CD < 7. MRI index of activity was used at baseline and during follow-up in those patients in whom lesions could not be assessed by ileocolonoscopy; mucosal healing was defined as segmental index <7 in all ileocolonic segments. These data are published  24, 30. Selection of donor samples for transfer in germ-free mice. In this cohort of CD patients, fecal samples were collected at three main time points (baseline, week 26 (T26) and week 52 (T52) post-transplant.
Histological scoring. Cecal Swiss-roll tissues were fixed in 4% formaldehyde/PBS for 24 h at room temperature, subsequently dehydrated (Leica TP1020), and embedded in paraffin (McCormick; Leica EG1150C). In total, 5-µm-thick tissue sections were prepared, deparaffinized, and Hematoxylin and Eosin (H&E) staining was performed by using a Leica ST5020 Multistainer system. Scoring of H&E stained tissue sections was performed blindly by single observer through evaluation of lamina propria mononuclear cell infiltration, crypt hyperplasia, goblet cell depletion and architectural distortion as described previously  49  , resulting in a score ranging from 0 (non-inflamed) to 12 (highly-inflamed). Images were acquired by using the Digital microscope M8 and MicroPoint software (PreciPoint GmbH). Isolation of immune cells and flow cytometry analysis.
7 dilution 1:100), PerCP/Cyanine5. 5-conjugated anti-CD62L (MEL-14, dilution 1:100), and Allophycocyanin-conjugated anti-CD25 (PC61. 5, dilution 1:100) were from BioLegend. Allophycocyanin-conjugated anti-CD44 (IM7, dilution 1:100) was from BD Pharmingen Biosciences. Data output was subsequently analyzed by using FlowJo software. Metagenomic DNA extraction from fecal samples. DNA was extracted from frozen mouse colon content or pulverized human fecal samples by bead-beating followed by a modified version of the protocol by Godon et al. 50. Briefly, a volume of 600 µL DNA stabilizing solution (Stratec Biomedical, Germany) was added to the fecal aliquots in 2-ml screw-cap polypropylene microcentrifuge tube containing sterile 500 mg Silica beads (0. 1-mm-diameter; BioSpec Products) and kept on ice.
Sequences with <300 and >600 nucleotides and paired reads with an expected error >3 were excluded from the analysis. Remaining reads were trimmed by five nucleotides on each end to avoid GC bias and nonrandom base composition. The presence of chimeras was additionally tested using UCHIME  55. Clustering of Operational taxonomic units (OTUs) was done at 97% sequence similarity. OTUs with a relative abundance <0. 25% across all samples were removed to exclude spurious OTUs. Taxonomic binning was assigned at 80% confidence level using the RDP classifier  56  and compared to that of the SILVA ribosomal RNA gene database project  57. EzBioCloud database was used for precise identification of OTU sequences of interest  58. Downstream analysis was performed using R-package Rhea  59. Rarefaction curves were used to estimate sequencing depth.
Performing the same analysis on microbial profiles of fecal samples collected longitudinally from all patients showed disease state (R2 = 0. 0181924, p = 0. 003), clinical outcome post-HSCT (R2 = 0. 0296583, p = 0. 001) to be significant. Sex and BMI did not show statistical significance. Between-sample diversity is calculated by generalized UniFrac using GUniFrac v1. 1. distances. De-novo clustering is based on Ward hierarchical clustering, the selected number of clusters is based on the Calinski and Harabasz index, performed with the R package NbClust v. 3. 0. For the analysis of prevalence of categorical variables between groups, a non-parametric Fisher test is used. Correction for multiple testing was performed using the Benjamini-Hochberg false discovery rate control procedure. These statistical analyses have been implemented in pipeline Rhea analysis pipeline  59.
For each dataset, we ran random forest (RF) models to classify disease state and treatment response separately. OTU tables from each dataset was preprocessed and normalized as described in the 16S rRNA gene sequencing analysis section. Random Forest implemented in the WEKA software suite  61  was used as a baseclassifier and the number of trees was set to 100. The model was evaluated in a 10fold cross-validation. Shotgun metagenomics library preparation. DNA concentrations were measured using Quant-It™ PicoGreen® dsDNA Assay Kit (ThermoFisher Scientific, MA, USA) and a spectrofluorometer (SpectraMax Gemini EM microplate reader Molecular Devices, LLC, USA). DNA purity check was assessed spectrophotometrically (Nano Drop 1000, ThermoFisher Scientific, USA). In total, 200 ng of DNA per sample was sheared using an E220 Focused-ultrasonicator (Covaris® Inc.
Quality control: raw metagenome samples (with an average of ~19 million readpairs per sample) were processed using Trimmomatic version 0. 36  62. First the adapter sequences were removed, retained reads with target length of at least 90 bp and strictness parameter 0. 4 (MAXINFO:30:0. 4) were further processed to obtain good quality of ~15 million read pairs in average per sample. Reads coding for ribosomal genes were removed from the samples using sortmeRNA version 2. 1b  63  by mapping the quality-controlled samples against SILVA database  64  version 132 to obtain non-rRNA good quality read-pairs of ~15 million read-pairs per sample in average. Reads related to the human (~12 million read-pairs per sample in average) and mouse genome (~11 million read-pairs per sample in average) were further removed from the samples by mapping against their genomes using hisat2  65.
Finally, PhiX contaminant reads were also removed human (~11 million read pairs per sample in average). Taxonomic profiling: preprocessed reads from samples from the same treatments were pooled and assembled using megahit version 1. 1. 3-0  66. Assembled contigs were used for taxonomic annotation using CAT database  67  , available on (https://github. com/dutilh/CAT). This tool internally uses prodigal v2. 6. 3  68  for gene prediction and DIAMOND v 0. 9. 14  69  for the alignment against the nonredundant (nr) protein database. Annotated contigs were used as reference and mapped against corresponding samples from each treatment to the reference contigs using bbmap  70. The taxonomic profile was used as a measure for relative abundance. LEfSe analysis  71  was performed from this relative taxonomic abundance profile using http://huttenhower.
sph. harvard. edu/lefse/. Those taxa with a Kruskal-Wallis p-value <5% and LDA score with at least ×100 fold change (log10 fold change of 2) were considered as potential taxonomic biomarkers. Functional profiling: gene-annotation was performed for the assembled contigs from each treatment using prodigal v2. 6. 3  68. In order to obtain KEGG annotation and to reconstruct KEGG Genes and thereby the functionally associated gene sets named KEGG Modules, the amino acid sequences were used in the KEGG internal annotation tool named GHOSTKOALA  72. The fraction of KEGG Modules present in a particular treatment was obtained using R package 'metQy'  73. This allowed a characterization of the functional capabilities of the microbial communities based on the complete or incomplete presence of functional units.