dataesr/scientific-paragraphs-categorization huggingface dataset:
The annotated gene coding sequences obtained from prodigal were used as reference to map for the post-processed reads from the corresponding samples using bbmap to obtain the relative abundance of the KEGG Modules. The taxonomic distribution of KEGG Modules were identified using https://www. kegg. jp/kegg-bin/check_module_ taxonomy. cgi and KEGG Modules that were not completely prokaryotic were removed. The relative abundance of KEGG Modules of samples from different treatments was obtained by LEfSe analysis  71. KEGG Modules with a Kruskal-Wallis p-value <5% and LDA score with at least ×10 fold change (log10 fold change of 1) were considered as potential functional biomarkers (http://www. huttenhower. sph. harvard. edu/lefse/). Untargeted metabolomics. Untargeted metabolomics measurement was performed using ultra-high-performance liquid chromatography/time-of-flight mass spectrometry.
Samples for metabolomics analysis originated from 20 CD patients recruited in (TrIM). Those included 26 fecal samples collected during periods of active disease (baseline or relapse) and 36 samples collected during periods of inactive state disease (remission). Samples from gnotobiotic mice originated from 32 humanized mice (4-6 mice/colonization group; Supplementary Data 4). For sample preparation mouse colon content (20 mg) was mixed with 1 mL methanolbased Dehydrocholic acid extraction solvent (1. 3 µmol/L) as an internal standard in a 2 mL bead beater tube (CKMix 2 mL, Bertin Technologies, Montigny-le-Bretonneux, France) filled with ceramic beads (mixture of 1. 4 mm and 2. 8 mm ceramic beads i. d. ). The samples were homogenized by bead beating using a bead beater (Precellys Evolution, Bertin Technologies) supplied with a Cryolys cooling module (Bertin Technologies, cooled with liquid nitrogen) three times each for 20 s with 15 s breaks in between, at a speed of 7322×g.
1 mm, 100 Å, 1. 7 μm, Kinetex C18 column (Phenomenex, Aschaffenburg, Germany). Chromatography was performed with a constant flow rate of 0. 35 mL/ min using a mobile phase consisted of water (eluent A) and acetonitrile/methanol (3/1, v/v, eluent B), both containing 10 mM ammonium acetate and 0. 1% formic acid. The gradient elution started with 32% B for 1. 5 min, increased in 4. 5 min to 50% B, in 2 min to 60% B, in 1 min to 62% B, increased in 2 min to 80% B, held for 0. 5 min, increased in 0. 5 min to 100% B; held 2 min isocratically at 100% B, decreased in 0. 5 min to the initial ratio of 32% B, followed by 2 min of reequilibration. The injection volume for all samples was 1 μL, the column oven temperature was set to 40 °C, and the auto-sampler was kept at 10 °C. Data acquisition and instrumental control were performed with Analyst 1.
Declustering potential was set to 80 V for all experiments while the collision energy was 10 V for precursor ion scans and 35 V including 20 V collision energy spread for the fragmentation in the individual SWATH windows. Data analysis and databases used for annotations. Preprocessing: raw data files from UHPLC-TOF-MS were converted into Reifycs Abf (Analysis Base File) files and subsequent untargeted peak picking was performed by means of MS-DIAL software version 3. 52  74  using the SWATH windows reported above. Alignment was performed across the human and mice samples and areas of individual features were exported for further data analysis using the R statistical computing environment. Normalization of peak areas was performed based on quality check samples and the method Q previously reported  75  for each individual acquisition method (positive and negative electrospray ionization, reversed phase (RP), and hydrophilic interaction (HILIC) separation methods).
Quality check (QC) samples were used to correct for intensity changes along the injection sequence using method Q. QC samples were injected after every fifth sample and were prepared by mixing aliquots (20 µL each) of every sample. Median relative standard deviations (RSD) were subsequently determined for every feature in the QC samples and median values of RSDs were reported for each method. (RSD) for RP (positive) and (negative) measurement modes was 8. 6 and 3. 5, respectively. While for HILIC (positive) and (negative) modes, it was 7. 4 and 5. 2, respectively. All features were combined into a single feature table. Metabolite data obtained by LC-MS were adjusted after peak picking by its Total Ion Current (TIC). For each feature in a sample the intensity value is divided by total intensity for the sample.
Given a constant biomass in fecal samples (100 mg for human samples and 20 mg for mouse samples), a similar summed TIC can be expected. Normalization of TIC between samples therefore can partly adjust for the different water content in fecal samples, as a lower TIC is assumed here to be indicative for a higher water content. Near-zero variance MS features were subsequently removed using the caret package (R package version 6. 0-71, www. CRAN. R-project. org). Multivariate modeling of metabolomics data. Unsupervised (PCA) and supervised multivariate data analysis of metabolomics data (PLS-DA) was performed by application of the ropls package  76  , while integrative analysis of microbiome and metabolome data was based on the methodologies used in the boruta and mixomics package within R  77, 78.
The data-independent MS/MS SWATH mode was applied due to its capability to acquire all MS2 spectra and the high quantitative accuracy. Preprocessing was performed by filtering out near-zero-variance features, resulting only in a negligible reduction of the total feature number. Furthermore, feature selection for integration of metagenomics and metabolomics information was not based on the PLS-DA model, instead fold change and significance as calculated for the volcano plots were used as selection criteria. The total count of metabolomics features in the multi-omics models was therefore 1252 (mice) and 451 (human). The predictive performance of the PLS-DA model is assessed by the cumulative Q2Y metric which can have values between 0 and 1. The higher the Q2Y, the better the performance. The Q2Y value for the human dataset was (0.
64) and for the mouse dataset (0. 839). To estimate the significance of Q2Y (and R2Y), permutation testing  79  was used. Additional models were built after random permutation (n = 1000) of the outcome variable (disease activity), and Q2Yperm are computed and compared to the Q2Y value. The p-value is equal to the proportion of Q2Yperm above Q2Y value. Cross-validation of the PLS-DA models was performed using the RVAideMemoire package within the R programming environment. Metabolite features selected after volcano plot visualization based on p-value and fold-change, as well as OTU count data pre-filtered to remove features with a low sum of counts 0. 01% (adapted from Arumugam et al. 80  ) followed by total sum scaling and Centered Log Ratio transformation (CLR) were used for integrative multi-omics analysis.
Mapping of metabolite features containing a potential sulfate moiety was based on filtering for tandem mass spectra containing a fragment with m/z 97 representing the [HSO4]-ion with at least 10% intensity and a raw spectral count of 100. As additional confirmation step, spectra were checked for presence of a fragment with 79. 96 (±0. 01) indicating [SO3]-fragment ions  81. Multi-omics data integration. For the multi-omics data integration, the first step inputs multiple omics datasets measured on the same individual, that were previously normalized and filtered. This is followed by a multivariate dimension reduction method that seeks for latent componentslinear combinations of variables from each omics dataset, that are maximally correlated as specified in a design matrix. The design matrix indicates which datasets should be connected such that their pairwise correlations are maximized.
The identification of a multiomics panel is performed via penalties that shrink the variable coefficients defining the latent components to zero. The performance of the model and associated multiomics panel is then assessed using cross-validation repeated several times to ensure reliable evaluation and the balanced error rate (BER) or area under the receiver operating curve (AUC) are reported. Statistical analysis. Statistical analysis was performed with GraphPad Prism (version 7. 00; GraphPad Software, San Diego, CA). For comparison between two groups, Student's two-tailed unpaired t test was used. For comparison between more than two groups one-way ANOVA followed by pairwise comparison testing (Bonferroni post-hoc test). P < 0. 05 was considered significant. *p < 0. 05; **p < 0. 01; ***p < 0.
Acknowledgements This work was supported by the  Leona Harry Helmsley Charitable Trust  ( IBDOT Consortium ). We thank  Sigrid Kisling  for histopathological scoring,  Silvia Pitariu ,  Caroline Ziegler ,  Susanne Kublik , and  Nico Gebhardt  for outstanding technical work. D. H. received funding by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ),  SFB 1371 (Projektnummer 395357507 ;  P01 ), and  Priority Program SPP 1656. D. H. , A. S. , J. P. , and M. A. received funding from the  Helmsley Charitable Trust (IBDOT). Open access funding provided by Projekt DEAL.
Data availability The demultiplexed reads for all 16S amplicon sequencing datasets from both patients and gnotobiotic humanized mice have been deposited to the NCBI Sequence Read Archive [http://www. ncbi. nlm. nih. gov/sra] under the accession No. PRJNA565903 and No. PRJNA565980, respectively. Metagenomics sequences generated during this study have been deposited at SRA NCBI under the accession No. PRJNA575186. Full list of identified metabolite features is included as Supplementary Data 5. Raw peak areas of metabolomics data generated during this study have been enclosed as Supplementary Data 6 to the manuscript. Source data are provided with this paper.
T cells gated in CD4+T cells were assessed by flow cytometry. Flow cytometry analysis was performed on mice colonized with microbiota from CD patients at baseline (n = 14), during active disease post-HSCT (n = 11) and during inactive disease post-HSCT (n = 9). 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  was used as a base-classifier and the number of trees was set to 100.
Their children's allowance registration number. 2. The postcode of their current main address. 3. Their identification number in the French tax system. 4. Their last yearly tax receipt. Items 1. and 2. are required to retrieve the user's children's allowance information thanks to the CNAF attribute source. Similarly, items 3. and 4. are required to retrieve the user's tax information thanks to the DGFIP attribute source. All four items are retrieved through the API Particulier 5. Put in place in 2017 by DINUM 6 , this API offers an access to the two aforementioned attribute sources through two different endpoints, accessible for TCPAs after registration and obtention of a client-specific token. When necessary, the identity matching, either automated or performed manually by the agent as described later in Section 5, happens on the PII provided by FranceConnect and the API Particulier endpoints.
These security hypotheses are strongly linked to the technologies used either with the identity-matching procedure or more generally with the citizen-relationship management software environment itself. These hypotheses are listed below: • Server-side SSL/TLS authentication is used, as for usual Web technologies. • The identity information is provided by the FranceConnect service according to the OIDC protocol. • The DGFIP and CNAF endpoints are restricted: they are accessible after registration only, which involves per-case validation. The endpoints expose read-only resources according the the Representational state transfer architecture style  (Fielding, 2000).
Presentation of the Use Case Identity Matching Procedure According to the use case defined in Section 3, the user-relationship management platform, acting as a service provider to the identity-and personallyidentifiable-attribute sources, relies on the identity provided by the FranceConnect service. The user-relationship management platform has to ensure that the identity provided by one of the France-Connect identity providers matches with the identity attributes contained in the data returned by the CNAF and the DGFIP endpoints. The FranceConnect service returns a "pivot" identity, containing a set of user attributes (i) a blankspace-delimited list of the user's first and middle names, (ii) the user's family name and (iii) a string representation of the user's birthdate. The CNAF endpoint returns (i) the user's full postal address, containing their postcode, (ii) a string representation of the user's birthdate (ii) a string representation of the user's full name (i.
Format Unification The comparison as well as the detection mechanisms are based on an ASCII representation of identity information (i), (ii) and (iii) (see Section 5. 2). More generally, for any given PII type, defining a formatunification procedure is necessary. As explained in the problem statement (Section 3), depending on the sources, the identity information is presented under different formats. That's why a format-unification step is required before performing the comparison between the information available across the sources. For instance, identity information (iii) -i. e. , the date of birth of the user -string representation differs between the France-Connect data source (YYYY-MM-DD), the DGFIP data source (DD/MM/YYYY) and the CNAF data source (DDMMYYYY). Similarly, the DGFIP source provides the user's postcode of main residence, as part of a longer string representing the postal address of the user (34 Rue des Lilas 75001 Paris).
However, in our specific case, the plain Levenshtein distance algorithm is sufficient. For instance, computing the Levenshtein distance between Smicz and Smics is straightforward: this distance is 1. Cases where the distance is relatively low are considered. Such cases help to detect variations between personally-identifiable-attribute values across several sources. One Python software implementation is the python-levenshtein software module 13  , providing a simple API for computing the edit distance between two strings. The notion of distance and its application to our result vectors need to be explained. Each element of a result vector is firstly normalized (Section 5. 4. 2), and the global distance of a result vector is the following matrix representation: M(result i ) ∈ M mm (R + ) where i ∈ {1,.
Birth Date The user's birth date is considered as complete for our three sources. For each source, it is provided in a specific format: 1. FranceConnect adopts the OIDC ISO 8601:2004 YYYY-MM-DD format  (Sakimura et al. , 2014 , Chapter 5. 1). 2. The DGFIP API returns dates according to the DD/MM/YYYY format. 3. The CNAF adopts yet another format, returning dates according to the DDMMYYYY format. Sources are considered trustworthy regarding date consistency, therefore no semantic date validation is performed. Django custom template filters are therefore used to perform format unification. This template filter is based on the python standard datetime library. When providing the input format, it makes it possible for us to compare the several dates provided by the available data sources.
Implementation Considerations This section relies on the Publik URM software suite. Licensed as AGPLv3 free software, its sources are available on its project management webpage 14. This software can either be installed as Debian packages or directly from sources using an Ansible playbook. For our experimental setup, a development instance of the Publik software suite is installed, using the community documentation 15  of the software installation process. This URM platform is made of three types of software entities: • User-oriented software entities, offering URM features such as content management, appointment-making with TCPA agents, or scanned documents depository. • Similarly, the URM platform is also made of agent-and administrator-oriented software entities, offering form and workflow design, or collect and expose statistics about the platform usage.
This normalization includes splitting the different fields for a consistent information comparison, as well as performing Normalization Form Canonical Decomposition (NFKD) over potentially non-ASCII strings. This normalization form means that the Unicode characters of the strings retrieved from the data sources are translated into a set of characters known to be stable. 2. compute the distance between elements of a result vector of normalized PII from our three identity-and personally-identifiable-attribute sources. 3. display the identity-matching result information in a human-readable manner. Adding template filters is performed directly according to the Django template engine. A simple procedure that generates the result vectors based on the PII retrieved from the multiple sources needs to be implemented.
CONCLUSION Identity matching across multiple identity-and personally-identifiable-attribute sources in a federated-identity environment has become a challenging concern as the number of official sources is increasing. These sources tend to adopt widely accepted authentication  (Sakimura et al. , 2014)  and authorization  (Hardt, 2012)  standards. However, these standards do not offer out-of-the-box solutions for matching the users' digital identities and personallyidentifiable attributes accross multiple sources, and as a result identity mismatch errors happen. The proposed identity matching solution supports efficient automated processing, that requires human assistance for a limited number of corner cases. These corner cases involve interactions with the user for possible identity verification, as well as more subtle and subjective validation At the time of writing, the French government is experimenting the use of FranceConnect data providers and has published technical documentation about such providers.
We present a novel longitudinal multimodal corpus of physiological and behavioral data collected from direct clinical providers in a hospital workplace. We designed the study to investigate the use of offthe-shelf wearable and environmental sensors to understand individual-specific constructs such as job performance, interpersonal interaction, and well-being of hospital workers over time in their natural day-to-day job settings. We collected behavioral and physiological data from n = 212 participants through Internet-of-Things Bluetooth data hubs, wearable sensors (including a wristband, a biometricstracking garment, a smartphone, and an audio-feature recorder), together with a battery of surveys to assess personality traits, behavioral states, job performance, and well-being over time. Besides the default use of the data set, we envision several novel research opportunities and potential applications, including multi-modal and multi-task behavioral modeling, authentication through biometrics, and privacy-aware and privacy-preserving machine learning.
Materials. This section describes the materials employed in the data collection: Surveys, sensors, and phone applications (apps). Surveys. To get an understanding of the participants' mental states, traits, and physical and emotional well-being, they were asked to take different surveys throughout the study; these also serve as targets (or labels) for statistical modeling. At the beginning of the study, participants were asked to answer a baseline survey over two different sessions, assessing constructs related to job performance, cognitive abilities, and health. Throughout the data collection, participants answered daily Ecological Momentary Assessments (EMAs) for health, job performance, personality, psychological flexibility, and psychological capital. After the conclusion of the sensor data collection, participants completed a post-study survey.
These surveys are described in the following sections, where labels in parenthesis correspond to how the measures are referred to in the data set. Baseline survey. Due to the length of the baseline survey, it was split into two different sessions. A first part of the baseline survey was administered at the study enrollment session (described in the Study Procedures section) for participants, and assessed demographics and a number of cognitive and psychological constructs pertaining to job performance, cognitive ability, and health. Later, and before the start of the sensor-based data collection, participants answered the second part of the baseline survey at home (or another place of their choice). This survey assessed demographics, health, satisfaction with life, perceived stress, psychological flexibility, work acceptance and action, work engagement, psychological capital, and challenge and hindrance stressors (measures were administered in the above order).
The score is computed by adding all the responses, for a total score between 20 and 100. This session was followed by the second part of the baseline survey and then by 10 weeks of data collection, during which participants wore multiple wearable sensors (wristband, garment and an audio badge) and answered two daily EMAs (Ecological Momentary Assessments) through their personal smartphones. During the off-boarding session, participants handed in their sensors and finished uploading data. After the sensor data collection, they completed a post-study survey. • Task Performance was assessed using two different measures: -In-Role Behavior (IRB)  11  : Consists of 7 items, each being a Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). A scored is obtained by adding all the responses, for a total score between 7 and 49.
Rooms shaded in gray contain an Owl-in-One, while the blue circles show their exact locations. pat denotes a patient room, ns a nursing station, med a medication room, and lounge represents the lounge or break area for the workers. becoming uncomfortable or no longer wanting to receive daily surveys. The data of these participants has been kept in the dataset. Participant enrollment session. After providing their consent to participate, interested individuals signed up for a two-hour in-person enrollment session at the hospital through the TILES app. Upon arrival at the enrollment session, each participant was assigned a unique participant ID. During the first hour, participants completed part I of the baseline survey, under the supervision of a trained research team member. During the second hour, participants received their package of wearable sensors and instructions for use.
To properly set-up this sensor, each participant created a Fitbit account and registered the Fitbit Charge 2 as a new device as well as synced the Fitbit app to the TILES app. When prompted by the Fitbit app, participants were asked to give Bluetooth permissions and deny location permissions. OMsignal garments. Next, participants were given an OMbox and OMsignal garments; men were given five shirts and women were given three bras. The OMbox contains the hardware and software to process, collect, and transmit the information. Participants were asked to charge the sensor prior to each work shift, then connect it to the OMsignal garment, wear the OMsignal garment with the OMbox attached during their work shifts at the hospital, and start OMsignal recordings in the OMsignal app installed in their phones at the beginning of each work shift and stop, save and upload the recording at the end of each work shift.