dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison of Principal Component Analysis and Functional Principal Component Analysis According to our tuning criteria, we obtained four clusters with PCA (i. e. , two components). We therefore set the same number of clusters in fPCA for comparative purposes. PCA clustering outperformed fPCA for each delivery mode dataset that was analyzed (see Table  2 ). The resulting fPCA clustering is displayed in Figure  6  for babies born via vaginal delivery. We found that the EM approach in fPCA tended to cluster a larger number of uncorrelated OTUs compared to the k-CFC approach (average silhouette coefficient = 0. 07 for EM and 0. 61 for k-CFC). We used sPCA to select key OTU profiles for each cluster. This step is essential for discarding profiles that are distant from the FIGURE 5 | Simulated study: overall accuracy of clustering when time points are missing.
Pre-Processing and Modeling A total of 90 OTUs were identified in the 12 samples of the initial dataset (Table  4 ). After pre-processing, 51 OTUs were retained. Approximately 60% (resp. 50%) of the OTUs (resp. metabolites) were fitted with linear regression models (1), and 40% (resp. 50%) were modeled by more complex spline models (2)-(4). All performance measures were also modeled by splines. During the filtering step, seven OTUs and four metabolites that were fitted with linear regression models were discarded. The small number of profiles that were filtered out indicated that the variability between the three bioreactors was relatively low.
Sparse PCA on Concatenated Datasets As a first and naive attempt to jointly analyze microbial, metabolomic, and performance measures, all three datasets were concatenated then analyzed with sPCA. Only a very small number of profiles from the different datasets were selected. This small selection is likely due to the high variability in each data type. Selected variables included mainly OTUs and performance measures. These were assigned to four clusters and included respectively 1, 3, 2, and 3 OTUs with 0, 1, 2, and 0 metabolites and 2, 0, 1, and 0 performance measures. The average silhouette coefficient was 0. 744, a potentially suboptimal clustering compared to our analyses presented in the next section. This preliminary investigation highlighted the limitation of sPCA to identify a sufficient number of associated profiles from disparate sources.
Integration of Microbiome, Metabolomic and Performance Data with MultiBlock sPLS Figure  9  illustrates the results from the integration of the three datasets, where the performance data are considered as the response of interest. Similar to the sPLS analysis, block sPLS assigned profiles to four clusters, with an average silhouette coefficient of 0. 909. The proportionality distances are summarized in Figure  10  and in Supplementary Table  5  and show a greater level of association between profiles within each cluster, compared to the associations with all other profiles outside the cluster (see Supplementary Figure  8  per omic variable). Two performance variables (methane and carbon dioxide productions) were assigned to cluster 1 (component 1 negative). This result is biologically relevant, as biogas is the final output of   the AD reaction and is known to be associated with microbial activity and growth.
, 2014). It was biologically relevant to find it associated with hydrocinnamic and 3,4-dihydroxyhydrocinnamic acids, which are also difficult to degrade. Cluster 4 (component 2 negative) was composed of only OTUs and metabolites and was similar to the one obtained with sPLS on component 2 positive. In summary, our framework allowed us to integrate different omic datasets measured longitudinally and identify subsets of relevant microorganisms that were highly associated with metabolites abundance and performance measures through the biodegradation process. These analyses constitute a first step toward generating novel hypotheses about the biological mechanisms underpinning the dynamics in AD.
DISCUSSION Advances in technology and reduced sequencing costs have resulted in the emergence of new and more complex experimental designs that combine multiple omic datasets and several sampling times from the same biological material. Thus, the challenge is to integrate longitudinal, multi-omic data to capture the complex interactions between these omic layers and obtain a holistic view of biological systems. In order to integrate longitudinal data from microbial communities with other omics, meta-omics, or other clinical variables, we proposed a data-driven analytical framework to identify highly associated temporal profiles between these multiple and heterogeneous datasets. The application of this method allows the identification of similar expression profiles within a particular dataset (e.
g. , the waste degradation study). Lastly, we clustered the data using multivariate dimension reduction techniques on the spline models that further allowed integration between different data types, and the identification of the main patterns of longitudinal variation. Ribicic et al. (2018)  proposed an approach similar to ours, but they applied individual PCA or sPCA on each dataset (chemical loss and microbial community) after local polynomial regression modeling. Integration was performed in a second stage of the analysis with PLS by using hierarchical clustering (Cluster Image Maps visualization) to identify correlations between the two datasets. In comparison, we offer a more complete framework that accommodates complex scenarios, across several omics and across replicates, and handles compositional data.
The LMMS allows for the modeling of expression over time for each compound across biological replicates while taking into account the overall individual variability. We used sPCA, sPLS, and block sPLS as clustering means by leveraging on the loading vectors from these methods while selecting meaningful profile signatures. Integrating different types of microbiome longitudinal data (e. g. , abundance, activity, metabolic pathways, or macroscopic output) can be naively performed by concatenating all datasets. However, we showed that this approach was unsuccessful at selecting a sufficiently large number of profiles of different types and thus did not shed light on the holistic view of the ecosystem dynamics (bioreactor study). Our integrative multivariate methods sPLS and block sPLS were better suited for the integration task, as they do not merge but rather statistically correlate components built on each dataset, and thus avoid unbalance in the signature when one dataset is either more informative, less noisy, or larger than the other datasets.
For example, 16S data describes the abundance of the microorganisms, with metabolites as the consequence of their activity, and performance as the macroscopic resulting output. Potential delays between these molecules can be detected using other techniques, such as the fast Fourier transform approach from  Straube et al. (2017) , and will be further investigated in our future work. To summarize, we have proposed one of the first computational frameworks to integrate longitudinal microbiome data with other omics data or other variables generated on the same biological samples or material. The identification of highly associated key omics features can help generate novel hypotheses to better understand the dynamics of biological and biosystem interactions. Thus, our data-driven approach will open new avenues for the exploration and analyses of multi-omics studies.
DATA AVAILABILITY STATEMENT Infant gut microbiota phylochip raw data can be found in  Palmer et al. (2007). The microbiome and performance datasets for the bioreactor study can be found in  Poirier and Chapleur (2018) ; metabolomic data are available on request. In-house scripts and code to conduct both case study analysis are available in a Github public repository: https://github. com/abodein/timeOmics.
Safety objectives The safety objectives in this study will initially be assessed as part of the embedded pilot study. The Data Monitoring and Ethics Committee (DMEC) will assess whether low-dose cyclophosphamide and G-CSF is a safe and effective mobilisation regimen for patients with refractory CD. Ongoing monitoring of toxicity of chemotherapy using the National Cancer Institute Common Terminology Criteria for Adverse Events (NCI CTCAE) will continue throughout the trial.
Randomisation Once eligibility has been confirmed and baseline data recorded, participants will be centrally randomised to either the HSCTlite arm or usual care, in the ratio 2:1, using the CTRU online randomisation system (SCRAM). The randomisation schedule will be generated by the blinded trial statistician prior to the start of the study. The trial statistician will generate the schedule via SCRAM but will remain blinded to the allocation as they will not be able to access the schedule. Randomisation will be stratified by centre using permuted blocks of variable size to ensure that sufficient participants are allocated in the correct ratio each arm of the trial within each centre. Day 0 for the group allocated to receive usual care will be calculated as 49 days after the date of randomisation in order to align the length of time that both groups are within the trial, given the time taken to go through HSCTlite.
Stem cells will be reinfused on day 0, and G-CSF (filgrastim or biosimilar, 5 μg/kg/day) will start on day + 5 until the absolute neutrophil counts reach > 1. 0 × 10 9 /L for 2 consecutive days. Supportive care will follow local standard procedures at each centre and is at the discretion of the transplant physician, but should not include additional immunosuppression. All transplants must be reported by the transplant centres to the EBMT database, as per standard practice. This will include any transplants that are received outside of the trial, while the EBMT follow-up study is in progress.
Data collection and management The Sheffield CTRU's bespoke database (Prospect) will be used to collect all trial data for the ASTIClite RCT. Site staff have access levels granted based on their study role, and evidence of appropriate training. Access is controlled by individual usernames and encrypted passwords. Site staff will enter data from source documents into Prospect, and electronic validation rules built into the system will ensure data queries are identified and resolved in a timely manner, on an ongoing basis. Questionnaires are completed by participants onto paper copies and entered onto Prospect by site staff. Data will be stored and managed in accordance with CTRU Standard Operating Procedures (SOPs). For the EBMT follow-up, the EBMT Registry database system MACRO (Elsevier) will be used for the capture and storage of participant data.
ASTIClite RCT Analyses will be reported in line with the Consolidated Standards of Reporting Trials (CONSORT) 2010 Statement. Detailed descriptions of statistical analyses can be found in the ASTIClite statistical analysis plan. The trial statistician(s) will remain blinded throughout the study, but will be unblinded at database freeze, for analysis. The Senior Statistician within the CTRU will be unblinded to the treatment allocation throughout the trial, but will review and approve the statistical analysis plan before seeing any outcome data. Confidence intervals will be two-sided, 95% intervals and hypothesis tests will use a two-sided 5% level of significance. The primary analyses will be carried out using the intention to treat principle with data from all participants included in the analysis, including those who do not complete therapy and with participants analysed by the group to which they were randomised.
To test the primary hypothesis of a between group difference in the proportion of patients with absence of ulceration, we will estimate the proportions for each group. A mixed effects logistic regression will be used to estimate odds ratios for the disease remission in HSCTlite in comparison to conventional therapy. Baseline SES-CD ulcer subscore will be included as a fixed effect and study centre as a random effect. A number of sensitivity analyses will be carried out on the primary outcome, including assessing the impact of missing outcome data, adjustment for baseline predictors of missing outcome data and complier average causal effect (CACE) analysis. For the secondary outcomes, analogous parametric regression models will be used as appropriate to the distributional form of the outcome, accounting for study centre, and the corresponding baseline assessment for the outcome under investigation where appropriate.
Mechanistic immunology The complex datasets will be integrated, analysed and interpreted using established artificial neural network (ANN) and computational intelligence, machine-learning based approaches. Adaptions of existing neuro-fuzzy computational intelligence models  [21]  will be used to answer the questions posed. These approaches will provide important mechanistic insight into therapeutic responsiveness, underlying responsiveness to anti-TNF and events that are associated with patients becoming refractory to it after HSCT. Further details of the planned mechanistic analyses have been published elsewhere  [22].
Acknowledgements The ASTIClite group (not including named authors): Liam Whitby,  Yash Mahida ,  Gordon Moran ,  Sree Subramanian ,  Peter Irving ,  Shahida Din ,  Jenny Byrne ,  Ben Uttenthal ,  Amit Patel ,  Peter Johnson ,  Majid Kazmi ,  Andy Peniket ,  Sergio Rutella ,  Mike Bradburn ,  Amanda Loban. The authors would also like to acknowledge the contributions of all site research staff and participants, and the support of the  NIHR Clinical Research Network , as well as the Trial Steering Committee members and Data Monitoring and Ethics Committee members.
Acknowledgements We thank  SOLEIL Synchrotron  for provision of beamtime (project  20171046 ) and C. Mocuta for assistance at the DiffAbs beamline. This work is a contribution to the  ANR  project  AFTER  ( ANR-13-JS06-0001-01 ) and was also supported by the  French "Investissements d'Avenir" program , project  ISITE-BFC  ( ANR-15-IDEX-03 ). We thank M. Iniesto for his comments on an earlier version of this work. We acknowledge the Bear Lake County road department and private land owner  D. M. Clow  ( Ogden, Utah ) for allowing access to studied exposures, as well as A. S. Grosjean,  P. Guenser  and  T. Hurd  for their help on the field.
Single-Cell RNA Sequencing of Pancreatic Islets Islet cell subpopulations were analyzed for PSMB8, PSMB9 and PSMB10 genes expression using published human islet single-cell sequencing data  (37). FastQ files were downloaded from ArrayExpress (accession: E-MTAB-5061). Data was analyzed with bcbio-nextgen (https://github. com/chapmanb/bcbio-nextgen), using the hisat2 algorithm  (38)  to align reads to human genome version hg38 and the Salmon algorithm  (39)  for quantitation of gene counts. Only cells that passed the quality control in the original study  (37)  were maintained for further analysis, and the cell type classification from the original study was also maintained.
INS-1E cells contain two major types of proteasomes We next investigated the composition of proteasomes in INS-1E cells through immunoprecipitation (IP) of the 20S α2 proteasome subunit that is an obligatory member of all types of assembled proteasomes  (42). In vivo gently cross-linked INS-1E cells proteasomes were immunoprecipitated and samples analyzed by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). About 86 % of the total proteasomes were found to be s-proteasomes with enzymatically active rings composed of β1-β2-β5 subunits (Fig. 2 ). However, almost 14 % of the precipitated proteasomes contained β5i replacing the standard β5 subunit, forming an intermediate proteasome β1-β2-β5i. We found neither β1i nor β2i in protein complexes precipitated with α2 subunit. Finally, our MS data indicated the presence of proteasomal regulatory particles 19S and 11S (PA28αγ) within α2 proteasome complexes at the rate of 55.
Acknowledgements The authors are thankful to JDRF award  31-2008-416  ( ECIT Islet for Basic Research program ) for providing human islets for this study. The authors also acknowledge  C. Wollheim  and  P. Maechler  ( University Medical Centre, Geneva, Switzerland ) for providing INS-1E cell line. Soren Buus , ( Department of International Health, Immunology and Microbiology, Copenhagen, Denmark ) generously provided the A20 and Jurkat cells.
ng of extracted RNA by means of TruSeq® Stranded mRNA Library Prep (Illumina®, Copenhagen, Denmark), and library sequencing was done with the HiSeq 4000 System (Illumina®, Copenhagen, Denmark). Sequence files were drawn to the UCSC mouse genome NCB137/mm9. Further technical and analysis details in (36) and RNA-seq raw data are accessible here: https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE110691.
While the complex of wild-type AD1-ACTR with MBP-fused NCBD did not yield crystals, we succeeded in solving an X-ray structure of the analogous complex with the best binder, [1055meLeu;1076meLeu]AD1-ACTR (Fig. 4a , Supplementary Figs. 11-15 and Table  12 ). The X-ray diffraction data were collected at 2. 2 Å resolution and the structure was solved by molecular replacement using a high resolution MBP crystal structure and the previously reported NMR structure of AD1-ACTR  25. A comparison of the crystallized complex with the two NMR structures of activation domain of ACTR and NCBD, as well as other structures of NCBD is provided in Supplementary Fig. 12. The overall arrangement of the three AD1-ACTR helices in the crystal structure is most similar to the NMR structure with PDB ID: 1KBH. However, the interaction interface with NCBD in the crystal is less tight than in the NMR structure, with the overall buried surface area (BSA) decreasing to 1066 Å from 1655 Å for 1KBH structure (Supplementary Fig.
14 ). The most prominent difference is seen in the C-terminal helix, which no longer forms a tight interface with NCBD in the X-ray structure. Analysis of crystal packing did not reveal any interactions with symmetry related molecules (Supplementary Fig. 15 ), suggesting that this is not a crystal packing artifact. In [meLeu1055;meLeu1076]AD1-ACTR, both meLeu residues adopt canonical α-helical backbone conformations with (φ,ψ)-angles of -58. 2º, -65. 3º for meLeu1055 and -39. 9º, -57. 0º for meLeu1076, respectively (Fig. 4 a ). This result further supports our hypothesis that α-methylation can be used to stabilize α-helical structures in IDPs. A comparison with another available NMR structure of the complex (PDB ID: 6ES7)  39  , shows much larger deviations including a completely different orientation of the C-terminal helix of AD1-ACTR.
The same is true for the NMR structure of the complex of NCBD with a homologous activation domain from SRC-1 isoform of p160 (PDB ID: 2C52)  40  in which only the NCBD chain and N-terminal helix of SRC-1 can be reasonably superimposed, whereas the remaining part of SRC-1 adopts a completely different conformation. α-Methylation attenuates the conformational dynamics of AD1-ACTR/NCBD complex. Explicit-solvent all-atom molecular dynamics (MD) simulations were performed for 200 ns for the wild-type AD1-ACTR/NCBD complex as well as for two complexes with the αmethylated analogues [1055meLeu]AD1-ACTR and [1055meLeu,1076meLeu]AD1-ACTR. As can be seen from the reduced RMSF (root-mean-square fluctuations) of backbone atom positions in the complexes with the variants compared to wild type (Fig. 4b ), α-methylation at positions 1055 and 1076 causes a substantial decrease in the amplitudes of motion.
Peptides (H)-DERALLDQLHTLLSN-(NH2) and (H)-DERALLDQLHTL-meLeu-SN-(NH2) (meLeu = α-methyl-L-leucine) and shorter LXXLL motif containing peptides (see above) were purified on a C18 column (Phenomenex, particle size 10 μm, pore size 300 Å, dimensions 250 × 21. 00 mm) at a flow rate of 10 mL/min and a gradient of 5-30 % of eluent B (0. 08% TFA in acetonitrile) in eluent A (0. 1% TFA in H2O) within 40 min. After purification, pure fractions were identified by analytical HPLC and LC/MS, combined and lyophilized. Mass-spectrometry: Peptide masses were determined using a LC/MS instrument containing a Thermo Scientific Accela UHPLC (Hypers II GOLD column, 50 × 2. 1 mm, 1. 9 μm) integrated with a Thermo Scientific LCQ Fleet ion trap. Deconvolution of experimental data was performed using the Zscore algorithm with the help of MagTran 1.
Circular dichroism (CD) CD spectra were recorded using a J-1500 (Jasco) spectrophotometer. All samples were prepared in a quartz cuvette (thickness of 1 mm and a volume of 300 μL). For every measurement protein or protein complexes were dissolved in a buffered solution (20 mM phosphate, pH 7. 4) at a concentration of 25 μM. For data collection the following parameters were set: scan range 280-185 nm, band width 1. 00 nm, scanning speed 100 nm/min, data pitch 0. 1 nm. Every CD curve was obtained by averaging of 5 scans and subtracting the background signal. The data are depicted in Supplementary Fig. 3. The thermal stability of complexes of several AD1-ACTR variants with NCBD (concentration of complex 50 μM) was evaluated by monitoring the ellipticity at 222 nm as a function of temperature from 20 to 90 °C.
On the remaining 3 channels biotinylated AD1-ACTR variants at a concentration of 100 nM were injected at a flow rate of 20 µL/min for 10 sec yielding around 25 to 35 RU. For regeneration between each sensogram recording a solution of 6 M guanidine-HCl, 250 mM NaOH was injected into all channels at 5 µL/min for 60 sec. The parameters for the binding measurement were set as follows: temperature varied from 20 to 31 °C, initial delay 60 sec, injection of NCBD 120 sec at 50 µL/min, dissociation 120 sec. NCBD solutions were prepared with a two-fold cascade dilution with a range of concentrations from 1 µM to 1. 95 nM (10 dilutions). Data analysis was performed using BiaEvaluation 3. 2 software (GE Healthcare). After subtraction of the background signal from the reference channel, the curves were fitted assuming a simple 1:1 binding isotherm model.
More advanced "two state" kinetic model involving a conformational change did not improve the fitting. The apparent dissociation constants (KD), as well as association and dissociation kinetics (kon and koff) obtained upon fitting the data are provided in Supplementary Table  15. These values are listed in order to make a qualitative comparison (rather than quantitative) of binding properties by four different AD1-ACTR variants, given the complexity of coupled binding and folding and difficulties to obtain the exact fitting of the experimental curves. Steady-state analysis was made to obtain dissociation constants and the corresponding experimental uncertainties (Supplementary Fig. 19 )  32. The steady-state binding (Req) was derived by averaging the signals at equilibrium. Subsequently, steady-state analysis using in-house Python scripts was performed by fitting the average signal Req as a function of analyte concentration, assuming a simple 1:1 interaction binding isotherm model, leading to 3 fitted parameters: the minimal signal (Rmin), the maximum capacity of the surface (Rmax) and the affinity (KD).
A Monte Carlo approach was further used in order to estimate the values and uncertainties of the Rmax and KD fitted parameters (Supplementary Table  16 ). This method consists of reproducing the fit using 1000 datasets in which noise fluctuations were introduced based on experimental uncertainty, and then the mean and the standard deviation of the fitted parameters were calculated. The SPR measurements with full length 270 kDa CBP were also performed using Biotin CAPture kit, Series S (GE Healthcare-Biacore) on a Biacore T200 instrument (GE Healthcare -Biacore). The running buffer was 50 mM Tris, pH 7. 5, 300 mM NaCl, 1 mM TCEP, 0. 01% P20 supplemented with Roche Complete EDTA free protease inhibitor and the running temperature was set to 10 °C. First, the Biotin capture reagent was immobilized on the chip surface followed by the immobilization of the biotinylated ACTR variants (WT, [1055mL;1076mL]AD1-ACTR and [1064mL;1071mL]AD1-ACTR) as ligands.