dataesr/scientific-paragraphs-categorization huggingface dataset:
Protein identifications were set at >90. 0% probability and contained at least one identified peptide. Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony as described originally  (Nesvizhskii et al. , 2003). Label-free relative peptide quantification LFQ methods (spectral counting and precursor intensity) were used to analyze and contrast the proteomic profiles of lymph, plasma, and EVs from each melanoma patient. LFQ based on the precursor intensity was performed using the quantification algorithm supported by the PEAKS Q module (version 8. 0; Bioinformatics Solutions). MS/MS spectra were then extracted using the same software and used to search against the target-decoy database containing all SwissProt entries for Homo sapiens (January 2017; 158,154 entries) as described above.
The Quantitative module (Q) of PEAKS 8. 0 was used to perform the LFQ analysis using the precursor ion (MS1) quantification built-in algorithm. The relative protein abundance was displayed as heat maps including representative proteins of each protein group after normalization of the corresponding averaged areas (abundances) with respect to the total ion current. The un-clustered heat maps were generated using the CIMminer free software provided by the National Institutes of Health (https://discover. nci. nih. gov/cimminer/) using log2 transformed quantitative values of MS1. The positive values reflect fold increases (red color), and negative values reflect fold decreases (green color). Only proteins that passed a selected significance statistical threshold (ANOVA, P < 0. 05 and FDR <1% for protein and peptide expression) and had been identified with at least threefold differential expression across all samples are shown in the representative heat maps.
An additional LFQ analysis using the spectral counts intensity (MS/MS or MS2) was employed for the same set of lymph, plasma, and EVs samples using the normalized weighted spectrum count option provided in "perSPECtives" (version 2. 0. 6; Proteome Software). The corresponding ". mzIdentML" files generated in Scaffold (version 4. 7. 3) for each set of sample from LAN neg , LAN pos , and HCs were imported in "perSPECtives" where the threshold of one unique peptide/sample and the significance level of P < 0. 05 were set up throughout the control of FDR with the standard Benjamini-Hochberg procedure. Gene ontology, molecular, and cellular pathways enrichment analysis Networks, functional analyses, biochemical, and cellular pathways were generated using IPA (Ingenuity Systems) and the list of proteins extracted from LFQ analyses.
Specifically, the experimentally determined protein ratios were used to calculate the experimental fold changes by rescaling their values using a log2 transformation, such that positive values reflected fold increases while the negative values reflected fold decreases. For network generation, datasets containing gene identifiers (gene symbols) for the LAN neg , LAN pos , and HCs were uploaded into the IPA application together with their rescaled log2 transformation of the protein's area ratios. For all quantitative IPA analysis, we used datasets identified to represent less than threefold differentially abundant expressed proteins across all analyzed samples. These molecules were overlaid onto a global molecular network contained in the Ingenuity Knowledge Base. The networks were then algorithmically generated based on their connectivity index using the built-in IPA algorithm.
The probability of having a relationship between each IPA indexed biological function and the experimentally determined genes was calculated by a right-tailed Fisher's exact test. The level of significance was set to P < 0. 05. Accordingly, the IPA analysis identified the molecular and cellular pathways from the IPA library of canonical pathways that were most significant to the dataset (-log (P value) > 2. 0). For the quantitative analysis of the expression profiles, IPA assigned the z-score function to all eligible canonical and cellular pathways (where z < -2 represents significant down-regulation while z > 2. 0 represents a significant up-regulation of the selected pathways). The specific melanoma-associated proteins were assigned to each proteome dataset using the cancer-related database reported in the Human Protein Atlas (http://www.
proteinatlas. org/cancer) and are presented in Table  S1. miRNA analysis 35 µl each of EV and non-EV fractions from lymphatic exudate and plasma were analyzed for their miRNA content with the FirePlex miRNA Assay from Biofluids (Abcam), which allows the detection of up to 68 miRNAs in customized panel using a low sample volume. Samples were shipped to Abcam and profiled (detailed protocol published in  Royo et al. , 2016). Results were analyzed using the FirePlex Analysis Workbench that allowed geNorm normalization of the samples. Data were presented as log2 transformed after the addition of one pseudoread. Cell lines B16-F10 melanoma cells (American Type Culture Collection) were maintained in high-glucose DMEM (10569044; Invitrogen) supplemented with 10% heat-inactivated FBS (A3840002; Invitrogen) and used for in vitro EV production in DMEM 2% heat-inactivated, exosome-depleted FBS (A2720801; Invitrogen).
Acknowledgments The authors are grateful to the patients that participated in this study. They are grateful to  Samia Abed-Maillard ,  Eleftheria Anastasiu ,  Hélène Maby-El Hajjami ,  Paula Marcos Mondéjar , and  Loredana Leyvraz  for clinical sample handling and information;  Witold Kilarski ,  Yassin Ben Saida ,  Petra Baumgartner , and  Anne-Christine Thierry  for technical assistance;  Tera Lavoie  ( Advanced Electron Microscopy Facility, University of Chicago ) for assistance with TEM sample preparation and imaging;  Kari Alitalo  ( Translational Biology Laboratory, University of Helsinki, Helsinki, Finland ) for the K14-VEGFR3-Ig mice; the  École Polytechnique Fédérale de Lausanne Flow Cytometry  and Bioimaging core facilities; and  Douglas Hanahan ,  Olivier Michielin ,  Manuel Fankhauser , and  Sachiko Hirosue  for helpful discussions and advice.
The size distribution profile of EVs was estimated by the dynamic light scattering (DLS) measurement using a Zetasizer Nano-ZS90 instrument (Malvern Instruments, Malvern, UK). Fifty microliters of EV sample was analyzed at 20°C immediately after isolation in disposable low-volume cuvettes (ZEN0118, Malvern Instruments, Malvern, UK). The dispersant refractive index was 1. 330 (ICN PBS Tablets), and the equilibration time was set for 30 s. The results were the average of six measurements each consisting of 10 runs (Malvern Zetasizer Software 7. 12. was used for data analysis).
Protein identification was performed using the Swiss-Prot human database (release 2017_03 containing 137 404 sequence entries) with a precision tolerance of 10 ppm for peptide masses and 0. 08 Da for fragment ion masses. All raw data obtained for each dataset were imported into Protein Discoverer v. 1. 4 (Thermo Fisher Scientific) < Thermo raw files >. Mascot engine (Matrix Science, Boston, USA) was used for database searches for protein identification and quantification. Protein was considered as positively identified if at least two peptides per protein were found by both search engines, and a peptide score reached the significance threshold FDR = 0. 01 (assessed by the Percolator algorithm). The abundance of identified proteins were estimated in the Proteome Discoverer using the Precursor Ions Area detector node (Thermo Fisher Scientific, Waltham, USA), which calculates the abundance of a given protein based on the average intensity of the three most intensive distinct peptides for this protein, with further normalization to the total ion current.
The modelbased approach was used as an alternative to the classical Pvalue-based approach due to sample size-limited power of statistical testing and the 'semi-quantitative' character of label-free proteomics implemented in the current study. A list of genes corresponding to IR-affected proteins was annotated at the Gene Ontology database (using GO Database version 05. 2016, Gene Ontology Consortium). A list of genes corresponding to all identified proteins was used as the reference set, and then statistical overrepresentation of gene ontology (GO) terms associated with proteins present in a given subset was estimated based on a hypergeometrical distribution.
Thermogravimetric Analysis (TGA) The thermal stability of CNF-Nafion ® membranes was determined using a TA Instruments Q500 thermogravimetric analyzer (New Castle, PA, USA). Thermographs were analyzed using the TA Instruments Universal Analysis 2000 software. Two samples from each group were evaluated at a 10 • C/min ramp, over a temperature range of 20-600 • C, in nitrogen atmosphere. Sample weights varied from 2 to 8 mg.
Morphology and Microstructure The cross section of produced films from pure Nafion ® , pure CNF and CNF-Nafion ® hybrids with different ratios (20:80, 35:65, 50:50, 65:35, and 80:20 CNF to Nafion ® ) were investigated by SEM, and the results are displayed in Figure  2. Fractured surfaces of specimens (from tensile analysis) were imaged after failure to examine differences in morphology and ductility. As shown in Figure  2A , pure Nafion ® was condensed and exhibited lower porosity and fewer cracks relative to CNF. The observed necking (data not shown) after prolonged strain may also suggest its ductility. Figure  2G  shows pure CNF films with higher porosity, consisting of many horizontally stacked sheets. CNF films did not undergo as large of strains as Nafion ® , but under SEM, the CNF breaking point shows rope-like cellulose fibrils that extend perpendicular off the cross section of the break.
Glycosyltransferases (GTs) are also classified within the CAZy database as they are involved in the biosynthesis of glycosidic bonds from phosphate-activated sugars donors. GHs hydrolyze and/or transglycosylate the glycosidic bonds, CEs hydrolyze ester bonds, and AAs are redox enzymes acting in conjunction with other CAZymes, many of which are involved in lignin degradation. PLs cleave bonds from uronic acid-containing polysaccharide chains (nonhydrolytic cleavage of glycosidic bonds). With the emergence of the high-throughput sequencing technologies, owing to the reduced cost and easier access, researchers have investigated the metabolic potential (referring to the gene content) of the microbial communities present in anaerobic environments. In a variety of studies, microbial communities of full-scale and lab-scale anaerobic digesters under optimal (10) and dysfunctioning conditions have been characterized  (4, (11) (12) (13).
1. 23 (␤-galactosidase; 5. 3% Ϯ 0. 5%) (Fig. 1F ). Similar to abundance of GHs, the average metagenomic abundance of CBM coding genes was highly correlated to the total gene copy numbers in the microbiome (R 2 ϭ 0. 91, P Ͻ 0. 001) (Fig. 2B ). The set of putative CBMs fell into 36 families (Fig. S1E ), with CBM50 being the most abundant. In total, 91 coding genes from CBM50 were identified, and 20. 9% of them were associated with a GH domain in a single protein. Out of the identified CAZyme genes, 65. 3% were further assigned to the different reconstructed MAGs. BLAST analysis against the NCBI nonredundant protein database showed overall low identities of the identified CAZymes for most of the MAGs (Fig. 2D ). Roughly, 13. 5% of predicted CAZymes showed high similarity (Ն95%) to the entries in the nonredundant protein database.
The diversity of CAZyme-coding genes in MAG15 might explain the metagenomic abundance of this species. MAG15 was quite abundant during the whole anaerobic digestion process and especially under the optimal digestion conditions (Fig. 1A , phase I). Using the database of 8,000 newly reconstructed MAGs from public metagenomic studies (37), we realized that MAG15 was absent from any other environment except our digesters. In addition, the novelty of putative CAZymes from this MAG was confirmed by their low similarities to other CAZyme proteins in the nonredundant protein database (Fig. 4A ). CAZyme-coding genes represented 9. 1% of the genomic content of MAG15 (Table  S1 ). Among the different CAZy domains identified, GH domains were the most dominant (representing 62. 4% of the total CAZy domains in MAG15 genome).
MATERIALS AND METHODS Sampling, metagenomics, and data processing. Samples were taken from an R3 lab-scale continuously stirred tank reactor (CSTR) of 100-liter capacity fed with dry sugar beet pulp as described in a previous study. Decreasing HRT and increasing OLR resulted in a pH decrease and lower biomass-tomethane conversion ratio (4). In total, seven time points were analyzed, corresponding to the different stages of the experiment (Fig. 1 ). Total genomic DNA was extracted with a PowerSoil DNA isolation kit (MoBio Laboratories, Inc. ), according to the manufacturer's instructions. Metagenomic DNA libraries were constructed, sequenced, and analyzed as previously described  (31). Binning of assembled contigs resulted in over 30 MAGs with average genome completeness of 76. 6% Ϯ19. 8% and contamination below 5%.
MAGs described in this study correspond to the bacterial bins listed in Table  2  in a previous publication  (31). The taxonomic assignment of MAGs was done with Phylophlan  (69). Prodigal was used to predict coding sequences on the coassembled contigs  (70). CAZyme-coding genes were searched with the dbCAN (dbCAN-fam-HMMs. txt. v6 [27] ) against a CAZy database (http://www. cazy. org/). The resulting CAZyme-coding genes were manually curated. A difference in abundances of MAGs/GHs/ECs was tested by a chi-square test. PULs were predicted according to the PUL database  (71) , and the presence of at least one susD or susC gene was mandatory for partial PUL prediction. Homology to peptide pattern (Hotpep) was used to assign an EC class to the identified CAZymes  (72). Taxonomic and functional annotation of the coassembled contigs was done with IMG-MER (73) and corresponds to the project identification number 3300002079.
2. 1. 21, ␤-glucosidase; EC 2. 4. 1. 18, 1,4-␣-glucan branching enzyme; EC 3. 2. 1. 20, ␣-glucosidase; EC 3. 2. 1. 40, ␣-L-rhamnosidase. (D) Box plot representations of the percentage identity of the CAZymes to proteins in the NCBI nonredundant protein database. The different CAZy families represented include auxiliary enzymes (AA), carbohydrate-binding modules (CBM), carbohydrate esterases (CE), glycoside hydrolases (GH), glycosyltransferases (GT), and polysaccharide lyases (PL).
ACKNOWLEDGMENTS We thank  Dominika Klimek ,  Sebastien Lemaigre , and  Stephanie Giusti-Miller  for their technical support and  Kjell Sergent  for his expertise in protein identification analysis. This work was supported by the  Luxembourg National Research Fund , as follows: by  FNR   CORE 2011  project  GASPOP  ( C11/SR/1280949 :  Influence of the Reactor Design  and the  Operational Parameters on the Dynamics of the Microbial Consortia Involved in the Biomethanation Process ) and by  FNR CORE   2014  project  OPTILYS  ( C14/SR/8286517 : Exploring the higher termite lignocellulolytic system to optimize the conversion of biomass into energy and useful platform molecules).
Background: Biomedical research often requires large cohorts and necessitates the sharing of biomedical data with researchers around the world, which raises many privacy, ethical, and legal concerns. In the face of these concerns, privacy experts are trying to explore approaches to analyzing the distributed data while protecting its privacy. Many of these approaches are based on secure multiparty computations (SMCs). SMC is an attractive approach allowing multiple parties to collectively carry out calculations on their datasets without having to reveal their own raw data; however, it incurs heavy computation time and requires extensive communication between the involved parties. Objective: This study aimed to develop usable and efficient SMC applications that meet the needs of the potential end-users and to raise general awareness about SMC as a tool that supports data sharing.
Methods: We have introduced distributed statistical computing (DSC) into the design of secure multiparty protocols, which allows us to conduct computations on each of the parties' sites independently and then combine these computations to form 1 estimator for the collective dataset, thus limiting communication to the final step and reducing complexity. The effectiveness of our privacy-preserving model is demonstrated through a linear regression application. Results: Our secure linear regression algorithm was tested for accuracy and performance using real and synthetic datasets. The results showed no loss of accuracy (over nonsecure regression) and very good performance (20 min for 100 million records).
However, recent studies have shown that it is possible to deduce the identity of research participants from clinical data that were considered anonymized. DNA sequencing aggravates this problem as the genome is unique to every individual and can be used to predict future ailments for individuals and their blood relatives (such as Alzheimer's or schizophrenia). Such information has the potential to deny jobs and to isolate subjects socially  [2]. In the face of these growing concerns, privacy experts are trying to explore alternative approaches to privacy protection. Many of the new strategies are based on cryptography, particularly secure multiparty computations (SMCs). SMC is an attractive approach that allows a set of multiple parties, S 1 ,. ,S m , each holding a private fraction of the data to be analyzed, to collectively carry out a computation f on the overall dataset, without any party having to reveal their own private raw data.
In almost all existing research in SMC, one of the main goals is to minimize the total number of messages communicated between the different parties and, thus, minimize the performance gap between secure and regular protocols  [9]. One of the approaches taken is to relax security and privacy requirements (such as allowing some information leakage)  [10]. Our goal with this line of research is to develop usable and efficient SMC applications that meet the needs of the potential end-users and to raise general awareness about SMC as a tool that supports data sharing. Thus, we proposed a divergence from current research efforts. Instead of lowering the security requirements, we proposed to introduce distributed statistical computing (DSC) into the design of secure protocols. Through DSC, we will study the possibility of conducting computations on each of the parties' sites independently and then combine these (local) computations to form 1 (accurate) estimator for the collective dataset, thus limiting communication to the final step and significantly reducing complexity.
Contributions The main contribution of this study is introducing a model for privacy-preserving distributed data mining in which local models are produced separately and SMC is used to aggregate the results privately. The study applies these novel ideas to linear regression and introduces the first secure linear regression model that does model selection and parameter estimation efficiently (all previous secure multiparty algorithms perform parameter estimation only). The paper then presents experiments on real and synthetic datasets to demonstrate the accuracy and efficiency of the algorithm. The paper is organized as follows: the next section defines our problem formally and introduces the theory behind DSC; the following section demonstrates the effectiveness of our privacy-preserving model through a linear regression application; and finally, the paper is concluded with a discussion of the results and limitations and a proposal for future research directions.
Problem Definition A researcher wants to estimate a population parameter θ by running a computation f over the private inputs of several remote databases, d 1 ,. ,d m while keeping these inputs private (Figure  1 ); (where f (d 1 ,. ,d m ) is a mechanism for the estimation of θ). The goal is to achieve a good approximation θ* of θ using as little communication as possible and without any party learning anything about other parties' input aside from the final output θ* (Figure  1 ). Interinstitutional data sharing is generally motivated by multiple scenarios such as (1) increasing results' accuracy and lowering bias, (2) performing benchmarking studies, or (3) attaining the cohort required for a study. In what follows, we illustrate each with a scenario: 1. m hospitals want to collectively study factors that affect the survival rate for breast cancer patients.
Running the regression problem on the m datasets will provide better properties by increasing sample size and will reduce data bias (such as environmental and location bias). Sharing data in the open may not be easy as medical data are governed by privacy legislations. 2. Hospitals in a given geographical area are interested in calculating the average rate of hospital-acquired bacterial infection (across all the hospitals in the said area) for the purpose of self-evaluation. In this case, the hospitals have an additional incentive against data sharing as it may implicate them negatively. 3. Monogenic diseases are very rare genetic disorders associated with single gene variations observed in few subjects per 1000 to 10,000 individuals. Some are well-characterized such as cystic fibrosis (frequency of disease 1:2000), sickle cell anemia, phenylketonuria (frequency of disease 1:8000), and some primary immunodeficiency diseases  [11].
Overview A common approach in statistical learning with big data is to split the data (along observations) into multiple subsets. Each subset conducts the required computation completely independently. The final result is then obtained by combining these local computations. Thus, communication (and sharing of information) is reduced to the final step only. This will significantly reduce the complexity and provide simpler algorithms. The problem is illustrated in Figure  2  and explained below. A researcher is interested in estimating a population parameter θ from a sample database D with N records and p attributes. Traditionally, θ is estimated from the whole dataset D as: θ*=f (D) (referred to as the centralized estimator), where f is a mechanism for the estimation of θ. In this split and merge statistical learning approach, the database D is split equally among m sites as d 1 ,.
,d m. The number of records in the resulting databases is denoted by n=N/m. Each site i performs the estimation of θ on its local dataset as: θ i =f (d i ), then the final estimate is obtained by combining the local estimates: θ # =g (θ 1 ,. ,θ m ). The N observations are assumed to be independent and identically distributed. They are evenly and randomly allocated along the m different sites. McDonald et al, who advocated for this split and merge method in  [17] , claim that, given the stated assumptions, it provides a good balance between accuracy and efficiency. As for merging strategies, few were considered in the literature, the most common ones being averaging  [18]  and median  [19].
Relevant Theory The split and merge strategy is simple to execute and is communication-efficient. Splitting is always done along observations (rather than attributes), and each site performs the estimation on its local dataset. Averaging of the m sites estimates is the simplest and most popular strategy. In what follows, we review the available literature while trying to answer the following questions: • What is the error of the averaged estimator versus the centralized one? • What affects the optimality of the averaged estimator? • How many sites to include in a given study? And how many samples to include from these sites? The accuracy of averaging depends on the relationship between the number of observations (N), the number of sites (m), and the number of parameters (p). As a general insight, averaging provides estimates that are as accurate as the centralized solution when there are many observations per parameter on each local machine  [20].