dataesr/scientific-paragraphs-categorization huggingface dataset:
The size of ABO-1 is 2. 79 KB which is very small in comparison with ABO-2 size, which is 45. 4 KB (approx. 23 pages in. doc extension, 11 pt "times new roman" size of content). A total of 2504 patients are included in this study. The size of each patient dataset is 17. 7 KB, in total we have 44. 3 MB datasets for the exper-imental analysis. Each dataset is defined in table format, which has six columns. The sample of each patient's dataset is defined in Table  1. In first row of Table  1 , the "9" indicates chromosome, an integer number "136125819" that corresponds to the position at the reference chromosome, "C" which is the nucleotide at the reference chromosome, and "T" which indicates the nucleotides of the alternative allele. In the columns 5 and 6, both values are "0", which means that the both alleles of an individual have no change in the nucleotide compared to reference chromosome.
HElib Parameter Settings We present the HElib parameter setting for secure genomic datasets analysis in Table  3. The HElib context is mainly defined by the plaintext base (p), the security parameter (λ) and the circuit depth (L). All the parameters provide 128-bit security level. In particular, L can be considered as the number of ciphertext moduli in the BGV scheme. We choose the following parameter values: p = 2 and λ = 128. The values of L are presented for evaluation of both rules in Table  3. The security of BGV relies on the hardness of the RLWE assumption  (Brakerski et al. , 2012).
Experimental Results We measure the running time of key generation, encryption, evaluation, and decryption to evaluate the overall performance as well as the impact of the HElib in our considered genome datasets. This segment presents the experimental results of both rules, which are shown in Tables 3, and 4 respectively. In our datasets, each of genome sequence is expressed in a binary representation. ABO-1 Rule: We implement and analyze the computation cost of running ABO-1 rule on genomic datasets. Our ABO-1 rule is presented in Table  2. In ABO-1, first column indicates the blood group type and the second column indicates the conditions that are required to have that specific blood group type. We define 15 rules (O1O1, A2O1, A2A2, B1O1, B1B1, A2B1, A1O1, O1O3, A1A1, A1O3, O3O3, A1B1, B1O3, A1A2, A2O3), which are presented in Table  2.
We implement these ABO-1, and ABO-2 rules using HElib to identify the presence of ABO blood types in patient's blood for further analysis and better treatment. The results are shown in Tables 3, and 4 respectively. Table  3  describes the parameters which have been used in Table  4  for our experimental analysis. We use default value of plaintext base (p) which is 2. The value of circuit depth (L) depends on the number of multiplications in ABO-1, and ABO-2 rules. We use tree-structured multiplications to achieve low multiplicative depth in evaluation of ABO-1, and ABO-2 rules. Tables 3, and 4 present one patient dataset analysis. We can run these experiments using paralleliza-   tion method and calculate average value of all patients' results, and get the same performance results, which are presented in both Tables 3, and 4 respectively.
Similarly, the value of L for ABO-2 is L ≥ 15. lic cloud. The model protects not only genomic sequences but also the intermediate and final computation results when processing on public cloud. We evaluate our proposed framework through intensive experiments using real genomic datasets. This study assessed the steps required for deployment of privacypreserving genetic testing in personalized medicine scenario. We test the applicability of homomorphic encryption techniques for genetic testing with ABO rules. This includes protection of the genomic datasets itself and the possibility to conduct various operations such as ABO analysis within an encrypted environment. The testing results have proven that HElib performance is close to be practical in genomic datasets evaluation. Our next step is to enable transciphering  (Canteaut et al.
Participants Thirty male Caucasian participants with normal or correctedto-normal vision and no history of neurological or psychiatric illness were tested. Data from two participants was excluded from the analysis due to poor performance, and a further one dataset due to equipment failure during EEG recording. Of the remaining 27 participants (mean age 27. 9 ± 6. 1 years), 25 were right-handed and two were left-handed according to results from the Edinburgh Handedness Inventory  (Oldfield, 1971). Participants gave written informed consent in accordance with the Declaration of Helsinki. The study was approved by the University of Queensland's ethics committee. All participants underwent a rigorous clinical interview to exclude confounding conditions such as major psychiatric illness, neurologic illnesses or drug dependency.
EEG Recording and Analysis Continuous EEG data were recorded using an ActiveTwo Biosemi electrode system from 64 scalp electrodes, digitized at 1024 Hz. Vertical eye movements were recorded with two vertical electrooculogram (EOG) electrodes above and below the left eye, while horizontal eye movements were recorded from electrodes placed at the outer canthus of each eye. Data were analyzed using eeglab 9. 0. 4. 4. (Delorme and Makeig, 2004 ) and FASTER v1. 2b. (Nolan et al. , 2010) , implemented in Matlab. The resulting EEG data were down-sampled to 512 Hz, re-referenced off-line to the average of all scalp electrodes and segmented into epochs of -1000 to +2000 ms surrounding cue onset. Epochs were baseline corrected relative to the pre-cue interval (-200 to 0 ms), high-pass filtered to 0.
In this regard, grapevine is an interesting case showing a highly heterozygous genome and a high genetic variation among cultivars and accessions. From the comparison of the coding region of single copy genes (for a total length of 43000 bp) in 157 cultivars of Vitis vinifera, a total of 96 polymorphic sites were recorded with an average frequency of 1 SNP/34. 55 nucleotides. 79 everal tools are publicly accessible on the web for designing sgRNAs that target unique locations in the plant genome  80, 81  and a recently developed database  82  for facilitating the use of the CRISPR/Cas9 system is available for public use at the Grape-CRISPR website (http://biodb. sdau. edu. cn/gc/index. html). For selecting the best target sequences according to the criteria required by the different systems (ZFN, TALEN, CRISPR) the grapevine reference genomes  83, 84  can be used.
Patients and samples This study was approved by the Institutional Review Board of the Hospital Germans Trias i Pujol (PI-14-016) and by the Ethics Committees of all the participating institutions and conducted in accordance with the Declaration of Helsinki. We selected eleven cases from a database of 432 GBM patients for whom we had both FFPE and FF tumour samples. All patients had primary glioblastomas, as confirmed by pathological review (FA, SB, CC, TR, RL). Two samples had been obtained from the same tumour from each patient, one of which had been stored as FFPE and one as FF.
Bioinformatics The bioinformatic analyses included alignment and quantification, sample quality metrics, differential gene expression analysis, gene variant calling, and prediction of GBM molecular subtype. Alignment and quantification. RNA-Seq reads were aligned to the human reference genome (GRCh38) using STAR (version 2. 5. 1b) [18]  with ENCODE parameters for long RNA. The Y chromosome was removed from the reference genome to map the female samples. Genes were quantified using RSEM (version 1. 2. 28)  [19]  with default parameters. Human gene annotation file was downloaded from gencode release 24. Sample quality metrics. Several quality metrics were calculated to evaluate the differences within each FF-FFPE pair and across the different preservation conditions. For categorical data,a Fisher's exact test was applied for each pair.
For differences in means between the two conditions, a t-test was applied. PCR duplicates were calculated with sambamba  [20]. The number of detected genes was calculated taking into account genes with at least one pairedend read mapped. The number of genes consuming 25% of the reads was calculated by ranking the genes according to expression values (read counts) and then computing the cumulative sum until the number of reads was equal to 25% of the total sum. Mapping statistics were calculated with the tool 'gtfcounts' using GEMtools (http://gemtools. github. io/). Gene body coverage, GC content, paired-end inner distances, median transcript integrity number (TIN) across all the transcripts and distribution of mismatches across reads were computed with RSeQC  [21, 22]. The percent spliced index (PSI) values were calculated with Spladder  [23].
Correlation plots and principal component analysis were done with custom R scripts. Differential gene sampling. RSEM read counts were used as input for DESeq2 (version 1. 10. 1) [24, 25]. The cut-off for considering a gene significantly up-sampled or down-sampled in the FFPE-derived samples was FDR<5%. Gene ontology enrichment analysis of the downsampled FFPE genes was performed with DAVID database beta version 6. 8  [26]. Gene variant calling. We counted the number of mismatches with respect to the reference genome for each GBM-associated gene directly from the mpileup generated by samtools without any filter  [27, 28]. Variant calling was done with samtools with minimum base quality of 13, mapping quality >20, PCR duplicates removal, and minimum read depth of 10. Prediction of GBM molecular subtype.
The glmnet R package  [29]  was used to fit a multinomial logistic regression model with alpha = 1 lasso penalty. The cross-validation RNA-seq dataset was downloaded from the The Cancer Genome Atlas (TCGA) repository using the RTCGAToolbox R package (http://mksamur. github. io/RTCGAToolbox/). The core function 'getFirehoseData' with 'dataset = GBM' and 'runDate = 20151101" was used to access and download the data. The associated clinical annotation for each sample was downloaded using the cgdsr R package (https://github. com/cBioPortal/cgdsr). The TCGA RNA-seq dataset comprised 145 RNA-seq samples grouped into the five established GBM molecular subtypes (Classical, Mesenchymal, Neural, Proneural and G-CIMP). Prediction was made for the four FF and the three informative FFPE samples (excluding FFPE_AA6365) using the largest value of lambda such that error was within 1 standard error of the minimum.
In addition, only 50% of patients receive standard treatment-often due to low performance status or older age-which further reduces the possibility of obtaining comprehensive data on disease progression and patient outcome  [36]. In this setting, FFPE tissues can provide a large volume of biospecimens and may thus represent an opportunity to investigate genetic changes that drive clinical outcome. However, it is not clear whether genomic data obtained from FFPE tissue is as reliable as that obtained from FF tissue. In the present study, we have found that although many FFPE samples were highly degraded and thus could not be included in the study, RNA from those FFPE samples that were not degraded maintained transcriptomic similarities to that obtained from FF samples. The GLIOCAT project recruited patients with GBM who had all been treated with the standard treatment of radiotherapy with concomitant and adjuvant temozolomide, who had clinical information available, and for whom there was sufficient FFPE tumour tissue to perform genomic studies.
Tenfold serial dilutions ranging from 5. 12 × 10 2 to 5. 12 × 10 8 copies μ L -1 and 3. 72 × 10 2 to 3. 72 × 10 8 copies μ L -1 were used to construct standard curves for methanotrophic bacteria and methanogenic archaea, respectively. The qPCR amplification efficiencies of 99. 5% (R 2 = 99. 8%) and 97. 6% (R 2 = 99. 7%) were obtained for methanogenic archaea and methanotrophic bacteria, respectively. The copy numbers for the pmoA gene and methanogenic archaeal 16S rRNA gene were calculated according to the standard curve within the linear range. Fluorescence data were measured when the temperature was raised from 65 °C to 95 °C to confirm the PCR amplification specificity. CFX Manager ™ software (version 1. 6) was employed to perform thermal cycling, fluorescent data col- lection, and data analysis according to the manufacturer's instructions.
5 mM dNTPs, 0. 8 μ L of each primer (5 μ M), 0. 4 μ L of FastPfu Polymerase, and 10 ng of template DNA. Amplicons were extracted from 2% (w/v) agarose gels and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, USA) according to the manufacturer's instructions and quantified using QuantiFluor ™ -ST (Promega, USA). Purified amplicons were pooled in equimolar and paired-end sequenced (2 × 300 bp) on an Illumina MiSeq platform according to the standard protocols. Processing of Illumina MiSeq sequencing data. Raw fastq files were demultiplexed and quality filtered using QIIME (version 1. 17) with the following criteria: (i) the reads with approximately 300 bp were truncated at any site receiving an average quality score < 20 over a 10-bp sliding window, and the truncated reads that were shorter than 50 bp were excluded from further study; (ii) exact barcode matching, 2 nucleotide mismatch in primer matching, and reads containing ambiguous characters were removed; and (iii) only sequences that overlapped longer than 10 bp were assembled according to their overlap sequence.
Reads that could not be assembled were discarded  47. Operational taxonomic units (OTUs) were clustered with 97% similarity cutoff using UPARSE (version 7. 1 http://drive5. com/uparse/), and chimeric sequences were identified and removed using UCHIME. The phylogenetic affiliation of each 16S rRNA gene sequence and each pmoA gene sequence was determined with RDP Classifier (http://rdp. cme. msu. edu/) against the silva (SSU115) 16S rRNA database and FGR functional gene database, respectively, using a confidence threshold of 70%  48, 49. The Chao 1 estimator of community richness, Simpson index of community diversity and Coverage indicating sequencing depth were chosen to evaluate community alpha diversity using the mothur program, version v. 1. 30. 1 (http://www. mothur. org/wiki/Schloss_SOP#Alpha_diversity)  50.
Hierarchical clustering analysis (Jiang et al. , 2013) was conducted based on UPGMA (unweighted pair group method with arithmetic mean) using QIIME (Bray-Curtis) for the calculation of beta diversity distance matrix. Venn diagrams  51  and community composition histograms  52  were generated using the 'Vennerable' package and 'graphics' package, respectively, in R, version 3. 2. 1  53. Nucleotide sequence accession number. The MiSeq sequencing data of the 16S rRNA and pmoA genes are publicly available in the NCBI Short Read Archive (SRA) under accession no. SRP076506.
Statistical analysis. Statistical analyses of the methane emission rate and abundance data were conducted using the LSD (least significant difference) test at the 0. 05 probability level using IBM SPSS Statistics 20. According to the detrended correspondence analysis (DCA) of species-sample (OTUs with 97% similarity cutoff), the length of the gradient in the first axis indicated that redundancy analysis (RDA) could be used to investigate the correlations among environmental parameters, soil samples and methanogenic archaeal/methanotrophic bacterial communities. Therefore, in the R program environment (http://cran. r-project. org)  25  , RDA was conducted using the package 'VEGAN' , and its statistical significance level was detected via the Permutest function  24, 54. Pearson correlation coefficients and p values between the environmental parameters and methane emission rates were calculated using IBM SPSS Statistics 20.
Acknowledgements This study was supported by the  National Natural Science Foundation of China  (No. 31171509  and  30671222 ),  Special Fund for Agro-scientific Research in the Public Interest  (No. 201103001 ), and the  National Key Technology R&D Program  for the 12th five-year plan of China (No. 2012BAD14B03 ). Particular thanks go to  Wang Entao  ( Departamento de Microbiología, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional ) and  Eugene L. Madsen  ( Department of Microbiology Cornell University ) for giving thoughtful suggestions that have helped improved this paper substantially.
Acknowledgements: This work is partially supported by the grants  ANR-12-BS05-0002  and  ANR-13-BS05-0005  of the  French Agence Nationale de la Recherche. CU and EP are supported by the  Delta-ITP  consortium, a program of the Netherlands organization for scientific research (NWO) that is funded by the  Dutch Ministry of Education, Culture and Science (OCW). CU thanks IAP for hospitality when this project was initiated. TN acknowledges financial support from  JSPS KAKENHI  Grant Number  17K14273  and  JST CREST  Grant Number  JPMJCR1414. Numerical simulations presented in this paper were carried out on Cray XC30 at  Center for Computational Astrophysics, National Astronomical Observatory of Japan. CU thanks  Guido D'Amico ,  Matteo Biagetti ,  Kwan Chuen Chan ,  Vincent Desjacques ,  Nico Hamaus ,  Eiichiro Komatsu ,  Mikhail Ivanov ,  Marcello Musso ,  Marilena LoVerde ,  Mark Neyrinck ,  Emiliano Sefusatti ,  Fabian Schmidt ,  Ravi Sheth ,  Sergey Sibiryakov ,  Yvette Welling ,  Drian van der Woude  for discussions and  Toyokazu Sekiguchi  together with  Shuichiro Yokoyama  for information.
Homology modelling The crystal structure of TLR2/TLR6 bound to Pam 2 CSK 4 is known for mouse proteins (Protein Data Bank (PDB) code 3A79). A set of twenty human homology models were generated based on the mouse structure using Modeller software 9 version 10  [64]. Among these 20 models, the model with overall lowest discrete optimized protein energy (DOPE) score was chosen for further characterization. The quality of the best model was then compared to the mouse crystal structure at individual amino acid levels with an additional DOPE energy profile, smoothed over a 15 residue window and normalized by the number of restraints acting on each residue. The profile was subsequently visualized in Excel and revealed the adequacy of the model and the template. The quality of the model was good due to the high sequence identity between mouse and human species (about 70% in the region of interest corresponding to residues 33 to 482).
In silico construction of lipopolyamines The atomic coordinates of RPR ligands were generated using Sybyl software (Tripos). The geometry of the molecules were optimized using the Powell minimization method with initial optimization based Fig. 3. Liposomes (A) and lipoplexes (B) activate NF-κB through a mechanism dependent on TLR2. HEK 293 cells were transfected with plasmids encoding hTLR2 and hCD14 (black), or hTLR4, hMD2 and hCD14 (grey) together with a luciferase reporter plasmid dependent on NF-κB activation. Two days after transfection, cells were incubated for 1 h in serum-free medium, washed and then incubated for 6 h with serum free medium alone (Ctrl, 0) or the indicate amount of liposomes or lipoplexes (μM, serum free medium) or LPS (ng/mL, complete medium) or Pam 2/3 CSK 4 (μM, complete medium).
Molecular docking The human crystal structure of TLR2 in complex with TLR1 (PDB code 2Z7X) and its homology model in complex with TLR6 were used for docking upon removal of the synthetic ligands bound to the protein complexes. RPR molecules were docked into TLR2 heterodimers using Autodock Vina  [67]. The receptor was kept rigid, whereas the ligand was allowed total flexibility. The Autogrid parameters were computed on an initial grid size of 40 × 40 × 50 Å 3 , with a spacing of 1 Å. The grid was centered on the complex at x = + 13. 706; y = -23. 174; z = +3. 332. The default optimization parameters were used for the iterated search in Vina, with the default value of 8 for exhaustiveness. Flexible docking performed with LPAs generated a number of poses ranked according to their binding energies.
Conclusion Tested LPAs activate the innate system via TLR2 whether they are delivered in liposome or lipoplex forms. Docking predicts binding poses suitable for either TLR1 or TLR6-driven heterodimerization, while binding energies and biological data seem to favour the former. We determined the structural parameters responsible for TLR2 recognition, extending our results from three very specific lipids to a large number of molecules currently commercialized or proposed to be used as transfection agents. These parameters are most likely to be chosen in the future synthesis of transfection agents because they confer ease of synthesis, while combining biocompatibility and efficiency of transfection. Therefore our study emphasizes the importance of assessing the inflammatory properties of lipoplexes to estimate the safety of transfection, and also aims at warning and guiding on planning new synthesis of non-immunostimulatory nanocarriers.
Acknowledgements MP would like to thank the  FRIA-FNRS  ( F3/5/5-MCF/XH/FC-17514 ) for its financial support. NG and MG would like to thank the  Wellcome Trust  ( WT100321/z/12/Z ) for financial support. CL is grateful to the foundation  Wiener Anspach  and the  Marie Curie Actions  ( TLR4-CAT PIEF-GA-2012-326481 ) for financial support.
05). These effects were accompanied by down-regulation of both VEGF-A and CXCL-12 gene expressions (P < 0. 001). LC-MS dereplication using taxonomy filters and molecular networking databases identified isovitexin in FD1c; and oleanolic acid, moretenol, betulin, lupenone, and lupeol in FD2c. In conclusion, FD1c and FD2c were able to overcome three main hallmarks of cancer in PC3 cells: (1) apoptosis by activating of the intrinsic pathway,.
Microfraction Collection Using HPLC-DAD (High Performance Liquid Chromatography-Diode Array Detector) High performance liquid chromatography-diode array detector (HPLC-DAD) chromatograms were obtained on an Agilent 1200 series HPLC system (Agilent, United States). The stationary phase was Agilent C18 column (250 mm × 4. 6 mm id, 5 µm). The microfractions were collected using Agilent 1200 series fraction collector. The data was collected and processed with the Agilent OpenLab CDS Chemstation Edition software (Agilent).
Ultra High Performance Liquid Chromatography (UHPLC) and Mass Spectrometry (MS) Analysis The UV (200-500 nm) and ELSD profiles were acquired to provide indication of the type of compound and a quantitative estimation of each components presence in the active fractions respectively. This was performed on Waters Acquity UHPLC system consisted of Solvent Manager, Sample Manager, Photodiode Array Detector (Waters, Milford, MA, United States) and SEDEX Model 85 LT-ELSD detector (SEDERE, Olivet, France). Data acquisition and processing was carried out using Pro Empower 3 software (Waters, Milford, MA, United States). The accurate mass measurement was acquired on Waters Acquity UHPLC system coupled with a Waters Micromass LCT Premier Time-of-Flight mass spectrometer (Waters, Milford, MA, United States) and controlled by Mass Lynx 4.
1 mm, 1. 7 µm) that was protected with a pre-column (Waters VanGuard BEH C18, 1. 7 µm). Column and sample temperature was fixed at 40 and 10 • C respectively. The mobile phase consisted of water (solvent A) and acetonitrile (solvent B), each containing 0. 1% formic acid (v/v). A generic 50 min gradient at a flowrate of 0. 46 mL/min was applied as follows: 0-30 min, 5-95% B; 30-40 min, 95% B; 40-40. 20 min, 95-5% B and end at 50 min isocratic step for column equilibration to initial conditions. The acquired MS1 raw data was converted to mzXml format using Proteowizard 1  and imported to an open source software, MzMine 2. 11 2. Peak list was generated using 2D GridMass, followed by deisotoped step and peak alignment. Identification was performed based on a custom database created from Dictionary of Natural Products (DNPs on DVD, Windows 7 version 6.