dataesr/scientific-paragraphs-categorization huggingface dataset:
05). These results supported the hypothesis regarding the potential role of several DEMs in milk production and composition. However, the direct role of these miRNAs in milk synthesis needs to be confirmed Fig. 4  Two-way hierarchical clustering dendrogram of differentially expressed miRNAs. Clustering performed with data of the 11 mammary samples of the 19 DEMS (P adj < 0. 05), and using PermutMatrix software (Caraux and Pinloche, 2005) using Euclidean distance calculation and Ward's minimum variance method. Columns represent each mammary sample for Holstein (H, samples 1 to 5) and Montbéliarde (M, samples 1 to 6) cows. Rows represent each miRNA grouped according to abundance (from Group 1 with higher to Group 3 with lesser abundance) by functional analyses, which would provide compelling evidence of their role.
The sequence reads were aligned against the Bos taurus btau5. 0. 1 genome as miR-Base_v22. 1 using the miRDeep2 package  [55]. Precursors and mature miRNAs were identified using the miR-Deep2 core module, miRDeep2. pl. Potential miRNAs were annotated accordingly against ortholog miRNAs in goat, sheep and humans (miRBase release 22. 1). We used a miRDeep2 score ≥ 0 as a cut-off threshold. The accession number of the RNA-Seq data is GSE131057. The X-(Holstein) and Y-(Montbéliarde) axes show the Log 2 of the mean of the normalized counts of expressed miRNAs (Additional file 1: Figure  S2 ).
Statistical and bioinformatics analyses Statistical analyses to compare breed effects on the milk production means per cow were performed using mixed models of SAS (version 9. 4; SAS Institute INC, Cary, NC). The normalization and differential expression analysis of the known and predicted miRNAs were conducted with the DESeq2 R package v1. 18. 1. Significance was considered at P adj ≤ 0. 05. Relationships among production variables and DEMs were explored by Spearman correlations and significance were considered at P ≤ 0. 05. Target genes of studied miRNAs and corresponding putative pathways were investigated using the miRWalk database (version 3. 0) and Metacore™ software (release 6). Clustering was performed using the reads count for each animal of the DEMs using Permutmatrix software using Euclidean distance calculation and Ward's minimum variance method  [60].
Acknowledgements The authors thank the staff at Herbipôle  INRA  ( UE1414 ,  Theix, France) ,  Simon Collange ,  Julien Mongiat  and  Denys Durand  for performing mammary gland biopsies,  Sébastien Bes ,  Karol Pawlowski ,  Arnaud Delavaud , and  David Chadeyron  for technical assistance and for sample collection;  Sébastien Bes  for RT-qPCR analyses. Sequencing was performed by the  IGBMC Microarray and Sequencing platform , a member of the ' France Génomique' consortium  ( ANR-10-INBS-0009 ).
Introduction Along with the rapid progress of genome sequencing technologies, many bioinformatics tools have been developed for characterizing genomic variants based on genome sequencing data. While there is an increasing availability of experimentally validated gold-standard genome sequencing dataset from real biological samples, in silico simulation remains a powerful approach for gauging and comparing the performance of bioinformatics tools. Correspondingly, many read simulators have been developed for different sequencing technologies, such as ART  (Huang et al. , 2012)  for Illumina and 454, SimLoRD  (Sto ¨cker et al. , 2016)  for PacBio and DeepSimulator  (Li et al. , 2018)  for Oxford Nanopore. However, when it comes to tools for simulating genome sequences with embedded variants, the choices appear much more limited.
The current available tools are either too simple or too specialized. For example, SInC  (Pattnaik et al. , 2014)  can introduce random single nucleotide polymorphisms (SNPs), Insertions/Deletions (INDELs) and copy number variants (CNVs) into a user-provided reference genome but lacks the ability to simulate known variants, which is actually highly relevant in some simulation applications. Simulome  (Price et al. , 2017)  is another random variant simulator that provides finer control options, but it is designed for prokaryote genomes only. More sophisticated tools exist, such as VarSim  (Mu et al. , 2015)  and Xome-Blender  (Semeraro et al. , 2018) , but these tools are mostly tailored for human cancer genome simulation and often require additional third-party databases. Therefore, we feel there is need for a genome simulator that strikes a balance between simplicity and versatility.
For random variants, simuG supports a wide-spectrum of fine control options, such as '-titv_ratio' for specifying the transition/transversion ratio of SNPs, '-indel_si-ze_powerlaw_alpha' and '-indel_size_powerlaw_constant' for specifying the size distribution of INDELs, '-cnv_gain_loss_ratio' for specifying the ratio of segmental duplication versus segmental deletion, '-duplication_tandem_dispersed_ratio' for specifying the ratio of tandem versus dispersed duplications and '-centromere_gff' for specifying the location of centromeres so that simulated random CNVs, inversions and translocations will not disrupt the specified centromeres. An ancillary script vcf2model. pl is further provided to directly calculate the best parameter combinations for the random SNP/INDEL simulation-based on real data.
Funding This work was supported by  Agence Nationale de la Recherche  [ ANR-16-CE12-0019 ,  ANR-16-JPEC-0005 ,  ANR-15-IDEX-01 ]. J. -X. Y. was supported by a postdoctoral fellowship from  Fondation ARC pour la Recherche sur le Cancer  [PDF20150602803]. Part of computation involved in this work was performed via the  Extreme Science and Engineering Discovery Environment (XSEDE)  [ TG-BIO170065 ].
The authors built a formal probabilistic model and verified the adaptation behavior with discrete event simulation  [186]. Paolo et al. provided a formal rule based validation method for service-oriented applications and validated the behavior by scenario-based simulation  [142]. Youngil et al. introduced a vehicle-driver closed-loop simulation method to verify the self-adaptation algorithm for integrated speed and steering control with the CarSim and MATLAB/Simulink  [187]. A rule based self-adaptation algorithm are proposed for traffic light control, the authors analyzed the safety and liveness with Simulink  [188]. Simulation-based methods can evaluate the safety by checking predefined rules/thresholds but also the statistics of unacceptable results. Obviously, simulation-based V&V methods can be regarded as a part of MDE solution, which suffer all the challenges that have been introduced in the Section 4.
time approaches are fragmented and dedicated to limited scenarios, no integrated safety assurance framework is available  [191] , which can't well describe the multi-level architecture and model the dynamic behavior of SCPS. To model and verify continuous SCPS scenarios, model@run. time solutions should dynamically adjust the rules and cases, and to overcome the limited resources of embedded subsystem, we need to develop compositional models and rules, taking full advantage of the hybrid multi-role self-adaptive architecture, and build multi-level databases to cache the rules and cases. Moreover, the DSS should be able to recognize the scenarios and subsystems should be able to dynamically rebuild models to fit the scenarios. As a kind of safety V&V method, the model@run. time solutions also suffer from the issues introduced in Section 5.
ACKNOWLEDGMENTS The authors want to thank  Drs. Debby van Riel ,  Vincent Munster , and  Thijs Kuiken  for virus labeling. The authors also want to thank the people involved in tissue collection and preparation, with special thanks to  Jorge Hernandez ,  Björn Hermann ,  Johan Stedt , and  Jonas Bonnedahl  for their help with tissue sampling in Sweden and  Daniela Rojas Mancilla ,  Claudio Lecocq Parra , and  Manuel Quezada Orellana  for tissue collection in Chile. Finally, the authors also want to thank the staff at  Ottenby Bird Observatory  for bird trapping and sampling. This is contribution 311 from  Ottenby Bird Observatory.
5% NP40, and phosphatase inhibitor cocktails) with 60 minutes incubation on ice followed by centrifugation (15 minutes, 12,000g, 4°C). Proteins were separated by SDS-PAGE and transferred onto polyvinylidene difluoride membranes using the iBlot system (Life Technologies). Membranes were incubated with specific antibodies, as shown in Supplemental Table  1B. Protein expression was visualized using the Odyssey infrared system (LI-COR Biosciences). Protein bands were quantified using the Odyssey system, subtracting background noise from a similarly sized area just below the band, and presented as ratio of the expression of proteins of interest versus betaactin expression. Data Processing and Statistics. Quantitative data were analyzed with SPSS Statistics 24. 0 (IBM Inc. , Armon, NY) and GraphPad Prism 8 (San Diego, CA) for Windows.
Results CDA Activity in Cell-Free Culture Media. We investigated whether cell-free culture media had any CDA activity, which would be of importance in the subsequent interpretation of data from cell lines incubated with gemcitabine. We found CDA activity only in DMEM supplemented with horse serum used for culturing MIA PaCa-2 cells. Within the maximum duration of our cell experiments (24 hours), the highest dFdU/(gemcitabine 1 dFdU) ratio at both gemcitabine concentrations was 6. 3% at 37°C (Supplemental Fig. 1 ). No CDA activity was found in either RPMI or DMEM media without horse serum. Accumulation of Inactive Gemcitabine Metabolite in Culture Media. To quantify inactivation of gemcitabine in PDAC cells, we measured extracellular dFdU concentrations after incubation with 10 and 100 mM gemcitabine for 60 minutes or 24 hours, with or without inhibition of CDA.
Problem complexity Before investigating new algorithms to solve the RRH-BBU assignment problem, we address in this section the problem's complexity. We provide a theorem and a proof confirming the problem's NP-Hardness. Theorem 3. 1. Finding the optimal assignment of the antennas (RRHs) demands to available edge data centers (BBU pools) is an NP-Hard problem. Proof As it is described above, the constrained resource allocation problem consists in finding the optimal assignment of antennas demands to the available edge data centers with the aim of satisfying latency and processing requirements and minimizing network resource utilization. Our problem is close to the Generalized Assignment Problem (GAP) (see  [30]  for more details), which is a classical generalization of both multiple knapsack problem  [31]  and bin packing problem  [32].
The body of the annulus was fabricated from Pyrex glass to enable visualization of the bubble formation phenomenon. Four k-type thermocouples were used to measure the stainless steel rod's surface temperature, which were installed close to the surface of the rod. All thermocouples, flow meters, and pressure sensors were connected to a data logger with a frequency of 1 kHz to log the data. The data logger was connected to a PC to record and process the data. Figure  1b  shows the details of the rod, which was made of stainless steel. To measure the bubble diameter, a high-speed camera (Casio EX-F1) was utilized and the images were processed in Matlab software using image processing tools to estimate the bubble diameter. To improve the quality of assessment, a contrast filter, together with sharpness effect, was utilized to clearly distinguish the border of the bubbles.
The software and detailed information of the visualization technique has already been used and verified in our previous publication  [47]. formation phenomenon. Four k-type thermocouples were used to measure the stainless steel rod's surface temperature, which were installed close to the surface of the rod. All thermocouples, flow meters, and pressure sensors were connected to a data logger with a frequency of 1 kHz to log the data. The data logger was connected to a PC to record and process the data. Figure  1b  shows the details of the rod, which was made of stainless steel. To measure the bubble diameter, a high-speed camera (Casio EX-F1) was utilized and the images were processed in Matlab software using image processing tools to estimate the bubble diameter. To improve the quality of assessment, a contrast filter, together with sharpness effect, was utilized to clearly distinguish the border of the bubbles.
However such data for adult T-ALL are still lacking. In this work, we report genome-wide promoter methylation profiling by methylation-dependent immunoprecipitation (MeDIP) in a cohort of adult T-ALL. Subsequently, a nine-promoter classifier was applied to a large series of 168 adult T-ALL included in the GRAALL 03/05 trial that distinguished two subgroups with highly significant differences in the clinical outcome. The multicenter randomized GRAALL-2005 phase 3 trial was very similar to the GRAALL-2003 trial, with the addition of a randomized evaluation of an intensified sequence of hyperfractionated cyclophosphamide during induction and late intensification. 13 Between May 2006 and May 2010, 337 adults with T-ALL were randomized in the GRAALL-2005, of which 185 had available diagnostic material.
MeDIP-assay Global DNA methylation was assessed by a MeDIP assay on an initial series of 24 T-ALL and three human thymi and a second (confirmatory series) of 17 T-lymphoblastic lymphomas (T-LBL) and three human thymi. Briefly, methylated DNA was immunoprecipitated as described previously 17 using 2 mg of sonicated genomic DNA. MeDIP samples were directly subjected to labeling and hybridization to previously described custom human promoter arrays (Agilent, Santa Clara, CA, USA) covering either 17,970 promoters  17  (T-ALL series) or 25,490 promoters  18  (T-LBL series), following the manufacturer's instructions. The median-normalized log2 enrichment ratios (MeDIP/Input) were calculated for each probe using the CoCAS software  19  and visualized using the IGB tool (http://bioviz. org/igb). Finally, a methylation score was computed for each promoter by calculating the median enrichment ratio of overlapping probes.
Clustering of methylation profiles Hierarchical clustering analysis (Average Linkage) based on the methylation signal of the top 5% genes with highest variance was performed with the TIGR MeV v. 4. 9. 0 program, 20 using the -1 Spearman rank correlation method. Analysis of the differential methylation signal between the groups was performed using the significant analysis of microarrays (SAM) algorithm (threshold value: FDR<0. 121 and delta=2. 144). The graphical clustering representation of the clustering was done with the GenePattern software. 21 The list of differentially methylated promoters in T-ALL and T-LBL is provided in Online Supplementary Table  S3.
Validation of DNA methylation signature Direct methylation levels were analyzed by methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA) with custom probes (Online Supplementary Table  S4 ) and, SALSA ® MLPA ® P200 Reference-1 probemix and EK1 reagent kits from MRC-Holland (Amsterdam, the Netherlands), according to manufacturer's recommendations. Data were analyzed with the Coffalyser software (MRC-Holland, Amsterdam, the Netherlands). In addition, the promoter methylation patterns were verified by quantitative PCR (qPCR) analysis of MeDIP samples and by bisulfite sequencing using specific primers for the MEIS1 gene promoter.
Performing a stratified epistasis analysis by lung cancer histology subtype will provide insights concerning tumor-subtype specific genetic interactions and gene networks. The availability of large lung cancer GWAS data from international collaboration enables us to conduct a large-scale epistasis analysis among oncogenesis-related genes in overall lung cancer as well as lung cancer subtypes. In this study, we collected the genotype data from 44,438 individuals with Caucasian ancestry, including 20,401 controls and 24,037 cases, from three independent cohorts. It is currently the largest genetic interaction analysis in lung cancer study to our knowledge.
Discovery study By use of the "fast-epistasis" option in PLINK to quickly screen for interactions, an exhaustive pairwise interaction analysis among cancer-related genes was conducted in the overall lung cancer cohort (ALL) as well as the stratified group by NSCLC, ADE and SQC histology subtype using lung cancer OncoArray GWAS data in the discovery stage. The sample size in the cohorts included 14,260 controls plus 18,401 overall lung cancer cases, 13,593 NSCLC cases, 7,157 ADE cases and 4,612 SQC cases, respectively. In the ALL interaction analysis, there were 102, 734, 7113 and 70145 SNP pairs when we used p value < 1x10 -7 , 1x10 -6 , 1x10 -5 and 1x10 -4 as the significance threshold, respectively. We decided to use 1x10 -6 as the significance cutoff value to provide a balance between including excess false positive results versus missing potential signals.
Replication study In the replication study, we first performed logistic gene-gene interaction studies for the selected SNP pairs in Affymetrix GWAS and GELCC GWAS datasets separately and then conducted meta-analysis to combine the information from both studies. 37, 50, 33 and 67 SNP pairs had a replication meta-analysis p value < 0. 05 with consistent interaction effects (either risk or protective effect) across all three different datasets in ALL, NSCLC, ADE and SQC, respectively (Appendix 1). A final meta- There are 37, 50, 33 and 67 SNP pairs with meta-analysis p value < 0. 05 in replication study and only SNP pairs with interaction p value < 0. 1 in both the two replication datasets are reported in Table  2. OR, p values from each individual dataset as well as the joint meta-analysis are reported, and numbers highlighted in red color indicate the overall meta-analysis p values < 1.
95x10 -10 (Bonferroni corrected significance cutoff). analysis was conducted to combine the information from both discovery and replication studies at these replicated SNP pairs and overall ORs and p values were reported. Table  2  displays the results for SNP pairs with logistic interaction p value < 1x10 -5 in discovery dataset and < 0. 1 from each of the two replication datasets. In ALL lung cancer cohort, we identified a significant interaction effect between rs74826777, located in gene RGL1, and five SNPs located at gene RAD51B. The interaction OR varied from 0. 46 to 0. 49 with p values ranging from 1. 14x10 -8 to 2. 83x10 -7 in discovery study. In the replication study, the interaction OR varied from 0. 16 to 0. 30 with p values ranging from 3. 56x10 -4 to 9. 63x10 -3 in Affymetrix replication data; and OR varied from 0.
Suggestive evidence for genetic interactions between SYNPO2 and BRCA1 gene were found in ADE cohort. 8 SNP pairs had significant interaction effect in the OncoArray discovery dataset and GELCC replication study. However, no supporting evidence was identified in Affymetrix replication data (Appendix 1). The most significant signal came from rs6828669:rs16941 with overall OR of 0. 85 and p value of 3. 95x10 -9.
We further validated the signals in replication datasets and performed joint analysis to combine all information. Figure  2B  displayed the significant signals from the imputed genotype analysis. In each plot, X and Y axis denoted the location of SNPs at each of the gene and Z axis displayed the -log10(p) in the joint meta-analysis, the plane in dash line indicated Bonferroni corrected p value threshold (1. 95x10 -10 ). For the genetic interaction between RGL1 and RAD51B gene, we identified 7 and 3 SNP pairs with joint p value < 1. 95x10 -10 from the ALL lung cancer and NSCLC cohort, respectively. The most significant SNP pair in ALL cohort is rs74826777:rs8006890 with an OR of 0. 39 and p value of 1. 68x10 -11 ; and rs74826777:rs17835244 with OR 0. 38 and p value of 3. 76x10 -11 in NSCLC cohort (Figure  2B  1&2, Appendix 2).
The interaction analysis using imputed genotype data reinforced the finding for interaction between RGL1 and RAD51B gene. In the ADE and SQC cohort, we identified some evidence for genetic interactions in SYNE1:RNF43 and FHIT:TSPAN8 gene pairs in discovery and replication study but none of the SNP pairs achieved the significance level (Table  2 ). Fortunately, the intensive interaction mapping using imputed genotype provided us strong evidence for epistasis between these two gene pairs. In ADE subtype, we identified 111 SNP pairs with metaanalysis p value < 1. 95x10 -10 coming from 14 SNPs at SYNE1 and 8 SNPs at RNF43 gene. The most significant interaction came from SNP pair rs12213593:rs11079348 with OR 0. 73 and p value of 1. 01x10 -12. In SQC cohort, 21 SNP pairs, coming from 16 SNPs at FHIT and 3 SNPs at TSPAN8, had meta-analysis p value < 1.
Genetic interactions on gene expression in the lung We further explored the impact of identified genetic interactions on the expression of involved cancer-related genes using the lung eQTL dataset including a total of 409 subjects with both genotyping and gene expression data  [23] [24]. We evaluated the interaction effects of the significant SNP pairs on gene expression levels. For the significant SNP pair rs10515157:rs1554783 Figure  3 : Interaction analysis at candidate gene pairs. A. Stratified lung cancer risk analysis at the identified significant SNP pairs using genotype data in discovery OncoArray data. MAF1 and MAF2 indicate the minor allele frequency of each SNP in the pair. 0 and 1 indicate the genotype without or with at least one copy of minor allele in second SNP. P values and ORs of SNP1 in main effect association conditioned on genotype at SNP2 were displayed.
82x10 -3 ). With a dominant genotype model in both of the SNPs, the individuals with 1/0 and 0/1 joint genotype have lower SYNE1 expression compared with 0/0 and 1/1 groups. Another differentially expressed gene is TSPAN8 from SNP pair rs1882898:rs1705235 (Figure  3C-3 , P = 4. 67x10 - 2 ). We detected decreased gene expression in 1/0, 0/1 and 1/1 groups compared with 0/0 genotype group. For the SNP pair rs17835244:rs74826777 from RGL1:RAD51B gene pair, the rs74826777 is not available in the lung eQTL dataset and was replaced by the best available proxy (rs12733281, D' = 1, R 2 = 0. 33 in European population). Because the low allele frequency in rs74826777 (and proxy rs12733281) (MAF ~ 0. 01), we had a limited number of samples to test the interaction. We did not identify differential gene expression in the gene pairs, but we did see a trend of increased expression in 1/1 genotype group (Figure  3C-1 , P = 0.
Gene set enrichment analysis The statistical analysis using GWAS data provided very significant statistical evidence for genetic interactions between selected genes. However, we wanted to further explore the underlying biological mechanisms behind the statistical findings and understand the genetic architecture of epistasis acting more generally. In ALL lung cancer cohort, 37 SNP pairs had meta-analysis p value < 0. 05 in replication analysis and displayed consistent interaction effect across three independent datasets. These 37 SNP pairs came from 38 unique cancer-related genes and these genes were submitted to IPA program for pathway and network analysis. Similarly, 43, 36 and 76 genes were submitted to IPA program from NSCLC, ADE and SQC cohorts, respectively. The top 5 canonical pathways from each lung cancer histology subtype are listed in Table  3  and all of them had a Fisher exact test p value less than 0.