dataesr/scientific-paragraphs-categorization huggingface dataset:
test function of the Bioconductor package DAPAR. Our work could be extended in several ways. First the experimental design of some proteomic analyses may be more complex than the one accounted for in our evaluations. The fast version of our statistic introduced in Proposition 1 is derived for a model with a protein and peptide fixed effect only, as opposed to e. g. technical replicates. Proposition 1 could be generalized to more grouping factors with fixed effects. Alternatively, or for random effects, one can use model (  3 ) with additional factors and (slower) out of the box implementations of mixed model to compute the likelihood ratio statistic. Another possible extension regards the misspecification described in Section 5. 3: using a Wald test instead of likelihood ratio test, one could simply estimate all protein abundances jointly instead of relying on models in which all proteins but one are differentially abundant.
The results showed that the proposed model is in excellent agreement with experimental data in prediction of pure and impure CO 2 -oil MMP. Also, the swarm intelligence and artificial neural network (ANN) models  [37]  were provided for determining the dew point pressure. The models can aid in better understanding reservoir fluid behavior through reservoir simulation scenarios. Meanwhile, Mohammadet al. [38]  also provided a novel method to predict dew point pressure in gas condensate reservoirs. The proposed intelligent model can be regarded as a feasible method to calculate the dew point pressure of condensate gas reservoirs when the required real data cannot be acquired. In 2017, artificial neural network and piecewise linear (PWL) algorithm  [39]  were integrated to simulate four datasets of the carbon capture process system.
Model establishment and parameters optimization In this work, the MATLAB software and LIBSVM-3. 1-Faruto Ultimate 3. 1 Mode toolbox (by faruto)  [54]  were used to establish the PSO-SVM model and then train it. The 3-SVR regression model during training was chosen as the kernel function (Usually RBF kernel function) to obtain the good performance. The performance of 3-SVR regression model strongly depends on the selection of 3-SVR regression parameters, mainly including: penalty factor C and kernel function parameter g and the allowable error 3. Therefore, the key process of model establishment is the optimization of parameter (C, g and 3)  [55]. Currently, the methods commonly used to optimize parameters of 3-SVR regression model are as follows: empirical method, grid search method, Bayesian framework method, genetic algorithm and so on.
Our implementation of the bioinformatics pipeline aimed to identify potential piRNA reads by eliminating other forms of small RNAs instead of considering only TE-derived reads  [52]. Briefly, after genomic mapping, reads that annotated to rRNA, tRNA, and miRNA were removed. To ensure the elimination of false positives, we also eliminated reads that may have originated from predicted microRNA precursors. Given the expression of chicken PIWI/piRNA pathwayassociated genes found in blastoderm  [48, 52] , cultured PGC  [15, 48] , and adult testes  [49] , we validated that these genes are also expressed in enriched embryonic germ cells around spermatogonia formation stages (Additional file 1: Figure  S2, S3 ). Our in-house bioinformatic pipeline was then applied over 3′-end-2'O methylation-enriched small RNA-seq datasets from blastodermal cells (BC), cultured PGCs from E7 gonads (E7PGC), and E11 and E14 embryonic gonads (E11G and E14G, respectively) generated in our lab.
PiRNA datasets of adult testes (AT) published by Li et al. [49]  were included to evaluate the accuracy of our piRNA candidate analysis pipeline and provide a piRNA dataset of germ cells undergoing meiosis. The small RNA categories and read densities through each step of filtering and final piRNA candidacy are summarized in Additional file 1: Table  S1. The high proportion of mapped reads was categorized as piRNA candidates based on our analysis pipeline predictions (Additional file 1: Figure  S1C ). The reduction of reads mapping to miRNAs in the oxidation-treated libraries compared to the untreated counterparts demonstrated the success of enriching 3'end modified small RNAs. The characterization analysis results showed that the adult testicular piRNA candidates are shorter in length, mostly ranging from 24 to 27 nt, relative to piRNA candidates from other samples, which range from 26 to 29 nt (Fig.
We found 510 transcripts that were potential piRNA associated transcripts of E11G and E14G in union (Fig. 3a ), among which 193 transcripts were differentially expressed (fold change > 2) before and after spermatogenesis (E11G vs E14G). Among these candidate transcripts, we identified 58 protein-coding genes, 132 uncharacterized genes from Ensembl, and 3 snoR-NAs (Fig. 3b ). However, none of the Gene Ontology (GO) terms was significantly enriched with these genes. The differential expressions of 15 of the targets between E11 and E14 germ cells were examined by RT-qPCR (Fig. 3c-f  ). Three of these genes showed negative correlations between gene expression and the associated piRNAs, which were predominantly mapped on or approximate to TEs within those genes (Fig. 3c, e;  Additional file 1: Figure  S6 ).
The intersection of joined overlapping piRNA clusters across samples and transcriptome information allowed us to better predict the piRNA cluster boundaries (Additional file 1: Figure  S8 ; see Material and Method). This adjustment granted a slight increase in the number of piRNA candidates that may be assigned to piRNA clusters despite being initially discarded by proTRAC, a software program for piRNA cluster detection (Additional file 1: Table  S3 ). Multidimensional scaling analysis showed a strong association between piRNA cluster expression patterns and developmental stages (Fig. 4b ). As demonstrated, the expression profiles of piRNA clusters between E11G and E14G were less distinguishable, suggesting similarities in piRNA modulations before and after spermatogonia formation. Hence, we categorized the expression profiles of piRNA clusters according to the developmental stages, as BC-enriched piRNA clusters (BC-piRC), PGC-enriched piRNA clusters (PGC-piRC), embryonic gonadal-enriched piRNA clusters (EG-piRC), and adult testes-enriched piRNA clusters (AT-piRC).
Additionally, other methylation editing may also block the oxidation reaction. The RNA modification information has recently been summarized in RMBase (http://rna. sysu. edu. cn/rmbase/) [62]. Hence, despite RNA modifications in chicken are scarcely studied, the possibility exists that RNA methyltransferase may be highly active in chicken blastodermal cells and leads to 2'-O methylation on rRNA and tRNA, including 3′-end methylation. Alternatively, cleavage of rRNA and tRNA, may also expose 2'-O methylation site at 3′-end, and are therefore included in our 3′-end enriched small RNA population. Further studies may reveal their potential roles in early chicken embryo and germ cell development. Although our high throughput datasets were generated from multiple chicken breeds, the conclusion of stage-specific piRNA modulation should not be solely attributed to genetic background mediated bias.
When taking a full set of piRNA sequencing datasets from blastodermal cells (BC1, BC2), E11 (E11G2) and E14 (E14G2) and adult testes (Adult) sequencing results all from Leghorn breed, we confirmed that all of the stage dependent differences we demonstrated in this paper were also observed within the LH breed (Figs. 1a, c, 4c  and Additional file 1: Figure  S7B ). Studies of genomic occupancy profiling such as ChIP-seq and bisulfite genomic sequencing may further elucidate the links between the PIWI/piRNA pathway and epigenetic regulation along chicken germ cell developmental states. The developmental stage-dependent modulation of TE subfamilies and their importance in establishing or maintaining pluripotency have only recently been appreciated  [63] [64] [65].
Blastoderms were collected from non-incubated eggs. PGCs were isolated from E3 and E7 embryos of the Arbor Acres broiler (Gallus gallus) purchased from Chu Lin Farm Co. , Ltd. , Taiwan. Male gonads were dissected from chicken embryos incubated ranging from 8 days (E8, HH34) to 14 days (E14, HH40) for germ cell isolation. Samples were processed for total and small RNA isolation by TRIzol reagent (Thermo Fisher Scientific, Waltham, MA, USA) and miRNeasy Mini Kit (Qiagen, Valencia, CA, USA), respectively, according to the manufacturers' instructions. A full set of data solely from Leghorn layer has been extracted from blastodermal cells (BC1, BC2), E11 (E11G2) and E14 (E14G2) and adult testes (Adult) sequencing results to confirm the conclusion. High-throughput next-generation sequencing of 3′-end-2'O-methylated small RNAs RNA extracted from 10 to 15 blastodermal embryos, cultured E7 PGC, 8-10 E11 left-side embryonic gonads, and 8-10 E14 left-side embryonic gonads were preceded for small RNA-sequencing and strand-specific total RNA-sequencing (ssRNA-seq).
Bioinformatic filtering for piRNA candidates Periodate oxidation-treated small RNA-seq datasets for blastodermal cells, E7 PGCs, E11 and E14 gonads were generated in our laboratory. We also obtained the oxidized small RNA sequencing for adult testes (GSM1096613) from Li et al. [49]. For all in-house generated small RNA-seq, we applied adaptor trimming via cutadapt  [83]  with a 3′ adaptor sequence "TGGAATTCTCGGGT GCCAAGGAACTCCAGTCAC" and parameters -m 15 -M 45 to restrict a small RNA size for the follow-up analysis. We applied FastQC  [84]  to identify adaptor sequences to be trimmed by cutadapt for adult testes small RNA-seq. Adaptor-trimmed small RNA reads were mapped to the galGal5 genome via Tophat2 (v2. 0. 12)  [85] , with parameters -g 1 -N 0 -readgap-length 0 -read-edit-dist 0 -read-realign-edit-dist 0 -max-insertion-length 0 -max-deletion-length 0, to filter out non-chicken reads and reads containing indels or mismatches (Additional file 1: Table  S1 ).
We used chicken galGal5 Refseq transcriptome obtained from UCSC genome browser  [86, 87]  as the splice junction database to identify reads spanning splice junctions. For stringent piRNA candidate selection, we removed reads mapping to galGal5 rRNA and tRNA sequences from Rfam  [88]  and Ensembl database (release 87)  [89] , then to chicken miRNA precursors from miRBase (release 19)  [90]  and predicted novel miRNA precursors identified via miRDeep (v2. 0. 0. 7; default parameter setting), with parameters -n -c -j -l 18 -m -p  [91]. We applied Bowtie (v 1. 1. 1) [92]  with parameters allowing no mismatches for identifying miRNAs with complete sequence match. Finally, we reckoned sequencing reads with 24-34 nt as piRNA candidates (Additional file 1: Figure  S1B ).
Strand-specific RNA-seq analysis We constructed strand-specific paired-end mRNA sequencing for blastodermal cells and E11 and E14 gonads following Illumina TruSeq stranded total RNA sample preparation with 12 PCR-cycle amplification, and then sequenced via Illumina HiSeq sequencing system, with read length setting to 100 bp. The raw RNA-seq reads were curated by trimming adaptors and low score reads, followed by genomic mapping using Tophat2 (v2. 0. 12)  [85]  with standard settings for paired-end sequencing. We applied reference guided sequence assembly using Cufflinks (v2. 2. 1)  [93, 94]  with default parameters, followed by merging transcriptome assemblies and filtering out potential isoforms. We then annotated the merged contigs based on galGal5 Refseq and Ensembl databases (release 90).
piRNA cluster analysis We adapted proTRAC (v2. 0. 5) and reconfigured it to allow the discovery of dual-stranded piRNA clusters, in addition to single-and bi-directional piRNA clusters  [95]. For cross-stage comparison, we merged the piRNA clusters in each profile based on their genomic position. Considering that the transcriptional activities between an expressing piRNA precursor and its sense-overlapping expressing mRNA precursor may not be distinguishable by sequencing information, because both precursors are transcribed via RNA Pol II  [96] , we extended a candidate piRNA cluster to cover its overlapping transcripts with FPKM > = 1 in at least one in-house RNA-seq dataset (Additional file 1: Figure  S8 ). The expressions of piRNA clusters were measured as the number of piRNA reads per kilobase per million reads (piRPKM) for cross-sample comparison.
RT-qPCR primer design All in-house developed primers were designed via Primer3  [101, 102]  against gene or transcript sequences obtained from UCSC genome browser  [86, 87]. For the design of primers against chicken LINE sequences, we obtained 10 longest sequences for each of CR1-B to CR1-H from the galGal5 genome sequence, and then identified consensus sequences via R-packages  [99]  for Multiple Sequence Alignment (msa)  [103]  and Biological Sequences Retrieval and Analysis (seqinr)  [104]. Primer target specificity was validated via in-silico PCR in UCSC genome browser  [87]. Total RNA isolation from enriched germ cells and reverse transcription real-time PCR (RT-qPCR) For total RNA extraction, equal volume of ethanol was added to a sample in TRIzol, and then loaded onto a filter column provided by the miRNeasy Mini Kit (Qiagen, Valencia, CA, USA).
Acknowledgements This article is dedicated to the memory of  Prof. Hsei-Wei Wang  ( 1969~2015 ),  National Yan-Ming University, Taiwan. We would like to express our sincere gratitude to  Prof. Wang 's comments and support for this project. We thank  National Taiwan University -The Core Technological Platform and Services of the Center for System Biology  for assisting in developing the piRNA identification platform. Special thanks to  Dr. Hervé Seitz  from  Laboratoire de Biologie Moléculaire Eucaryote (LBME), Université de Toulouse, France , for providing critical intellectual input and extensive discussion, and the 3'-end-2'O-methylated small RNA enrichment protocol. We thank  Chun-Yi Hsiao ,  Yu-Yun Kao ,  Yih-Shien Chiang ,  Yih-Shien Chiang , and  Jui-Hua Riva Chu  ( Technology Commons, College of Life Science, NTU ) for support with experimental settings for oxidation-enriched small RNA-seq library preparation and the MiSeq platform analysis.
Funding This study is supported by the  Ministry of Science and Technology, Taiwan  ( 102-2314-B-002-070-MY3 , this Grant associated with the  MOST Ta-You Wu  memorial award;  106-2311-B-002-009-) ;  National Taiwan University -Cutting-Edge Steering Research Project  ( 101R7602D3 ;  102R7602D3 ;  103R7602D3 ). This work has benefited from the facilities and expertise of the  NGS platform of Institut Curie  (supported by the  Agence Nationale de la Recherche  and the  Canceropôle Ile-de-France). Morillon's lab is supported by the  Agence Nationale de la Recherche  ( REGULncRNA, DNA-Life ;  ANR-10-EQPX-03 ;  ANR10-INBS-09-08 ) and the  European Research Council  ( ERC DARK, Consolidator Grant  (CoG),  LS2 ,  ERC-2013-CoG ). Pain's lab is supported by the  Agence Nationale de la Recherche  ( CRB-ANIM-ANR-11-INBS-0003 ).
The framework EMULSION presented here intends to both tackle multiple modelling paradigms in epidemiology and facilitate the automation of model design. We therefore built both a domain-specific language (DSL) for the modular description of complex epidemiological models, and a generic simulation engine designed to embed existing modelling paradigms within a homogeneous architecture based on adaptive software agents. The diversity of concerns (biology, economics, human activities) involved in real pathosystems requires an explicit, comprehensive and intelligible way to describe epidemiological models, to involve experts without computer science skills throughout the modelling, simulation and output analysis steps. This approach was applied to compare hypotheses in modelling a zoonosis (Q fever), to study its transmission dynamics within and between cattle herds at a regional scale, and to assess the contribution of transmission pathways.
Separating model description from the simulation engine allowed epidemiologists to be involved in assumption revision, while guaranteeing very few code modifications. We assessed the added value of EMULSION by applying the DSL and the simulation engine to a concrete disease. Future extensions of EMULSION towards a broader range of epidemiological concerns will reduce significantly the time required to design and assess models and control measures against endemic and epidemic diseases. Ultimately, we believe this effort is a major lever to increase scientists' preparedness to face emerging threats for public health and provide rapid, reliable, and reasoned assessments of control measures.
Besides, even in object-oriented development, abstraction is rarely used (excepted e. g. in  [33]  on vector-borne disease mechanisms). However, classical general-purpose simulation platforms tend progressively to be used, for instance GAMA  [14] , NetLogo  [16]  or Repast  [34]. They provide indeed reliable, ready-made tools for calculations or data integration, leaving more time to focus on modelling itself. Yet, they are not specifically designed for epidemiology and still require a significant time spent on software development. Simulation libraries and platforms dedicated to epidemiological issues are rising, e. g. SimInf  [35] , a R library for data-driven CBM; MicroSim  [36] , an agent-based platform for several diseases; or GLEaMviz  [37] , a metapopulation-oriented platform. To our knowledge, the most advanced approach from a software engineering viewpoint is Broadwick  [38] , a framework for CBM and IBM with interaction networks, which still requires writing large portions of code to derive specific classes and carry out simulations on practical cases.
Another interesting approach, though dedicated to a specific study, relies upon geographical levels and a separation of activities to define efficient aggregations of individuals  [39]. KENDRICK  [40]  defines a DSL fostering a strong separation of concerns (infectious dynamics, spatial distribution, species), and generates C/C++ code to run simulations efficiently. But, it only targets CBM with theoretical (SIR-type) models. The framework EMULSION (for "Epidemiological Multi-Level Simulation") we propose is to our knowledge the only contribution that combines the capability of integrating several modelling paradigms and several scales with dynamic aggregation levels (through a multi-level multi-agent architecture to wrap them), and the explicit description of models (through a DSL dedicated to epidemiolocal issues)  [41]  (Fig  1 ).
Coupling a Domain-Specific Language with a generic simulation engine Designing a model using EMULSION involves three interdependent elements: 1) an explicit, modular and readable description of the model written using EMULSION's DSLthis step is intended to be accessible to non-computer scientists experts ; 2) the use of the generic simulation engine written by computer scientists, to capitalize, in an extensible, modular and reliable way, recurrent treatments and calculations that can be found in most epidemiological models (e. g. computation of states evolution over time, connection to data, etc. )this engine is aimed at building and running the appropriate simulation architecture based on the model specifications in the DSL; 3) small code add-ons which may be necessary to add features (calculations, actions, processes) either specific to each model or not yet incorporated into the generic engine (Fig  2 ).
A classical flow diagram is essentially a conceptual sketch of the model, requiring further programming to control transitions between states; conversely, the state machine diagram is informative enough to be processed directly by the generic simulation engine without further code writing. For instance, flow diagrams generally assume an exponential distribution of durations in health states; but, sometimes other distributions are required, e. g. a constant incubation duration. While a classical approach would require to rewrite the simulation code to switch from exponential to constant duration, with EMULSION the specification of a constant duration in the node of the state machine is automatically handled with the correct computation by the generic engine. Similar changes can be made or revised on the fly, since they require no more than adding or removing a few lines in the model description.
The elaboration of a DSL for epidemiological models is actually a first attempt towards standardization, which must be supported by an ability to encompass existing modelling paradigms and adapt to real use cases. The modelling language defined in EMULSION is an "internal" DSL  [47] , as it is based on another language, YAML (a human-friendly data serialization standard). Its syntax is quite simple, relying mainly on lists and on dictionaries (i. e. key-value pairs), which can be nested one in another and, for most components, do not require a special ordering. Contrary to most general-purpose programming languages, this DSL is aimed at describing declarative knowledge, i. e. the model components and their relations, the way to process them being implemented in the generic simulation engine.
Only the items of the two latter require subsequent implementation in the proper agent classes as specific code add-ons. Additional features can be specified in the model, such as time management (e. g. time unit, duration of discrete time steps, scheduling of events…) or desired outputs. The key point is that this description, which can be developed and consulted independently of the generic simulation engine and of any possible code add-ons, does not require any computer science skill to be understood and discussed. Hence, it fosters interactions with experts throughout modelling, from formulating initial assumptions to specifying parameter values and identifying relevant scenarios and outputs. Besides, an EMULSION model not only enforces an explicit specification of model items that otherwise would be hidden in the code, but also requires a textual description of their rationale and purpose, to keep track for instance of the evolution of assumptions or of the exact meaning of parameters.
An agent-based software implementation Several elements in the model description file rely upon the agent-based software architecture used in EMULSION, which is instantiated at runtime by the generic simulation to build the actual simulation from the model description. Multi-Agent Systems (MAS)  [48, 49]  have become a classical paradigm for the simulation of complex systems. Agents, endowed with behaviors reflecting assumptions of a mechanistic model, interact within a shared environment. They are quite flexible and can represent any kind of entity, since they are defined by their behaviors and interaction capabilities rather than by their structure. Their behaviors can be defined through rules, equations, probabilistic trials, etc. More recently, in multi-level MAS, agents are also used to explicitly represent intermediary organization levels within the system, with their own behavioral capabilities.
Metapopulation appears a gathering of lower-level agents, such as those built after one of the previous architectures. "MultiProcessManager" agents are designed to host them, provide a contact structure, and be automatically constructed by EMULSION with the underlying components, based on the specification of processes modelling the contact structure, key variables and state machines in the model description file. Thus, several concerns are handled at the same time, without any special development effort for the model designer. EMULSION models can be checked prior to simulation to identify missing or inconsistent information, and code templates can be generated to facilitate writing the specific add-ons. To run a simulation from a model, EMULSION parses the YAML description file to read parameters, resolve expressions, build the state machines, and instantiate the agent classes corresponding to the required levels and groupings, based both on the objects of the generic simulation engine and on the specific code add-ons.