dataesr/scientific-paragraphs-categorization huggingface dataset:
EMULSION is also currently used to model the spread of several diseases in real pathosystems. EMULSION provides a command-line tool for checking models, producing model diagrams, running simulations, and plotting outputs. Written in Python 3, EMULSION runs on Linux, MacOS, and Windows. It is released under Apache-2. 0 license. A comprehensive documentation with installation instructions, a tutorial and many examples are available from: https://sourcesup. renater. fr/www/emulsion-public.
On the other hand, enhancing reusability requires reproducibility (the capability for others to recode a model) and flexibility (the possibility to adapt it). Though necessary, best practices in software engineering  [9]  cannot settle those issues alone: ad hoc codes can be documented, tested, reproducible, and yet wrong  [10]. A first step to reduce development time and errors is to use simulation platforms, which provide high-level built-in features. In general-purpose platforms such as GAMA  [11] , NetLogo  [12]  or Repast  [13] , a substantial programming effort is still required as algorithms involved in epidemiological models are not provided as platform features. Simulation libraries and platforms dedicated to epidemiological issues are rising, e. g. SimInf  [14] , a R library for data-driven compartment-based models; MicroSim  [15] , an agent-based platform for several kinds of diseases; or GLEaMviz  [16] , a metapopulation-oriented platform.
Yet, KENDRICK is designed for compartment-based models only. To the best of our knowledge, none of existing solutions address those methodological challenges simultaneously, the most advanced approaches providing either a flexibility in modelling paradigms at the expense of software development efforts, or an enhanced readability through a DSL limited to a specific modelling paradigm. This article introduces EMULSION, an AI-based framework which tackles those challenges in an integrated approach. EMULSION intends to help modellers develop mechanistic stochastic models of complex systems in epidemiology at several scales using multiple paradigms, and to facilitate the co-construction and assessment of model components (biological assumptions, model structure, parameters, scenarios, etc. ) with epidemiologists or scientists from other relevant fields, throughout model development.
A domain-specific language for epidemiological models The main benefits of the DSL defined in EMULSION are a modular decomposition of the model, which reduces dependencies between model components and facilitates further extensions, and a high readability by non-modeller scientists. When EMULSION simulation engine reads the DSL, it selects the agent classes corresponding to the chosen paradigm and scales, instantiate agents depending on initial conditions, transforms expressions into Python functions (using SymPy, a library for symbolic computation), builds the state machines, and runs the simulations. Hence, the execution of simulations based on an EMULSION model is univocal, all required information being directly usable (yet human-readable) by the simulation engine. EMULSION comes with several built-in actions (changing the values of variables, creating new individuals, etc.
) which can be used states and transitions. When the user defines levels, groups, or states, EMULSION automatically provides associated variables (e. g. population counts, individual tests for each state) to make model description as self-sufficient as possible in model files. In very specific cases, processes or actions may require the definition of small code add-ons, as demonstrated below for data-driven events in a metapopulation. But in the majority of situations, no additional code is required. An EMULSION model is a text file based on the YAML format  [33] , which is mainly based on nested lists or key-value pairs, ending with strings, numbers or booleans (yes/no). Each model is composed of several first-level keys (called "sections" in what follows) to provide information by topic: model name, modelling paradigms and scales, processes occurring at each level, structure of state machines, list of parameters and expressions, individual actions, initial conditions, outputs.
Availability and future directions The EMULSION package documentation, with installation instructions (either from PyPI or by cloning the GIT source repository), tutorial and examples, can be found on SourceSup (a forge dedicated to French public research institutes) at: https://sourcesup. renater. fr/www/ emulsion-public. The package comes with 32 examples to illustrate features of EMULSION modelling language, from very simple SIR models declined in compartmental, individualbased, or hybrid approaches, to epidemiological models in structured populations mixing infectious process, demography, seasonality, forcing external variables, and non-exponential distributions of state durations. Examples also demonstrate how to move from within-to between-population models using data-driven movements.
The generic modelling approach we promote in EMULSION both has advantages and drawbacks. The gathering of various model features within a homogeneous software tool, which can be picked up and combined according to the modeller's needs with little or even no additional code, reduces development time, ensures that model assumptions and processes are well implemented, and allows optimized code generation as a mid-term perspective. As a common software limitation, potential bugs in the generic engine could impact all models developed with EMULSION: but its diffusion as an open-source software among a broad community of users is the best way to get feedback on potential malfunctions, identify their origin and correct them rapidly. To our knowledge, EMULSION is currently the only software which addresses all major computing issues in the design of mechanistic models in epidemiology.
(PDF) S1 File. Zip file containing all example files mentioned in the software paper. File "README. md" provides description and usage for each of them. (ZIP) S2 File. Zip file containing EMULSION source files. This zip file is a clone of EMULSION public Git repository. To install from this file rather than from PyPI, go to EMULSION documentation page: https://sourcesup. renater. fr/www/emulsion-public/pages/Install. html and follow Git-based installation instructions. (ZIP).
Funding: PE received funding from: the  French Research Agency  (ANR: https://anr. fr); Program Investments  for the  Future , project  ANR-10-BINF-07  ( MIHMES) ; project  ANR-16-CE32-0007-01  ( CADENCE) ; the  European Union  through the  European fund for the regional development (FEDER)  of  Pays-de-la-Loire  (http://www. europe. paysdelaloire. fr); the  INRA meta-programme "Sustainable management of animal health " through project  FORESEE. SP  was funded by the  Animal Health Division of the French National Institute for Agricultural Research (INRA) : http:// www. sa. inra. fr/. The funders did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The ratio between the radiation necrosis captation and the normal cortex uptake is lower for 18 F-FET compared with 18 F-FDG and 18 Ffluorocholine  (67). Metabolic characteristics were explored in patients with gliomas after treatment with various therapeutic approaches and suspicion of recurrence. An intense and focal uptake suggests relapse, whereas a weak homogeneous uptake on the resection bed was rather in favor of benign post-therapeutic modifications  (125). However, these results are still being debated, and Mehrkens and collaborators adjusted these results. The examination could not replace the stereotactic biopsy  (126). The additional dynamic data mentioned above can help in differentiating low-grade from high-grade glioma tumors but also allows the discrimination of recurrences from radiation necrosis and would have a prognostic contribution.
Study procedure The opinions, knowledge and perception of the participants were examined in one-on-one face-to-face semi-structured interviews. Each participant was interviewed twice: at one and three months after the delivery of the intervention in the school in order to assess the knowledge retention and to verify any changes in school settings. The interviews were recorded using a digital voice recorder to facilitate data collection and framing of themes. Each interviewer was trained on how to conduct the interviews via video conference training conducted by IDF staff in Brussels. Three different interview schedules were developed, one for each of the above listed types of participant. These were adapted to suit the specific cultures of each country, while ensuring that the content remained similar.
Data analysis The data obtained from interviews was transcribed and translated by the PHFI and HRIDAY data collection team in India and by ADJ in Brazil as required. Two researchers (a nutritionist and a psychologist, both also diabetes educators) were involved in data analysis in Brazil. In India five researchers were involved in the data analysis (an anthropologist and a dentist, both with research interests in child and adolescent health, two nutritionists involved in public health research and a public health scientist with research interest in health promotion). All the recordings were transcribed according to a pre-designed transcription protocol. As a first step, five transcripts were coded by two researchers independently and applied codes were compared to develop a list of a priori codes.
Limitations Our study had methodological and operational challenges. Data collection overlapped with annual examinations and summer break in schools, resulting in data for the second round of interviews being collected at variable points in time, ranging between 1 and 3 + months. Data saturation was limited for parents of children with diabetes due to small sample size. Representation of "parents of children with diabetes" from public schools in India could not be included in the interviews due to certain administrative challenges. A purposive sampling strategy was used for selection of participants. In advance of this, attempts were made to minimize inherent biases; during the analysis stage, credibility was ensured by independent analysis of data by more than one qualitative researcher.
Conturo et al. (1999)  laid the foundations for selection and analysis of tracts based on ''region of interest (ROI)'', which opened up a new era of tract-based investigations from neuroscience  (Lebel et al. , 2008; Thiebaut de Schotten et al. , 2011a , 2012)  to clinical applications (Thiebaut de  Schotten et al. , 2005; Bartolomeo et al. , 2007; Deprez et al. , 2012). The access to large samples made the construction of in vivo WM tract atlases and guidelines feasible  (Mori et al. , 2005; Wakana et al. , 2007; Hua et al. , 2008). In this work, we reconstruct the proposed structure using specific ROIs along with modeling of the diffusion MR signal based on constrained spherical deconvolution (CSD;  Tournier et al. , 2004; Tournier et al. , 2007). To minimize the possibility that these findings are based on acquisition or processing artifacts, we used datasets from different projects: young Adult human connectome project (HCP;  Glasser et al.
, 2013; Sotiropoulos et al. , 2013; Van Essen et al. , 2013) , Avon Longitudinal Study of Parents and Children (ALSPAC;  Golding, 2004) , Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation (MASSIVE;  Froeling et al. , 2017) , an in-house dataset  (Jeurissen et al. , 2011) , and robust processing pipelines to show these trajectories. While the main focus of this research was based on dMRI, we complemented our findings with other non-MRI techniques, such as polarized light imaging (PLI) microscopy  (Axer et al. , 2011)  and human brain dissections. Preliminary results of this work on the SAF have been presented at the 2015 ISMRM meeting in Toronto, ON, Canada  (Heemskerk et al. , 2015).
Dataset 1 Minimally processed dMRI data from the HCP S500 release were used. Briefly, the data were acquired with a 1. 25 mm isotropic voxel size; three shells with b = 1,000, 2,000 and 3,000 s/mm 2 in 90 DW volumes and six non-weighted images per shell. Every participant for whom all the 90 b = 1,000 s/mm 2 and 90 b = 3,000 s/mm 2 images were available along with all 18 b = 0 s/mm 2 volumes, and were not listed among participants with known anatomical anomalies or data quality issues were included in the analysis. The IDs of the excluded participants are listed on the HCP wiki page  (HCP wiki, 2018). The selection resulted in 409 healthy participants (243 females and 166 males), between the age of 22 and 36 years. Low b-value images were analyzed with DTI and CSD models separately using 90 b = 1,000 s/mm 2 and nine unweighted volumes, while the higher b-shell was processed with CSD only using 90 b = 3,000 s/mm 2 and 18 unweighted volumes.
Dataset 1 Briefly, FSL  (Jenkinson et al. , 2012)  tools topup  (Andersson et al. , 2003)  and eddy  (Andersson and Sotiropoulos, 2016)  were used to correct for head motion and geometrical distortions arise from eddy currents and susceptibility artifacts. Finally, the DWIs were aligned to the structural T1 image. For the full description, see the work of  Sotiropoulos et al. (2013). DTI estimation of the low b-value shell for the full population was performed using REKINDLE  (Tax et al. , 2015). The fiber orientation distribution (FOD) in each voxel was estimated using CSD  (Tournier et al. , 2004 (Tournier et al. , , 2007) )  with the recursive calibration method (peak ratio threshold = 0. 01;  Tax et al. , 2014)  with maximum harmonic degree of L max = 8, in the high and low b-value shells separately.
Fiber Tractography For all four datasets, the deterministic FT framework of  Jeurissen et al. (2011)  was used for the CSD approach, with the following parameter settings: FOD threshold = 0. 1, angle deviation = 45 • , step size = 1 mm and minimal tract length = 20 mm. Wholebrain FT was performed with uniform distribution of seed points defined at a 2 mm resolution. For DTI based FT, the settings are: step size = 1 mm, minimal tract length = 20 mm, angle deviation = 45 • , FA seed and tracking threshold of 0. 2 and uniform seed point resolution of 2 mm.
ROI Configuration for Reconstructing the SAF Automated, large-scale tract selection on the HCP dataset was obtained by atlas-based tractography segmentation  (Lebel et al. , 2008). On the template dataset, we defined 4 Boolean ''AND'' and ''NOT'' ROIs: 2 axial AND, 1 sagittal NOT and 1 coronal NOT ROIs (Figure  1 ). The 2 axial AND ROIs were placed as follows: the first one was located at the height of approximately half the genu of the corpus callosum (CC) and the second ROI was placed 10 slices (equals to 12. 5 mm in the HCP dataset) superior. Both AND ROIs included the medial frontal area and excluded the cingulum. A NOT ROI was placed midsagittal to exclude fiber pathways from the CC. Additionally, one coronal NOT ROI was placed posterior of the frontal lobe and below the CC to exclude fibers from the inferior fronto-occipital fasciculus (iFOF).
Tract Description The architectural configuration of the SAF is shown in Figure  2  where dataset 3 was used with CSD based FT. The complex fiber architecture in the frontal area can be appreciated from the FODs overlaid on the sagittal view in Figures  2A-C  with the dotted yellow line identifying the interface between regions with locally different dominant fiber populations [''blue'' vs. ''green'' on the principal direction encoded color (DEC) map]. A medial and frontal view of the bilateral tract configuration is given in Figures  2D, E , respectively. Note that the SAF resembles the trajectory of the cingulum bundle (in red) but it is located more anteriorly, superiorly, and laterally. The SAF group composite map of the HCP participants (dataset 1) using CSD modeling with b = 3,000 s/mm 2 is shown in Figure  3.
Again, the bilateral tracts follow a similar trajectory as the cingulum, but more frontally [i. e. , in front of the cingulate sulcus or within the superior frontal gyrus (SFG)] and slightly more laterally. The SAF pathways appear to spread from the rostrum of the CC to the ascending ramus of the cingulate sulcus and are medial  to the corona radiata. Supplementary Figures  S1, S2  show the population level map using the same ROIs, but with the 1,000 s/mm 2 b-value dataset and modeled with DTI and CSD, respectively.
Across Data Acquisitions Figure  5  reveals that the SAF was present in all samples that we analyzed. This observation excludes the possibility that the SAF is merely a scanner-specific artifact. While there are differences in acquisition settings between the different acquisition sites, there is no impact on the extent of the SAF. An example is shown in Figure  6 , where the effect of using different b-values for the data acquisition on the SAF reconstruction is depicted. Low b-values result in less complex FODs leading to shorter and fewer streamlines, but CSD based modeling can still reveal the SAF configuration. CSD based tract coverage from low b-value data shows large overlap with the high b-value based CSD tracts, revealing that proper modeling of crossing fibers is imperative for reconstruction of the SAF.
Impact of Modeling The choice of diffusion signal modeling was critical as the SAF cannot be revealed with DTI. The tract is formed mostly from locally non-dominant fiber populations. Therefore, it can be mapped only with techniques, which can resolve crossing fibers. The single location where the tract is locally dominant is the most anterior part with up-down orientation, as depicted in blue in Figures  1A-C , and is also the only location revealed by DTI based population level tract mapping shown in Supplementary Figure  S1. Following this line of reasoning, yet a system of non-dominant, interwoven fiber bundles are to be found with the help of high data quality and advanced modeling. Another example of mapping a challenging fiber population without any well-known underlying architecture is showcased in the MyConnectome project  (Poldrack et al.
On the one hand, the brain dissections in this work could not clearly verify the existence of the SAF. On the other hand, the dissection results could not rule out the existence of the SAF given the limitations of this approach, especially in regions where multiple fiber systems exhibit crossing configurations. The brain data used for the FT results in this work are obtained from healthy volunteers. Hence, ex vivo investigations (dissection and PLI) on the same brains were not feasible. As such, a direct comparison was not possible.
FUNDING The research of AL, AH and SD is supported by  VIDI  Grant  639. 072. 411from the  Netherlands Organization for Scientific Research (NWO). The  UK Medical Research Council  and the  Wellcome Trust  (Grant ref:  092731 ) and the  University of Bristol  provided core support for ALSPAC. Acquisition of MRI data was funded by the  National Institutes of Health  ( R01MH085772  to TP).
Direct Whole Genome Sequencing Methyl-directed enrichment was applied to all examined clinical samples (n = 25) prior to NGS (details in section "Materials and Methods"). We used the preliminary sequencing to calculate the appropriate number of reads needed for the best possible coverage in the next sequencing runs. The NGS statistics are given in the Supplementary Table  S3. In order to determine if the new sequencing approach was comparable to the traditional pooled segment genome sequencing (PSGS) approach, we sequenced isolates from the same organism (strain Phi-1 and Grady) following each method and did not find any discrepancies between the genomes (data not shown). According to the preliminary sequencing results, we selected samples containing treponemal DNA showing the highest breadth coverage (>97%, n = 11) as candidates for complete genome sequencing and samples with lower coverage (69-97%, n = 8) as candidates for genome-wide analyses (with a depth of coverage of or greater).
9 pM. All runs were configured to obtain 149 nucleotide pairedend read lengths. The bioinformatic analysis was performed according to the pipeline described previously  (Grillová et al. , 2018b). Briefly, the quality check of the raw reads was performed using  FastQC (v0. 11. 5, Andrews, 2010)  and the raw reads were preprocessed using  Cutadapt (v1. 15, Martin, 2011)  and  Fastx-toolkit (v0. 0. 14, Gordon, 2014). The total set of pre-processed reads was mapped to the human genome reference (hg38) and the human-matching reads were removed using  BBMap (v37. 25, Bushnell, 2017). Subsequently, the remaining reads were mapped to the TPA reference genomes using BWA  MEM (v0. 7. 15, Li, 2014). SS14-like strains were mapped to the SS14 TPA reference genome (GenBank Acc. No. CP004011. 1) and Nicholslike strains were mapped to the Nichols TPA reference genome (GenBank Acc.
In parallel, de novo assemblies were performed by SPAdes assembler  (v 3. 11. 1, Bankevich et al. , 2012)  using unfiltered reads aligned to the TPA reference  (kmer = 15, 21, 27, 33, 55, 77, 99, 127 ; minimal contig coverage 5). QUAST (v4. 5, Gurevich et al. , 2013)  was used to evaluate the completeness of the assembly. Consensus sequences from reference-guided mapping and the de novo assemblies of TPA genomes were manually combined to create a final WGS for each sample. Jupyter Notebook containing workflow for methyldirected enrichment sequencing analyses can be found at https: //github. com/opplatek/bacterial_genome_analysis.
SNV and Indel Identification Single nucleotide variants and insertions and deletions (indels) were identified using FreeBayes (v0. 9. 21-19-gc003c1e,  Garrison and Marth, 2012)  and filtered by vcflib  (v 0. 1. 15, Garrison, 2016). Prior to the variant call, mappings were indel realigned (GATK) and PCR duplicates were  removed (v3. 7, Broad Institute, 2015). FreeBayes was run with recommended settings for bacterial samples by the tool authors (ploidy 1; minimal coverage depth 3; minimal variant quality 50). Filtered variants were annotated using  SnpEff (v4. 2, Cingolani et al. , 2012)  with corresponding GenBank annotations.
The Sanger sequencing reads were combined with the Illumina sequencing reads using Lasergene software (DNASTAR v. 7. 1. 0. ; DNASTAR, Madison, WI, United States). The workflow to obtain the whole genome sequences of TPA is given in the Supplementary Figure  S4. Sequences of treponemal rRNA operons (rrn1, rrn2) were searched against the de novo assemblies (BLAST) (v2. 2. 31+,  Camacho et al. , 2009)  and all the hits with more than 90% identity, and alignment length of 100 bp were extracted in fasta and bed format (Bedtools) (v2. 27. 0, Quinlan and Hall, 2010). We have used BLAST to discover all potential sequences of rRNA regions or their fragments. We manually inspected all the BLAST hits and selected those that most likely represented the real rRNA regions.
To separate paralogous regions, the amplified TP intervals were labeled with multiplex identifier adapters and sequenced as four different samples (Nextera TM XT DNA Sample Preparation Kit, Illumina Inc. , Madison, WI, United States). The sequencing reads were trimmed  (Trimmomatic) (v0. 32, Bolger et al. , 2014) , and low-quality bases were removed with a sliding window (window length of 4 nt; average quality of at least Phred 17). The sequencing reads shorter than 50 bp were omitted from the analyses. Reads were analyzed with respect to four distinct pools and were de novo assembled using SeqMan NGen software (v4. 1. 0, DNASTAR, Madison, WI, United States) as well as mapped to the TPA reference genome (GenBank Acc. No. CP004011. 1).