dataesr/scientific-paragraphs-categorization huggingface dataset:
Stacked histograms options include the possibility to plot LOH represented as a blue line indicating the percentage of samples presenting LOH in negative ordinates (see option "lohTo-Plot" in Table  The stacked histograms are also represented in text format with the list and percentage of samples for each copy number value C allowing the user to easily identify samples of interest (Fig  1A ). GISTIC results with focal and broad copy number events with their associated statistics are also available if the "runGISTIC" option has been enabled giving the user the choice of the criteria for selecting potentially interesting events. Heterozygous/homozygous CNVs. Similarly to the construction of cn-M' and cn-M s , we can construct hH-M' and hH-M s by considering raw CNV data as segments with the following features: sample name, gain, no net change of copy number or loss and a status indicating whether the segment is heterozygous (both alleles have non null copy number values) or homozygous (at least one the alleles has a null copy number value).
Output files and options For each processed sample, all the output files produced by ASCAT and Sequenza are fully available and listed in Table A in S1 File. The resulting CNV data are then used as an input data in aCNViewer. The user may manually exclude samples from the analysis if desired (see options "sampleToExcludeList" and "sampleToProcessList" in Table  1 ) and the automated ploidy estimation of each sample can also be modified at the user's convenience (see "ploidy-File" option in Table  1 ). The user can define different groups of samples according to any characteristics (age groups, gender, tumor stage, etc. ) to be processed by aCNViewer (see "sampleFile" option in Table  1 ). aCNViewer allows three types of high quality graphical outputs suitable for publication: dendrograms, bi-dimensional heatmaps, and stacked histograms in jpg/png/tiff/bmp/pdf format whose resolution is defined by the user (see "outputFormat" option in Table  1 ).
Availability and future directions Following the accelerating global trend towards precision medicine, there is an increased need for evermore precise tools to help physicians gain insights from the rapidly accumulating available data. Having access to clear and precise pictures summarizing CNVs and cn-LOH genome-wide could help to achieve more comprehensive interpretations. We have shown that aCNViewer can help identifying rapidly recurrent CNVs in datasets from Affymetrix SNP arrays as well as WES/WGS data. The application and source code are available as open source on GitHub and Docker and a demo can be found at https://github. com/FJD-CEPH/ aCNViewer. Future developments of aCNViewer include the adaptation of the quantitative histograms and statistics on methylation data where copy number events would be replaced by variations in methylation levels.
Acknowledgments We thank  Dr Gregory Robert Grant  ( University of Pennsylvania ) for giving us access to STAC [35]. We thank  Julien Pele ,  Emmanuel Tubacher , and  Jeremy Semhoun  ( Foundation Jean Dausset-CEPH ) for their help and useful comments in testing aCNViewer. We thank  Anthony Herzig  ( INSERM, UMR 946 ) for his careful editing of the manuscript and improvement of the English.
However, reaching at a full genome scaffolds remains an important goal for these model fungi  [20]. In the case of T. reesei, getting the sequence and exact position of centromeres would provide invaluable information for the emerging sexual crossing field in this species. More broadly, information on centromeres in filamentous fungi remains sparse, and these sequences would bring interesting highlights onto their evolution and metabolism  [21]. Using chromosome conformation capture data (3C; or also dubbed proximity ligation data)  [22]  and the homemade program GRAAL (Genome Re-Assembly Assessing Likelihood from 3D), our groups recently published the first proximity ligation scaffolding of an incomplete eukaryotic genome sequence. The 89 scaffolds of the T. reesei QM6a strain were re-scaffolded into seven chromosomes  [23, 24].
In addition, the "Rabl" structure of chromosomes in fungi nuclei, where centromeres are clustered together at the microtubule organization center (spindle pole body in yeast), generates contacts enrichment between these sequences. When quantified, we also showed that the signal resulting from these 3D contacts allows the identification of centromere positions  [25]. Although the QM6a contact map displayed such signal, we did not at the time characterize precisely these sequences. The published sequence from this past work was not thoroughly integrated within the JGI reference genome database, though it was nevertheless exploited in independent analyses by others  [26]. Here, we provide an updated version of the QM6a chromosome scaffolding using an extra polishing step after GRAAL output. GRAAL is a scaffolding pipeline that processes pre-assembled contigs; as a result, the resulting assembly displays the same sequence as in the original genome.
Gene enrichment analysis To calculate the enrichment in genes close to the centromeres, we used the gene annotations (GO terms and EC numbers) from the JGI  [43]  and from the FungiPath database  [44] [45] [46] , and performed the enrichment analysis with the Pathway Tools software  [47]. A 50-kb window was defined around the centromeres, which resulted in a set of 238 genes (2. 6% of the genome).
Simulation Design To evaluate the performance of the proposed approach, we conducted two simulation studies, the first using simulated data and the second using a real dataset relating to ankylosing spondylitis. In each case we compared the proposed G-GEE model to the two other interaction variable modeling approaches. The first simulation corresponds to a simplified context where all parameters were controlled and external interference limited, while the second simulation corresponds to a realistic context with a realistic pattern of MAF and LD.
In the second (realistic) simulation study using a real ankylosing spondylitis dataset, genes are randomly selected. The number of SNPs composing each genes varies according to the selection. Phenotypes For both simulation studies, we generated phenotype vectors using two different schemes. Our first scheme corresponds to the model proposed by Wang et al. [17]  (which, for the sake of brevity, we will refer to hereafter as the "Wang Pathway" model): Y i = β 0 + g β g k∈C X g ik + rs γ rs   (j,k)∈C 2 X r ij X s ik   + i , (3) where C and C 2 are respectively the set of causal SNPs and causal interactions, and i a random Gaussian variable. For each causal gene g, we consider two causal SNPs and a coefficient β g is assigned to the standardized sum of these causal SNPs. In the same way, for the interactions, all the causal SNPs from a causal pair (r, s) are pairwise multiplied and a coefficient γ rs is assigned to the standardized sum of the product.
Scenarios In the first (simplified) simulation study, genotypes are simulated as described in the design. We considered six genes, each composed of six SNPs and for 600 subjects. We define one causal interaction between genes and two causal genes with main effects, and the simulation takes place using two alternative simulation settings: • (1) one interaction and two main effects involving the same genes • (2) one interaction and two different main effects For these two settings, different coefficients of determination, from 0. 05 to 0. 7, are considered and 1000 iterations are performed. In the second (realistic) simulation study, genotypes come from a real dataset comprising 763 individuals. At each iteration we randomly select six genes of various size (from 1 to 1119 with a median of 2 SNPs) in the dataset.
Real data illustration To illustrate our approach we applied the proposed method on three real datasets related to ankylosing spondylitis, thyroid carcinomas and inflammatory bowel disease. The dataset regarding ankylosing spondylitis consists of the French subset of the large study of the International Genetics of Ankylosing Spondylitis (IGAS) study  [22]. For this subset, unrelated cases were recruited through the Rheumatology clinic of Ambroise Paré Hospital (Boulogne-Billancourt, France) or through the national self-help patients' association: "Association Française des Spondylarthritiques". Population-matched unrelated controls were obtained from the "Centre d'Etude du Polymorphisme Humain", or were recruited as healthy spouses of cases. The protocol was reviewed and approved by the Ethics committee of the Ambroise Paré hospital.
A number of updated reviews on AS genetics, including genome-wide association study (GWAS) results, identified new ankylosing spondylitis-susceptibility genes outside of the MHC region  [28, 29]. We applied all the methods described above to the AS dataset. The data contain 408 cases and 358 controls, and each individual was genotyped for 116, 513 SNPs with Immunochip technology. For each SNP we obtained detailed genetic information, such as gene affiliation, with the NCBI2R package  [30]  which annotates lists of SNPs with current information from NCBI. We considered only SNPs located within a single gene in order to form gene groups without overlap. We focused our analysis on a list of 29 genes previously identified as having a main effect in GWAS. The three methods tested yield different results, and only the PLS and G-GEE methods identify interactions.
Thyroid carcinomas Thyroid cancers are thought to be related to a number of environmental and genetic predisposing factors and can be classified in various types and subtypes. Most association studies have focused on main effects but only a limited number of genes were identified. Recently, some papers focus on the detection of epistatic interactions  [31, 23]. We applied our proposed approach on the two data sets used in Luzón-Toro el al. [23]  regarding two rare tumours, sporadic medullary thyroid carcinoma (sMTC) and juvenile papillary thyroid carcinoma (jPTC). Affymetrix Genome-Wide Human SNP 6. 0 arrays were used to hybridized DNA. The data set related to sMTC contains 66 cases and the jPTC data set 30 cases. The same 125 healthy controls and 232, 607 SNPs were used for both studies. As for the ankylosing spondylitis dataset, we obtained gene affiliation for each SNP with the NCBI2R package and considered only SNPs located within a single gene.
We focused the analysis of the sMTC data set on a list of 10 genes, 3 of these genes (CHFR, AC016582. 2 and C8orf37) were chosen following the conclusions of Luzón-Toro el al. , the others because they contained markers that were susceptible to be associated with the disease from univariate analysis. The analysis of the jPTC data set was realized on a list of 20 genes among them we can cite DIO, RP11-648K4. 2, LOXL1, DMGDH, PAX8 and STK17B from which epistatic interaction were already detected (even if the interaction between PAX8 and STK17B was identified in a study concerning papillary thyroid and not the juvenile form). The 14 others genes contained susceptible associated individual markers from univariate analysis. Regarding the sMTC study, G-GEE identifies one interaction between genes NCK1 and TRIQK.
PCA detects only one main effect for the gene TRIQK whereas none effects were identified with PLS. Concerning the jPTC data set, 3 interactions were identified by G-GEE (NCAM1 and MNDA, MNDA and STK17B, LOC105370481 and STX3). PLS identifies 2 interactions (LOC105370236 and LOC105370481, LOC105370236 and PIKFYVE) and PCA detects only one main effect for the gene LOC105370481. We note that the effects detected with our approach concerned different genes that the ones identified in the presented previous studies (except for the gene STK17B). More analysese are needed to better understand these differences.
proach to the Wellcome Trust Case-Control Consortium genome-wide association dataset for Inflammatory Bowel Disease. The data contains 1949 case for 159 960 SNPs genotyped by Affymetrix. The control group was constituted of 1972 individuals from the Wellcome Trust Case-Control Consortium genomewide association dataset for hypertension. As for the two previous real data analysis, we obtained gene affiliation for each SNP with the NCBI2R package and considered only SNPs located within a single gene. The analysis was real-.
LD: Linkage Disequilibrium • MAF: Minor Allele Frequency • MHC: Major Histocompatibility Complex • PC: Principal Components • PCA: Principal Component Analysis • PLS: Partial Least Squares • SNP: Single-Nucleotide Polymorphism Ethics (and consent to participate) The dataset regarding ankylosing spondylitis consists of the French subset of the large study of the International Genetics of Ankylosing Spondylitis (IGAS) study [22]. For this subset, unrelated cases were recruited through the Rheumatology clinic of Ambroise Paré Hospital (Boulogne-Billancourt, France) or through the national self-help patients' association: "Association Française des Spondylarthritiques". Population-matched unrelated controls were obtained from the "Centre d'Etude du Polymorphisme Humain", or were recruited as healthy spouses of cases.
The protocol was reviewed and approved by the Ethics committee of the Ambroise Paré hospital. All participants gave their informed consent to the study. The application on thyroid carcinomas was carried out on a public dataset that came from the study of Luzón-Toro el al. on identification of epistatic interactions in two different types of thyroid carcinomas [23]. This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/), which permits unrestricted use, distribution, and reproduction. All subjects underwent peripheral blood extraction for genomic DNA isolation using MagNA Pure LC system (Roche, Indianapolis, IN) according to the manufacturer's instructions. A writ-.
• LD: Linkage Disequilibrium • MAF: Minor Allele Frequency • MHC: Major Histocompatibility Complex • PC: Principal Components • PCA: Principal Component Analysis • PLS: Partial Least Squares • SNP: Single-Nucleotide Polymorphism Ethics (and consent to participate) The dataset regarding ankylosing spondylitis consists of the French subset of the large study of the International Genetics of Ankylosing Spondylitis (IGAS) study [22]. For this subset, unrelated cases were recruited through the Rheumatology clinic of Ambroise Paré Hospital (Boulogne-Billancourt, France) or through the national self-help patients' association: "Association Française des Spondylarthritiques". Population-matched unrelated controls were obtained from the "Centre d'Etude du Polymorphisme Humain", or were recruited as healthy spouses of cases.
We chose the Molecfit software  (Smette et al. 2015; Kausch et al. 2015)  to correct for telluric absorption in the CRIRES-POP spectra. Molecfit uses radiative transfer to model the telluric transmission spectrum at the time and location of the observations, using a combination of an atmospheric profile, atmospheric observations, and meteorological data, fitting the telluric absorption lines, stellar continuum, instrumental resolution, and wavelength dispersion of the observed spectra. Once the model is interactively and iteratively perfected, it can be used to correct the observed spectrum, producing a telluric subtraction that is at least as good as that produced in the traditional way. More details of the Molecfit algorithm and its usage are given in  Smette et al. (2015). A benefit, particularly for CRIRES spectra, is that because Molecfit fits the wavelength dispersion, its results can be used to improve the wavelength calibration.
Results The final CRIRES-POP spectrum of 10 Leo covers most of the range from 0. 962 to 5. 245 µm at a resolving power of 90 000. Excluded from our spectrum are regions where the Earth's atmosphere is opaque, and gaps that are due to poor coverage or discarded chips. Table  3  lists the wavelength coverage of the spectrum of 10 Leo and denotes gaps (but note it does not specify the locations of the many masks). Owing to the large amount of data, it would not be informative to include a plot of the entire spectrum here, but we show in Figs. 5 to 11 some examples of the spectrum zoomed to interesting wavelength ranges. These give a sense of the S/N, resolution, frequency of masks, and overall quality of the spectrum of 10 Leo and of future CRIRES-POP data products. The spectrum has not been smoothed, and there are some areas of resulting low S/N that are due to both low S/N in some observational settings, and to fewer overlapping settings A&A proofs: manuscript no.
Full detailed line identifications will be published with the final atlas for 10 Leo. The full CRIRES-POP 10 Leo spectrum is available online at the CDS 6. The data are provided as five ASCII files, each containing the fully corrected spectral data for one bandpass. The file structure is simple, with Col. 1 containing the wavelength data and Col. 2 containing the flux data. The data are easy to read for both humans and computers, with all numbers printed in scientific E notation. Masks are represented with 'nan' replacing the relevant fluxes, a value that plotting packages like Python matplotlib easily ignore, allowing the user to read in and plot the entire bandpass quickly, without having to define masked regions or deal with missing data.
The raw and pipeline-reduced spectra are available on the CRIRES-POP webpage, and the spectrum of 10 Leo presented in this paper is also now available. The final atlas data will be pro-vided in ASCII format, and possibly in a visual format similar to the printed Arcturus atlas. Comparison of the UVES-POP (blue) and CRIRES-POP (red) spectra of 10 Leo in a small wavelength region of the overlap between the two spectra. The UVES-POP spectrum has not been corrected for telluric lines, and this causes the majority of the differences between the two spectra.
Acknowledgements. CPN acknowledges support by the  Austrian Science Fund (FWF)  in the form of a  Meitner Fellowship  under project number  M1696-N27. TL acknowledges support by the  Austrian Science Fund (FWF)  under project number  P23737-N16. HH acknowledges support by the  Swedish Research Council (VR)  under project number  2011-4206. M-FN acknowledges support by the  Austrian Science Fund (FWF)  in the form of a  Meitner Fellowship  under project number  N1868-NBL. Based on observations collected at the  European Organisation for Astronomical Research in the Southern Hemisphere  under  ESO  programmes  084. D-0912 (A),  085. D-0161 (A),  086. D-0066 (A),  087. D-0195 (A),  088. D-0109 (A),  088. D-0109 (B), and  088. D-0109 (C). This research has made use of the SIMBAD database and the VizieR catalogue access tool, operated at  CDS, Strasbourg, France.
The design methodology of an embedded system should start with a system-level partitioning dividing functions into hardware and software. However, since this partitioning decision is taken at a high level of abstraction, we propose regularly validating the selected partitioning during software development. The paper introduces a new model-based engineering process with a supporting toolkit, first performing system-level partitioning, and then assessing the partitioning choices thus obtained at different levels of abstraction during software design. This assessment shall in particular validate the assumptions made on system-level (e. g. on cache miss rates) that cannot be precisely determined without low-level hardware model. High-level partitioning simulations/verification rely on custom model-checkers and abstract models of software and hardware, while low-level prototyping simulations rely on automatically generated C-POSIX software code executing on a cycle-precise virtual prototyping platform.
Our toolkit, TTool  (Apvrille, 2015) , supports both stages, and makes it possible, at the push of a button, to evaluate the design at a given development stage, and to propagate the results to enhance the system at another development state, thus easing development iterations. We previously described  (Li et al. , 2016)  our approach towards multi-level Design Space Exploration, but without the ability to generate detailed performance metrics during prototyping that we present in this paper. Section 2 presents related work. Section 3 presents the overall design method. Section 4 details an automotive case study used to exemplify the highlevel design space exploration (Section 5), as well as software component design and performance evalua-tion (Section 6). A final discussion and perspectives on future work are presented in Section 7.
MDGen from Sodius (Sodius Corporation, 2016) adds timing and hardware specific artifacts such as clock/reset lines automatically to Rhapsody models, generates synthesizable, cycle-accurate SystemC implementations, and automates exploration of architectures. The Architecture Analysis & Design Language AADL  (Feiler et al. , 2004)  allows the use of formal methods for safety-critical real-time systems. Similar to our environment, a processor model can have different underlying implementations and its characteristics can easily be changed at the modeling stage. Recently,  (Yu et al. , 2015)  developed a model-based formal integration framework which endows AADL with a language for expressing timing relationships. Capella  (Polarsys, 2008)  relies on Arcadia, a comprehensive model-based engineering method.
Hardware components are highly abstracted. For example, a CPU can be defined with a set of parameters such as an average cache-miss ratio, power-saving mode activation, context switch penalty, etc. After mapping, software components can also be verified independently of any hardware architecture in terms of safety and security. For example, when designing a component implementing a security protocol, the reachability of the states and absence of security vulnerabilities can be verified. When the soft- SoCLib also contains a set of performance evaluation tools  (Genius et al. , 2011). Last but not least, the SoCLib prototyping platform comes with an operating system well adapted to multiprocessor-on-chip  (Becoulet, 2009). If the performance results of the SystemC simulation differ too greatly from the ones obtained during the design space exploration stage -e.
Software Components Figure  3  shows the software components of the active braking use case modeled using an AVATAR block diagram. These modeling elements have been selected during the previous modeling stage (partitioning). Software components are grouped according to their destination ECU: • Communication ECU manages communication with neighboring vehicles. • Chassis Safety Controller ECU (CSCU) processes emergency messages and sends orders to brake to ECUs. • Braking Controller ECU (BCU) contains two blocks: DangerAvoidanceStrategy determines how to efficiently and safely reduce the vehicle speed, or brake if necessary. BrakeManager operates the brake for a given duration. • Power Train Controller ECU (PTC) enforces the engine torque modification request. The AVATAR model can be functionally simulated using the integrated simulator of our toolkit, which takes into account temporal operators but completely ignores hardware, operating systems and middleware.
Prototyping To prototype the software components with the other elements of the destination platform (hardware components, operating system), a user must first map them to a model of the target system. Mapping can be performed using the new deployment features recently introduced in  (Genius and Apvrille, 2016 ). An AVATAR Deployment Diagram is used for that purpose. It features a set of hardware components, their interconnection, tasks, and channels. The partitioning phase selected an architecture with five clusters. Some tasks are destined to be software tasks (they are mapped onto CPUs), and the others are expected to be realized as hardware accelerators. Yet, each specific hardware accelerator in SoCLib needs to be developed specifically which requires a significant effort. We do not consider that case in the paper since all AVATAR tasks are software tasks.