dataesr/scientific-paragraphs-categorization huggingface dataset:
Although these examples seem unsual, they are designed to be minimal and expose possible problems, similar to those detected in a Fuel Control System (FCS) benchmark provided by Toyota  [8]. It is common practice to mix, in languages that allow it, Boolean and numeric values in a way exposed by these examples. We have tested this behavior extensively and we have observed that other languages that perform automatic conversions between Boolean and numeric values behave consistently with Simulink (e. g. , C: (double)3 + (bool)2 = 4. 0, Python: float(3) + bool(2) = 4. 0). Now, consider the diagram shown in Fig. 2 , where the output of the same Boolean constant block as the one used in the previous diagrams is fed directly into the integrator. In this case, Simulink rejects this diagram (meaning it refuses to simulate it).
The challenge of this work is embedding the more relaxed type system of Simulink into the formal type system of Isabelle, while preserving the semantics, and as much typing information as possible. We apply this technique to several case studies, including the FCS benchmark. This work is part of a larger project on translating Simulink diagrams into Isabelle theories suitable for analysis and verification. Because Isabelle's language is formal and precise, we can directly obtain concise and correct code in other languages that can be used for processing Simulink models. For example from the Isabelle model we easily obtain Python code for simulations, and Z3 SMT solver  [3]  model for automatically checking properties.
Therefore, the framework proposed in this paper is much more lightweight. In this work we use the Isabelle theorem prover which has a standard type inference mechanism. Our goal is to give an embedding of Simulink into a language and framework suitable for further processing (simplifications, checking of properties, and even simulation). Other systems for logical reasoning (PVS  [12] , Z3,. ) could also be used for this purpose. Some of the problems encountered in our translation may have simpler solutions in these systems, but some other aspects may become more difficult. All translation attempts to languages that do not allow automatic conversions between Boolean and numeric values would have similar challenges as the ones that we solve here.
Isabelle Isabelle/HOL is an interactive theorem prover based on higher order logic. Isabelle provides an environment which consists of a powerful specification and proving language and it has a rich theory library of formally verified mathematics. Notable features of Isabelle include a type system with type inference, polymorphism and overloading, and axiomatic type classes. Isabelle's type system includes the basic types bool, real, int, nat, type variables a, b, etc. , and predefined type constructors a → b (functions from a to b) and a × b (Cartesian product of a and b). For term f (x, g(y)) we can specify that it has a type t by using : t after the term f (x, g(y)) : t. Isabelle uses type inference to deduce the type of a term. For example the next declaration introduces a function f : a → b → ( a → b → c) → c definition f (x)(y)(g) = g(x)(y) The type of f is the most general type such that the expression f (x)(y)(g) = g(x)(y) is well typed.
Boolean Blocks Simulink Boolean blocks are also challenging to implement due to the fact that, for example, the inputs to a conjunction block could have different types (real, Boolean, unspecified), as illustrated in Fig. 6c. In languages that allow it (e. g. , C, Python), it is common practice to use numerical values in Boolean expressions, with the meaning that non-zero is true. Similarly, it is common practice to use Boolean values in numeric expressions. Simulink also allows these cases, but Isabelle does not. We show in this and next section how to solve these problems. Consider the example from Fig. 6c. If we would simply take the conjunction of all inputs (Const(1. 5 : real) Const(1 : bool) Const(3)) • [x, y, z x ∧ y ∧ z] we will obtain in Isabelle a type error, because x has type real, y has type bool and z has type a : numeral, and their conjunction is not well typed.
Obtaining a more general typing ensures the correctness of this approach. When Boolean constants and operations are implemented using numeric constants and operations, the correctness is also ensured because this implementation is quite straightforward and standard. We have extensively tested all combinations of interactions of numeric and Boolean blocks, and we carefully implemented the observed behavior. We have also tested this technique on several case studies, as well as an industrial example: the Fuel Control System (FCS) benchmark from Toyota  [8]. We validated our approach by simulation. We translated the Isabelle systems into Python and we simulated them, resulting in the same behavior (modulo a small simulation error) as the Simulink simulation. All examples have been translated using all combinations of the options.
In each case the results obtained were as expected: either a type mismatch or the same values as those provided by Simulink simulation. For the FCS case study, when translating it only with the -const option, we have detected a type mismatch which was not signaled by Simulink. An easy correction is to add a conversion block. Even though Simulink, as well as other programming languages, implicitly perform this conversion we believe that having a fully correct type checked model will increase the confidence of the generated code.
RNA sequencing and data analysis RNA extraction and sequencing were performed as previously described. 25, 26 Briefly, after transfer to ice-cold RNeasy RLT lysis buffer (Qiagen), LT tissue samples were homogenized using a Precellys tissue homogeniser (Bertin Technologie). Total RNA was isolated using RNeasy Midi columns (Qiagen) and then treated with RNAse-free DNase I (Qiagen) for 15 min at room temperature according to the manufacturer's protocols. The concentration of total RNA was measured with a Nanodrop ND-100 instrument (Thermo Scientific) and the quality was assessed with an RNA 6000 Nano Labchip kit using an Agilent 2100 Bioanalyzer (Agilent Technologies). All 11 samples had an RNA integrity number (RIN) value greater than eight. The mRNA-Seq libraries were prepared using the TruSeq RNA Sample Preparation Kit (Illumina) according to the manufacturer's instructions.
After sequencing, the samples were demultiplexed and the indexed adapter sequences were trimmed using the CASAVA v1. 8. 2 software (Illumina). The Bos taurus reference transcriptome was downloaded from Ensembl (version 63, Bos_taurus. Btau_4. 0. 63. cdna. all. fa). To align the reads back to the assembled reference transcriptome the BWA programme (version 0. 5. 9-r16) was used. 27 Reads were mapped for each sample separately to the assembled transcriptome. The BWA default values were used for mapping. Properly paired reads with a mapping quality of at least 30 (Àq ¼ 30) were extracted from the resulting BAM file using SAMtools 28 for further analyses. Properly paired is defined as both left and right reads mapped in opposite directions on the same transcript at a distance compatible with the expected mean size of the fragments (<500 bp).
SNP identification BWA was also used to map reads onto the bovine genome assembly UMD3. 1 version. 29 Only reliable properly paired BWA mapped reads were considered for SNP calling. Indels were not considered because alternative splicing impedes reliable indel discovery. SNPs were called using the SAMtools software package. Genotype likelihoods were computed using the SAMtools utilities and variable positions in the aligned reads compared with the reference were called with the BCFtools utilities. 30 SNPs were called only for positions with a minimal mapping quality (ÀQ) of 30, a minimum coverage (Àd) of 4 and a maximum read depth (ÀD) of 10,000,000.
Data and statistical analyses Information on bovine genes were obtained from the Ensembl Genome Browser (version 84) using the BioMart tool. 31 Functional annotation analysis of genes sharing putative bidirectional promoters was done using the FATIGO tool of the online software suite Babelomics. 32 Genes were assigned their Ensembl identities as input for Babelomics. Only one copy of each gene was used. Default parameter settings were used for the analysis. Statistical assessment of annotation differences between the two sets of sequences (SNP-containing genes versus all the other bovine genes) was carried out for each FATIGO analysis, using the Fisher Exact Test with correction for multiple testing. Prediction of putative TFBSs was performed using the bioinformatics tools MATCH. 33 It uses a library of positional weight matrices from TRANSFAC 6.
Bidirectional promoter validation Each putative bidirectional promoter region was PCR amplified from bovine genomic DNA using a pair of primers. The genomic DNA sequences were retrieved from the UCSC Genome Database. Sequence repeats were masked using RepeatMasker  35  and Primer3 36 was then used to design primer pairs to amplify each putative bidirectional promoter region. PCR primers were synthesized by Eurofins MWG Operon. Restriction enzyme cutting sites for EcoRI (GAATTC) and BamHI (GGATCC) were artificially added into the PCR primers to facilitate directional cloning. Primer sequences are presented in Supplementary Table  S1. Polymerase chain reactions were performed in 50 ll using 120 ng genomic DNA, 1 Â PCR buffer, 1. 5 mM MgCl 2 , 0. 2 mM of each dNTP, 0. 3 lM of each primer and 1U AccuPrime GC-rich Taq DNA polymerase (Invitrogen).
37 All transient transfection experiments were done in triplicate and repeated three times. Thirty-six hours after transfection cells were washed two times with PBS 1Â, and then viewed with an Axio Observer Z1 microscope (Zeiss) and images were acquired using a CoolSNAP HQ2 camera (Photometrics) driven by the Axiovision imaging system software. Analyses of micrographies were performed using the AxioVision 4. 7. 2 software (Zeiss). Filter sets 38HE [excitation BP470/40(HE) and emission BP525/50(HE)] and 43HE [excitation BP545/30(HE) and emission BP620/60(HE)] were used to visualize AcGFP1 and DsRed monomer signals, respectively. Each reporter gene assay was performed in triplicate and with three independent transfection experiments.
Bovine muscle whole-transcriptome analysis To identify bidirectional promoters active in bovine muscle, we first obtained a global view of the bovine LT transcriptome using pairedend RNA sequencing (RNA-Seq) from twenty Limousin bull calves. We used already-published data from nine Limousin animals  25, 26  and poly(A)-enriched mRNA from 11 new Limousin bull calves were retrotranscribed and subjected to high-throughput sequencing. The 11 RNA-Seq libraries were barcode-tagged and sequenced on five lanes (3 libraries per lane) of an Illumina HiSeq2000 sequencer. Sequencing of cDNA libraries generated a total of 1,303 million raw paired-end reads with a length of 100 bases, resulting in a total of 130 gigabases. The reads were then de-multiplexed to assign reads to each sequenced sample according to its barcode index.
Approximately 17-48 million paired-end reads were obtained for each library. Reads from each sample were then mapped back to the bovine reference transcriptome. We used the set of Bos taurus Ensembl transcripts v63 RefSeq genes as the reference transcriptome. This set contains transcripts for 22,915 known or novel genes but also pseudogenes. Based on mappings done using the Burrows-Wheeler Aligner (BWA) programme, 67-81% of the mapped reads were aligned properly paired (Supplementary Table  S2 ). A total of 22,025 transcripts (18,055 genes) were identified, with at least one paired-end read in all samples analysed. Similar RNA-Seq read mapping rate and the number of genes identified were obtained in other RNA-Seq bovine studies. For example, Chitwood et al. 38  found that $69% of the RNA-Seq reads they generated while sequencing the transcriptomes of single bovine blastocysts mapped uniquely onto the bovine genome.
Identification of putative bidirectional promoters shared by genes co-expressed in bovine LT In order to identify putative bidirectional promoters active in bovine muscle, the chromosomal locations of all the bovine genes were retreived from the Ensembl database (version 84). 31 The Ensembl gene start sites were used as the TSS. Pair of genes in a head-to-head arrangement and separated by less than 1 kb between their TSSs were identified and the region between paired-genes were considered as putative bidirectional promoters. Among the 24,616 bovine genes annotated on the UMD3. 1 genome assembly, we found 563 putative bidirectional promoters (Supplementary Table  S3 ). The number of bovine bidirectional promoters we predicted is very similar to the one calculated by Xu et al. ,  39  who found 574 putative bidirectional promoters in cattle.
To overcome confusion during the subsequent analyses, we then used four filters (Supplementary Fig. S1 ). First, we removed all gene pairs for which at least one gene had paralogous sequences identified in the Ensembl database and predicted 192 bidirectional promoters. Second, we retained only gene pairs associated with these putative BiPs and for which we could detect the expression for both pairedgenes in our RNA-Seq dataset, leaving 171 cases. Third, to avoid quantification problems relative to transcript isoforms, genes with known alternative transcripts were excluded from the study. Finally, for validation purposes, we only selected bidirectional promoters between protein-coding gene pairs. In total, 120 BiPs remained, each with both genes expressed in muscle (Supplementary Table  S4 ).
Experimental validation of some predicted bidirectional promoters To assess the promoter activity of the predicted bidirectional promoters, some bidirectional promoters were selected and cloned into the promoterless pBiP0 reporter vector we recently constructed. 37 his plasmid is a dual reporter vector with the Green Fluorescent Protein gene from the Aequorea coerulescens jellyfish (AcGFP1) inserted in a head-to-head configuration relative to a gene expressing a monomer mutant form of the Red Fluorescent Protein gene from the Discosoma striata reef coral (DsRed monomer). Both fluorescent protein genes are devoid of promoter and separated by a short multiple cloning region. The sequences of predicted bidirectional promoters were downloaded from the UCSC database. 41 To ensure that all regulatory elements were included each sequence was extended until the first ATG (ATG not included) of both paired genes.
We identified these two different types of gene pairs using the chromosomal locations of all the bovine genes from the Ensembl database and using the same filters as for the identification of gene pairs sharing BiPs (Supplementary Fig. S1 ). We found 218 and 163 gene pairs in the convergent and unidirectional configurations (Supplementary Tables  S7  and S8 ), respectively. The Spearman's rank correlation coefficient for each gene pair was then calculated using the normalized expressed levels measured in the 20 samples. Thirty-five percent (76/ 218) and 30. 1% (49/163) of the gene pairs in the convergent and unidirectional configurations, respectively, were co-expressed (Supplementary Tables  S9  and S10 ). The percentage of co-expressed genes is comparable for the bidirectional, convergent and unidirectional configurations, suggesting that co-expression is most likely mediated by shared chromatin environment rather than only by specific regulatory regions.
The 11 cSNPs might have an impact on the regulation of the corresponding gene, which in turn might affect the activity of some bidirectional promoters. Spearman's rank correlation coefficients were calculated for the genotypes of these 11 cSNPs and the normalized expression level of the corresponding gene (Table  5 ). Significant correlations were found at P-value <0. 05 for 4 cSNPs, suggesting that these cSNPs have an effect on the regulation of the expression of their corresponding gene. The seven other cSNPs, including the variant found in Taf9, might have most probably an effect on the posttranscriptional regulation of the corresponding gene, which consequently alter the amount of proteins which then affect the activity of the bidirectional promoter. 3. 9. Some bidirectional promoter regions are located within quantitative trait loci for meat-related phenotypes The positions of the 120 bidirectional promoters sharing genes expressed in bovine LT were compared with the position of known quantitative trait loci (QTLs) deposited in the public database AnimalQTLdb.
99 1. 78E-24 ENSBTAT00000005923_585 25 2,977,744 T/C upstream_gene_variant ENSBTAG00000004509 ENSBTAT00000005923 SLX4 118 À0. 99 1. 78E-24 ENSBTAT00000008129_240 16 19,500,611 G/C intron_variant ENSBTAG00000006186 ENSBTAT00000008129 KCTD3 118 0. 99 1. 78E-24 ENSBTAT00000008507_123 15 82,265,194 T/C intron_variant ENSBTAG00000006493 ENSBTAT00000008507 CLP1 118 À0. 99 1. 78E-24 ENSBTAT00000008728_747 12 18,197,212 C/T intron_variant ENSBTAG00000006640 ENSBTAT00000008728 RB1 118 0. 99 1. 78E-24 ENSBTAT00000018163_665 21 66,866,347 T/G upstream_gene_variant ENSBTAG00000013666 ENSBTAT00000018163 SLC25A29 104 À0. 99 1. 78E-24 ENSBTAT00000020493_130 7 62,925,278 A/G intron_variant ENSBTAG00000015419 ENSBTAT00000020493 ARHGEF37 64 À0. 99 1. 95E-20 ENSBTAT00000025492_402 19 55,675,666 T/C intron_variant ENSBTAG00000019153 ENSBTAT00000025492 JMJD6 24 À1 6.
38E-137 ENSBTAT00000028060_520 21 16,720,681 C/T intron_variant ENSBTAG00000037383 ENSBTAT00000028060 AKAP13 118 À0. 99 1. 78E-24 ENSBTAT00000028277_365 15 76,757,181 T/G intron_variant ENSBTAG00000021223 ENSBTAT00000028277 CRY2 118 À0. 99 1. 78E-24 ENSBTAT00000028656_569 14 75,708,914 T/G intron_variant ENSBTAG00000039968 ENSBTAT00000028656 TMEM55A 104 0. 99 1. 78E-24 ENSBTAT00000028865_103 25 39,432,034 G/A intron_variant ENSBTAG00000037400 ENSBTAT00000028865 TNRC18 118 0. 99 1. 78E-24 ENSBTAT00000029400_195 17 73,658,214 C/T upstream_gene_variant ENSBTAG00000021656 ENSBTAT00000029400 SPECC1L 104 0. 99 1. 78E-24 ENSBTAT00000029403_203 18 62,789,937 A/G intron_variant ENSBTAG00000030393 ENSBTAT00000029403 RDH13 104 À0. 99 1. 78E-24 ENSBTAT00000033704_233 6 113,701,647 rs442770236 G/T upstream_gene_variant ENSBTAG00000004316 ENSBTAT00000033704 BOD1L 104 0.
99 1. 78E-24 ENSBTAT00000035362_466 20 10,305,279 G/A downstream_gene_variant ENSBTAG00000027980 ENSBTAT00000035362 TAF9 118 À0. 99 1. 78E-24 ENSBTAT00000037465_138 25 10,053,612 T/C intron_variant ENSBTAG00000026375 ENSBTAT00000037465 RMI2 118 À0. 99 1. 78E-24 ENSBTAT00000043122_702 10 86,384,804 A/G upstream_gene_variant ENSBTAG00000020379 ENSBTAT00000043122 AREL1 118 0. 99 1. 78E-24 ENSBTAT00000043260_900 10 80,246,761 A/G upstream_gene_variant ENSBTAG00000014334 ENSBTAT00000043260 ZFYVE26 118 0. 99 1. 78E-24 ENSBTAT00000043778_165 15 82,285,586 T/C intron_variant ENSBTAG00000002411 ENSBTAT00000043778 CTNND1 118 0. 99 1. 78E-24 ENSBTAT00000043778_492 15 82,284,426 G/A intron_variant ENSBTAG00000002411 ENSBTAT00000043778 CTNND1 118 À0. 99 1. 78E-24 ENSBTAT00000043778_760 15 82,284,694 C/T intron_variant ENSBTAG00000002411 ENSBTAT00000043778 CTNND1 118 À0.
99 1. 78E-24 ENSBTAT00000054096_509 8 103,426,705 G/A upstream_gene_variant ENSBTAG00000038335 ENSBTAT00000054096 IGBP1 118 0. 99 1. 78E-24 ENSBTAT00000056520_352 23 27,378,739 C/T upstream_gene_variant ENSBTAG00000039620 ENSBTAT00000056520 MGC151586 104 0. 99 1. 78E-24 ENSBTAT00000061199_185 14 20,989,316 A/G upstream_gene_variant ENSBTAG00000044106 ENSBTAT00000061199 SPIDR 118 À0. 99 1. 78E-24 ENSBTAT00000061451_269 23 3,434,098 G/A intron_variant ENSBTAG00000021237 ENSBTAT00000061451 DST 118 À0. 99 1. 78E-24 ENSBTAT00000061451_704 23 3,432,111 G/T intron_variant ENSBTAG00000021237 ENSBTAT00000061451 DST 118 0. 99 1. 78E-24 ENSBTAT00000034195_104 8 53,736,890 G/A intron_variant ENSBTAG00000017734 ENSBTAT00000034195 VPS13A 118 0. 99 1. 78E-24.
Introduction In the Digital Humanities (DH), one focus of research centers on the development and advancement of methods and respective tools that can support them. Burdick et al. 1 understand data curation and data analysis as well as editing and modeling as the tasks most relevant to the DH. Using digital tools and services for these tasks is at the core of DH practice. There is an interest in convincing traditional scholars to adopt digital methods and tools and in demonstrating the potential of computing for the humanities. Creating a positive user experience can increase the adoption and usage of tools2 and therefore, over the past decade, requests have increased for development in line with results from user-centered research methods. In light of the numerous sources that claim a lack of usability studies for the DH3 or that developed tools are not intuitive or difficult to use and thus fail user expec-tations4, building digital tools with better usability seems difficult to put into practice.
Here, user experience and usability aspects may play a role in investigating user satisfaction with developed features and components. One example is MONK, a webbased text-mining software. In an extensive study, web analytics data and user interviews were analyzed to gain knowledge about the usage of the application18. There was an overall satisfaction with the tool offered but it was ob-served that the functionalities were geared more towards easy access and approachability than towards offering a flexible tool for the expert user19. Here, one can see that researchers are more willing to go to considerable lengths to learn a tool when it provides expert functionalities and enables flexible adaptation to their specific needs. Tools such as MONK have been praised for their ability to teach and their provision of gateway entry into learning more about text mining and its capabilities.
Using such a standardized set of guidelines helps to establish a common measure by which to evaluate different projects. As an additional step, the tools were checked against the specific DARIAH-DE guidelines, which were primarily developed for the tools built for that infrastructure37. Secondly, a thinking aloud test was performed in which a subject was asked to fulfill common tasks concerning the tool in question. During that test, the subject is observed by one or two persons who take notes during the experiment and also remind the subject to continue to think aloud while completing the individual tasks. Ideally, a recording of both the audio data and the computer screen are made to better analyze the experiment. To understand if the interchangeable use of tools and devices during the research workflow is possible and acceptable for researchers, an exemplary digital workflow was identified, designed and studied.
The workflow comprised several steps that were performed on various devices such as desktop, laptop, digital camera, tablet and custom displays (see Figure  1 ). First, existing collections of tombstone images were integrated into a research data management (RDM) system (based on the imeji38 software). Next, using a specialized app39 on a smart device, new nearby locations with tombstones were identified. In a subsequent excursion in the field, using another specialized app40 installed on a digital camera, a smart phone and a tablet, researchers took images of those tombstones. These images were automatically uploaded to the RDM system by the app. The uploaded images were further enriched with domain-specific metadata by using the RDM system web application on a desktop computer. As a final step, the image collections were visualized both on desktop computers and on a so-called Hyperwall display (an array of four 4K screens).