dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, it is also worth mentioning the NCBI reference sequence database (Ref-Seq) that provides automatic annotation of lncRNAs and mRNAs in > 55,000 organisms, including domesticated species. In particular, NCBI/RefSeq makes use of the "eukaryotic genome annotation pipeline" with the Gnomon program, which combines homology searching with ab initio modelling  (O'Leary et al. 2016 ) and comprises 10,823, 5 147, 5 605, and 5 183 lncRNA loci in dog, chicken, pig, and cow, respectively (Table  1 ). Although these publicly available catalogues represent a rich resource for digging into trait-associated loci, involving annotated lncRNAs, a limited genomic overlap still exist between these annotations (Fig. 2 ), most likely reflecting the high specificity of lncRNA expression profiles and the different origins of the input transcriptomic sequencing data.
2011), Wucher et al. integrated 20  additional RNAseq data to build a new canine reference annotation  (Wucher et al. 2017). Using the dedicated FEELnc program to automate the annotation of lncRNAs and their genomic classification (lincRNA, antisense, and other subclasses), the authors provided an extended set of canine lncRNAs comprising 22,880 lncRNA transcripts gathered into 10,444 gene loci. A deeper analysis of this extended RNAseq dataset revealed that, as in humans, canine lncRNAs are more tissuespecific than protein-coding genes (44 versus 17%, respectively) with 65% of all tissue-specific lncRNAs expressed in canine testis  (Le Béguec et al. 2018). This catalogue was Fig. 2  Distribution of reads supporting lncRNAs and mRNAs (A) and gene overlap between NCBI and Ensembl resources according to both biotypes (B).
A For each gene biotype (lncRNAs in blue and mRNAs in red), the dark, intermediate and light shades correspond to the percentage of reads supporting all expressed genes, 25% of the most expressed genes and the 10 most expressed genes respectively. RNAseq data correspond to the chicken PRJEB28745 project and 4 tissues (adip adipose tissue, livr liver, blod blood, hypt hypothalamus) of the same population (Rhode Island Red). B) Percentages of chicken lncRNA gene overlap-using 1 bp or more-between the GRCg6a-V104 Ensembl and NCBI gene catalogues. Note that these overlaps have been computed at the gene level given the uncertainty of isoform modelling with short-reads as explained in the main text first analysed in the context of dog breed phenotypic variations, such as the "drop ear" phenotype, in which case using GWAS one lncRNA was found to be closely associated to the MSRB3 gene involved in human deafness  (Plassais et al.
Comprehensive atlas based on multi-tissue studies in farm animals Starting in the mid-2010s, a number of multi-tissue studies has been performed in the three main livestock species with the aim to provide more comprehensive annotation of lncRNAs given their high level of tissue specificity. In cow, Koufariotis et al. provided a catalogue of 9778 lncRNA transcripts resulting from the RNAseq analysis of 18 tissues, which were sampled from a single lactating cow  (Koufariotis et al. 2015). In pig, in addition to different studies focusing on the detection of lncRNAs in various tissues, the Pig LncRNANet database (http:// lnc. rnanet. org), is the most comprehensive pig lncRNA catalogue to date  (Liang et al. 2018). This database contains 53,468 lncRNAs, of which 30,175 lncRNAs were retrieved from published studies and extended by 23,293 non-overlapping lncRNAs from NONCODEV4.
In chicken, the most comprehensive lncRNA catalogue provided by Jehl et al. , was built using the Ensembl gene atlas as reference that was extended by non-overlapping lncRNAs from four public databases (NCBI, NONCODE, ALDB, Fr-AgEncode) and other lncRNAs modelled from a few hundred RNAseq samples using cufflinks for gene modelling and FEELnc for lncRNA prediction  (Jehl et al. 2020 ). This extensive chicken atlas is renewed at each important update of the Ensembl annotation including significant changes in the numbers of gene loci or new genome assembly version. To date, two versions related to the two last chicken genome assemblies and Ensembl annotation ("Galgal5-Ensem-blv94" + "GRCg6a-Ensemblv101 (equivalent to v104") are available at http:// www. frage ncode. org/. The Ensembl reference has grown from around 5000 to more than 25,000 lncRNAs, of which 59% and 41% with an expression level ≥ 0.
A limited overlap between lncRNA resources The comparison of lncRNA catalogues from different resources has revealed a limited overlap between monoand multi-tissue resources or between different multi-tissue resources, including the public datasets presented above. (Jehl et al. 2020). These limited overlap between different resources could be explained by three main explanations. The first one is due to the low expression of lncRNAs, which are known to be globally 10-to 20-fold less expressed than mRNA transcripts both in human and domesticated animals  (Derrien et al. 2012; Le Béguec et al. 2018; Jehl et al. 2020). Therefore, for each sequenced sample, the sampling of the reads has a stronger impact on the annotation of captured lncRNAs as compared to the more highly expressed mRNAs, because of the rarity of the lncRNAs in the population of sampled transcripts.
The low expression level of lncRNAs is illustrated in Fig. 2A  with the analysis of a RNAseq dataset of different chicken tissues mapped on the lncRNA-enriched atlas previously described (with around 20,000 mRNA loci and 25,000 lncRNA loci). Using four different tissues, it showed that the majority of reads mapped to mRNA loci with more than 80% (95. 7%) of the mapped reads aligning to the 25% of the most expressed (100%) genes, all of them being protein-coding genes. The second reason involves the higher tissue-, temporal-, and condition-specificity of lncRNA expression profiles as compared to mRNAs  (Derrien et al. 2012; Le Béguec et al. 2018; Jehl et al. 2020) , making critical the number and origin of samples to be analyzed. So far, the reference annotations provided by Ensembl or NCBI/RefSeq for domesticated species are based on a very few set of RNAseq samples (e.
g. for chicken, 21 samples from a unique project for Ensembl and 129 samples from different projects for NCBI/RefSeq) in comparison to the thousands of RNAseq samples generated over the past decade and publicly available in ENA or SRA databases. Therefore, these reference gene sets do not recapitulate the diversity of tissues, ages and physiological stages of lncRNA expression patterns. Consequently, lncRNA gene models are highly sample-dependent in comparison to more broadly expressed mRNAs, as illustrated by the little overlap of lncRNA loci between Ensembl and NCBI/RefSeq (about 13. 7%), whereas almost all mRNA loci are common to both resources (87%) (Fig. 2B ). Finally, as previously illustrated in Table  1 , lncRNA databases also make use of different bioinformatic tools at each step of the lncRNA annotation process (Table  1 ).
This most likely influences gene structure boundaries (especially given the limitations of tools for the reconstruction of full transcripts from short-read RNAseq) together with the correct attribution of gene biotypes (mRNA versus lncRNA), and therefore, the extent of overlap between lncRNA sets. In conclusion of this section, unlike protein-coding genes, genome annotation for lncRNAs (transcript and gene loci) requires considering the entire diversity of tissues, stages, conditions available in public sequences databases. In combination with standard computational procedures and benchmarked tools, the inclusion of many more projects and associated RNAseq samples within the same species both using short-read RNASeq and, in the coming years, long-read RNAseq technologies will most likely increase the completeness of lncRNA sets in domesticated animals.
Long non-coding RNAs and comparative genomics sequence might be sufficient to support the function of a lncRNA  (Quinn and Chang 2016). Recently, a new tool, called lncLOOM, based on a graph representation of a multiple sequence alignment (MSA) and integer linear programming, has been published for the functional prediction of lncRNA short motifs positionally conserved between species  (Ross et al. 2021). Applied to vertebrate species, the tool allowed the identification of functional domains in known lncRNAs, such as Cyrano and CHASERR, as well as in the 3′-UTR of protein-coding transcripts  (Ross et al. 2021). However, while lncRNA gene structures change rapidly and might therefore be an obstacle to the detection of homologous sequences, other important features can be used in the detection of lncRNAs by comparative genomics.
3  Phylogenetic divergence between domesticated species, mouse, and human. A Red numbers correspond to the common ancestor of different species. This tree was generated using the Time-Tree database  (Kumar et al. 2017). Distances were calculated from estimated molecular time. B. Genomic conservation of 2 lncRNAs (in green) in divergent position extracted from Foissac et al. (Foissac et al. 2019)  the number of annotated orthologous lncRNAs. Indeed, we observed 427 (2150) versus 190 (256) ''1-1'' (''n-1'') orthologous lncRNAs between chicken and human species when comparing the two lncRNAs chicken atlas (v104 Ensembl catalogue versus lncRNA-enriched v104 Ensembl catalogue from  Jehl et al. 2020). The increase in the number of ''n-1'' versus ''1-1'' orthologous lncRNAs for chicken, pig, and dog species is probably due to less accurate modeling of gene structures in these species compared to that in humans where transcript boundaries are validated by manual curation and 5′/3′ experimental supports (e.
2017a, b; Esposito et al. 2019 ) could efficiently modulate the expression (up-or downregulate) of lncRNAs; however, 2 main limitations need to be mentioned. First, the single guide RNA (sgRNA) libraries have been designed from lncRNA databases, such as Ensembl or GENCODE, built on models reconstructed from RNAseq data of different cell types or differentiation states and therefore not specific for a given cell type/tissue; thus, many sgRNA might not be functional in the studied cell model given the high tissue-and condition-specific feature of lncRNAs. Second, the design of a sgRNA library might be sometimes hazardous because of the imperfect knowledge of lncRNA promoter regions, despite the recent advancements in 5′ end annotation in human, dog, and chicken  (Hon et al. 2017). To the best of our knowledge, such CRISPR libraries are not yet available for domesticated species.
The first one relies on exploiting and combining the wealth of public RNASeq data available in public repositories (SRA/ENA) in order to include as many as possible tissues, physiological/pathological stages and environmental conditions. Although feasible in theory, this requires efficient programs and large computational infrastructures to regularly cope with the thousands of data now available for domesticated species and to carefully version each newly produced catalogues  (Seal et al. 2020). As mentioned previously  (Steijger et al. 2013 ), one of the major bottlenecks in the bioinformatic process of annotating gene models can be related to the transcript reconstruction step i. e. the process of connecting multiple exons into correct spliced isoforms. The growing interest in long-read RNA sequencing, provided by technologies such as ONT or PacBio, will likely facilitate the reconstruction of full-length non-coding (and coding) gene models for domesticated species in the near future.
Yet, these technologies still produce shallow sequencing depths compared to short-read RNAseq. This could be an issue for lowly expressed transcripts such as lncRNAs although capture strategies followed by LR-RNAseq have been recently applied with success in human and mouse  (Lagarde et al. 2017). The availability of these catalogues of lncRNAs in domesticated species, even if not perfect, has allowed researchers to include these new types of regulatory genes in their studies, by showing some of these lncRNAs to be differentially expressed across treatments, conditions, or genotypes. To go further on some lncRNAs of interest, it is important to keep in mind that multiple evidence should be considered to assess lncRNA functionality in domesticated animals. The identification of an orthologous lncRNA, by sequence or positional conservation, in human databases is a good proxy for its real existence but would involve that the phenotype of interest is evolutionary conserved between the studied domesticated species and human.
Vehicle localisation is an important and challenging task in achieving autonomous driving. This work presents a box particle filter framework for vehicle selflocalisation in the presence of sensor and map uncertainties. The proposed feature-refined box particle filter incorporates line features extracted from a multi-layer Light Detection And Ranging (LiDAR) sensor and information from OpenStreetMap to estimate the vehicle state. A particle weight balance strategy is incorporated to account for the OpenStreetMap inaccuracy, which is assessed by comparing it to a high definition road map. The performance of the proposed framework is evaluated on a LiDAR dataset and compared with box particle filter variants. Experimental results show that the proposed framework achieves respectively 10% and 53% localisation accuracy improvement with reduced box volumes of 25% and 41%, when compared with the state-of-the-art interval analysis based box regularisation particle filter and the box particle filter.
The second group of accurate digital maps comes with service charges and often limited access to metadata. The crowdsourced OpenStreetMap (OSM) could be a cheaper replacement for expensive digital maps and can provide flexible solutions since almost all the metadata can be accessed and customised by end-users. OSM has already been applied in urban navigation  (Suger and Burgard, 2017). However, the accuracy of OSM remains a challenge  (Vargas-Munoz et al. , 2021). It could vary from centimeters to meters from city to city, thus bringing in additional uncertainties apart from those caused by sensors  (Brovelli et al. , 2016 , Senaratne et al. , 2017). Hence, it is beneficial to develop robust localisation frameworks when using inaccurate SLAM maps or OSM for autonomous systems. In this paper, a feature-refined BPF (FRBPF) that stems from the box particle filtering (BPF) approach  (Abdallah et al.
, 2008 , Gning et al. , 2013)  is proposed to achieve accurate and robust localisation results based on an inaccurate yet free OSM. In our case, the OSM serves as a reference map for localising a vehicle. A real-time kinematic (RTK) sensor suite provides the ground truth information. The vehicle is equipped with an Inertial Measurement Unit (IMU) and a LiDAR sensor to fulfill localisation. In the proposed approach, line features are firstly extracted from raw LiDAR data obtained by the vehicle at time k. The distances and angles of the line features with respect to the vehicle are adopted as measurements and are denoted as [y k ]. The line features are next associated with line features corresponding to building footprints on the OSM. The accuracy of the OSM is assessed with respect to a high-definition map (HDM) maintained by the Université de Technologie de Compiègne (UTC) so that map uncertainties are also considered during localisation.
Measurements of the matched line features are fed to the proposed FRBPF for vehicle state updating. With N box particles representing the vehicle states [x i k ], i = 1, • • • , N and upon the arrival of measurements [y k ], the filter propagates state estimates through the box contraction and update steps as time evolves. In contrast of performing contraction per measurement, a feature-refined contraction merges line features before the contraction step. This is also a way of coping with OSM and sensor data uncertainties, henceforth reduces boxes. As interval analysis based methods do not provide point estimates by nature, this paper takes box centers to achieve point estimates by statistical metrics such as expectation and covariance to evaluate the proposed FRBPF and compares it with the BPF and the box regularised particle filter (BRPF)  (Merlinge et al.
, 2019). The main contributions of this paper can be summarised as follows: (1) A LiDAR features-refined box particle filter is proposed that is able to deal effectively with OSM and sensor data uncertainties; (2) A contraction algorithm is developed that incorporates the abundant line features from structured urban environments to reduce the volume of box particles; (3) Theoretical proofs about the features-refined contractions are derived; (4) A box particle weight balance strategy is designed to cope with OSM uncertainties and further improves the localisation performance. The rest of this paper is organised as follows. Section 2 presents an overview of related works. Section 3 gives the necessary theoretical background knowledge. Section 4 elaborates the proposed approach. Section 5 includes validation and discussions of the proposed approach.
OpenStreetMap based Localisation OSM is the most well-known crowdsourced map whose metadata is structured by entities such as nodes, ways, and relations  (Zheng and Izzat, 2018). Nodes represent points of interest. Ways are a collection of nodes that correspond to buildings and roads, and relations indicate the relationships between nodes and ways. Generally, the exterior surface of buildings can be projected into a two dimensional (2D) plane as line segments or can be approximated by line segments. Hence, the OSM is equivalent to a feature map represented by a set of linear equations. Compared with highly precise maps, maintained by local authorities, the OSM accuracy needs to be further improved. For instance, building footprints of Milan on OSM show a systematic translation of 0. 4 m on the defined X and Y directions in  (Brovelli et al.
Models and Experiment Settings The evolution model [f ] uses the measured speed v k and yaw rate ω k and is given as            [x k+1 ] = [x k ] + T • v k • cos([θ k ] + T • ω k 2 ) + [µ x k ], [y k+1 ] = [y k ] + T • v k • sin([θ k ] + T • ω k 2 ) + [µ y k ], [θ k+1 ] = [θ k ] + T • ω k + [µ θ k ], (48) where LiDAR data collected by a Velodyne ® VLP-16 sensor mounted on the roof of a vehicle are processed. Sixteen layers of point clouds are obtained. This paper extracts line segments from these layers directly and they are next associated with OSM line features. Please note that it is possible that one can extract line features from point clouds reflected by trees, but they will be filtered out by data association (no line features corresponding to the tree exist on the OSM) and q-satisfied intersection (line features corresponding to the same footprint on the OSM tend to be 'closer' to each other than the features from the trees, hence features extracted from the trees will be filtered out).
One can accelerate the q-satisfied intersection by decreasing the box number by a greater step than 1. In addition, this paper uses Matlab for FRBPF implementation, which is   conclude that FRBPF helps in box areas reduction, which holds when q in the qsatisfied intersection equals or slightly smaller than the number of line features. In the worst case, i. e. q = 1 and one randomly selects one measurement for contraction, it could lead to the increase of the box-hull area. Alternatively, if all the measurements are used for contraction one by one, then FRBPR degenerates to BRPF, hence the box-hull area would be similar to BRPF. This can be observed from Figs. 7 and 8.
Acknowledgment Experiments were carried out in the framework of the  Equipex ROBOTEX  ( ANR-10-EQPX-44-01 ). We are grateful also to the support by the  National Natural Science Foundation of China  (Grant No. 61703387 ),  UK EPSRC  for funding this work through  EP/T013265/1  project  NSF-EPSRC:ShiRAS -Towards Safe and Reliable Autonomy in Sensor Driven Systems  and the  USA National Science Foundation  under Grant  NSF ECCS 1903466.
In the methodology, the documents are news collected from the Web. The advantage with big data is that the collected news may span a wide variety of geographical, societal, and organizational borders  [9]. This is especially important because, as mentioned above, researchers in emerging technologies should identify research directions by reaching out to individuals and society, beyond organizations. Document sampling should pay special attention to sampling bias, a major risk for Internetmediated research  [13]. In the methodology, we divide the sample into three subsamples: one for training the relation extraction algorithm, one for testing it, and one for executing it after training and testing. Document profiling follows document acquisition and sampling. In the big data analytics cycle, data preparation, comprising data profiling and data transformation  [21] , is crucial.
Document profiling. Document profiling is performed manually to avoid redundancies, check style (avoid familiarity) and relevance (e. g. , documents investigating the rates of cryptocurrencies are not very relevant for this study). Even though we filter the documents as much as possible before manual profiling, we inspect 11000 documents, anticipating that we will keep about half of them. Ideally, artificial intelligence could assist in profiling, but to the best of our knowledge, application of artificial intelligence to data profiling is mostly restricted to structured data. 4. 3. 3. Document syntactic pre-processing. Syntactic pre-processing consists in applying the Stanford parser, used in IEPY.
Participants were also provided with information of how to install the Inquisit Software in order to launch all the experimental tasks. Based on the results from the online pre-selection stage, we calculated each participant's individual inhibition capacity index. In particular, we examined the standard interference effect, i. e. , the difference between the mean response times in incongruent and congruent trials divided by the mean response time in congruent trials, which represents the time needed to inhibit interference and, importantly, is considered a reliable indicator of the efficacy of cognitive control (e. g. [18]  ): the lower the interference (i. e. , the faster the interference is resolved), the stronger one's inhibitory capacity. First, we calculated the interference ratios separately for the Stroop task and the Eriksen flanker task.
Materials • Inquisit Web software: All instructions and tasks were created and displayed using Inquisit Web software (Millisecond software) in an online setting. • The Stroop-like task: This task consisted of four color words (red, green, blue, and yellow) in Polish, printed in one of these four colors (e. g. , the word red could be printed in red, green, blue or yellow). Participants were instructed to identify the color of the ink of the word as quickly as possible by pressing a key corresponding to the color of the ink without paying attention to the meaning of the word. Each word was displayed until the response was given (latencies were measured from the onset of stimuli), with a 400 ms interval between trials and a 400 ms error feedback screen after each error trial. While the meaning of the word and the color of the ink were the same in congruent trials, the meaning differed from the color of the ink in incongruent trials.