dataesr/scientific-paragraphs-categorization huggingface dataset:
This would be the case of genomes with particularly low levels of heterozygosity (<0. 1%, or an average of 1 heterozygous SNP per kb) or datasets in which the sequencing data consists of reads that are shorter than the distance between heterozygous positions, inevitably leading to fragmented haplotypes. Long reads are particularly interesting for phasing applications due to how phase-informative they are. Each long read can contain significantly more heterozygous positions than its short read counterparts. However, none of the objective function optimization methods cited here take long reads into account. The intuition behind the optimization of an objective function is typically guided by the notion that the predicted haplotypes must conform in some way to the information present in the set of mapped reads.
This assumption holds fairly well only if the read dataset is known to be of high quality and not errorprone. These methods are more appropriate for relatively error-free reads. Objective function minimization strategies, like the graph Population inference methods typically cast the mapped reads to a matrix and compare them to a panel composed of haplotype information obtained from sequencing either a large population of individuals, or a smaller group of individuals related to the sample. Haplotypes are predicted through statistical inference based on the frequency of jointly observed genotypes.
Graph partitioning The graph partitioning strategy casts the dataset of mapped reads as a graph. Typically, each mapped read is a node and each edge represents how similar two nodes are. The goal is then to determine the optimal way to split the graph into subgraphs that represent the different predicted haplotypes (Fig. 4 ). It departs from the objective function strategy by seeking to group similar mapped reads together, away from dissimilar mapped reads, rather than seeking to optimize for coherence of the predicted haplotypes with the set of mapped reads. It achieves this through the use of the graph model and its associated mathematical tools and algorithms. To this end, graph partitioning algorithms are implemented or developed and applied, outputting subgraphs which are then converted to haplotype sequences, usually through majority voting.
They should then naturally form tightly connected graphs if attributed weights which correspond to their similarity (or dissimilarity). This strategy leverages well-established algorithms which efficiently split graphs into wellconnected components. WhatsHap Polyphase's application of a graph partitioning strategy to long read datasets and its handling of part of the complexity brought on by the variability in genomic architectures is encouraging for the handling of the more complex problems of polyploid phasing. However, most graph partitioning algorithms, and all methods Fig. 4. Graph partitioning strategy. Graph partitioning strategies cast the mapped reads to a graph in which typically the reads are nodes and the edges between them correspond to a measure of how similar or dissimilar the reads are to each other based on the variants they carry.
Validation datasets and performance metrics Once a polyploid phasing method has been developed, its performance must be evaluated. To that end, a validation dataset which corresponds to a set of reads obtained from a polyploid must be given as input to the phasing method, and the output haplotype predictions must be evaluated by performance metrics. The validation dataset can be simulated or real. In the case of simulated datasets, it is possible to know the optimal phasing result, which allows for the use of detailed metrics to better understand the performance of the polyploid phasing algorithm. A validation dataset can be fully simulated, such as in Haptree  [20] , which randomly generates haplotypes and simulates reads derived from these haplotypes. Validation datasets can also be partially simulated, or reconstructed.
This is the case for WhatsHap Polyphase and nPhase, which both merge real sequencing reads of organisms with known haplotypes. WhatsHap Polyphase combines human datasets with known haplotypes, while nPhase combines S. cerevisiae datasets of haploid and homozygous diploid individuals. Fully simulated datasets have a high degree of control over all characteristics of the genome, which allows them to test the effects of different ploidy levels, heterozygosity levels, genome architectures, coverage levels. However, these methods are highly dependent on the accuracy of their simulations of genomes and sequencing results. Partially simulated datasets are more faithful simulations of real haplotype phasing scenarios as they use real genomes, with real SNPs and real sequencing reads. However, these genomes are still artificially produced, typically presenting relatively uniform distance between haplotypes, and there is less control over their characteristics, which limits the testing space.
Some parameters, such as the effects of coverage level and heterozygosity rate, can still be queried by downsampling the number of reads or the variable positions input to the phasing algorithm, however this process is less straight-forward than it is for a fully simulated dataset. For all simulated datasets, the ground truth is known and can be used to evaluate the predicted haplotypes. A variety of metrics have been implemented, here we discuss those most commonly used in the field. The MEC score is not only an objective function used in a number of phasing methods, but also a metric which has been routinely used as evidence of good phasing. Individual reads are, barring sequencing errors, considered to be naturally phased sequences. It can therefore appear intuitive that comparing the phases of predicted haplotypes with the phases of individual reads can be a useful proxy for phasing quality.
It is also trivial to obtain a perfect MEC score by not clustering reads together at all, and simply comparing the input set of reads to itself. Due to the significantly higher error rate of long read sequencing, any method relying on these reads will necessarily obtain worse MEC scores despite the obvious advantages of long reads, further limiting the usefulness of this metric for the evaluation of polyploid phasing methods, which exploit different input read types (such as comparisons between short read and long read methods). Finally, the MEC score is necessarily dependent on the error rate of the sequencing and base-calling technology, the read coverage used, and therefore gives no direct, quantitative indication on the trustworthiness of a prediction. It can only qualitatively be used to compare different datasets to each other.
These metrics are all applicable when the ground truth is known, which is the case for simulated datasets. However, it is less straightforward to evaluate the performance of these methods with real polyploid data due the absence of a ground truth. A few proxies have been developed to tackle this problem. We have already discussed the MEC metric, which is one of the main metrics used to evaluate performance on real polyploids. Ranbow  [37]  phases the sweet potato, Ipomoea batatas, and uses long, accurate Roche 454 reads to validate its haplotype predictions. WhatsHap Polyphase and nPhase both phase the autotetraploid potato plant, S. tuberosum, and show qualitatively that its genes appear well-phased  [32, 39]. In their paper  [40] , Motazedi et al. develop haplosim, a simulation pipeline which can generate simulated haplotypes and associated reads.
This tool has been used by several polyploid phasing methods for their validation steps, such as Hap10  [30]  and Ranbow  [37]. However, there is no widely used benchmarking dataset which can systematically be compared against, and haplosim does not appear to have been updated in the past three years to reflect the significant improvements in quality achieved in long read sequencing methods. A well-maintained gold standard benchmark would be of benefit to the field of polyploid phasing. It would be interesting for such a resource to carefully consider the performance metrics to evaluate, the diversity of read sequencing methods and the effects of variable ploidy, genome architecture, heterozygosity level, genome size, structural variation, indels, polyallelic sites and local variations in the number of haplotypes.
Call for a community benchmarking project Alignment-based polyploid phasing algorithms have significantly evolved since their inception. We consider the graph partitioning and cluster building strategies to be the most promising for future methods, in particular for the relative ease with which long reads can be leveraged. More nuanced understanding of the wide variety in the genome architectures of polyploids will have to be considered and taken into account in the design of future strategies. Additionally, it will be important not to ignore indels and polyallelic sites, and to seek to obtain accurate and contiguous phasing results. We believe that the polyploid phasing field would greatly benefit from the development of a common gold standard dataset of simulated, partially simulated, or real polyploids, with carefully selected performance metrics.
Systematic benchmarks against the same well-designed datasets would be beneficial in several ways by reducing the effort required to demonstrate the performance of a new phasing method and standardizing the metrics being used, allowing a user to more easily compare two polyploid phasing methods. It would also help show the conditions under which some algorithms perform highly, or meet their limitations. In the interest of furthering this goal, we developed a toolkit to Fig. 6. Behavior of the SWER and contiguity performance metrics. A We illustrate the unpredictable nature of the SWitch Error Rate (SWER) metric with two examples. In both cases we suppose we have a haplotype prediction of 100 variants. In the first, top case, two consecutive switch errors lead to a 2% SWER, but due to being consecutive the accuracy is at a very high 99%.
In the second, lower case, there is only one switch error, giving a better SWER score of 1%, however the accuracy is reduced by half to 50% due to it occurring in the middle of the prediction. This behavior of the SWER metric makes it unpredictable and unreliable. B We illustrate the importance of contiguity to the interpretation of accuracy results with two examples. In both cases we show haplotype predictions for a diploid sequence. In the first, low contiguity example, we illustrate how it can be trivial to obtain extremely accurate predictions if they are sufficiently fragmented. Through the second, high contiguity example, we show how the accuracy of the previous example could dramatically decrease by increasing contiguity. benchmark alignment-based polyploid phasing strategies and tested it on a previously described dataset.
We hope that this can serve as a starting point for the community to develop into a robust gold standard benchmarking process and associated datasets. This Phasing Toolkit is a series of scripts which perform all of the necessary steps for polyploid benchmarking (Fig. 7 ). It is an open source project which welcomes contributions and strives to become a useful resource first for benchmarking but also potentially for real use cases of phasing tools and subsequent analysis of the results. We will present here the current state of the toolkit and how we have used it to generate an initial example benchmark of three tools on one dataset: flopp, nPhase and WhatsHap polyphase. The first step of the benchmarking process is to obtain the data we will be testing different phasing methods on. For simplicity, we reused the virtual polyploid dataset described in the nPhase paper  [39].
We provide the downloadData. py script with the names and NCBI accession codes of the sequencing datasets we want to download. In this case, we provide the accession codes for the short and long read sequencing data of four strains of S. cerevisiae which are either haploid or homozygous diploid. We also need to download the reference sequence of S. cerevisiae by providing the downloadData. py script with its taxid and taxonomic group according to the ncbi (559,292, fungi). The downloadData. py script uses sra-downloader (https://github. com/s-andrews/sradownloader) to download sequencing data and ncbi-genome-download (https://github. com/kblin/ncbi-genome-down load) to download reference sequences. In the second step, we launch the processReads. py script and the short reads are mapped to the reference with bwa-mem  [41] , sorted with samtools  [42]  and variant called using GATK  [43].
This provides us with a ground truth dataset which will later be used in the performance evaluation. For the third step, we use the hybridGenerator. py script to generate virtual polyploids, in this case a 2n, 3n and 4n dataset of short and long reads. These datasets are obtained by merging the reads of individual samples and can be set to a custom coverage level (set to 20× in our example). We can then run the processReads. py script to process the short reads as described before to obtain variant calls for the short reads. In order to evaluate the effect of heterozygosity rate on the performance of phasing tools, we can also automatically subset these variants to obtain specific heterozygosity rates. In our example we subset the variants to 1%, 0. 5%, 0. 1% and 0. 05% heterozygosity rate.
We also indicate that we want to obtain variant calling results with and without indels, to be able to evaluate how well indels are phased. Finally, this script will also process the long reads by mapping them to the reference using NGM-LR  [44]. We now have a dataset of a 2n, 3n and 4n sample, each at 1%, 0. 5%, 0. 1% and 0. 05% heterozygosity rate, with and without indels, for a total of 3*4*2 = 24 datasets ready to be phased, and the corresponding ground truth. We then run the phaseToolRunner. py script on each dataset and select the tools we want to test. For this initial release of the Phasing Toolkit we have only implemented flopp, nPhase and Whatshap polyphase, which we all test on our benchmarking dataset. The phase-ToolRunner. py script will keep track of runtime and memory usage for each tool and phase each dataset with every tool selected.
The benchmarking strategy we describe here consists in obtaining long and short read data of individual haploids of the same species and using them to construct virtual polyploids which, once phased, will be compared to the ground truth obtained from the original samples. A We generate a virtual polyploid's long and short read datasets by combining the long and short read datasets of n haploid individuals. At this step we can subsample the reads to reach a coverage level of X to evaluate their effects on phasing quality. We then map and variant call these reads. Once variant called, we can subsample the variants to a heterozygosity level of x% to evaluate its effect on phasing quality and choose to include or exclude indels for the same reason. The final dataset is then phased using the available phasing algorithms.
Mobility is a subject of increasing importance in a time when cities have gained prominence, as they are home to over 56% of the world's population and generate over 80% of global GDP. Urban planning principles have traditionally been developed to promote urban efficiency and enhance productivity. The emergence of 'Smart Mobility' has provided researchers and policy practitioners new ways to understand and plan cities. With rapid urbanization growth and the sustained mobility challenges faced in most global cities, this paper sets forth to understand and map the evolution of the concept of 'Smart Urban Mobility' through a bibliometric analysis and science mapping techniques using VOSviewer. In total, 6079 articles were retrieved from the Web of Science database over 5 decades, from 1968 to 2021, and divided into four sub-periods, namely 1968 to 2010, 2011 to 2015, 2016 to 2019, and 2020 to 2021.
Materials and Methods Various types of methods exist for literature reviews, including systematic reviews and metanalyses. While such methods are ideal for detailed analysis of the literature, the current pace of academic publication requires using other methods such as bibliometric reviews to understand the structure and trends of academic fields. Accordingly, bibliometric reviews have become popular in the last few years and many such reviews exist in the literature  [15, 16]. Input data for this overview analysis was obtained from the Web of Science (WoS), a widely known database for indexing peer-reviewed literature. Other databases such as Scopus also exist. However, WoS was selected as it only indexes quality peer-reviewed research, and also its bibliographic outputs allow more accurate bibliometric analysis.
Next, the full bibliographic details and cited references of these articles were downloaded from the WoS for bibliometric analysis. Over the past two decades, different software tools have been developed that can be used to understand the overall structure of research fields. Tools such as SciMAT, Citespace, and VOSviewer have been used more frequently. VOSviewer has been used for this study as it has a more user-friendly interface, provides more informative outputs, and allows better analysis of trends using term co-occurrence maps  [17]. The software enables various types of analyses, including term co-occurrence analysis, co-citation analysis, and bibliographic coupling analysis. Term co-occurrence analysis identifies and maps terms that have co-occurred more frequently in the selected documents.
This variability is explained by the identification of the starting ATG, which is not the same between homeologs and leads to variability in the protein length (381 aa for TaGNAC1-7A, for 346 aa for TaGNAC1-7B and 365 aa for TaGNAC1-7D). Similarly, TaGNAC4-7A and TaGNAC4-7D have a length of 1050 bp while TaGNAC4-7B is 972 bp length. This 78 bp truncation is, in that case, due to the appearance of a premature stop codon on the TaGNAC4-7B sequence, truncating the end of the protein by 26 aa (324 aa vs. 350 aa for the A and D homeologs). The protein sequences of the four TaGNACs all have the signature domain of the NAC family (PF02365). The four TaGNAC and the four TmGNAC protein sequences harbor a probable non-cytoplasmic location (more than 80% of probability) without any signal peptide nor transmembrane helix for all the sequences.
We manually added the protein sequences of the four T. monococcum GNAC (TmGNAC1, TmGNAC2, TmGNAC3 and TmGNAC4) previously sequenced to this pool of orthologous sequences. Furthermore, the presence of the NAC domain (IPR003441, PS51005 and PF02365) was validated for all the sequences; along with a biological process of regulation of transcription, DNA-templated (GO:0006355) and a molecular function of DNA-binding (GO:0003677). Those results are consistent with the NAC protein role as transcription factors. No peptide signal nor transmembrane domain was identified on those orthologous sequences. A high conservation was observed in GNAC1, GNAC2, GNAC3 and GNAC4 sequences among the Triticeae group (Triticum turgidum, T. aestivum, T. spelta, T. dicoccoides, T. monococcum, T. urartu, Aegilops tauschii and Hordeum vulgare) (Figure  1 ).
However, for the seven non-Triticeae species, the orthologous sequences of the TaGNAC genes seem to be less organized (they are orthologous to several TaNAC genes). For example, in the case of two species (B. distachyon and S. italica), there is only one orthologous sequence corresponding to all the homeologs of the four TaGNACs genes: the sequence KQK02751 in B. distachyon, and the sequence KQL24250 in S. italica. Int. J. Mol. Sci. 2022, 23, x FOR PEER  REVIEW 4  the sequences; along with a biological process of regulation of transcription, DNAplated (GO:0006355) and a molecular function of DNA-binding (GO:0003677). Thos sults are consistent with the NAC protein role as transcription factors. No peptide si nor transmembrane domain was identified on those orthologous sequences. A high conservation was observed in GNAC1, GNAC2, GNAC3 and GNAC4 quences among the Triticeae group (Triticum turgidum, T.
We therefore hypothesize that the TaGNAC3 gene from bread wheat does not have a d ortholog in Hordeum vulgare. However, for the seven non-Triticeae species, the ortholog sequences of the TaGNAC genes seem to be less organized (they are orthologous to sev TaNAC genes). For example, in the case of two species (B. distachyon and S. italica), t is only one orthologous sequence corresponding to all the homeologs of the four TaGN genes: the sequence KQK02751 in B. distachyon, and the sequence KQL24250 in S. itali.
Structure and Functional Domains of GNAC Proteins To investigate the conservation of the protein structure in the NAC preferentially expressed in grain, we modelled the tertiary structure of the TaGNAC and TmGNAC as an example (Figure  2 ). This result shows that the structure of the studied proteins is conserved, with four main beta-sheets and two alpha-helix in a central position of each protein. After comparing the set of 488 TaNACs from the RefSeqv1. 0 database  [12] , only 454 sequences were retrieved from the new protein database (RefSeq v2. 1). The presence of conserved motifs was investigated among these protein sequences. Among the 20 motifs identified, a 68 aa motif was present in the C-terminal part of 17 sequences belonging to a clade of the phylogenetic tree consisting of 21 sequences in total (Figure  3 ) corresponding to the NAC-d subfamily, with an e-value of 1.
9 x 10 -182. Guérin et al. (2019) identified 19 TaNAC genes preferentially expressed in grain  [12]. Sixteen of these 19 sequences are contained in the same clade. Of these, 14 sequences carry the 68 aa-motif. After comparing the set of 488 TaNACs from the RefSeqv1. 0 database  [12] , only 454 sequences were retrieved from the new protein database (RefSeq v2. 1). The presence of conserved motifs was investigated among these protein sequences. Among the 20 motifs identified, a 68 aa motif was present in the C-terminal part of 17 sequences belonging to a clade of the phylogenetic tree consisting of 21 sequences in total (Figure  3 ) corresponding to the NAC-d subfamily, with an e-value of 1. 9 × 10 -182. Guérin et al. (2019) identified 19 TaNAC genes preferentially expressed in grain  [12]. Sixteen of these 19 sequences are contained in the same clade.
Sequence Identification and Analysis in Bread Wheat The genomic, CDS and protein sequences of the 10 available homeologs of the four bread wheat TaGNAC genes were retrieved from the RefSeq v2. 1 database of the International Wheat Genome Sequencing Consortium  [56]. The three homeologs of TaGNAC1, TaGNAC2 and TaGNAC4 were identified. For TaGNAC3, only the copy carried by chromosome 5A has a Traes id on v2. 1 of the bread wheat pseudomolecule (Table  S1 ). Chromosomal anchorage of the 10 TaGNAC homeologs was performed using the MapChart software  [57] , and chromosomal length and gene location provided by the IWGSC. We used the RefSeq v2. 1 protein database to retrieve the 488 Traes id identified in bread wheat (in the RefSeqv1. 0 database) by  Guérin et al. (2019)   [12]. Only 454 sequences were retrieved, the others probably have been removed during the database updates.
An unrooted phylogenetic tree of those 454 TaGNAC sequences was designed using the maximum-likelihood method available in MEGA-X software  [58] , based on the alignment of the protein sequences using the ClustaW method. Then, the sequences anchored in the clade containing the homeologs of our four TaGNACs were retrieved. On the MEME Suite platform (http://meme-suite. org/tools/meme, accessed on 15 January 2022), we used the MEME tool to search for the presence of conserved motifs among these protein sequences with parameters as follows: motif number = 20, width = 10-150 amino acids. Subsequently, we imported these motifs into the FIMO tool to check for their presence in the 64 orthologous sequences of bread wheat TaGNACs (the results were filtered using a p-value < 1 x 10 -10 ).