dataesr/scientific-paragraphs-categorization huggingface dataset:
A gdhB Paralog Complicates in silico Determination of the Oxford ST 730 genomes of the Acb complex were selected and downloaded to be used as dataset. The database included mostly genomes of A. baumannii isolates (703, 96. 3%), but also genomes of A. nosocomialis, A. seifertii, A. dijkshoorniae, A. pittii, and A. calcoaceticus isolates. MLST alleles of both schemes were first defined from genomic sequences. While extracting alleles, a consistent proportion of the genomes appeared to have two variants of the gdhB gene. This issue was investigated further, revealing that an alternative gdhB locus (corresponding to alleles 182, 189 and variants of them) was present in 553 (76%) of 730 strains, all belonging to the A. baumannii species. This locus is often annotated in these genomes as gdhB2 and has a sequence similarity with allele 1 of gdhB ranging from 73.
Oxford and Pasteur Comparison Minimum spanning tree analysis demonstrated that both MLST schemes gene sets discriminate the existing species within the Acb complex (Figures  3A, B , MLST-based species identification). ST assignments and eBURST analyses using our 730 genomes dataset generated a convenient and expandable table of correspondence between the two MLST schemes, represented in Table  2  and in Figure  4  and Supplementary Figure  S2  as Sankey diagrams. Then, we extracted CCs from both schemes and checked their monophyly on the core genome phylogeny. The Pasteur scheme had 12 CCs comprising a total of 47 STs, i. e. , 35. 34% of all STs. Additionally, there were 86 singleton Pasteur STs, while nine CCs are monophyletic. The Oxford scheme had 16 CCs comprising in total 82 STs, i. e.
, 44. 56% of all STs. Additionally, there were 102 singleton Oxford STs and 11 monophyletic CCs. The Pasteur scheme appeared to be less discriminant, but more appropriate for precise strain classification into clonal groups. On the other hand, the Oxford scheme was able to identify additional genotypes and to differentiate isolates belonging to international clone II into three distinct clonal groups (Table  2  and Figures  3C, D ). The Gini-Simpson index of the 730 genomes was almost one order of magnitude higher when classifying the dataset using the Pasteur STs (0. 70-0. 93 for Oxford). When repeating the calculation on the genomes of the three International Clones, the score difference was lower but still important. The Pasteur scheme obtained values close to 0, being of low discrimination within the three ICs (Table  3 ).
Topologies Comparison and Statistical Analysis To evaluate the two MLST schemes with respect to phylogenetic inference, we constructed four phylogenies (Figure  5 ): two using the concatenated alleles of the Pasteur and Oxford scheme as input, and two references using genome-wide data (a coregenome of 1409 high quality genes, and an alignment of 68,340 SNPs). We then statistically compared the trees to evaluate the reliability of MLST concatenates in relationship with the two references. All tests applied suggested a better congruence between the Oxford scheme and the references. Full results are reported in Table  4 , which includes the results of cross check tests between the two reference phylogenies. In Figure  5 , International Clones I, II, and III (calculated according to the Pasteur CC 1, 2, and 3) are highlighted in all four trees.
Recombination Analysis A recombination analysis was run on the alignments of all MLST gene fragments. All gene sequences presented signs of recombination in the non-baumannii genomes. This suggested that the analysis was biased by an uneven evolutionary distance and was repeated only on the 703 A. baumannii genomes. This step allowed detecting a recombination in the gpi locus of the Oxford scheme, while all other loci appeared to be recombination free. These data are in partial agreement with a previous study, which detected recombination in the topologies of the phylogenetic trees generated for the gyrB and gpi genes using the Oxford MLST scheme  (Hamouda et al. , 2010). High recombinogenicity of the gpi locus was also detected in other studies, which focused on the genomic plasticity of the capsular loci  (Kenyon and Hall, 2013; Holt et al.
DISCUSSION The aim of this investigation was to compare the two MLST schemes that are widely used to genotype isolates of the Acb species complex. We decided to tackle the problem evaluating the two schemes, their reproducibility, discrimination, strain classification into CCs and compatibility among MLST-based phylogenies and genome-based phylogenies. Starting from a curated dataset of 730 genomes, two phylogenomic trees were obtained from information collected throughout the whole genomes (core genes and core SNPs). The two resulting trees showed highly similar topologies and can be considered close approximations of the real evolutionary history of the Acb species complex. For this reason, they were used as reliable and unbiased references for the analyses. A phylogeny was obtained from the concatenate alignment of the alleles of each of the two MLST schemes and compared with the two references using three different statistical methods.
baumannii species, followed by a quick sequence divergence of gdhB2 and by the loss of this gene in a number of representatives of the Acb complex (177 out of 730 in our dataset). Nucleotide composition analysis shows that both gdhB and gdhB2 have higher than average codon adaptation index, thus not allowing to understand which of the two events could be more likely. The incorrect alleles are, to date (14 September 2018) 182 and 189 and have led to the determination of 30  STs: 1567 STs: , 1604 STs: , 1677 STs: , 1678 STs: , 1793 STs: , 1794 STs: , 1796 STs: , 1800 STs: , 1804 STs: , 1805 STs: , 1806 STs: , 1807 STs: , 1808 STs: , 1809 STs: , 1813 STs: , 1815 STs: , 1816 STs: , 1833 STs: , 1834 STs: , 1835 STs: , 1836 STs: , 1837 STs: , 1838 STs: , 1839 STs: , 1840 STs: , 1841 STs: , 1843 STs: , 1851 STs: , 1852 STs: , 1857.
Other 38 unregistered 182like alleles were found in the dataset used in this project and should not be registered if submitted to the MLST database. We suggest that these gdhB2-based alleles should be removed from the database, and each of the genomes belonging to these STs should be re-analyzed excluding the paralog gdhB2 (a stringent allele calling filter on genetic similarity could be useful for this purpose) to find the correct gdhB allele and subsequently the current ST. The inclusion of the paralog allele in the database is due to the bioinformatics methods used, which did not take into account the possible presence of such paralog. This does not appear to have ever happened in PCR-based classification, as the gdhB primers are sufficiently specific to amplify only the correct locus. While this issue can be solved using bioinformatics, in silico MLST can be performed with different software tools including in-house scripts, so we cannot rule out the possibility of novel gdhB2-based alleles to appear in the future.
For this reason, it is important to study how urban projects became smarter through the wise management of natural resources and participatory governance with the involvement of multi-stakeholders such as citizens, users, engineers, researchers, elected representatives, etc. A large number of new ideas, techniques, and approaches have contributed to the field of research on decision-making in smart cities in recent years  [4]. However, no general consensus yet exists on the optimal decision-making tool to use to support stakeholders at each level of decision-making, each stage of the project, and for each particular context. Therefore, this paper aims to propose a systematic literature review of formalization tools for decision-making processes in collaborative smart city projects. To achieve this, 606 research articles published between 2005 and 2018 extracted from the Scopus ® database were found and analyzed using a co-occurrence approach conducted with VOSviewer software.
Thus, among this huge panel of scientific data, 76 significant references in terms of different decision-making methods were selected and then sorted according to the main characteristics of smart cities. Based on the categorical reviews in  [5] , decision-making methods were classified into four groups: multi-criteria decision-making (MCDM), mathematical programming (MP), artificial intelligence (AI), and integrated methods (IM). In this study, decision making methods were reviewed according to three aspects: (1) levels of decision-making, (2) phases of implementation, and (3) involvement of and negotiation between stakeholders. This review, supported by algorithms and database analysis, is necessary to increase the understanding of how decision-making methods are applied in smart city projects and, subsequently, to inform researchers how to choose the correct approaches in order to formulate and optimize the decision-making process.
Materials and Methods In this study, the approach was the systematic literature selection process using different filters to narrow the number of scientific publications relating to decision-making in Smart Cities. Figure  1  shows a flow diagram as a process including four filters with different search strategies and exclusion criteria. The large number of articles collected after the first filter form the data for a qualitative data analysis carried out by VOSviewer ® software. VOSviewer is freely available software at www. vosviewer. com. This software tool provides bibliometric mapping based on a network of keywords. It implements the mapping technique, and co-occurrence data analysis following a similarity matrix  [27]. The VOS (visualization of similarities) mapping provides a high quality of visual representation to explore the maps in detail by offering zooming and scrolling options.
Materials and Methods In this study, the approach was the systematic literature selection process using different filters to narrow the number of scientific publications relating to decision-making in Smart Cities. Figure  1  shows a flow diagram as a process including four filters with different search strategies and exclusion criteria. The large number of articles collected after the first filter form the data for a qualitative data analysis carried out by VOSviewer® software. VOSviewer is freely available software at www. vosviewer. com. This software tool provides bibliometric mapping based on a network of keywords. It implements the mapping technique, and co-occurrence data analysis following a similarity matrix  [27]. The VOS (visualization of similarities) mapping provides a high quality of visual representation to explore the maps in detail by offering zooming and scrolling options.
First Search Strategy: The First Filter This step consisted of defining the first filter as a search equation, which was composed of keywords, period of time, database, and type of documents to be analyzed (Table  1 ). The keyword field was divided into two complementary parts. The first concerned "decision-making", and the second "smart city". These keywords were defined according to a preliminary literature review  [16] , whereas the period of time was selected from the year 2018 backwards. The focus was only on articles and conference papers; therefore, unpublished articles, working papers, and magazine articles were excluded during the data refinement process. After applying the first search strategy, 606 initial scientific publications were found in the Scopus ® database. The whole database of 606 papers according to their title, keyword statistics, citation counts, author names, and affiliations were collected and exported from the Scopus website into a CSV data file.
Second Search Strategy: The Second Filter In this search equation, the proximity operator "w/50" was used between two sets of keywords: "decision-making" and "smart city". This is a search tip provided by the Scopus website to make sure all search terms appear in the title or in the same paragraph. By using this tip, the number of selected publications was narrowed down and the topic of these studies was more relevant to the present objectives. In this step, the period of time searched, database, and type of documents were similar to the first search step. After applying this search strategy, 213 potential publications were found on the Scopus database.
Qualitative Review: Data Statistics Generated by VOSviewer The 606 publications selected from the first search strategy were transferred as bibliographical data to the VOSviewer software. These bibliographical data were used to develop a network of keywords by co-occurrence links. The whole network of keywords and their links is mapped out in Figure  2. In this network, the circles are a representation of keywords and the diameter of circles represents for the frequency of occurrence of each keyword. The distance between two keywords indicates their relatedness in terms of co-occurrence links. These co-occurrence links were determined based on the number of documents in which keywords occur together. So, the closer two keywords are located to each other, the stronger their relatedness. The 606 publications selected from the first search strategy were transferred as bibliographical data to the VOSviewer software.
These bibliographical data were used to develop a network of keywords by co-occurrence links. The whole network of keywords and their links is mapped out in Figure  2. In this network, the circles are a representation of keywords and the diameter of circles represents for the frequency of occurrence of each keyword. The distance between two keywords indicates their relatedness in terms of co-occurrence links. These co-occurrence links were determined based on the number of documents in which keywords occur together. So, the closer two keywords are located to each other, the stronger their relatedness. In the network, a collection of keywords having a strong relationship creates clusters, each presented with different colors. Using all the keywords in one cluster for the search equation in the Scopus database, a list of papers was found that represented the research trend of each cluster.
The seven main clusters of networks identified by VOSviewer are described in Table  2 , which includes the color, components of clusters, some example publications, and current research mentioned in each cluster. In the network, a collection of keywords having a strong relationship creates clusters, each presented with different colors. Using all the keywords in one cluster for the search equation in the Scopus database, a list of papers was found that represented the research trend of each cluster. The seven main clusters of networks identified by VOSviewer are described in Table  2 , which includes the color, components of clusters, some example publications, and current research mentioned in each cluster. After exploring the cluster network, it is interesting to continue to discover the distribution and evolution over time of keywords in the network.
Trend of Publications Figure  5  shows a notable increase in published studies regarding decision-making processes in Smart City projects over the past 10 years. The evolution of the 76 selected studies over the years is represented in column data. In order to compare this trend, the red line represents the total 213 articles collected from the Scopus database after the second filter of the selection process was applied and the yellow line represents normalized data. The normalization process starts from the actual number of publications per year that were published at https://dblp. unitrier. de/statistics/publicationsperyear. After computing the increase rate for each year, we normalized the data from the Scopus database. In general, the evolution begins with a handful of papers per year in the period of 2008 to 2011, then rises dramatically from 2012 and peaks in 2018.
Trend of Publications Figure  5  shows a notable increase in published studies regarding decision-making processes in Smart City projects over the past 10 years. The evolution of the 76 selected studies over the years is represented in column data. In order to compare this trend, the red line represents the total 213 articles collected from the Scopus database after the second filter of the selection process was applied and the yellow line represents normalized data. The normalization process starts from the actual number of publications per year that were published at https://dblp. uni-trier. de/statistics/publicationsperyear. After computing the increase rate for each year, we normalized the data from the Scopus database. In general, the evolution begins with a handful of papers per year in the period of 2008 to 2011, then rises dramatically from 2012 and peaks in 2018.
Conclusions In many studies, urban and smart city projects have been considered within complex contexts and over long time scales that revealed multi-stakeholders and organizations with conflicting interests. Therefore, smart city planning and decision-making are not only data-driven multi-level scaling practices, but are also a collective learning procedure supported by advanced ICT-based technologies and visualizations of available data, constant processes, and local history and stories  [57]. In order to increase the efficiency of discussions and negotiations, there is a need for situational-awareness tools and approaches enabling collaborative decision-making processes that deal with the complex nature of smart city projects. In this study, a systematic literature review methodology with four filters was used to narrow the number of potential articles collected from the Scopus database.
Starting from a huge database of 606 papers, collaborative decision-making methods in smart city projects was undertaken through the qualitative analysis of 76 selected research documents. This literature study contributes a general mapping of different decision-making methods applied at the levels of decision-making and the phases of implementation. The main contributions of this study are the following: -Even though smart government with citizen participation is an important characteristic of a smart city, few articles focus on this issue. -MCDM is a popular method at every level of decision-making and throughout all stages of smart city projects. Meanwhile, MP and AI are widely used for making operational and tactical decisions. -Regarding the implementation process, several papers cover all phases, from concept to modelling and assessing decision-making tools.
In order to use word embedding with pre-trained vectors on part of Google News dataset  34  , we translated children's productions from French into English. Word-embedding is a method of language modeling and feature learning techniques in natural language processing (NLP), where words or phrases from the vocabulary are mapped to vectors of real numbers. This method involves a mathematical embedding from a space with one dimension per word to a continuous vector space and has been demonstrated to be a relevant method to measure semantic distance between words  35. We performed the analysis using the Python implementation of word2vec in the Gensim package  36. This method gives a vectorial representation of words in 300 dimensions, and the cosine of two vectors provides a measure of the semantic similarity of two words, ranging from 0 (no similarity) to 1 (identity, see Mikolov et al.
We tested significance by means of bootstrapping (N = 100) with a cluster-based significance level of 0. 05. We performed statistical analysis using R (R Core Team, 2014), with the lme4 package for mixed models  37  , the lmerTest package to perform likelihood ratio tests  38  and the lqmm package for quantile regressions  39. Raw data, translation of the lists of words and analyses scripts are available on the Open Science Framework at https://osf. io/2n4q9/.
For illustrative purposes only, we here represent the semantic space by means of the t-distributed stochastic neighbor embedding (t-SNE, tsne package in R  56  ) algorithm for dimensionality reduction, in order to obtain a two dimensions map of the 300 dimensions vectors from the word2vec google. news model  34. Insets contain the histograms for the distributions of distances. distances above said quantile for each child, with group (high or low ADHD score) as a predictor. By means of bootstrapping (N = 100), with a cluster-based significance level of 0. 05, we found that the two groups differed from the quantile 0. 56 to the quantile 0. 99 (see Fig. 2A ), with participants high on the ADHD-rs having, again, an excess of long distances compared to participants low on the scale. Thus, children with higher ratings on the ADHD scale "travelled" on average significantly longer distances between two bells, notably because they inserted more "long jumps" in their search paths, but without incurring significant costs to their performance.
The purity of regional synaptic preparations was verified with quantitative enrichment analyses using the raw proteomic data (Figures  1C  and S1 ). The normalized average abundance of the well-established synaptic markers synaptic vesicle glycoprotein 2A (SV2A) and synapsin-1 was calculated for each region at the young time point and compared to isolated cortical mitochondria of the same age. To ensure parity between the synaptic samples and the isolated mitochondria, all preparations were loaded onto the mass spectrometer during the same experiment. SV2A and synapsin-1 levels indicated significant enrichment in all respective NHP and human samples versus isolated cortical mitochondria, suggesting purification of synaptic compartments (Figure  1C ). In addition to demonstrating purification of synaptic fractions, we employed quantitative fluorescent western blotting (QFWB) to determine the veracity of the proteomic data.
These filtered proteins were used for all analyses. (C) Purity of regional synaptic preparations. Purity of regional synaptic isolates was verified with quantitative enrichment analyses using the raw regional proteomic data and isolated cortical mitochondria. Comparative expression of the synaptic markers SV2A and synaptotagmin indicate synaptic enrichment of all regional preparations. (D and E) Validation of regional temporal proteomic data with quantitative fluorescent western blotting in NHP (D) and human patient (E) samples. (i) Actin loading control for pooled hippocampal and occipital NHP synaptosomes. Samples were pooled according to age group. Bar charts demonstrate there is no significant difference in total protein between ages or regions. (ii-iv) Left bar chart displays the proteomic average normalized expression values of proteins in regional synapses during aging.
regions aged in a similar manner in both NHPs and human patients. Using the objective network visualization software BioLayout Express 3D  (Theocharidis et al. , 2009) , we generated brain regional Pearson correlation graphs, in addition to principal-component analyses (PCAs) and heatmaps, demonstrating relative age-dependent similarities (Figure  S1 ). Examination of the Pearson correlation networks and PCAs confirmed distinct region-dependent clustering profiles. Resistant NHP synaptic populations (occipital cortex) exhibited single networks, suggesting congruence among young, mid-age, and old samples (Figure  S1 ); however, the aged synaptic population appears to demonstrate reduced equivalence with the mid-age synapses and enhanced similitude with the young samples (Fig-  ures S1  and S2 ).