dataesr/scientific-paragraphs-categorization huggingface dataset:
Significance of the chi-square statistic was assessed either with the chi-square curve ("conventional chi-square") or using a null distribution as described in  Foster (2004). Note that four loci generally have low probability of homogeneity throughout. n missing, mitogenomes in the matrix not sequenced for a locus; no rogue, analysis conducted with rogue taxa omitted; protein, analysis based on amino acid sequence. -The likelihood of the data under each partitioning scheme was assessed on the fixed topology of a randomized parsimony tree under a GTR + G model, with the number of partitions, free parameters, and ln(L) scores used in the calculations given. ÁAIC refers to the decrease in likelihood relative to the most complex model (partitioning by gene and codon). Values for 2 Â ln ÁBF 10 > 10 are usually considered to be highly significant.
Compositional heterogeneity was investigated for a reduced data set that had these 33 taxa excluded. The results were very similar to those obtained with the full matrix, with heterogeneity in 2nd positions and in the two-state model of RY-recoded 1st position sites limited to nad2, nad4, nad5, and nad6 partitions (table 2). Rogue taxa instead seemed to be affected by slightly lower data completion, specifically the sequences for the short amplicon coding for nad2 and cox1-5', which was missing from 22 or 23 respectively of the 33 rogue taxa. Yet, the average completion of the data set for rogue taxa was similar to the complete data set (12. 24 vs. 12. 40 protein-coding loci per taxon; supplementary table S4, Supplementary Material online), and >120 other taxa in the matrix were also lacking the short fragment (supplementary table S2, Supplementary Material online).
We then compared the topology from the nhPhyML model, which adds a separate parameter for the nucleotide composition for each branch. The nhPhyML tree (supplementary fig. S1 , Supplementary Material online) was greatly improved, including the monophyly of the suborders and all infraorders. However, in the Cucujiformia only the (reciprocal) monophyly of Tenebrionoidea and Lymexyloidea was recovered, whereas paraphyly remained surrounding Chrysomeloidea, Curculionoidea, and Cucujoidea. The RAxML software was used to assess nonhomogeneity across the data (not across the tree, as in nhPhyML) implementing independent GTR models for different partitions of the matrix (although without allowing among-partition rate variation that is not implemented in this software). A tree from the unpartitioned data had many of the same undesirable features as the PhyML tree, including the nonmonophyly of Adephaga and Polyphaga, although with a better outcome overall including the recovery of three of five infraorders.
Although most of the correctly recovered higher groupings were robust to the specific data treatment, there was a general decrease in power with the removal of data, and none of these analyses performed better than the partitioned analysis of all nucleotides. Notably, the removal of the rate-heterogeneous genes (nad2, nad4, nad5, nad6) resulted in the loss of monophyly of both small suborders, Myxophaga and Archostemata (supplementary fig. S2 , Supplementary Material online, for a tree from a matrix RY recoded for 1st positions and 3rd positions removed). Equally, the amino acid coding resulted in the failure to recover several key groups, including the suborder Polyphaga that was paraphyletic due to the misplaced Tetraphalerus and Priacma (Archostemata). Hence, the  -RAxML trees were obtained with the RY-coded 1st positions and 3rd positions removed, or on all data (including the rRNA genes).
Finally, the Bayesian analysis was run again after removal of rogue taxa, which produced a tree very similar to that based on the complete data set, with the main improvement simply due to the absence of the inconsistently placed rogue taxa themselves. For example, only after removing several rogue taxa, in particular the divergent sequence for Sphindus (Sphindidae), the Nitidulid and Cucujid series of Cucujoidea each resolved as monophyletic and combined they were the sister group to Curculionoidea + Chrysomeloidea (supplementary table  S4 , Supplementary Material online).
The Branch Length across Superfamilies Root-to-tip branch lengths were investigated on the RY-coded ML tree (supplementary fig. S2 , Supplementary Material online) and the Bayesian amino acid tree (supplementary fig. S3 , Supplementary Material online) for each suborder and polyphagan superfamily (fig. 2  and supplementary fig. S4 , Supplementary Material online). Variation among these groups was very similar for each data set. The Adephaga and Myxophaga showed substantially shorter branches than the two other suborders Archostemata and Polyphaga. Shorter branches were found in several polyphagan superfamilies, compared with Bostrichiformia and all superfamilies of Cucujiformia, which are sister groups in most analyses and occupy a derived position in the tree. Within some superfamilies branch lengths were highly variable, for example, the two sequences of Passalidae with extremely long branches, which were responsible for shifting the average branch length in Scarabaeoidea beyond the rate of other staphyliniform lineages.
2010). These findings suggest that compositional heterogeneity is increased between distantly related taxa and therefore greater sampling density, as available in the Polyphaga, ameliorates the problem, although residual heterogeneity remains even in very densely sampled mitogenome trees, for example, in a tree of ~100 taxa in the family Curculionoidea  (Gillett et al. 2014). In this study, rather than using pairwise comparisons, heterogeneity was assessed for the matrix as a whole, but only after the data were partitioned by gene. This analysis showed that compositional heterogeneity is concentrated in four genes, all of them NADH dehydrogenases. Two of these (nad4 and nad5) are on the reverse strand, while nad6, but not nad2, is in proximity to these genes, encoded by the forward strand.
2010; Song et al. 2010; Timmermans et al. 2010) , which was also supported by the RAxML and PhyML analyses conducted here, and which could easily be explained by the convergent low evolutionary rates in both suborders (fig. 2  and supplementary fig. S4 , Supplementary Material online). Previous studies combining mitochondrial data with nuclear rRNA genes generally support a yet different topology of Polyphaga + Adephaga  (Caterino et al. 2002; Hunt et al. 2007; Bocak et al. 2014). If indeed Polyphaga is the sister to the other suborders, this would reduce the imbalance of species diversity at the basal node of the tree, given that in previous work Archostemata and Myxophaga with less than 100 species each were thought to be the sister of all other Coleoptera and the Polyphaga, respectively.
Conclusion The possibilities for rapid sequencing of mitochondrial genomes have brought a new perspective to the phylogenetics of Coleoptera. Although compositional heterogeneity is pervasive in these data sets, the study joins others  (Talavera and Vila 2011; Li et al. 2015)  in suggesting the power of the CAT model that produced highly satisfactory trees. Partitioned likelihood models with the RAxML software were not much worse, but missed a few critical relationships apparently affected by different rates of molecular change. The problem of compositional heterogeneity has been considered to be a major driver of long-branch attraction, and is frequently thought to be reduced by RY coding and removal of 3rd codon positions, or by using the translated protein sequence. Here we show that these strategies cannot remove compositional heterogeneity completely, and that heterogeneity is not uniformly distributed among the various mitochondrial genes.
Acknowledgments This work was supported by a grant from the  Leverhulme Trust  to A. P. V. , C. B. , D. A. , and L. B. (grant  F/00969/H ). M. T. J. N. was supported by an  NERC   Postdoctoral Fellowship  ( NE/I021578/1 ). We thank  Junying Lim  and  Benjamin Linard  for assistance with the editing and annotation of the mitochondrial sequences. We are obliged to  M. Balke ,  M. Barclay ,  M. Bednarik ,  S. Fabrizi ,  J. Gomez-Zurita ,  P. Hammond ,  R. A. B. Leschen , and  C. Murria  for donating specimens, and to two anonymous reviewers for valuable comments.
Surface integrity is extremely affected by manufacturing processes conditions especially the evolution of cutting tool performance. Many researchers, in steel machining field, focused their works on the elaboration of models predicting tool wear combining both numerical and experimental approaches. Most of published numerical cutting models simulate the chip formation for a few micro to millisecond time periods. For that, all corresponding experimental tests, needed for the model validation, are usually carried-out in special time conditions which is not reflecting industrial situations. Indeed, the latters are characterized by tool wear whose evolution can affect extremely cutting force levels and the final workpiece integrity. In this context, the main purpose of the proposed research work is to develop a FEM model, based on the commercial code ABAQUS©, to simulate wear phenomenon of tool insert in the case of orthogonal cutting operation.
First of all, a Finite Element (FE) multi-part model will be presented based on the commercial code ABAQUS© following a lagrangian approach. It simulates chip formation in the case of AISI 4140 machining with a carbide tool. Then, a strategy that aims to reach high machining time simulated with moderate computational time is presented. It is based on ensuring the thermal continuity of the tool from one calculation to another. Results will be compared with experimental data found in literature.
Methods and results To facilitate VUS analysis, we have developed a visualisation resource that compiles and displays functional data on all documented BRCA1 missense variants. BRCA1 Circos is a web-based visualisation tool based on the freely available Circos software package. The BRCA1 Circos web tool (http:// research. nhgri. nih. gov/bic/circos/) aggregates data from all published BRCA1 missense variants for functional studies, harmonises their results and presents various functionalities to search and interpret individual-level functional information for each BRCA1 missense variant. Conclusions This research visualisation tool will serve as a quick one-stop publically available reference for all the BRCA1 missense variants that have been functionally assessed. It will facilitate meta-analysis of functional data and improve assessment of pathogenicity of VUS.
Besides the visualisation resource, we have developed a single comprehensive database that incorporates all the present information on BRCA1 missense variants. We used PERL-based Circos modules to generate the BRCA1 Circos images and developed a webpage for the Circos to be used as an interactive tool along with a searchable database of all the variants (the webpage can be viewed at http://research. nhgri. nih. gov/bic/circos/).
Literature curation We annotated all the missense variants that had been reported in (a) the literature or (b) the National Heart, Lung, and Blood Institute (NHLBI) Exome Sequencing Project (ESP6500SI-V2 release; http://evs. gs. washington. edu/EVS/) or (c) the BIC database totalling 698 variants. We performed an extensive literature search for all functional assays. If the variants were present in the BIC, we have indicated the frequency of the variant in Circos. In addition to the traditional search tools and databases such as the National Center for Biotechnology Information's PubMed and Google Scholars, we also used publically available databases such as Align-GVGD (http://brca. iarc. fr/PRIORS/ BRCA1/index. php) and the BRCA1 variant literature database (http://chromium. liacs. nl/LOVD2/cancer/home.
Circos plot Circos (http://circos. ca) is a freely available web resource primarily used to represent genomic data. 21 Circos provides PERL-based modules that can be used as the basis to develop circular visualisation of data. We used these modules to install Circos and wrote our own script to represent the data present in our BRCA1 missense variant database. In Circos, each concentric ring is called a 'track'. Figure  1  shows all the exons of the BRCA1 gene in the outermost track (track 1). Tracks 2 and 3 represent the frequency of the variant in the BIC database and the IARC class, respectively. Tracks 4 and 5 depict results from the VarCall computational model, with track 4 representing the likelihood of being pathogenic and track 5 showing the associated Bayes' factor. Track 6 indicates whether the variant is present in the NHLBI Exome Sequencing Project.
23 A brief description for each track can be seen in table  1  and detailed descriptions and the key to interpreting them can be found in the online supplementary material 1 (http://research. nhgri. nih. gov/bic/circos/trackdescriptions/). Missense variants are shown in tracks 7-9 (reference amino acid: track 7, codon: track 8, variant amino acid: track 9). The remaining tracks show the functional impact on the variant as assessed by different functional assays (tracks 11-53). The functional assessment by each assay/prediction algorithm is colour coded as 'red' (functional impact), 'blue' (no functional impact), 'yellow' (mild/intermediate functional impact) or 'white' (no data available). The script generates an image file that was used as the basis for the webpage. We generated two Circos figures: one with all variants in all coding exons (figure  2 ) and one removing data from exon 11 of BRCA1 (figure  1 ).
Interpreting BRCA1 Circos The BRCA1 Circos plot should be read radially with track 1 being the outermost and track 53 being the innermost track. Each track is annotated by a track number and the corresponding data source (table  1 ). The data in each track are colour coded, and mouse-over on any data point on the figure on the website will allow the track number, track name and variant information to be displayed. As an example, we look at two variants: M1775K and M1775R (figure  3 ). The first track shows the exon; the variants lie in exon 21. Track 2 shows the BIC entries for each variant as a histogram (numerical values can be found in the functional database or can be seen using the mouse-over function on the website figure). In this case, there are no entries for M1775K but 31 entries in BIC for M1775R.
Functional overlap One of the goals of the BRCA1 Circos is to help in the meta-analysis of results from individual variants. For example, a variant 'A' that was tested multiple times and scored as of 'no functional impact' in every assay is considered to have stronger evidence against functional impact than a variant 'B' with only one assay reported in which it scored as no functional impact. But if all the assays performed for 'A' are functionally redundant (they assay the exact same function) and were performed in similar conditions, then the evidence against functional impact for 'A' is not stronger than the evidence for 'B'. Data in tracks 11-53 represent results from several functional assays. Some tracks will represent data from the exact same assay reported in two or more separate papers.
Website development Using the Circos program, we generated html image maps that represent the co-ordinates of every data point on the BRCA1 Circos image. The BRCA1 Circos website (http://research. nhgri. nih. gov/bic/circos/) also displays a searchable database with information on the functional assays and functional prediction algorithms for BRCA1 variants. The functionalities of the website are ▸ mouse-over 'magnifying glass' to zoom into any region of the Circos plot to visualise the data in that region; ▸ mouse-over on every data point on the tracks shows a label with 'track number, variant (amino acid change), track name and base change'; ▸ track descriptions and key to interpret the functional data on every track; ▸ all tracks hyperlinked to the original article where the data were extracted from; ▸ double-clicking any part of the figure zooms into that region and allows the user to pan through the image; ▸ every data point in the Circos figure is hyperlinked to the information in the BRCA1 Missense variants functional database (http://research.
nhgri. nih. gov/bic/circos/allvariants/). All codes to develop the BRCA1 Circos and raw files are available on request. In the BRCA1 Circos, each track represents one set of functional assays or prediction algorithms that indicate the functional classification of a specific BRCA1 missense variant. Two papers reporting the same assay would be displayed in two separate tracks. Two separate assays reported in the same paper would also be displayed as two separate tracks. The current version of BRCA1 Circos depicts a specific functional assay per track. As more assays are published, the BRCA1 Circos can be updated by increasing the radius of a Circos plot which allows for more tracks to be incorporated; however, this may become unwieldy as the tool needs to fit in a standard computer screen. There are several options to solve to address this problem when it arises: (a) adding multiple BRCA1 Circos plots is a simple but the least preferable solution because we would lose the comprehensive view in one image; (b) taking advantage of our interactive interface (zooming in and mouse-over functions) and hide or show tracks (assays/studies) according to the user's choice and, finally, (c) tracks that have few variants (eg, <5) would be removed from the plot and the data would be available on the database but would not be depicted in the BRCA1 Circos.
Nevertheless, results from functional assay show a strong correlation with pathogenicity and provide an important source of information to help in the classification of VUS. 26  Recently, a computational model, called VarCall, was developed to convert results of functional assays into likelihood ratios of pathogenicity, allowing for functional data to be incorporated into multifactorial models of variant classification. 20 Thus, we believe that using functional information will improve and accelerate the classification of variants for BRCA1. To facilitate the process of analysing functional data, we developed a database and a visualisation tool that can display all the available functional data for individual BRCA1 variants. This tool allows for the direct comparison of results from different functional tests and provides a global picture of the functional coverage in BRCA1.
It also provides a resource in which non-specialists can navigate around different functional assays and weigh information from several sources. There have been multiple efforts to facilitate systematic integration of all the data available on these variants such as the BIC (http://research. nhgri. nih. gov/bic/) and the LOVD BRCA1/2 literature (http:// chromium. liacs. nl/LOVD2/cancer/home. php) database. However, there is no comprehensive resource to help interpret and visualise data available on these variants in the current literature along with the information from multiple databases. We have developed a novel web-based resource for the interpretation and visualisation of BRCA1 missense variants. BRCA1 Circos is the first interactive tool that combines data for all documented BRCA1 missense variants with an easy-to-interpret visualisation structure.
It combines information from multiple functional assays and databases in one comprehensive layout. Besides the detailed information on every  variant, this tool is also useful to obtain a global view at the current data available for missense variants of BRCA1 to guide future efforts on VUS characterisation. The data in the BRCA1 missense variant database and, consequently, BRCA1 Circos use controlled vocabulary as discussed by Lindor et al  16  classifying the variants as having a 'functional impact', 'no functional impact' or 'mild/intermediate functional impact'. This makes it easier to consolidate data from multiple studies regardless of whether the results were quantitative or qualitative. These data have been colour coded and represented in an easy-to-use manner with many different features such as zooming into a particular exon, variant and obtaining information on multiple variants at a glance.
We also provide the original articles for every track in the Circos and it is advisable to read the original publications to obtain any experimental data. Several limitations apply to the data in the BRCA1 Circos. In particular, because most assays described here have not been validated extensively nor were conducted with specific standard operating procedures in a clinical environment, results should be taken with caution. In addition, individual-level data could suffer from clerical errors, swapped samples, mistakes in constructs and other problems that are inherent to research. 13 As the gold standard for VUS classification  [14] [15] [16]  requires that more than one independent source of data to classify a variant, functional data represented in the BRCA1 Circos should not, at the present stage, be considered as a single source for the purposes of clinical decisions.
It is expected that as assays are validated and reported as likelihood ratios (eg, as in VarCall), the functional data will be incorporated in the multifactorial model. For rare variants for which genetic data may not be available, it is possible that properly validated assays could be used as a single source for clinical decisions but this approach needs to be further assessed in future studies. In conclusion, we have developed a web-based tool to visualise complex data on BRCA1 variants using an open-source resource -Circos. 21 This study can be used as a platform for the development of a comprehensive functional database for other cancer susceptibility genes such as BRCA2 and can be used by researchers and genetic counsellors to better interpret the function of VUS.
In this paper, we present MOSAIIC, an agent-based model to simulate the road traffic of a city in the context of a catastrophic event. Whether natural (cyclone, earthquake, flood) or human (industrial accident) in origin, catastrophic situations modify both infrastructures (buildings, road networks) and human behaviors, which can have a huge impact on human safety. Because the heterogeneities of human behaviors, of landuses and of network topology have a great impact on the traffic flows, the agent-based modeling is particularly adapted to this subject. In this paper, we focus on the new traffic model itself: the way geographical data is used to build a network, the various behaviors of our agents, from the individual to the collective level.
But in a ¿½ disaster individual decisions can lead to important collective consequences. Two ¿¾ drivers can leave their car, thus blocking hundred behind them. Drivers may react ¿¿ to things they see, fleeing by taking one-way streets in reverse, creating a jam in ¿ the road leading to this street. Individual-based modeling and micro-simulation are ¿ the only way to incorporate those possibilities, not just origin destination matrices ¿ and shortest path algorithms. For modelers without high level programming skills, ¿ adapting these platforms to specific application contexts is out of reach as they ¿ require to write code in JAVA or C++. As a result, many simulators are still ¿ developed from scratch or with a generic platform (e. g. [8] [9] [10] ).