dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Extraction The SPM model deployment was conducted in Ann Arbor, Michigan. The field test includes 75 miles of instrumented roadway. Our dataset sample covered the amount of five days traffic activity that occurred over 6 h, during the period 6:00 a. m. to 12:00 p. m. (including congested and smooth traffic regimes); across a distance of 11. 2 miles, as shown in Figure  14. Vehicles that participated in this study include light/passenger vehicles, heavy/commercial trucks, and busses. Based on DE_VehicleType data elements, vehicles were classified in three main categories: Passenger car, Bus, and Trailer. Traffic volume can be determined by counting the number of vehicles that cross through a point on a road segment at a specific time and denoted by vehicles per hour (v/h). We manually calculated the TV using the Position data frame by counting the number of vehicles traversing the road segment illustrated in Figure  14.
Model Development We performed the traffic volume prediction using the Multilayer Perceptron (MLP) artificial neural network which is a feedforward ANN. This kind of ANN relies on backpropagation for the training stage. It has multiple input layers connected as a directed graph with output layers. Analyzed dataset contains the following features shown in Table  13:   13  and is observable in Figure  15. Generally measured in units of vehicles per mile (v/m), the traffic density is referred to the volume of vehicles on a road fragment. As a preprocessing step, we first randomized our dataset and then divided it into three sub-sets. The first sub-set represented 10% of the whole dataset and it was used for training. The second sub-set was taken for cross-validation in the ratio of 10%. The remaining part that represent 80% of the dataset was used for testing purposes.
Distinct architectures of ANN have been designed to determine an efficient network. Table  14 , presents several ANN models that were prepared to train on the dataset. The testing data sets along with cross-validation were used to identify the performance of every ANN architecture. As well as, to evaluate the predicted results, other parameters were exploited, such as Normalized Mean Square Error (NMSE), the coefficient of correlation (r), and Mean Absolute Error (MAE). The desired neural network is that of three hidden neurons, according to Table  14. Therefore, Figure  16  shows, in detail, the ANN structure used in our work.
Results and Discussion The linear correlation between the predicted and real volume values that are illustrated in Figure  17 , as well as the output summary of the qualified ANN test stage presented in Table  15 , confirm that our developed model gives accurate results. The scatterplot that is shown in Figure  17  determines the solid relationship between predicted traffic volume (Y) and real traffic volume (X) confirmed by the correlation coefficient value very close to 1. This can be explained by the fact that the predicted traffic volume values are highly fitted to the real traffic volume values. Bearing in mind that our primary goal one hand and considering the good value of the coefficient of correlation and very small errors, on the other hand, it can be argued that our developed ANN has successfully reused stored BSM as past data during training, cross-validation, and testing stage to accurately predict future traffic volume despite for a short time.
Thus, stored BSM can serve as a rich dataset for machine learning in the transportation field. Echart is an open-sourced, web-based, cross-platform framework that has a powerful function, friendly interface, excellent performance to enhance data visualization. This excellent development tool for front-end developers can help to present huge amounts of data to users in a very appropriate way, and users can analyze valuable information through charts. Using Echart, we can visualize a bunch of information based on data elements and data frames or according to time and geofencing technique. BSMs are stored according to data elements and Data frames data, as shown in Figure  7. An overview of different elements visualization is shown in Figures  18a,b  and 19a,b ). This element visualization provides a ready means to tell stories from the atomic data as well as provides us with analysis at various levels of detail.
Conclusions Given our primary goal of not constraining BSM use contextually and geographically, in this paper, we introduced a new philosophy that aims at conserving collected BSMs and adopting the In-Vehicle Computing paradigm in order to create a reliable and useful transport data source. We then proposed our new BSM reuse model based on three-stage process. In its first stage, our proposed model captures generated and acquired BSMs. Then, in the second stage, it would perform a series of procedures on the raw BSMs to be storable according to the proposed model. In the third stage, our model aims at opening up new possibilities for the endless reuse of stored BSMs. Later in our study case, we built an embedded data platform accrediting the Model-View-Controller design commonly used for developing user interfaces.
This new platform has accomplished several purposes of data reduction, delivery, and visualization. We were able to perform lossless data compression and considerably reduce the data size; the thing that has a positive impact on bandwidth and storage requirements that have been reduced by about 80%. We have also achieved different data delivery according to the Pull and Push mechanisms to cater to the different data consumer use case requirements. Adopting the ANN paradigm, we obtained an accuracy of 0. 9988 in carrying out traffic volume prediction. We attained the visualization of some data elements to enhance analytics and support decisions-making for transportation. Our work bears certain limitations that should be recognized. First, the proposed model was partially developed using a pre-collected BSMs, forthcoming works should focus on the data capture stage.
Given that the 'genetic variance' in equation (  1 ) is positive, the mutation will increase in frequency as long as its 'selection coeffi cient' is positive; that is, if it transmits better, leads to fewer asymptomatic cases, remains infectious longer, or some combination of the three. We next use these results to examine the nature of selection acting on the life history traits of SARS-CoV-2. We start by considering selection acting on each life history trait on its own and then explore how mutations that affect multiple traits will evolve. Throughout, we use the full model, with selection described by equation (S6) in the Supplemental Information, using the special case represented by equation (  1 ) only to help explain the results. The accompanying Mathematica package provides all code and explores a range of parameters to confi rm the robustness of the illustrated results (available from DRYAD: https://doi.
We recommend that metadata be sought when viral samples are collected that provide information about disease outcomes (time since symptom onset, severity of symptoms at time of sampling) and, where available, with data on hospitalization rates, death rates, and transmission (if contact tracing is conducted). Relating genomic variants with patient health and epidemiological data is the most direct way to establish whether mutations are functionally important or not (as recently investigated by  [37]  using hospitalization data). These data are also needed to apply many of the new phylogenomic methods, as argued by  [1]. Finally, if functionally important mutations are verified, how might this then inform intervention strategies? Although the answer to this question will depend on the details, it can be helpful to consider a few possibilities to reinforce the value of tracking evolution during a pandemic.
ACKNOWLEDGEMENTS We are grateful to four anonymous reviewers,  Ailene MacPherson ,  Leithen M'Gonigle ,  Arne Mooers ,  Bernie Crespi ,  Richard Neher , and  Michael Whitlock  for helpful comments on previous drafts that helped refi ne this work. Funding was provided by the  Natural Sciences and Engineering Research Council of Canada  to T. D. ( RGPIN-2013-217636 ) and to S. P. O. ( RGPIN-2016-03711 ) and by the  Agence Nationale de la Recherche  to  S. G. ( ANR-17-CE35-0012 ) and S. L. ( ANR-16-CE35-0012-01 ).
The mutational patterns of indels was dominated by single nucleotide deletions of C:G and T:A bases in homopolymer stretches and dinucleotide deletions in repeats (Supplementary Fig. 1b ). The dinucleotide substitutions were not overrepresented by specific classes and demonstrated a broad range of contexts (Supplementary Fig. 1c ). To better understand mutational processes operating in XP-C cancers we extracted mutational signatures from XP-C and sporadic tissue-matched tumors with the non-negative matrix factorization approach  (Alexandrov et al. , 2013a)  (NMF). Seven signatures were extracted from this dataset (Supplementary Figs. 2a, b ) and one of them, Signature "C" explained on average 83. 1% of mutations in the XP-C samples (57% in breast sarcoma, 88. 9% in rhabdomyosarcoma and 84.
5b ). Future studies on the identification of nature of this mutational process and its link with particular genotoxins (for ex. free radicals, aldehydes, food mutagens) producing bulky lesions may result in elaboration of preventive measures for XP patients. Except the breast sarcoma sample from Comorian Archipelago with IVS12 mutation, our dataset mainly represents a XP-C population of the Northern african origin and single XPC mutation (delTG) urging the importance of expanding the investigation of internal tumorigenesis and underlaying mutagenesis in different XP populations. c, TRB strength depends on the level of gene expression and is most pronounced in highly expressed genes (SEMs are indicated for leukemia; Poisson, two-sided test used for breast sarcoma (n=1) and rhabdomyosarcoma (n=1); Wilcoxon signedrank, two-sided test for leukemia (n=6), P : ns -nonsignificant, * < 0.
Genome sequencing and data processing The genomes were sequenced using BGISEQ-500 or Illumina Hiseq 2500 (SA008T6) sequencers according to the manufacturer protocols to the mean coverage after deduplication equal to 45x for tumor and 30x for normal DNA (Supplementary Table  1 ) using 100bp paired-end reads. Reads were mapped using BWA-MEM (v0. 7. 12) software  (Li and Durbin, 2009)  to the GRCh37 human reference genome and then used the standard GATK best practice pipeline  (Van der Auwera et al. , 2002)  to process the samples and call somatic genetic variants.
Mutational signatures analysis To convert the VCF files into a catalog of mutational matrixes we used the MutationalPatterns software v. 1. 11. 0  (Blokzijl et al. , 2018). Profiling of the mutational matrixes of indels and double nucleotides substitutions was performed with SigProfilerMatrixGenerator v. 1. 0 software  (Bergstrom et al. , 2019). For comparison with XP-C tumors we used 190 tissue-matched whole cancer genomes from the ICGC PCAWG collection  (Yung et al. , 2017)  which included cancers from the following projects: Chronic Myeloid Disorders -UK (n=57), Acute Myeloid Leukaemia -KR (n=8), Breast Cancer TCGA US (n=91), Sarcoma -TCGA US (n=34). We used only high-quality variants and additionally filtered out mutations in low-mappability and blacklisted regions of the human genome.
To construct the multidimension scaling plot (MDS) we computed pairwise Cosine similarity distance between all pairs of the samples using MutationalPatterns package  (Blokzijl et al. , 2018)  and then processed the matrix of distances between the samples in prcomp () function in R. To perform Non-negative Matrix Factorization approach and extract de novo mutational signatures we used the XP-C samples along with tissue-matched dataset of PCAWG samples (n = 190) in NMF framework realized in MutatioanlPatterns R package  (Blokzijl et al. , 2018)  with 500 initialization runs. After examination of the diagnostic plots (Supplementary Fig. 2a ) we choose K=7 (with RSS at inflation point, according to  Hatchins et al. (Hutchins et al. , 2008) ) to extract mutational signatures  (Supplementary Figs.
2b ) and then assigned them to the known mutational signatures based on the Cosine similarity (Fig. 2c , Supplementary Fig. 2e ). Choosing of lower (K=4) or higher factorization rank (K=9) did not influence significantly the extracted Signature "C" and its proportion in samples (data not shown). To quantify the contribution of the NMF-derived mutational signatures (A-G) in XP-C tumors and tissue-matched PCAWG cancers we used the quadratic programming-based algorithm  (Huang et al. , 2018)  realized in SigsPack R package  (Schumann et al. , 2019)  (Fig. 2b ). To better understand and quantify the contribution of the NMF-derived mutational signatures in XP-C dataset we additionally used bootstrapping (n=10000) on substitution classes to receive the confidence intervals of each signature contribution (Supplementary Fig.
Transcriptional strand bias analysis Transcriptional strand bias (TRB) was quantified for each sample and six mutational classes using MutationalPatterns package  (Blokzijl et al. , 2018). The function computed inequality between mutations from pyrimidines (C>A,T,G; T>A,C,G) to mutations from purines (G>A,C,T; A>C,G,T) for genes located on the sense and antisense strands of DNA relative to the reference human genome. Inequality in number of mutations from purines and pyrimidines was considered as evidence of transcriptional bias and statistical significance was assessed using Poisson test. To compute tissue-specific TRB between genes expressed at low and high level we used RPKM values of RNA-seq from Epigenetic Roadmap Project  (Consortium et al. , 2015)  (E028 for breast sarcoma, E050 for leukemia, E100 for rhabdomyosarcoma).
TSSs of all annotated genes  (GENECODE v30 (Frankish et al. , 2019) ) were retrieved using BEDTools v2. 29. 0 (Quinlan, 2014) and then regions located ± 50kb of TSSs were split into 1kb intervals. The 1kb intervals which overlapped with other intergenic or genic intervals (represented mainly by overlapped or closely located genes) were removed. This approach rendered 237 Mbp of 5' proximal to TSS intergenic regions and 151 Mbp of genic regions.
Epigenetic marks and mutational density To infer relationship between mutation density and intensity of various epigenetic marks (methylation, H3K27ac, H3K27me3, H3K36me3, H3K4me1, H3K9me3) we downloaded bigwig files of the Roadmap Epigenomics Project  (Consortium et al. , 2015)  and converted them to wig and then bed files (tissue E050). The mean intensity of each mark was calculated for 1kb nonoverlapping windows across autosomes with BEDOPS v2. 4. 37 (bedmap) software  (Neph et al. , 2012). We used only genomic windows with high alignability (equal to 1) along at least 90% of a window. Mark intensities were normalized to 1-100 range. For each window we split mark intensities into 5 quantiles (cut2() function in R (R Development Core Team, 2011)) and calculated relative mutation density of each mark for intergenic regions, transcribed and untranscibed strands of genes.
Clustered mutations To evaluate the distribution of mutations across the genome for the presence of clustered mutations in our dataset, we performed Monte Carlo simulations for the intermutation distances distribution of random mutations for ranges between 2 and 10000bp for each studied sample. We developed a mathematical model of the Monte Carlo method for random mutations generation based on the following statements: 1) positions of mutations are random and uniformly distributed along the genome; 2) random positions are selected from the same set of genomic intervals as original somatic mutations; 3) the number and nucleotide context spectrum of randomly generated mutations exactly matches somatic mutations in the corresponding sample. As follows, our simulations are based on the discrete homogeneous Poisson point process.
The Monte Carlo simulations were performed using Java programming language, discrete random positions were generated with standard Java Random class (Supplementary code). Data analysis was carried out with MathWorks MATLAB. We randomly assigned mutations giving their trinucleotide (3bp) contexts and repeated the procedure 30000 times for each sample (Supplementary Fig. 5 ). To compute statistics for the distances between neighbors for randomly placed mutations within mapability sections for chromosomes and whole genome we used the following algorithm: We next verified that random mutations at small distances produced by random generations followed the Poisson distribution. Then, the means for simulated distributions were compared with the observed intermutation distances for XP-C leukemia samples (n=6) using Wilcoxon signed-rank, two-sided test in 5bp overlapping (1bp step) windows to define length of clusters (for 2-10000bp intervals).
Resulted P-values were corrected with Bonferroni approach. Significant enrichment of clustered mutations at short distances remained when simulations were performed without taking into account the context of mutations or in 5-bp context of mutations; or when only euploid parts of the genomes were taken into account (data not shown). 4 exomes of XP-C samples were independently sequenced on Illumina Hiseq 2500 with ~100X sequence coverage. Out of 6 clusters that overlapped exonic regions all 6 were validated. Additionally, we assessed the number of mutations located on the same read or different reads for clusters up to 16 bp located in diploid genomic regions. 1: input: G ► mappable.
Relative number of mutations before and after SCNAs To infer relative number of mutations which occurred before and after SCNA we followed to the previously described methodology (Jolly and Van Loo, 2018) and identified SCNA of two classes in our dataset: copy gain or cnLOH (Supplementary Table  2 ). In these SCNA regions taking into account tumor purity and ploidy of the regions we determined conservative variant allele frequency (VAF) thresholds to separate mutations on occurred before and after SCNA given their VAF. The number of mutations was then normalized per haploid copy of a genomic segment. Bradford, P. T. , Goldstein, A. M. , Tamura, D. , Khan, S. G. , Ueda, T. , Boyle, J. , Oh, K. S. , Imoto, K. , Inui, H. ,  Moriwaki, S. I. , et al. (2011)  Consortium, R. E. , Kundaje, A. , Meuleman, W.
ACKNOWLEDGEMENTS S. N. was supported by grant  Foundation ARC 2017 ,  Foundation Gustave Roussy and Swiss Cancer League   KFC-3985-08-2016. The authors would like to thank  Dr. Patricia Kannouche  and  Dr. V. B. Seplyarskiy  for fruitful discussions and participation, and  Dr. F. Rajabi ,  Dr. Catherine Genestie  and  Dr. Samuel Quentin  for DNA extraction and providing samples. The authors are also very thankful to  Dr. C. Genestie  ( IGR, Villejuif, France ),  Dr. Z. Tata  ( Algiers, Algeria ) and  Dr. S. Duquenne  and  Dr. F. Cartault  ( Saint-Pierre, La Réunion, France ) for giving us or for manipulating biopsies of tumors and  Xiaole Xu  ( BGI ) for perfect management of sequencing.
CODE AVAILABILITY All software used is published and/or in the public domain. Custom Java code for the clustered mutation analysis is available as the Supplementary code. T>C T>G sample_193 sample_191 sample_194 sample_198 sample_197 sample_196 sample_192 sample_195 sample_153 sample_161 sample_147 sample_156 sample_135 sample_26 sample_173 sample_134 sample_169 sample_181 sample_141 sample_175 sample_179 sample_140 sample_162 sample_187 sample_40 sample_137 sample_167 sample_174 sample_184 sample_152 sample_159 sample_129 sample_185 sample_132 sample_142 sample_182 sample_131 sample_149 sample_133 sample_178 sample_78 sample_87 sample_150 sample_144 sample_176 sample_148 sample_163 sample_180 sample_158 sample_130 sample_143 sample_164 sample_136 sample_154 sample_171 sample_139 sample_183 sample_157 sample_151 sample_155 sample_168 sample_177 sample_165 sample_172 sample_170 sample_166 sample_146 sample_160 sample_82 sample_1 sample_34 sample_108 sample_114 sample_70 sample_74 sample_85 sample_112 sample_58 sample_104 sample_145 sample_13 sample_48 sample_67 sample_32 sample_138 sample_31 sample_72 sample_51 sample_88 sample_53 sample_113 sample_27 sample_79 sample_75 sample_105 sample_28 sample_30 sample_46 sample_54 sample_116 sample_44 sample_84 sample_103 sample_95 sample_11 sample_107 sample_109 sample_36 sample_99 sample_50 sample_80 sample_3 sample_17 sample_10 sample_19 sample_60 sample_106 sample_126 sample_68 sample_189 sample_91 sample_110 sample_121 sample_102 sample_16 sample_14 sample_33 sample_29 sample_39 sample_59 sample_98 sample_5 sample_123 sample_92 sample_77 sample_127 sample_56 sample_190 sample_12 sample_97 sample_6 sample_52 sample_37 sample_61 sample_120 sample_73 sample_4 sample_49 sample_42 sample_86 sample_115 sample_65 sample_18 sample_35 sample_20 sample_117 sample_9 sample_2 sample_118 sample_7 sample_63 sample_101 sample_24 sample_8 sample_45 sample_57 sample_23 sample_64 sample_93 sample_128 sample_89 sample_47 sample_96 sample_15 sample_25 sample_90 sample_94 sample_43 sample_69 sample_186 sample_21 sample_76 sample_100 sample_22 sample_83 sample_119 sample_38 sample_111 sample_41 sample_55 sample_125 sample_62 sample_71 sample_122 sample_124 sample_188 sample_66.
The discrepancy between the data and the model results are probably due to saturation of the florescence reporter used. (7 th and 8 th bars) When PLC is activated, ENaC activity is reduced. DGK inhibition further reduces the channel activity. The data provided by Almaça et al. suggest a decrease after DGK inhibition, which however is not significant. *Data for WT and WT/DGK-were collected using transepithelial voltage measurements; otherwise, a voltage dependent FMP assay was used (cf. Almaça et al. 21 for further details). ENaC is composed of three subunits, which are usually called α, β and γ  37. Channels with alternate stoichiometries have been reported to have very low activity  38  , so that some ENaC channels could be considered constitutively closed. Also, not all ENaC channels are necessarily equal, due to variability introduced by alternative splicing, alternative folding, glycosylation and ubiquitination.
Methods The equations and parameters of the ENaC-ASL module used here are exactly the same as those presented in  29. The way in which the phosphoinositide model was designed is flexible enough to simulate membrane patches from different compartments if supporting data are available. For example, it is easy to block or greatly slow down a given reaction a priori if we know that it is not present in a particular compartment. Alternately, one would obtain very low enzyme activities for some reactions as a direct result of the parameter optimization to fit experimental observations. The expansion of the phosphoinositide pathway model introduced some new parameters that were not used in the earlier model  24, 29. For some of these new parameters it was possible to retrieve values from public databases (Table  S2 ).
The remaining parameters were adjusted to fit phosphoinositide steady-state values and other phenomena observed experimentally (described in  24  and Table  S1 ). The adjustment was performed gradually, first with manual adjustments, followed by Nelder-Mead optimization for each dataset separately, and finally using a hybrid genetic algorithm to identify the parameter set with the best fit for all calibration datasets [details in Supplements (Section 1)]. The extended phosphoinositide model was validated by comparing simulations with different datasets [Supplements (Secttion 1); Figures S3, S4, S5 and S6]. The combined model was validated using data from Almaça et al. 21 on the activity of ENaC with or without DGK inhibition (Fig. 4 ).
(7 th and 8 th bars) When PLC is activated, ENaC activity is reduced. DGK inhibition further reduces the channel activity. The data provided by Almaça et al. suggest a decrease after DGK inhibition, which however is not significant. *Data for WT and WT/DGK-were collected using transepithelial voltage measurements; otherwise, a voltage dependent FMP assay was used (cf. Almaça et al. 21  for further details).