dataesr/scientific-paragraphs-categorization huggingface dataset:
Preprocessing EEG data was analyzed as in previous studies using this approach (e. g. ,  Liu-Shuang et al. , 2014; Retter & Rossion, 2016; Zimmermann et al. , 2019) , with the open source software Letswave 5 (https://github. com/NOCIONS/Letswave5), running in MATLAB R2013a (MathWorks, USA). EEG data was first band-pass filtered between. 05 and 100 Hz with a 4th order zero-phase Butterworth filter and then down-sampled to 256 Hz for the ease of processing. The data sequence was then segmented relative to the starting trigger of each trial, with an additional 2 sec before and after each sequence (À2À76 sec). Eyeblink artifacts more than. 2 times/s on average  (Retter & Rossion, 2016)  were corrected by applying independent component analysis (ICA) on 8 participants (across two experiments). Individual channels with artifacts were interpolated by their three neighboring channels.
7 msec to all individual data was therefore applied to estimate the true stimulus onset time (Fig. 6 ). To determine the time-windows that showed significant response difference to the periodic presentation of familiar faces between the upright and inverted face conditions, we ran a cluster-based nonparametirc permutation t-test on the post-stimulus onset time-points (0 -1000 msec after stimulus onset) with the Fieldtrip toolbox (5000 permutations with the Monte Carlo method, minimum 3 neighborhood channels). The permutation t-test was run with a threshold of p <. 05 (two-tailed) for both the cluster statistic and the permutation test, using the percentile cut-off of the maximum of summed t-statistics within clusters  (Maris & Oostenveld, 2007; Yan et al. , 2019). Only participants who showed significant face inversion effect over the OT ROI were included in the permutation analysis.
Data and code availability The EEG data were analyzed using the open source Letswave software package (https://github. com/NOCIONS/Letswave5), running in MATLAB R2013a (MathWorks, USA). Data analysis steps are available at: https://osf. io/527gf/. The preprocessed time-domain data and behavioural data are available at: https://osf. io/527gf/. Stimuli: all face stimuli we used in the experiment were searched and downloaded online through Google Image. The name of the face identities used in the first version of the experiment is provided in the method part. Legal copyright restrictions prevent us from making the face stimuli publicly available or sharing them on request with any individual outside the author team. The experimental design was implemented using custom software written in Java.
Results Phenotypes distribution. Using the plugin BioVoxxel in ImageJ, we extracted quantitative measurements of the Area, Perimeter, Feret, Circularity, Solidity, Roundness, and the aspect ratio (AR) from root images collected in the field. The raw value datasets show similar ranges for root shape and size descriptors in GG and C1 populations (Supplementary Table  1 ). The individual root measurements with the maximum and minimum value of each trait in both populations are presented in Fig. 1. The frequency distribution of the mean value per plot of the GG and C1 populations is presented in Supplementary Fig. 2  and the mean values per trait within a population are presented in Supplementary Table  1. Some genotypes exhibited large differences in their mean values for Area, Perimeter and Feret.
For example, the maximum mean root Area in GG population was 339 cm 2 while the mean Area of the GG population was 121. 5 cm 2. Similarly, those genotypes exhibited a maximum mean root Perimeter of 135 cm and a maximum mean Feret of 50 cm while the mean Perimeter and Feret value in the GG population were 66 cm and 26 cm, respectively. In the C1 dataset, the maximum mean value for the root area in the C1 dataset was 372 cm 2, while the mean area of that population was 128 cm  2. The maximum values for Perimeter and Feret were 132 and 49 cm respectively, while the C1 population mean value for the two traits was 68 cm and 28 cm respectively. Correlation plots. Phenotypic correlations were calculated pairwise using de-regressed BLUPs of the mean values for each population separately (Fig. 2 ).
In the GG dataset, the highest correlation within yield traits corresponded to root number and root weight (r 2 =0. 79). Similarly, root number and root weight were highly correlated in C1 population (r 2 =0. 88). In both datasets, correlations between yield traits were significant and high (r 2 > 0. 5) and these traits were also positively correlated with Area, Perimeter and Feret. However, a low correlation (r 2 < 0. 1) was observed between yield traits and root shape descriptors such as Circularity, Roundness, Solidity and AR in both populations. Size-related traits derived from root images (Area, Perimeter and Feret) showed the highest positive correlation (r > 0. 7) with each other. In both datasets, the highest correlation between size-related traits corresponded to Perimeter and Feret (r=0.
97). Additionally, Feret and Perimeter were negatively correlated with shape-related traits (Circularity, Roundness and Solidity) and positively correlated with AR. In the GG dataset, Area showed a negative correlation with Circularity (r= -0. 26), Roundness (r= -0. 21), Solidity (r= -0. 19), and a positive correlation with AR (r= 0. 19). While in the C1 population, a low correlation was observed between Area and shape descriptors. Within the shape related traits, the highest correlation was found between Circularity and Roundness (GG r = 0. 89, C1 r = 0. 86) and Solidity (GG r = 0. 87, C1 r = 0. 84). AR showed a negative correlation with Circularity, Solidity and Roundness in both datasets.
20 were found for most traits except for shoot weight (0. 12). The top three heritability values corresponded to image extracted traits: AR (0. 48), roundness (0. 52) and circularity (0. 46). In addition, broad-sense heritability values (H 2 ) for root shape and yield-related traits were calculated for each population (Table  3 ). In the GG population, without adjusting the phenotypes for their CMD score, H 2 of root shape related traits ranged from 0. 17 (Perimeter and Circularity) to 0. 46 (aspect ratio) and for yield traits, H 2 ranged from 0. 29 root weight (RTWT) to 0. 44 shoot weight (SHTWT). In the GG dataset, Perimeter, Circularity and Solidity exhibited the lowest heritability values at 0. 17, 0. 17 and 0. 12, respectively. In the C1 population, the heritability of shape-related traits ranged from 0.
Genomic prediction. Using the parental (GG) and offspring generation (C1) datasets independently, we calculated the prediction accuracies of size and shape image traits and compared those to root yield traits accuracies using de-regressed BLUPs of 1) the mean phenotype value (average phenotype of 4 plants) (Fig. 5 , Supplementary Table  6 ), 2) the mean root size and shape phenotypes adjusted to account for the potential effect of cassava mosaic disease (CMD) on these traits (Fig. 6 , Supplementary Table  6 ) and 3) the standard deviation of the root shape and size measurements adjusted to remove the effect of CMD (Fig. 6 , Supplementary Table  6 ). Prediction accuracy, calculated as the correlation between the genomic estimated breeding values (GEBVs) and the de-regressed BLUPs of the mean phenotype value, ranged from 0.
We used fairly simple equipment including a green board platform, a cheap digital camera (can be substituted with a smartphone) and freely-available open source software for image processing. As expected, additional labour was required to lay and remove roots from the green board, a process that increase cost and time by about 15-20%. However, these costs could be offset through genomic prediction for shape-related traits in subsequent generations using training data from the parents and relatives. The methodology described here and the results obtained in this study are promising and open up the opportunity to apply high-throughput methods in cassava. The image capture and analysis can now be performed using the OneKK (one thousand kernels) app (https://github. com/PhenoApps/OneKK), an inexpensive and user-friendly tool for automated measurement of seed size, shape, and weight using smart phones.
Acknowledgements This research was supported by the  Bill & Melinda Gates Foundation  and the  Department for International Development of the United Kingdom  through the " Next Generation Cassava Breeding Project ", and  CGIAR-Research Program on Roots, Tubers and Bananas. The authors acknowledge the support of  Andrew Smith Ikpan ,  Ogunpaimo Kayode  and  Cynthia Idhigu  in acquiring the root images. We also acknowledge the  PhenoApp  project led by  Jesse Poland ,  Kansas State University , for technical support in image-based phenotyping using their OneKK (one thousand kernel) application.
As genome sequencing efforts are unveiling the genetic diversity of the biosphere with an unprecedented speed, there is a need to accurately describe the structural and functional properties of groups of extant species whose genomes have been sequenced, as well as their inferred ancestors, at any given taxonomic level of their phylogeny. Elaborate approaches for the reconstruction of ancestral states at the sequence level have been developed, subsequently augmented by methods based on gene content. While these approaches of sequence or gene-content reconstruction have been successfully deployed, there has been less progress on the explicit inference of functional properties of ancestral genomes, in terms of metabolic pathways and other cellular processes. Herein, we describe PathTrace, an efficient algorithm for parsimony-based reconstructions of the evolutionary history of individual metabolic pathways, pivotal representations of key functional modules of cellular function.
With respect to metabolic pathways, one of the earliest, most prominent advances on the subject has explored the varying degree of pathway evolution, with abrupt patterns of gain and loss -facilitated by massive lateral gene transfers  [21, 22]. Other directions that have been explored -not directly comparable with the inference of ancestral states per se -are represented by CLIME, which attempts to expand the initial pathway query and discover additional links based on phylogenetic profiles  [23] , MinPath, which detects pathway overpredictions in genomic and metagenomic datasets  [18]  and CoReCo, which estimates 'extant' (present) and 'extinct' (ancestral, i. e. inferred) genome-scale metabolic models  [24]. Here we describe a robust, efficient and scalable algorithm, named PathTrace, that addresses the issue of pathway inference across multiple ancestral genomes on a phylogeny for arbitrarily chosen target datasets.
► Step 1: extract protein sequences from BioPAX files. The metabolic pathway under study is represented by a BioPAX file  [27]. However, the content of any given BioPAX file may vary significantly. This step addresses the issue of extracting protein product sequence information either directly from the file or, if not available, through an online connection to a public database such as BioCyc  [28]  or KEGG  [29] , among others -see also http://www. biopax. org/ mediawiki/ index. php/ Data.
Data resources and algorithms Development and analysis were performed using both sequence and metabolic pathway information. In order to acquire high-quality, annotated protein sequences, we selected the entire Bacteria EnsemblGenomes database (http:// bacteria. ensembl. org/, release 12), from which we retrieved a total of 937 529 protein sequences from 249 entries, organized into ten major collections (original data available in URL ftp:// ftp. ensemblgenomes. org/ pub/ bacteria/ release-12). The ten collections provided by EnsemblGenomes, namely Mycobacterium, Streptococcus, Bacillus, Staphylococcus, Wolbachia, Buchnera, Escherichia/Shigella, Neisseria and Borrelia provide an extensive coverage of the domain Bacteria, while the collection for Pyrococcus represents an -arbitrarily chosen -representative from the domain Archaea, serving as an ougroup for phylogenetic tree construction (Table  1 ).
As a recommendation for users, the target set has to satisfy the criteria for the definition of a pangenome, for instance the saturation curves for close pangenomes or the bimodal frequency distribution of protein families  [32]. These simple and reasonable criteria have to be respected before a target set is built for querying by pathway sets, perhaps with the exception of including an outgroup, as in the present work (see below). Metabolic pathway data for target pathways were retrieved as Level 3 (L3) BioPAX files from the BioCyc database (https:// biocyc. org/). The selection of pathways was such that it can provide a wide coverage of representative pathways with multiple enzymes/reactions, including some of the bestcharacterized cases biochemically. Similarity searches that are used to detect the number of homologues were performed using BlastP (version 2.
E. C. numbers: reaction information in agreement with genome comparisons An alternative approach for the detection of presence or absence of a given pathway against a target genome list, is to utilize reaction information related to that pathway, i. e. the E. C. numbers of the participating enzymes  [38]. In order to perform this type of analysis, we have used the RSAT suite and specifically the 'NeAT -compare-classes' module  [39]. This algorithm accepts as input a list of pairs in the form of {E. C. Number, Genome ID} and a list of pairs in the form of {E. C. Number, Pathway ID} and produces an intersection metric, with a significance level estimated by a hypergeometric distribution  [39]. An interesting example in the case of leucine biosynthesis for E. coli DH10b can be seen (Fig. 5 , in column 198), as an indication of strain diversity within the pangenome.
3 , using several pangenomes as the target set. In this instance, the 249 columns correspond to the entire set of available genomes in the Bacteria EnsemblGenomes database (release 12), organized as a set of ten pangenomes (Table  1 ), outlined by the vertical purple lines. The mixture of representative genomes and pangenomes maintains the required heterogeneity of pathway information with an 'optimum' contrast, thus providing evidence of loss (i. e. absence) of reactions across entire pangenomes. This information is consequently used to construct the fuzzy-pathway profiles, even with a coarse-grained phylogeny. Scale from 0 to 9 (the number of homologues, arbitrary value) is shown on the right. are not due to the lack of other amino acids  [40]. Indeed, the operon leuLABCD is absent compared to the wild-type, 'causing both sensitivity to nutritional downshifts and slightly lower growth rates'  [40].
e. those most distant to the rest of the pangenome. To increase shows the plain homology matrix of the six enzymes involved in the pathway (rows), across the entire complement of the 249 genomes in the Bacteria EnsemblGenomes database (release 12) organized in ten pangenomes (Table  1 ), outlined with the red vertical lines for all panels. The bottom panel corresponds to the metric produced by NeAT  [39] , using a greyscale representation of values ranging from white (presence) to black (absence). The middle panel corresponds to the PathTrace representation of fuzzy-pathway profiles, with the horizontal purple line indicating the threshold for presence (above threshold) or absence (below threshold). It is evident that, although all three representations capture the essence of pathway absence or presence across all genomes and pangenomes, the fuzzy-profile approach of PathTrace (middle panel) provides increased sensitivity in the subtle variations within a single pangenome while still capturing the robust classification between absence and presence.
See text for details. Fig. 6. Comparison of three different representations of pathway status (presence/absence), using the TCA Cycle pathway as a case study. The top panel (identical to rows 7-24 of Fig. 4  -including scale) shows the homology matrix of the 18 enzymes involved in the pathway (rows), across the entire complement of the 249 genomes in the Bacteria EnsemblGenomes database (release 12) as organized in ten pangenomes (Table  1 ). The bottom panel corresponds to the metric produced by NeAT  [39]  (as in Fig. 5 ). The middle panel corresponds to the PathTrace representation (as in Fig. 5 ). Similarly to the Leucine biosynthesis example, subtle patterns of absence or presence across pangenomes can be seen -in this case, Streptococcus (block 3), Buchnera (block 7) and Borrelia (block 10).
See text for details. the necessary contrast, outgroups both in terms of query and target were also selected: the Glycolysis metabolic pathway Variant V (specific to Archaea) as a pathway query, and the Pyrococcus genomes (two genomes and the pangenome) as a species target set. In total, the homology matrix in this case study is composed of 86 enzymes×182 genomes (Fig. 7 ). For each reference pathway, the participating protein products were selected, according to the BioCyc database, and used as queries for similarity searches. Overall, three reference genomes were used as source organisms for the corresponding pathways, namely Escherichia coli K-12 substr. MG1655, Bacillus subtilis subsp. subtilis 168 and Pyrococcus horikoshii OT3 (Table  2 ). As the presence of these pathways in the source organisms is indisputable, they have served as internal, positive controls for the development of Path-Trace and the assessment of results.
S1 ). The taxonomic tree utilized here is identical to the tree used in the PathTrace use case (Fig. 9 ), with a 90 degree rotation. Inspection of the nine panels reveals that both Mesquite and PathTrace establish similar evolutionary scenarios of the query pathways. All the above results are available on the Figshare repository (see, Data Summary) and might be reproduced either in automated or interactive fashion, using as queries the sequences from the nine pathways documented in FASTA format in Data S1 and targets the specific databases restricted to the taxon (strain, species, genus or pangenome) of interest. The detailed case study results for the assessment of Path-Trace are consistent with our knowledge of pathway evolution, supported by corroborating evidence from the literature. It also demonstrates the difficulties of establishing pathway presence or absence, in particular with a single threshold -an ongoing challenge in fields such as similarity searches and clustering  [62].
It is evident that query pathways, represented as groups of genes, are less sensitive to parameters of parsimony-based algorithms for gain or loss  [22] ; however, the crisp decision for presence or absence remains a bottleneck. More sophisticated models using taxon-specific values and genome-size normalization  [63]  or dynamical models of gene acquisition and loss impacting pathways  [64]  are directions for future algorithm development. Issues that also need further investigation include the effect of multiple paralogues on parameter choice (thresholds, e. g. [65] ), the differential treatment of enzymes with respect to genome structure or pathway topology  [66] , the mode and tempo of horizontal gene transfers  [67] , the nature of ecological characteristics of target organisms with respect to speciation and innovation  [68] , as well as integration of PathTrace with related tools into software pipelines and relevant resources  [69].
Acknowledgements We thank our partners at the  Microme  project for feedback,  Rob Finn  (EBI),  Peter D. Karp  ( SRI International ),  Nikos Kyrpides  ( DoE-JGI) and Katie Pollard  ( UCSF ) for comments as well as  Laura Dadurian  ( CERTH ) for assistance. We thank  Dr. Didier Croes  ( UZ Brussel -Belgium ) for his input regarding alternative approaches to fuzzy pathway profiles using E. C. numbers and the RSAT platform.
Funding information This work has been supported by the  FP7 Collaborative Project Microme  (grant agreement no. 222886-2 ), funded by the  European Commission , and coordinated by the  European Bioinformatics Institute (EBI). C. A. O. acknowledges support by the project  Elixir-Gr , implemented under the Action ' Reinforcement of the Research and Innovation Infrastructure ', funded by the  Operational Programme 'Competitiveness, Entrepreneurship and Innovation'  ( NSRF 2014-2020 ) and co-financed by Greece and the  European Union  ( European Regional Development Fund).
Fission events form several 235 U samples have been detected using fission chambers and a set of parallel plate avalanche counters (PPAC)  [6]. To measure n-p scattering in presence of an intense γ flash and a continuous neutron energy distribution, three Proton Recoil Telescopes (PRTs) were specifically designed. Since fission fragments and recoil protons have been measured by different experimental set-ups, efficiencies must be precisely known and a fully characterization of the detectors is mandatory. Here, we present the model used to simulate the PRT systems employing the GEANT4 toolkit  [7]. Some preliminary results and comparisons to experimental data for the two of the three PRT detectors of INFN conception are also shown. * e-mail: nicholas. terranova@enea. it.
In the current study, we used miRNA target prediction algorithms to identify and comprehensively analyze the genome-wide sugarcane (Saccharum officinarum L. )-encoded microRNA (miRNA) targets against the SCBGAV. Mature miRNA target sequences were retrieved from the miRBase (miRNA database) and were further analyzed for hybridization to the SCBGAV genome. Multiple computational approaches-including miRNA-target seed pairing, multiple target positions, minimum free energy, target site accessibility, maximum complementarity, pattern recognition and minimum folding energy for attachmentswere considered by all algorithms. Among them, sof-miR396 was identified as the top effective candidate, capable of targeting the vital ORF3 of the SCBGAV genome. miRanda, RNA22 and RNAhybrid algorithms predicted hybridization of sof-miR396 at common locus position 3394.
Retrieval of mature miRNAs of sugarcane The sugarcane (Saccharum-officinarum L) and (Saccharum spp. ) plant miRNA sequences were accessed from the microRNA database, miRBase (http://www. mirbase. org/cgi-bin/ browse. pl/) (Griffiths-Jones et al. , 2006; Griffiths-Jones et al. , 2007; Kozomara, Birgaoanu & Griffiths-Jones, 2018; Kozomara & Griffiths-Jones, 2013). A total of 28 miRNAs were retrieved (Table  S1 ).
These properties yield output to weigh all matches and mismatch scores for gap penalties and for base pairs. It also promotes the prediction of multiple miRNA target sites including the ones with imperfect binding in the seed region within the 3 UTR of the target site thereby enhancing the specificity  (Betel et al. , 2008; Witkos, Koscianska & Krzyzosiak, 2011). miRanda software was downloaded from the source  website (http://cbio. mskcc. org/miRNA2003/miranda. html), and the SCBGAV genome was assessed to determine whether there were any possible targets for sugarcane miRNAs. O R F 2 SCBGAV 7444 bp O R F 1 O R F 3 The miRanda algorithm was run after defining standard settings. These include: Gap Open Penalty = -9. 0, Score Threshold = 130, Minimum free energy (MFE) threshold (= -15. 00 kcal/mol).
ACKNOWLEDGEMENTS We are highly thankful to our lab colleagues for their assistance in data analysis. We thank to  Dr. Zhiqiang Xia  (ITBB) for assistance in constructing Circos plot. We are thankful to our senior lab colleagues Xiao- Yan Feng  and  Lin-Bo Shen  for reading the revised manuscript as well as the editor and two referees for their valuable comments on this manuscript. We are highly thankful to a qualified native English speaking editor at the  PeerJ Experts  for editing proper English language.