dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to discover the contribution of each variable to the prediction, we add an analysis on the five firstly selected variables for each prediction task of all stocks of CAC40 (with allday dataset). Figures  7  and 8  show how many times a variable is selected as the first (second, third, fourth, fifth) selected variable by LASSO. We denote the i events lagged log volume on the bid (ask) limit price by VB th j i j    VAj i . Similarly, i events lagged log market order volume is denoted by and i events lagged binary variables are denoted by , VMO i  BMO i  AMO i  , BTT i  , ATT i  etc. For the sake of simplicity, for each selection order     ,5  1 VB BMO 0 1 VA 1, i i , we show the frequency distribution of the five most frequently selected variables among 746 backtests in each figure.
Conclusion In this paper, we provide an empirical result on the relationship between bid-ask limit order liquidity balance and trade sign and an analysis on the prediction of the inter-trade price jump occurrence by logistic regression. We show that limit order liquidity balance on best bid/ best ask is informative to predict the next market order's direction. We then use limit order volumes, limit order price gaps and market order size to construct the features of the limit order book for the prediction of inter-trade price jump occurrence. LASSO logistic regression is introduced to help us identify the most informative limit order book features for the prediction. Numerical analysis is done on two separated datasets: morning dataset and afternoon dataset. LASSO logistic regression gets very good prediction results in terms of AUC value.
The AUC value is consistently high on both datasets and all stocks whatever the liquidity is. This good prediction quality implies that the limit order book profile is quite informative for predicting the incoming market order event. The variable selection by LASSO logistic regression shows that several variables are quite informative for inter-trade price jump prediction. The trade sign and market order size and the liquidity on the best limit prices are the most informative variables. Nevertheless, the aggressiveness of market order, measured by tradethrough, has less important impact than we had expected. These results confirm that the limit order book is quite sensitive to the liquidity on the best limit prices and there is a long memory of order flow like what is shown by other authors.
SNP genotyping data from 3 separate cohorts were analysed using the cnvHap algorithm, and GSVs were identified that corresponded to the GSVs under investigation. For GSVs identified in at least one individual, association with obesity status (excluding overweight individuals from the analysis) was tested according to Fisher's exact test. For GSVs identified in at least 3 members of the NFBC1966 cohort, association with log 10 BMI as a quantitative trait was tested by 2-way ANOVA with gender as the second covariate. Italics denote a direction of effect opposite to that in the original report. We also investigated the occurrence of reciprocal GSVs at each locus (i. e. duplications instead of deletions and vice versa), applying the same calling criteria (Table  S1 ). Although the overall frequency of reciprocal GSVs was slightly lower compared to those showing reported association with obesity, there was a difference between the two sets of loci: Common obesity GSVs were identified approximately twice as frequently as their reciprocal counterparts, but extreme obesity GSVs were 30% less frequent than reciprocal aberrations.
The Contribution of Chromosome 16p11. 2 220 kb Deletions to Obesity Consistent with the original report for the SH2B1 locus  [10] , 5 of the affected subjects were from our cohort of severely obese children, a significant enrichment compared to our general population cohort (P = 1. 4610 23 ). Extending the analysis to include multiple other population cohorts (Table  2 ) unambiguously confirmed the association between this deletion and childhood obesity (P = 8. 7610 27 ; OR = 38. 4, [95% confidence interval = 10. 4-120. 6]). This finding was further strengthened (P = 3. 8610 210 , OR = 25. 0 [9. 9-60. 6]) by inclusion of previously published data  [10, 21]. Intriguingly, the association with adult obesity is less clear. We investigated by MLPA the parents of the 5 severely obese children carrying the deletion, finding that 4 deletions were inherited (one arising de novo).
Thus, there is a need for methods that properly address the structural complexity frequently observed at GSV loci. One straightforward approach might be to apply a locus-specific multiple-testing correction, according to GSV complexity, to reflect the number of independent tests made at a locus, in a manner similar to that used to correct for multiple tests of SNPs in linkage disequilibrium  [40]. Alternatively, more sophisticated methods developed for aggregating rare sequence variants in the absence of prior information, for instance as implemented in the 'thgenetics' R package  [41] , might be adapted for use with complex GSVs. Although problematic for the identification of an association, we nevertheless note that the existence of multiple different GSVs may be of great utility in dissecting a locus whose association has been conclusively demonstrated.
Even after the use of predefined criteria to select a subset of GSVs for analysis, the necessary correction remains substantial: In the previous studies, subjects with common obesity carried approximately 20 GSV calls per individual (1080 cases, 2500 controls)  [16] ; more than 300 separate rare GSV loci were identified in the extreme obesity discovery cohort alone  [10] ; and both used a point-wise method for assessing association at complex GSV regions. Although attempts were made to take account of multiple testing (e. g. exclude GSVs present in the Database of Genomic Variants, including only rare GSVs, empirical estimate of FDR), it is unclear to what extent these methods are effective -methods developed for association analysis of rare sequence variants are not consistently well-powered even for large sample sizes  [42].
GSV Identification The GSV regions were selected either as stated in the original report  [16]  or the region common to all GSVs identified in that region  [10] , and were analysed according to the GSV analysis pipeline illustrated in Figure  1. Intensity data from the French and NFBC1966 cohorts were exported from Illumina BeadStudio in the form of logR ratio (LRR) and B Allele Frequency (BAF); samples with a low SNP call rate (,95%) or a genome-wide LRR variance. 0. 3 were excluded. The cnvHap algorithm with default parameter settings (false discovery rate ,5%) was applied to each region under investigation plus additional 500 kbp flanking regions; using these parameters we expect high sensitivity for GSV detection -even a false discovery rate as low as 1% gives genome-wide sensitivity of ,40% for GSV detection in an individual, and.
Acknowledgments The NFBC authors thank  Prof. Paula Rantakallio  (launch of  NFBC1966  and initial data collection),  Ms Sarianna Vaara  (data collection),  Ms Tuula Ylitalo  (administration),  Mr Markku Koiranen  (data management),  Ms Outi Tornwall  and  Ms Minttu Jussila  (DNA biobanking). We acknowledge the contribution of  Prof. Vincent Mooser  to the CoLaus study. The CoLaus authors thank  Yolande Barreau ,  Mathieu Firmann ,  Vladimir Mayor ,  Anne-Lise Bastian ,  Binasa Ramic ,  Martine Moranville ,  Martine Baumer ,  Marcy Sagette ,  Jeanne Ecoffey , and  Sylvie Mermoud  for data collection. The SHIP authors are grateful to the contribution of  Anja Wiechert  and  Astrid Petersmann  in generating the genetic data. JSe-SM was supported by an  Imperial College Department of Medicine  PhD studentship.
Behavioral features of affiliative social behavior, i. e. , social contact duration and follow behavior  (24, 33, 43) , were first manually measured and compared between C57BL/6 and ␤2 Ϫ/Ϫ mice, as well as between lesioned and sham-treated mice. Furthermore, a more detailed analysis was carried out using MiceProfiler software  (33). Briefly, with this semiautomatic analysis, we distinguished contact events, defined as any position in which mice were at whiskers distance or less apart. Within contact events, we discriminated oraloral, oral-genital, and side-by-side contacts. Apart from contact events, we scored different postures in each mouse of the dyad. Among them, we distinguished stop behaviors, back-toback postures, and follow behaviors. Several previous studies have defined stop behaviors, that is short bouts of stopping in between locomotor sequences, as being important for the organization of actions in different animals  (44 -47).
Statistical analysis The main effects of genotype (2 levels: C57BL/6 and ␤2 Ϫ/Ϫ ) and of lesion (2 levels: sham treatment and global NE lesion, or NE PrL depletion), as well as statistical interactions between these factors, were assessed with ANOVA and Statview 4. 57 (SAS Institute, Cary, NC, USA). Post hoc analyses were performed on significant main effects or significant interactions using Bonferonni's test. Dixon's Q test  (38)  was used before any other statistical treatment to objectively detect a single outlier within a group of data for which a gaussian distribution applies. The criterion for statistical significance was set at P Ͻ 0. 05.
1, NS; Fig. 3 ). By contrast, in C57BL/6 mice, the index of aggressiveness increased dramatically by 6-fold following the selective depletion of NE from the PrL (Pϭ0. 0002; Fig. 3 ). This was unlikely to be due to anxiogenic effects, as anxiety was not altered by NE PrL depletion in C57BL/6 mice (see above). The same results obtained after global NE lesion were found in ␤2 Ϫ/Ϫ mice after specific NE PrL depletion. To investigate whether these effects were due to fine changes in the social repertoire, we assessed the key behavioral sequences of the social repertoire that were specifically altered by NE depletion. For this purpose, we analyzed social data with MiceProfiler (33) software that we designed for underpinning decision points during complex behavioral chains. NE PrL depletion produced a similar effect than that of the global lesion in ␤2 Ϫ/Ϫ mice, i.
A total of 365 molecular markers were added to the map, in addition to 1122 putative unique transcripts that were identified by microarray hybridization. The final map consists of 1180 FPC-based or 583 LTC-based contigs. Conclusions/Significance: The physical map presented here marks an important step forward in mapping of hexaploid bread wheat. The map is orders of magnitude more detailed than previously available maps of this chromosome, and the assignment of over a thousand putative expressed gene sequences to specific map locations will greatly assist future functional studies. This map will be an essential tool for future sequencing of and positional cloning within chromosome 1A.
While most of the physical maps generated by the project have not yet been published, they are already being used to advance a number of gene and QTL mapping projects  [10]. The wheat database established at INRA URGI (http://urgi. versailles. inra. fr/Species/Wheat) enables users to explore the group 1 & 3 chromosome physical maps in relation to other resources, such as 26 wheat genetic maps, using a Gbrowse interface  [10]. Under the umbrella of this project, we present the physical map for the 523 MB long arm of T. aestivum chromosome 1A (1AL) using fingerprint data from 70,537 BAC clones. Chromosome arm 1AL is known to harbour the Glu-A1 locus which is important in determining the end-use quality of bread wheat  [11] , as well as other genes involved in metabolism and disease resistance  [12] ; however, only the first of these genes has been cloned.
Construction of BAC-based physical maps of chromosome 1AL Our previous study  [1]  reported the production of BAC libraries covering chromosome 1AL to a depth of. 156 coverage, with an average insert size of 105 kb. Good quality fingerprints were obtained for 70,537 (76. 2%) of these clones by High-Information Content Fingerprinting (HICF), and then assembled using FingerPrinted Contig (FPC) software (see materials and methods). After the completion of a primary automated assembly, the build consisted of 2551 contigs with an average size of 233 kb and a total length of 623 MB (not including the 20,807 singletons). The Minimum Tiling Path (MTP) was generated from this assembly and used for marker development and anchoring (see below). The build was then edited manually to merge similar contigs at lower stringency and incorporate information gained from molecular markers, reducing the number of contigs to 2210.
LTC has been shown to give superior results to FPC in assembling simulated datasets derived from rice and maize  [6]. Therefore, in addition to the FPC assembly, LTC was also used to generate a second version of the physical map. Five hundred and seventy-six contigs were generated in the first round of adaptive clustering, after the removal of 1490 Q-clones (questionable clones) from the analysis. Eleven of these contigs were highlighted as non-linear by LTC, which led to a further exclusion of 46 clones from the analysis to eliminate discrepancies in contig topology. After removing these clones, a second and final round of adaptive clustering by LTC yielded 583 contigs, each containing 6 to 669 clones. In the final LTC assembly, 3 contigs -namely cl_20433, cl_20634 and cl_20677-were still indicated to have non-linear topology, which most likely resulted from the inclusion of one or two relatively low quality fingerprints in the MTP (Figure  S1 ).
Verification and refinement of the 1AL physical map by contig topology The LTC program described above also provides functionality for testing the linearity and integrity of contigs generated by FPC. This was used to evaluate the physical map of 1AL. First of all, the MTP generated by FPC was checked for inconsistencies by assessing the 2551 contigs produced at the end of the automated assembly process (Table  1 ). LTC was used to search these contigs both for non-significant clone overlaps, and for incorrectly ordered clones. By this approach, 763/5071 (15%) of MTP clone overlaps selected by FPC were found not to be significant at the recommended cutoff of 10 215  [6]. This does not usually indicate problems in the MTP, but reflects the different methods used to select MTP clones by the 2 programs: LTC requires a significant overlap between each pair of MTP clones at the specified cutoff, whereas the procedure used by FPC incorporates information from spanning and flanking clones to confirm the MTP selection  [17] , allowing relatively shorter overlaps at the conditions selected (see Materials and Methods) and therefore fewer clones to be selected to cover the whole assembly.
The final FPC assembly of chromosome 1AL was tested with the recently developed LTC software  [6]  to evaluate and enhance the quality of the physical map. LTC software reported nonsignificant overlaps between adjacent MTP clones of 548 contigs. This is not usually a concern, because FPC uses additional information from spanning and flanking clones  [17]  to determine the MTP, allowing smaller overlaps between MTP clones. Accordingly, for 536 of these, the order of clones was verified by LTC, thereby justifying the MTP selection; however, future fine mapping of these contigs might be facilitated by adding bridging clones between pairs of MTP clones if the overlaps cannot be confirmed. The order of MTP clones in 14 contigs re-analyzed by LTC was not consistent with that calculated by FPC. This situation is likely to result from redundant overlaps given by buried or chimeric clones, which render it impossible to determine the clone order precisely, or cause local non-linearity within contigs.
BAC library construction, fingerprinting and automated assembly Plant material, chromosome isolation from T. aestivum and construction of two BAC libraries giving a total of 15. 7x coverage of chromosome 1AL was previously described  [1, 4]. A total of 92,544 BAC clones with an average insert size of 105 kb was fingerprinted using the SNaPshot TM High-Information Content Fingerprinting (HICF) procedure  [20]. The number of BAC clones giving good quality fingerprint data was 72,724. These fingerprints were checked for cross-contamination between adjacent wells using GenoProfiler 2. 0 software  [21]  and 2187 suspect fingerprints were eliminated, leaving a total of 70,537 fingerprints (76. 2% of the original BAC clones). Initial assembly of the fingerprints was carried out using FPC software  [22]  with settings adjusted for the complex and repetitive wheat DNA as described previously  [3] ; briefly, an initial assembly of all clones was carried out under extremely stringent conditions (Sulston Score probability cutoff = 1e 275 ) to provide a core of highconfidence contigs.
Minimum tiling path and BAC pooling From the initial automated assembly at a cutoff of 1e 245 , a Minimum Tiling Path (MTP) consisting of 7470 clones covering the assembly was generated using the MTP module of FPC  [17]  with the following parameters: minimum FPC overlap 30, maximum overlap 250, FromEnd = 0, minimum shared bands 12, and preference given to large clones. BAC clones comprising the MTP were picked from the initial library and re-gridded into a 206384-well plate MTP library as previously described  [3]. To facilitate screening of the MTP library a 3D pooling approach was adopted. Each clone was grown independently and then pooled, giving a total of 16 row, 24 column and 20 plate pools, along with a single superpool containing all the clones. After organizing the pooled BAC clones into a single 96-well plate, DNA was amplified from each pool by the multiple displacement method with random primers and phi29 DNA polymerase (GenomiPhi V2 DNA polymerase Kit, GE Healthcare).
LTC assembly and LTC-check of the FPC assembly Fingerprinted 70,537 BAC clones were also assembled with LTC software  [6]. Briefly, a net of significant clone overlaps were generated with a liberal cutoff of 10 215. The average number of overlaps per clone was 15. 87, indicating a sufficient stringency cutoff. Prior to clustering, Q-clones and Q-overlaps were removed at cutoffs of 10 215 and 10 225. Then, first round of adaptive clustering was performed starting from the liberal cutoff of 10 215 and, for non-linear clusters, reducing the cutoff by a factor of 1000 each step for a total of 6 steps, to reach a maximum stringency cutoff of 10 233. Non-linear clusters that persisted at this stringency were individually visualized to identify problematic clones. A total of 46 clones causing non-linearity were identified and excluded from the second round of adaptive clustering.
Marker selection and development Markers that had already been genetically mapped to chromosome 1AL were retrieved from the GrainGenes online database (http://wheat. pw. usda. gov/GG2/index. shtml). These included a variety of SSR  [23, 24]  and EST markers  [25]. In addition, Conserved Ortholog Set (COS) markers were developed for the group 1 chromosomes from wheat EST sequences as previously described  [7]. To populate the non-gene coding sections of the chromosome with genetic markers, BAC-end sequencing had already been carried out for the MTP clones and putative insertion-site based polymorphisms (ISBP) identified in the BESs  [1]. In addition to the markers reported in that paper, a further 280 ISBPs were selected based on the following criteria designed to avoid marker redundancy: 1) a high-confidence junction between repetitive and non-repetitive DNA; 2) junctions where the end of the repetitive element could be clearly identified; 3) if a particular repetitive element occurred more than once in the BESs, only one possible ISBP was selected from all occurrences.
) at 2nm resolution with automatic gain adjustment. Scanned images were aligned and analyzed with DEVA software. Signal intensities were extracted from scan data and normalized and deconvoluted using automated R scripts (www. r-project. org), as described previously  [15]. Briefly, within each pool, if one probe among the 3 for each Unigene was found to be an outlier it was removed from the dataset. Pools were then normalized with respect to each other by subtracting the median and dividing by the standard deviation of all signal intensities within the pool.
Statistical analysis of microarray data Three files were generated containing the normalized signal intensities for row, column and plate pools respectively, and two complementary statistical methods used to identify positives within each pool type. In the first method, a signal was scored as positive if the median intensity for all the probes for a single gene in a single pool exceeded [Mean + C 6 Standard Deviation] of the intensities for that gene across all the pools, where C was a predefined threshold co-efficient. Three different thresholds were calculated for each pool type: High (C = 2. 6 for row pools, 2. 8 for column and plate pools), Medium (C = 2. 4 for row pools, 2. 6 for columns and plates) and Low (C = 2. 2 for row pools, 2. 4 for columns and plates). The second method used Student's t-Test to determine whether the intensities for a given gene in one pool were significantly different from all the other pools, assuming variances to be equal.
Similarity searches, annotation and synteny All similarity searches were carried out using the BLAST+ stand-alone toolkit, version 2. 2. 24, from the NCBI  [26]. Functional annotation of Unigene sequences was carried out using Blast2GO software  [27]  as follows: the Unigene sequences were searched against the non-redundant protein database at http:// www. ncbi. nlm. nih. gov/Blast. cgi using blastx, with a maximum evalue cutoff of 1e 210 and a minimum high-scoring pair length cutoff of 33. The best 20 hits for each sequence were retained and used for GO-mapping. After GO terms had been mapped to each sequence, automated annotation was carried out using an annotation score cutoff of 50 and an HSP-Hit coverage cutoff of 30% and other parameters at default values. The annotation was streamlined using GO-Slim_Plant to limit the results to plantrelevant nodes.
For detection of synteny with sequenced grass genomes, coding sequence datasets were downloaded from PlantsDB (http://mips. helmholtz-muenchen. de/plant/genomes. jsp) for the Brachypodium distachyon genome annotation v1. 2  [19] , Sorghum bicolor genome annotation v1. 4  [28] , and version 5 of the Oryza sativa pseudomolecules [29; rice. plantbiology. msu. edu/]. Similarity searches were carried out using blastn for higher stringency, with an empirically determined e-value cutoff of 1e 225 , and any hits shorter than 50 nt were rejected. The highest scoring hit for each UniGene sequence was retained. The syntenic gene matches were ordered using the virtual gene order for the group 1 chromosomes produced by Mayer et al. [5].
This work was supported by the  EU-FP7 - TriticeaeGenome  project and a  Sabanci University Internal Research Grant. HS ˇ, JS ˇ, and JD were supported by the  Ministry of Education, Youth and Sports of the Czech Republic  and the  European Regional Development Fund  ( Operational Programme Research and Development for Innovations  No. ED0007/01/01 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.