dataesr/scientific-paragraphs-categorization huggingface dataset:
If an iORF overlapped a masked region detected in only one strain, it was removed for all the other strains in order to not introduce presence-absence patterns due to strain specific masking. iORFs that do not overlap a known feature were then classified according to the conservation level: 1) conserved in both species, 2) specific and conserved within S. paradoxus, 3) fixed within lineages and divergent among, 4) specific and fixed in one lineage, 4) polymorphic in a least one lineage (Fig. S1 ). For iORFs with a minimum size of 60 nt, we also performed a sequence similarity search against the proteome of NCBI RefSeq database  (O'Leary et al. 2016)  for 417 species in the reference RefSeq category and the representative fungi RefSeq category (containing 237 fungi species). iORFs with a significant hit (e-value < 10 -3 ) were removed to exclude any risks of having an ancient pseudogene.
We then extracted the aligned read densities, subtracted by the P-offset estimates, per iORF or genes for next analyses. Metagene analyses were performed using the metagene, psite and get_count_vectors scripts from the Plastid package (Dunn and Weissman 2016), metagene figures were done using R script (R Core Team 2013). We identified translation initiation signal from ribosome profiling densities, by detecting peaks at the start codon. We defined 3 precision levels of peak initiation: 'p3' if the highest peak is located at the first nucleotide of the start codon, 'p2' there is a peak at the first position of the start codon and 'p1' if there is a peak at the first position of the start codon +/-1nucleotide because the peak position is less precise in low expressed feature. A minimum of 5 reads was required for peak detection.
Differential expression analysis Reads were strand-specifically mapped to iORFsT1 and conserved genes using the coverageBed command from the bedTools package version 2. 26. 0 (Quinlan and Hall 2010), with parameter -s. We then examined iORFsT1 significant expression changes between strains. Differential expression analysis was performed using DESeq2  (Love et al. 2014 ). Significant differences were identified using 5% FDR and 2-fold magnitude. We identified lineage specific expression increase when the expression of the iORFsT1 in the considered lineage was significantly more expressed than the others strains in all pairwise comparisons. For SpB-SpC increase, we selected iORFsT1 when SpB and SpC strains were both more expressed than YPS128 and SpA, and S. paradoxus increase when all S. paradoxus lineages were more expressed than YPS128.
Expression and sequence properties Normalized read counts for ribosome profiling and total mRNA samples were extracted with DESeq2 software  (Love et al. 2014 ) and we calculated the mean of the two replicates per library type. Translation efficiency (TE) was calculated as the ratio of RPF over total mRNA normalized read counts on the first 60 nt. We excluded iORFsT1 and genes with less than 10 total RNA reads in the first 60 nt for the TE calculation. Slope differences between Genes and iORFsT1 were tested with an ANCOVA. We confirmed the buffering effect on iORFsT1 annotated on the S. cerevisiae reference strain S288C with ribosome profiling and RNA sequencing data obtained in  (McManus et al. 2014 ) study (Fig. S6 ).
paradoxus alignments, by the total number of nucleotides in the region, as in  Agier and Fischer (2012)  study for intergenic sequences. Replication timing data per 1kb bin comes from  Muller et al. (2014)  study and were converted to the version R64-2-1 of the reference genome using liftOver (http://genome. ucsc. edu/cgi-bin/hgLiftOver). We used the codeml program from the PAML package version 4. 7  (Yang 2007)  to estimate the likelihood of the dN/dS ratio, using the same procedure as employed by  Carvunis et al. (2012)  with codon model 0. All analysis and figures were conducted with python and R script  (R Core Team 2013). between each cycle. The powder was gently thawed in the open grinding chamber at room temperature to collect the lysate which was cleared by two rounds of centrifugation for 5 minutes at 3000xg at 4°C, followed by one round of high speed centrifugation for 10 min at 20,000xg at 4°C.
Acknowledgments We thank  A. K. Dubé ,  G. Charron  and the IBIS sequencing platform ( B. Boyle ) for technical help and landry lab members for comments on the manuscript. This project was funded by a  FRQNT Team  grant to C. R. L and  Xavier Roucou  and  NSERC discovery  grant to C. R. L. C. R. L. holds the  Canada Research Chair in Evolutionary Cell and Systems Biology.
Data access High-throughput sequencing data have been submitted to the NCBI Sequence ReadArchive (SRA; http://www. ncbi. nlm. nih. gov/sra) and can be accessed under NCBI BioProject number PRJNA400476. De novo assemblies and annotations have been submitted to the NCBI nucleotide database (https://www. ncbi. nlm. nih. gov/nucleotide) under NCBI BioProject number PRJNA400476.
Introduction In recent years, the theory of stock market prediction has become more and more matured. From the random walk theory and the effective market hypothesis to the quantitative investment, the prediction of the time series and the financial trading are more closely linked with machine learning. Machine learning has one function named self-perfection, and with the development of artificial intelligence, machine learning has been widely used in financial field. This paper proposes a financial time series prediction model based on ensemble learning of long short-term memory neural network. The financial time series data set used in this paper is composed of 6 indices of the Chinese stock market. Each index converts the data from two-dimensional data into threedimensional data and normalizes each of the threedimensional data.
Quantitative investment has become an important way of investment in the US market. The proportion of US quantitative investment rose to over 30% in 2009  [4] , and about 85% of the US stock market transactions were completed through algorithmic trading  [5]. In 1988, James Simons, a data scientist, established Medallion fund, which used a quantitative model to invest, and has achieved return of an average of 34% per year since its establishment. Non-random walk theory and the practical application of quantitative investment prove the feasibility of stock price prediction. Machine learning improves the performance itself by means of computation and experience  [6]. Some scholars use various machine learning algorithms to sum up the historical rules of financial data in order to improve the accuracy of investment decisions.
The data were analyzed using 7000 System SDS software v1. 2 (Applied Biosystems). Primers used for qPCR are listed above. Illumina sequencing was performed at Fasteris (Geneva) using the Hiseq system. RNA IP experiments were performed using cells expressing RHS2-HA and RHS6-HA as described previously  (63). For isolation of RNA bound to RHS, anti-HA magnetic beads (Pierce) were used, and the RNA-protein complexes were resuspended in 20 mM Tris-HCl (pH 7. 5), 5 mM EDTA, 50mMNaCl, 0. 1% SDS, and 50 g ml -1 proteinase K and incubated at 68 • C for 40 min. RNA was purified by phenol-chloroform extraction and ethanol precipitation, subjected to DNase treatment, and quantified using a Qubit RNA HS Assay kit (Invitrogen).
NGS sequencing and bioinformatics Illumina reads were mapped and analysed using the Galaxy suite version 18. 05 (66), as described in Naguleswaran et al. (67). For Figures  4A  and 4B , the Chip-seq reads were mapped against release 8. 1 of T. b. brucei TREU927 genome downloaded from TritrypDB (http://tritrypdb. org/ tritrypdb/). The BWA software (Li H. , 2013, Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv:1303. 3997v1) with default parameters was launched in paired-end and single-end modes with respect to the replicate sequencing libraries. We discarded alignment matches shorter than 19 bp and allowed gaps shorter than 100 bp. The algorithm uses a scoring scheme (Z-dropoff) which avoids unnecessary extension of poor matching regions and reduces poor alignments in good matching regions by dynamically calculating the alignment score.
the full list of the default parameters can be found on the software website (http://bio-bwa. sourceforge. net/bwa. shtml). The output of BWA was sorted and indexed using SAMtools  (68). Peak calling was performed using MACS2 software (69) on RHS2, RHS4, RHS6 and Pol II using the replicates for each run. Reads from duplicate controls (DNA extracted from the same chromatin samples used for ChIP) were used as input. The pairwise Spearman correlations in Figure  4A  were computed using the R package GGally (B. Schloerke et al. , 2018, GGally: Extension to 'ggplot2. R package version 1. 4. 0). To generate the heatmap in Figure  4B , alignment coverage was normalized to Reads Per Kilobase Million (RPKM) using deepTools  (70)  with the option normalize using RPKM and converted to BigWig format. The normalized alignment as well as the peak enriched intervals (narrow peaks outputs) were used as input files in R Bioconductor package EnrichedHeatmap (Zuguang Gu, 2017, EnrichedHeatmap: Making Enriched Heatmaps.
As a control, we performed ChIP with the anti-RBP1 antiserum or with a monoclonal antibody against the large subunit of Pol I, followed by qPCR. This confirmed the differential binding of the two polymerases to their respective transcription units (Supplementary Figure  S3B ). In addition, we found that RHS and Pol II were strongly depleted from silent regions of the genome. One example is a region on chromosome 4, encoding VSG and Expression Site Associated Genes (Figure  4D ), which produces no steady state mRNA in this stage of the life cycle. The input genomic DNA gave good coverage of these regions, however, confirming that the lack of reads after ChIP was not due to a mapping artefact. Taken together, these data show that RHS are globally associated with active Pol II and suggest a potential role in transcription elongation.
ACKNOWLEDGEMENTS Marc B ühler ,  Sarah Allen  and  Ruth Etzensperger  are thanked for their constructive comments on the manuscript and  Kirk Deitsch  for thought-provoking discussions. Vivian Bellofatto  is thanked for providing an expression plasmid for RBP1,  Miguel Navarro  for a monoclonal antibody against Pol I and  Bernd Schimanski  for the Mex67 RNAi cell line.
Tb-NPs with concentrations of 0. 15 nM were sonicated and then incubated with HeLa cells in a standard culture medium (with serum and antibiotics) supplemented with HEPES buffer in order to prevent pH variations. Bright field images (DICdifferential interference contrast) and TG-PL images (with a delay time of 0. 01 ms and gate time of 1. 9 ms after the excitation pulse) of live HeLa cells were acquired in a live-cell chamber on an inverted wide-field microscope. Throughout the present study, we used 400 excitation pulses at a repetition rate of 100 Hz for the acquisition of TG-PL images. Although the resulting acquisition time of 4 s allowed for an enhanced signal- to-noise ratio, 200 pulses and repetition rates of up to 300 Hz (2/3 s acquisition time) were found to provide images of sufficient quality (data not shown) in case faster processes need to be analyzed.
This decrease was lower than expected (one would expect 50% of luminescent endosomes per cell after cell division) because we did not need to wash the cells after incubation (vide supra). Therefore, additional endocytosis of NPs that were still present in the cell medium was possible. The results confirm our earlier finding of an increasing uptake during the first 6 h of incubation and that cell division led to a distribution of the Tb-NPs in daughter cells. Further analysis of randomly selected cells (n = 16) at different times of incubation allowed us to observe the same tendency of NP uptake, which increased from 3 to 6 h (16%) and then progressively dropped (15% from 6 to 24 h and 16% from 24 to 72 h; data not shown). Although the distribution of NPs per cell was more heterogeneous for randomly selected cells (between 2 and 59 NPs, depending on the cell size), all cells were analyzed from three different images contained Tb-NPs.
). Luminescence signal was collected with a high numerical aperture (NA = 1. 35), immersion oil objective (Uplaspo 60xO, Olympus) and detected using 542 nm band-pass filter (FF01-542/20-25, Semrock Inc. ). Acquisition settings in Winview software controlling the camera were adjusted to optimal conditions (previously determined) regarding signal-to-noise ratio and were fixed to a delay time of 10 μs (after the excitation pulse) and a gate width of 1. 9 ms. If not mentioned otherwise, 400 gates per exposure and an intensifier gain of 100 V were used. Carbocyanine dye DIOC6(3) was illuminated with X-Cite 120Q mercury source (EXFO Photonic Solutions Inc. ) at ∼10 W through a 494 nm excitation band-pass filter (FF01-494/ 20-25, Semrock Inc. ) and 495 nm dichroic mirror (FF495-Di03, Semrock Inc.
). The fluorescent signal was detected using emission filter 522 nm (BP 522/12, Delta). This time, acquisition settings in Winview software were fixed to register in steady state mode with no delay time, gate width, 300 ms; gates/exposure, 1; and intensifier gain of 100 V. DIC images were acquired in similar conditions: with no delay time, gate width, 300 ms; gates/exposure, 1; accumulations, 10; and intensifier gain at 6 V. In order to remove camera dark charge from NPs intensity on TG PL images, the background acquired in the respect of the same acquisition settings (gating sequence, laser frequency) was subtracted from the raw images. Background acquisitions have been recorded by hiding incoming light with a shutter in front of the camera. The background images have also been acquired with an open shutter on the area with no cells and subtracted from DIC images in order to improve the contrast of these last.
Spot Detection. The presence of the NPs inside the cells was analyzed using Icy (Institut Pasteur). Cell area was determined from DIC images and respective ROIs transposed on time-gated photoluminescence images. In this way, NPs present inside the cells were analyzed using a spot detector plug-in with no specific size selection. Data such as mean spot area (μm 2 ), spot size (pixels), spots number, and Tb-NPs intensity (counts) were collected. Z-Stacking Microscopy. In order to avoid high fluorescent background from culture medium, HeLa cells costained with Tb-NPs and DiD were immersed in OptiMEM I reduced serum medium (Fisher Scientific, 11520386) supplemented with 10 mM HEPES. Fluorescent images of HeLa cells were acquired on a wide-field inverted microscope Olympus IX83 (Olympus) using X-Cite Exacte illumination source (Excelitas Technologies) and iXon Ultra 888 EMCCD camera (Andor).
) and transmitted by immersion oil objective (Uplaspo 60xO, Olympus) on the sample. Luminescence signal was filtered through a 542 nm band-pass filter (FF01-542/20-25, Semrock Inc. ) and collected on a PMT detector. The corresponding decay curve was acquired in a multichannel scaling mode during 11 ms and at 2666. 672 ns/channel. The curve was analyzed with FAST software (Edinburgh Instruments) using tail fitting.
* S Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10. 1021/acs. bioconjchem. 8b00069. TEM images and X-ray diffraction diagram of Tb-NPs from batch 2; photographs of Tb-NPs; absorption, excitation, and emission spectra, PL decays, and photophysical properties of Tb-NPs; influence of pH on HeLa cells; Tb-NP quantitation in HeLa cells; raw data PL decay curves, including a PL decay curve of a cell without any Tb-NPs (PDF).
17 ll proliferation, drug combinations and evaluation of synergism The antiproliferative activity of birabresib as single agent and in combination was assessed as previously described. 7 ased on the Chou-Talalay Combination Index (CI),  18  the effect of the combinations was defined as beneficial if synergistic (CI <0. 9) or additive (CI, 0. 9-1. 1). Real-time PCR and western blotting RNA was extracted using the RNeasy kit (Qiagen AG, Hombrechtikon, Switzerland). Real-time PCR and western blotting were performed as previously described. 15 ne expression profiling GEP was done as previously described. 19 Transcript mapping was based on HG19 using the manufacturer's supplied annotation. Data were quantile normalised and batch corrected using ComBat. 20 Transcripts were ranked based on their differential expression between conditions using the empirical Bayes (paired) moderated t-test as implemented in the LIMMA R-package  21  and functional annotated using Gene Set Enrichment Analysis with gene sets from MSigDB 5.
2  22  (hallmark, c2cpg, c2cp, c5), Signa-tureDB  23  and obtained in different experimental conditions,  7 13 15  applying a threshold based on false discovery rate (FDR) <0. 1. Probes presenting a FDR, controlled by Benjamini-Hochberg algorithm, <0. 05 and a log ratio >|0. 3| were considered differentially expressed. Profiling data are available at the National Center for Biotechnology Information Gene Expression Omnibus (http:// www. ncbi. nlm. nih. gov/ geo) database (GSE110134).
Gene expression profiling identifies synergistic combinations Since some of the transcripts and signalling pathways upregulated after birabresib exposure (signalling by FGFR, activation of MAPKK/ERK, DNA repair; Table  S2online  supplementary table 2) were suggestive of an adaptive resistance, and genes like FGFR3, c-JUN, STAT3 and MAP2K1 were upregulated after birabresib treatment (Figure  S7online  supplementary material 1), we  pharmacologically blocked some of these combining OTX015 with inhibitors of FGFR (PD173074), MEK (pimasertib), SRC kinases (dasatinib), ATR (AZD6738), CHK1 (PF00477736) or WEE1 (AZD1775). We first assessed these three combinations in two cell lines, characterised by a different degree of sensitivity to the BET inhibitor. 7 All but one of the six novel combinations were synergistic in the very sensitive cell line Jeko1, but in the less sensitive Granta519, synergism was only observed when birabresib was combined with inhibitors of MEK, ATR, WEE1 and CHK1 (CI 0.
14E+00 ILMN_2384056 GPER -1. 13E+00 ILMN_1782704 CD19 -1. 13E+00 ILMN_1795298 GPER -1. 12E+00 B ILMN_1651496 HIST1H2BD Based on these data, the combinations with the four compounds were expanded to five additional cell lines. The combination of birabresib with the ATR inhibitor was synergistic in 4/5 cell lines, and was associated with an increased DNA damage and induction of apoptosis, as shown by an increase in gamma H2AX and by cleaved PARP1, respectively, at immunoblotting (Figure  S8online  supplementary material 1). The other combinations showed benefit only in two or less cell lines (figure  2B ). Finally, we studied a MCL cell line with secondary resistance to the CHK1 inhibitor,  26  observing that the combination with PF00477736 was synergistic also in this model (median CI 0. 64; 95% CI 0.
CI values for birabresib/pimasertib and birabresib/ PF00477736 in Mino cell line are not plotted due to a median value >3. In each box plot, the line in the middle of the box represents the median and the box extends from the 25th to the 75th percentile (interquartile range, IQR); the whiskers extend to the upper and lower adjacent values (ie, ±1. 5 IQR); outside values have been omitted from the figure. Y-axis, C. I. (C. I. <0. 9, synergism; 0. 9<C. I. <1. 1, additive effect).
) values obtained in Maver1, Mino, REC1, SP53 and UPN1 treated with different concentrations of the BET inhibitor birabresib in combination with the MEK inhibitor pimasertib, the ATR inhibitor AZD6738, the WEE1 inhibitor AZD1775 and the CHK1 inhibitor PF00477736. CI values for birabresib/pimasertib and birabresib/ PF00477736 in Mino cell line are not plotted due to a median value >3. In each box plot, the line in the middle of the box represents the median and the box extends from the 25th to the 75th percentile (interquartile range, IQR); the whiskers extend to the upper and lower adjacent values (ie, ±1. 5 IQR); outside values have been omitted from the figure. Y-axis, C. I. (C. I. <0. 9, synergism; 0. 9<C. I. <1. 1, additive effect).
Sequencing and data processing Libraries and sequencing were performed with a HiSeq 2000 next-generation sequencing platform (Illumina) using a 40 bp single-end protocol, either at the Montpellier GenomiX (MGX; Montpellier, France) or at the Massachusetts Institute of Technology BioMicro center (Cambridge, MA, USA). Sequencing was performed on libraries prepared from duplicates of ChIP. Computational analyses are developed in supplemental experimental procedure. Genomic distribution of PLZF signal was visualized using cis-regulatory Element Annotation System (CEAS). For de novo TF motif discovery, PLZF, EZH2 or the common PLZF/EZH2 peaks were called with MACS2 (http://liulab. dfci. harvard. edu/MACS/) using a q-value cut-off set at 0. 01, and changed into summit regions (-50/+50 pb from peak summit).
The promoter and gene body peak summits were filtered and merged, and only the top 500 most significantly enriched regions (ranked by P-value) were analyzed using Regulatory Sequence Analysis Tools (RSAT)  (30). The related binding motifs were found using JASPAR and HOMER databases. Heatmap visualization of ChIP enrichments at EZH2 and PLZF-enriched non-redundant transcripts was performed using ngs. plot version 2. 47  (31). Plotting of the enrichment profiles were done by ranking the genes using kmeans clustering method (here set up at six clusters). The average profiles were calculated using the mean read coverage among the gene bodies in each resulted cluster. Regions that were differentially enriched for PLZF, H3K27me3 or H3K4me3 upon shPLZF or shEZH2 were identified using MACS2 bdgdiff module and diffReps with slight modifications (P-values re-adjusted in R using Bonferroni correc-tion)  (32)  depending on their localization to distinguish promoter and gene body regions and converted into official gene symbols using biotools.
Gene ontology (GO) analysis using the DAVID database confirmed that regions bound by PLZF were enriched for genes associated with cell signaling, metabolic and immune system processes (Supplementary Figure  S1 ). Interestingly, PLZFtarget genes enriched in H3K27me3 were more associated with developmental processes (Supplementary Figure  S1 ). Taken together our data indicate that PLZF correlates mainly with the H3K4me3 epigenetic mark in KG1 cells and suggest distinct target genes depending on its co-association with H3K4me3 or H3K27me3. ChIP H3K4me3 G A P D H C C N D 3 R A R A S P I 1 A S X L 2 E L M O 1 H O X A 1 0 K L F 2 I D 2 R U N X 2 N E O 1 M Y O 6 0 5 10 15 20 IgG H3K4me3 % bound / input ChIP H3K27me3 G A P D H C C N D 3 R A R A S P I 1 A S X L 2 E L M O 1 H O X A 1 0 K L F 2 I D 2 R U N X 2 N E O 1 M Y O 6 0.
De novo DNA motif searching using RSAT (30) at the 500 highest-ranked (by q-value) PLZF ChIP-seq peaks identified enrichment of an ERG-like binding motif, present at 40% of PLZF-occupied sites and a motif with no known identity in JASPAR and HOMER databases, present at 15% of PLZF-occupied sites (Figure  3E ). This novel motif is, however, matching perfectly to an experimentallydetermined PLZF binding site  (36). When motif discovery was done with the PLZF/EZH2 common peaks, only the ERG binding motif was overrepresented (Figure  3E ), suggesting that PLZF can be associated with two binding site signatures that are distinguishable by the presence or not of EZH2. Motif search for EZH2 did not reveal a strong consensus-binding site confirming the current knowledge on the non-sequence specificity of EZH2  (37).