dataesr/scientific-paragraphs-categorization huggingface dataset:
3G ) were 602 nM and 86 nM, respectively. The high affinity for the Ebox compared with the T-element was surprising. We therefore examined binding to both the TEAD and SOX motifs as well as a GATA element that did not feature in the list of Tbx2 targets identified in our ChIP-seq data set. The results (Supplemental Fig. S3E ) indicated that the affinity of Tbx2 binding to all three motifs was between that of T-element and the E-box, ranging from 292 nM to 173 nM. Collectively, these results may indicate that Tbx2 binding in vitro is relatively nonspecific, but that in vivo specificity is likely achieved through interaction with specific cofactors (see below).
Tbx2 regulates genes coordinating the cell cycle Our results so far suggest that Tbx2 controls positively genes implicated in promoting cell division and represses genes linked to cell cycle arrest. However, genes controlled by Tbx2 are not necessarily directly regulated by it. Therefore, to derive a robust list of genes bound and regulated by Tbx2, we integrated the results from the RNAseq analysis with those from the ChIP-seq data set. The results enabled us to generate a robust list (Supplemental Table  S3 ) of genes whose expression was up-regulated or down-regulated by Tbx2 depletion and that were also bound by Tbx2 in the ChIP-seq experiments. The overlap between the ChIP peaks and differentially expressed genes is shown in Supplemental Figure  S5A. Examples of Tbx2bound genes whose expression is changed on Tbx2 depletion is shown in Figure  5A , and the corresponding mapped ChIP-seq peaks are shown in Figure  5 , B-K, or for Pten in Figure  3B.
Pathology The antibody used for immunohistochemistry corresponding to human melanoma tissue was rabbit anti-Tbx2 HPA008586 (Sigma-Aldrich): low-expressing sample: male, aged 41, metastasis; high expressing sample: female, aged 82, metastasis. Images ob-tained from the Human Protein Atlas (https://www. proteinatlas. org/ENSG00000121068-TBX2/pathology/melanoma).
Chamber wells were mounted with 10 μL of VectaShield mounting medium (Vector Laboratories H-1000-10) and imaged using a Zeiss 710 with a 64× oil objective at 1280 × 740 resolution. Cells transfected with both plasmids were selected for imaging and quantification. To quantify the image data generated, DAPI staining was used to mask the nuclear area and nuclear area size (ns) was measured. mCherry staining was used to mask the nuclear focus (dot) area and the dot area size (ds) was measured. Average GFP intensity in the nuclear and dot area was measured (GFP n and GFP d ) as well as total GFP total fluorescence in nuclear and dot area (GFP tn and GFP td ). GFP dot/nucleus ratio was calculated as ratio = GFP d / (GFP tn -GFP td ) × (nsds).
qPCR reactions were performed with Brilliant II SYBR Green qPCR master mix (Agilent 600828) and analyzed using a Rotor-Gene Q. Melting curve analyses were carried out to ensure product specificity, and, to calculate the relative quantity of gene expression, a standard curve method was performed. Relative abundance of chromatin-immunoprecipitated DNA was normalized to 1% of input DNA. Three biological replicates were included in each experiment, and the data were represented as mean ± SD.
ChIP-seq analysis Prior to mapping to the mouse reference genome (mm10) with Bowtie2 (v. 2. 3. 5) (Langmead and Salzberg 2012) , quality of the raw sequencing data was evaluated using FastQC (v. 0. 11. 7), and adapter contamination was removed using CutAdapt (v. 2. 8) when necessary. Peak calling was performed using MACS2 (v. 2. 1. 2)  (Zhang et al. 2008 ) taking a q-value of 0. 01 as threshold. Bowtie2-generated SAM files were compressed to BAM files, indexed using SAMtools (v. 1. 9), sequentially converted to bigWig files using  USCStools (v. 373) , and uploaded to the UCSC genome browser for visualization. Input and ChIP bigWigs were overlayed in the same track for a more intuitive presentation for peak quality. HOMER (v. 4. 8)  was used to perform known motif enrichment and de novo motif identification, taking given peak size as input as we have paired-end data.
RNA-seq analysis The output raw Fastq files were examined for quality using Fastqc (version 0. 11. 7) and mapped against mm10 genome using STAR (version 2. 6. 1d). Reads per gene from STAR output BAM files were counted using FeatureCounts (subread/1. 6. 2). Counts per gene and sample information were converted to DeSeqDataSet objects and sequentially used as input for differential gene expression analysis using the R package Deseq2 (version 1. 28. 1). The Deseq function filters out lowly expressed genes, calculates normalization size factors, estimates dispersion, applies negative binominal GLM fitting, and calculates Wald statistics to identify differentially expressed genes (DEGs). Genes with an adjusted Pvalue <0. 05 were considered statistically significant DEGs. Heat maps of RNA-seq samples were generated from counted reads in DeSeqDataSet object using the R package ComplexHeatmap.
Raw reads were centered and scaled around the mean and hierarchically clustered. PCAs were generated from log transformed read counts using the R package ggfortify to visualize inter-and intragroup variability arising from transfection, clones, and replicates. Gene set enrichment analysis (GSEA) was carried out using the R package fgsea. One-thousand permutations were conducted for each gene set. Data were preranked by a gene significance score π-value combining expression fold change and statistical significance. π-value of the i-th gene in a data set was calculated as π i = -φ i (-log 10 ρ i ), where φ is the fold change and ρ is the adjusted P-value  (Xiao et al. 2014). Gene ontology (GO) was carried out at http://geneontology. org using statistically significant DEGs, and the top 15 pathways were visualized using the R package ggplot2.
Data-dependent acquisition MS analysis Mass spectrometry data were stored, searched, and analyzed using the ProHits laboratory information management system (LIMS) platform  (Liu et al. 2016). Within ProHits, AB SCIEX WIFF files were first converted to an MGF format using WIFF2MGF converter and to an mzML format using ProteoWizard (v3. 0. 4468) and the AB SCIEX MS data converter (V1. 3 beta). Thermo Fisher Scientific RAW mass spectrometry files were converted to mzML and mzXML using ProteoWizard (3. 0. 4468)  (Kessner et al. 2008). The mzML and mzXML files were then searched using Mascot (v2. 3. 02) and Comet (v2012. 02 rev. 0). The spectra were searched with the RefSeq database (version 57, January 30, 2013) acquired from NCBI against a total of 72,482 human and adenovirus sequences supplemented with "common contaminants" from the Max Planck Institute (http://lotus1.
gwdg. de/ mpg/mmbc/maxquant_input. nsf/7994124a4298328fc125748d00 48fee2/$FILE/contaminants. fasta) and the Global Proteome Machine (GPM; http://www. thegpm. org/crap/index. html). For the Tri-pleTOF 5600 files, the database parameters were set to search for tryptic cleavages, allowing up to two missed cleavage sites per peptide with a mass tolerance of 40 ppm for precursors with charges of 2 + to 4+ and a tolerance of ±0. 15 amu for fragment ions. Deamidated asparagine and glutamine and oxidized methionine were allowed as variable modifications. The results from each search engine were analyzed through TPP (the Trans-Proteomic Pipeline v4. 6 OCCU-PY rev 3)  (Deutsch et al. 2010 ) via the iProphet pipeline  (Shteynberg et al. 2011). SAINTexpress version 3. 3  (Teo et al. 2014 ) was used as a statistical tool to calculate the probability value of each potential protein-protein interaction from background contaminants using default parameters.
Unless otherwise specified, controls were compressed by half, to a minimum of eight controls, using a strategy first introduced by  Mellacheruvu et al. (2013). Two unique peptide ions and a minimum iProphet probability of 0. 95 were required for protein identification prior to running SAINTexpress. MS data visualization and archiving Functional enrichment analysis was performed using g:Profiler using the default parameters. Dot plots and heat maps were generated using ProHits-viz (https://prohits-viz. lunenfeld. ca) (Knight et al. 2017) , while Venn diagrams were generated using Venny 2. 1 (http://bioinfogp. cnb. csic. es/tools/venny/index. html) and refined using a local implementation of Chart Wizard (Google). All MS files used in this study were deposited at MassIVE (http://massive. ucsd.
Acknowledgments The Piggybac vectors were provided by  Kazuhiro Murakami  ( RIKEN, Kobe, Japan ). Maike Effern  kindly provided the px330 plasmid, and  Vincezo D'Angiolella  provided the peFLUT1a-NeoR vector. This work was funded by the  Ludwig Institute for Cancer Research  (C. R. G. , P. L. , J. C. , and J. S. ), the  China Scholars program  (S. L. ), a  Junior 1 salary award  from the  Fonds de Recherche du Québec-Santé (FRQ-S) , and an operating grant from the  Cancer Research Society  (J. -P. L. ). L. L. was supported by  La Ligue National Contre le Cancer , the  Institut National du Cancer , and the  Institut Thématique Multi-Organisme Cancer. M. G. R. was funded by a  Research Project award  ( 1041 ) from the  Oxford Health Services Research Committee , and by a  Basic Sciences and Clinical Translational Research  award from the  Oxford Cancer Centre.
Second, a convolutional encoderdecoder network using nested and dense skip connections is employed to finedelineate candidate masses. Unlike most previous studies based on segmentation from regions, our framework handles mass segmentation from native full mammograms without any user intervention. Trained on INbreast and DDSM-CBIS public datasets, the pipeline achieves an overall average Dice of 80. 44% on INbreast test images, outperforming state-of-the-art. Our system shows promising.
All these tasks are now routinely carried out in a purely data-driven fashion using convolutional neural networks (CNN). Specifically, many contributions have been proposed for breast imaging segmentation, which is an important and active research area. Deep learning-based segmentation is usually performed using convolutional encoder-decoder (CED) architectures such as fully convolutional networks (FCN)  [18] , U-Net  [8]  and Seg-Net  [19]  where the encoder performs multi-scale feature extraction whereas the symmetric decoder upsamples feature maps to recover spatial resolution. U-Net uses skip connections to combine corresponding encoder and decoder feature maps to better recover high-level details  [8]  and works quite well with relatively small datasets. The CED paradigm has been widely adopted by most of the recent approaches designed for breast mass segmentation.
Different from Faster R-CNN  [25]  which uses manually selected boxes, k-means clustering is used to recompute the 9 anchor settings to adapt YOLOv3 to the target mammography datasets. For training, we use pre-trained weights arising from ImageNet  [38]  pre-training. Extension using multi-scale fusion. Although recently proposed detection models  [24, 25, 26, 27]  have achieved excellent results on public common object detection datasets such as Pascal VOC  [39]  or Microsoft-COCO  [40] , they are not optimal to be applied directly to mammograms for two main reasons. First, they are still struggling with object size variance. Typically, most object detectors have worse performance for small objects than for medium or large structures. Especially in our context, this problem becomes more serious as the size and aspect ratio of masses vary strongly.
Experiments and results In what follows, we report experimental settings and results for image-based detection (Sect. The initial learning rate is set to 0. 001 and decreases by 0. 1 after 10k and 20k iterations. The INbreast dataset is too small to be representative if being divided into three subsets (train, validation and test sets). Therefore, we employ a ratio of 70% to split INbreast into train and test subsets containing respectively 74 and 33 images. In order to eliminate the bias error, we use 5 random splits (denoted as T1, T2,. , T5) to provide averaged results with cross-validation.
With a higher threshold, the false positives tend to be reduced while the TPR reaches the peak levels at around λ = 0. 5 ∼ 0. 6. We finally choose λ = 0. 6 considering the trade-off between true-positives and false-positives proposals. We also compare the image-based mass detection with respect to state-ofthe-art using TPR@FPavg (Tab. 3). Even if results are only for reference since datasets used for training and testing are not identical, it highlights that MSF (0. 94@0. 22) significantly outperforms  [13, 43, 28]  in both TPR and FPavg and shows consistent TPR with respect to  [33]  (0. 95@5) while providing less FP.
Conclusion In this paper, we studied the problem of automated mass segmentation from high-resolution full mammograms. We proposed a two-stage framework combining a deep, coarse-scale mass detection with a new multi-scale fusion strategy and a fine-scale mass segmentation using dense and nested skip connections. Our system works as an accurate and automatic mass localization and segmentation CAD system. Results on the INbreast dataset confirm that the proposed pipeline outperforms state-of-the-art with promising model robustness and generalizability. Our contributions make full-mammogram mass segmentation more reliable and steadily push forward the implementation of realistic CAD systems. Future research should consider the potential effects of fusing multi-view and contralateral symmetry information to increase the robustness of breast lesion detection and delineation and therefore improve clinical guidance.
Computational reconstruction of nearly complete genomes from metagenomic reads may identify thousands of new uncultured candidate bacterial species. We have shown that reconstructed prokaryotic genomes along with genomes of sequenced microbial isolates can be used to support more accurate gene prediction in novel metagenomic sequences. We have proposed an approach that used three types of gene prediction algorithms and found for all contigs in a metagenome nearly optimal models of protein-coding regions either in libraries of precomputed models or constructed de novo. The model selection process and gene annotation were done by the new GeneMark-HM pipeline. We have created a database of the species level pan-genomes for the human microbiome. To create a library of models representing each pan-genome we used a self-training algorithm GeneMarkS-2.
Genes initially predicted in each contig served as queries for a fast similarity search through the pan-genome database. The best matches led to selection of the model for gene prediction. Contigs not assigned to pan-genomes were analyzed by crude, but still accurate models designed for sequences with particular GC compositions. Tests of GeneMark-HM on simulated metagenomes demonstrated improvement in gene annotation of human metagenomic sequences in comparison with the current state-of-the-art gene prediction tools.
INTRODUCTION In recent years a number of sequenced microbial isolates has increased significantly; >310 000 annotated prokaryotic genomes, with the total length ∼1 Tb nt, were listed in Gen-Bank as of February 2021 (www. ncbi. nlm. nih. gov/genome/ browse#!/prokaryotes). The number of metagenomes with assembled contigs in the WGS section of GenBank has already reached above 75 000, with the total length of nucleotide sequences exceeding ∼2. 1 Tb nt (www. ncbi. nlm. nih. gov/Traces/wgs/?search=metagenome). Recent improvements in sequencing technology and assembly methods have led to increase in the average length of metagenomic contigs  (1). Still, a vast majority of them are not long enough to allow for estimation of parameters of the 'contig-specific' high order model with precision typical for the algorithms trained on complete or nearly complete prokaryotic genomes  (2) (3) (4) (5).
Therefore, in gene finders working with short sequences with unknown genomic context special methods for finding effective model parameters have to compensate the lack of information. Several algorithms for gene prediction in metagenomic sequences have been developed:  (6) (7) (8) (9) (10). Also, recently developed methods of reconstruction of nearly complete genomes from metagenomic datasets added to the picture thousands of genomes of uncultured candidate bacterial species. The goal of development of the GeneMark-HM pipeline (HM stands for human microbiome) was to improve accuracy of gene prediction in metagenomic contigs, those originated from a shotgun sequencing of human microbiome  (11, 12) , up to a level achievable by gene finding algorithms trained on complete prokaryotic genomes.
Importantly, genomes of sequenced isolates and reconstructed prokaryotic genomes could be used to support more accurate gene prediction in novel metagenomes. The main idea is as follows. Many metagenomic contigs may belong to organisms whose very close relatives have sequenced or nearly sequenced genomes. Still, many other contigs may belong to organisms distant from any known species. We argue that nearly optimal models of protein-coding regions could be determined for annotation of a vast majority of contigs in a metagenome. The solution of the annotation problem is implemented in GeneMark-HM, a pipeline for metagenomic contigs originated from human microbiome. The pipeline is using three gene prediction algorithms along with two reference databases (Figure  1 ). The pipeline components are (i) the newest version of MetaGeneMark, an algorithm using GC content specific models of protein-coding regions  (6, 5, 10).
This tool was designed for analysis of anonymous sequence fragments of arbitrary length, even as short as 400nt; (ii) GeneMarkS-2 (5) a self-training algorithm for gene prediction in sufficiently long sequences, up to complete genomes; (iii) GeneMark. hmm-2, an efficient algorithm that requires a model defined externally or by self-training. GeneMark. hmm-2 is an indispensable part of GeneMarkS-2 and the newest version of MetaGeneMark; (iv) a database of pan-genomes of the human microbiome; (v) a database of pan-genome specific models of proteincoding regions; the models are pre-computed by running GeneMarkS-2 on a representative genome of each pangenome. The GeneMark. hmm-2 algorithm was described in the GeneMarkS-2 publication  (5). All contigs in a metagenome are effectively divided into three groups: (i) contigs that can be analyzed by GeneMarkhmm-2 with models from genomes of close relatives, (ii) contigs with no close relatives with sequenced genomes which are sufficiently long to run self-training GeneMarkS-2 and (iii) short anonymous contigs analyzed by MetaGen-eMark with pre-computed GC specific models.
The database of the species level pan-genomes was built in the gene space, a non-redundant representation of the entire set of genes of a set of strains of the same species  (13). To create a database of representative models for the pan-genomes we used the self-training GeneMarkS-2 running on a representative genome of a clade. The genes initially predicted in each metagenomic contig were used for a fast similarity search against the database of genes of pangenomes. The top hits, if the similarity existed, were used to identify a model for GeneMark. hmm-2 that would make more precise gene predictions. Otherwise, depending on a length of the contig, the GeneMarkS-2 or MetaGeneMark were the right tools to have genes predicted in the anonymous contig (Figure  1 ). Testing of GeneMark-HM on simulated metagenomes demonstrated improvement in accuracy of gene annotation in metagenomic sequences from human microbiome in comparison with the current state-of-the-art gene prediction tools.
Thus, such SGBs are called 'known SGBs',  [kSGBs]. Remaining 77% or 3796 SGBs were deemed to be genome bins of strains of new species and were named 'unknown SGBs' [uSGBs]  (14). Besides the use of SGB rGenomes, we retrieved from the NCBI database genomes of those bacterial and archaeal species that were reported to inhabit human guts  (15) (16) (17) (18) (19) (20) 12, 21, 22). We grouped genomes of strains having the same Taxa ID into six archaeal and 695 bacterial genome clusters (tClusters). Annotation of the representative genomes of these 701 tClusters (Supplementary Figure  S4 ) was available in RefSeq  (23). The tClusters and the SGBs could overlap. Merging of the tClusters and the SGBs produced 4930 updated SGBs along with 152 tClusters that did not overlap any of the SGBs (see Supplementary Materials).
Construction of species level pan-genomes Each pan-genome was built in the gene space. A pangenome represented gene sequences from all the genomes from a particular tCluster or SGB. The purpose of making pan-genomes was the reduction of a reference database of gene sequences. The procedure was implemented as follows. In each SGB (tCluster), all the genome sequences were processed by GeneMark. hmm-2 to predict genes. In this analysis one and the same model of proteincoding region, the SGB (tCluster) specific model was used (see below). The initial set of gene sequences for the pan-genome of an SGB (tCluster) was a whole complement of genes predicted in the SGB (tCluster) representative genome (Figure  2 ). Then the construction process continued by taking one gene set after another in order of diminishing length of the rGenomes (genomes) from a given SGB (tCluster).
Initial gene prediction in an anonymous contig For accurate gene prediction, GeneMark-HM had to recognize the type of genetic code of an anonymous contig. Most frequent genetic code was one with the three standard stop codons TAA, TAG, TGA (genetic code 11), while the second in frequency was the one with two stop codons, TAA and TAG, while TGA was reassigned to code for either Tryptophan (genetic code 4) or Glycine (genetic code 25) (www. ncbi. nlm. nih. gov/Taxonomy/Utils/wprintgc. cgi). Other types of genetic code, e. g. with TAG or TAA reassignments, have not yet been observed in prokaryotes  (29). Parameters of the model of protein-coding region change depending on the role of TGA. GeneMark-HM determines the role of the TGA triplet by the following approach. The GC-specific models of Meta-GeneMark are diversified not only by 1% increment in GC content of contigs but also by the role of TGA (genetic code 11 or 4/25).
Improvement of initial gene predictions Each initially predicted gene is used as a query in BLASTn working in the MegaBLAST mode  (24)  for similarity search against the gene database of pan-genomes. Among the hits with scores having E-value better than 10 -5 we select those in which at least 90 nt of the query sequence and the target in the BLASTn alignment has the ANI value above 95%. Such hits are used to assign the query gene to a pan-genome. If, for a given query, the ANI values of several BLASTn alignments exceed the 95% threshold, the target that has an alignment with the highest BLASTn score is selected. A query gene that has no high enough scores and, thus, is not assigned to a pan-genome is not used in the contig classification. A contig is assigned to a pan-genome to which the majority of the contig's genes have been assigned.
Tests on simulated metagenomes Some SGBs were classified as 'known' SGBs  (14)  if there was a species whose genome in the NCBI database had a high ANI (>95%) with one of the rGenomes in the SGB. We used 625 NCBI genomes associated with 'known' SGBs to generate a set of contigs with lengths distribution like a contig length distribution observed in assembled rGenomes (Supplementary Figure  S2 ). To construct the test set of annotated genes we selected from the 625 genomes only those genes whose protein products had similarity to proteins in other organisms; such genes were supposed to be more reliably annotated by PGAP  (30). The genes co-ordinates determined by PGAP were transferred to the individual contigs. Minimum gene length of both the complete and incomplete genes in the test set was 90 nt.