dataesr/scientific-paragraphs-categorization huggingface dataset:
| Statistical methods For Figures  5A, B, D, G, I, K , kinetics results are expressed in mean ± SEM. For other figures results are expressed as scatter plot with line as mean, and statistical analyses were performed with Kruskal-Wallis test and Dunn's posttest, except for Figures  3, 5,  and 6  for which statistical analysis were performed as Mann-Whitney test. Tests were performed with R and GraphPad software (GraphPad, La Jolla, CA), statistically significance was fixed at P <. 05.
Moreover, CNN has also been applied to colon cancer diagnostics in an image classification style  [35] [36] [37]. Unfortunately, these methods require a large number of labeled training images, making them difficult to develop for clinical applications. Fortunately, recent advances on pattern recognition neural networks make it possible to detect and localize certain objects from a single image  [38] [39] [40]  using a small training dataset. These networks search for multiple patterns in each training image and allow PR-networks to be trained from fewer images as compared with older models. PR-networks have been previously explored in multiple settings  [29] , however, PR-networks paired with OCT have been unexplored in the colorectal cancer literature. Here, we report the first study of PR-OCT in differentiating normal from neoplastic colorectal tissue.
Colon specimen preparation Patients undergoing extirpative colonic resection at Washington University School of Medicine were recruited prospectively into our study. Immediately following surgical resection, colon specimens underwent imaging of both normal bowel wall as well as areas of known abnormality. For each 3-D imaging task, several volumes of 10 mm x 20 mm x 1. 6 mm or 5 mm x 10 mm x 1. 6 mm were selected depending on the available time for the image task. Each imaged volume has one 3-D dataset for further data processing. Those scanned volumes were far from each other to preserve independence between data sets. This study was approved by the Institutional Review Board of Washington University School of Medicine, and informed consent was obtained from all patients. All samples were imaged within a one-hour period prior to fixation in formalin for routine pathological evaluation.
OCT system setup Our swept-source OCT system (Figure  1A ) is based on a swept source (HSL-2000, Santec Corp. , Japan) with a 1310 nm center wavelength, 110 nm full width at half maximum bandwidth, and 20 kHz scan rate. The input light is split by a 50-50 fiber coupler and then directed to a reference arm and a sample arm by two circulators. Fiber polarization is controlled by two manual fiber polarization controllers. The reference arm is attenuated by a variable density filter. A galvo mirror system (GVS002, Thorlabs) is used to scan the sample arm light beam. The interference signal was detected by a balanced detector (PDB450C, Thorlabs) and sent to a data acquisition board (ATS9462, Alazartec Technologies Inc). Real-time OCT B-scan images are displayed on the monitor. The lateral resolution of the system in air was 10 µm, and the axial resolution was 6 µm by the FWHM definition.
OCT image labeling and pattern marking Prior to each imaging study, pathologists or surgical residents provided guidance to the researchers on the sample orientation and location of the examined tumor. Then OCT recorded several datasets accordingly. Each OCT image was labeled as "cancer", "normal", "adenomatous polyp", "treated complete responder", and "treated non-responder" based on the pathology record of each specimen. This manuscript focuses on identifying normal from malignant specimens, and the preliminary prediction results for other tissue types are also reported. Two key imaging patterns were then marked to identify normal colonic mucosa from malignancies: "Teeth" and "Noise". Literatures previously reported that normal colonic mucosa is associated with a dentate imaging structure, which we termed "Teeth" for this neural network  [41, 42].
Preparation of PR-OCT: establishing OCT dataset and training RetinaNet model A total of 20 tumor areas, 16 normal areas, 2 adenomatous polyp areas, 2 treated areas from complete responders, and 2 treated areas from nonresponders from 24 patients (mean age 69 years old, range: 53-91) were imaged and processed ex vivo from August 2017 to July 2019 in Washington University School of Medicine. Diagnoses were ascertained by subsequent surgical pathology examination. Details can be found in Table  1. In the training cohort of images, 838 labeled OCT images from 4 tumor areas and 4 normal areas acquired from 4 patients were included, where 2176 "Teeth" and 1875 "Noise" patterns were marked. We only used 4 tumor areas and 4 normal areas for training since the AUC of the ROC for our testing set did not improve too much as we included more areas in the training set, as shown in Supplementary Material (Figure  S1 ).
Qualitative OCT imaging results Distinct patterns were identified in normal colon tissues. Uniform crypt structures of normal colonic tissue created dentate structures within SS-OCT 3D-scanning images; likewise, the heterogeneous structure distribution of cancerous tissue yielded sparse dentate structures with little organized pattern. Representative images of normal colon tissues, cancerous tissues, and corresponding H&E slides are shown in Figure  3. Figure  3A  displays an en face image of a normal colon specimen formed by axial summation along the depth direction (z-dimension) of the entire 3D dataset for visualization. A clear crypt structure can be visualized as dot patterns in the image. When seen in cross-section (Figure  3B  in XZ plane and Figure  3C  in YZ plane), the uniform crypt structures create a dentate pattern that is replicated throughout normal colonic wall structure.
Acknowledgements Research reported in this publication was partially supported by the  NIH  ( R01CA151570 ,  R01EB 002136 , and  R01 CA228047 ), and by the  Washington University School of Medicine Surgical Oncology Basic Science  and  Translational Research Training Program  grant  T32CA009621  from  NCI ). We thank  Michelle Sperry , study coordinator, for consenting patients to the study.
The proper temperature profile for each gene was chosen after optimization following the standard protocol: 95 • C for 15 min for HOT FIREPol ® DNA Polymerase activation and 40 cycles at 95 • C for 5-10 s for denaturation, 60 • C for 10-30 s for annealing, and 72 • C for 15-30 s for the extension. The primers used for each examined gene are presented in Table  10. The PCR reactions were carried out using a thermocycler RotorGene Q (Qiagen, Germantown, MD, USA) with RotorGene Q software. The results obtained for the examined genes were normalized to the reference gene chosen from among three genes [glyceraldehyde 3-phosphate dehydrogenase (GAPDH), β-actin (ACTB) and histone deacetylase 1 (HDAC1)] using program NormFinder (Molecular Diagnostic Laboratory, Aarhus University Hospital, Aarhus, Denmark) for identification of the optimal normalization gene.
Li et al. [12]  used the Fire Dynamics Simulator (FDS) software to study the plugholing phenomenon that occurs under the mechanical smoke exhaust in a tunnel when fresh air is drawn directly into the smoke extraction system from the bottom layer. This phenomenon is undesirable and should be avoided as it greatly reduces the efficiency of smoke extraction in tunnel fires  [1, [13] [14] [15] [16] [17]. In recent years, further experimental  [18] [19] [20] [21]  and numerical  [22] [23] [24]  studies investigated the performance and efficiency of extraction systems on smoke control in the case of a fire focusing on the vent size and fire source location  [22] , the tunnel slope  [20] , the location and shape of the exhaust vents  [6]. The ventilation of road tunnels can be also controlled by combining both ventilation systems, transverse and longitudinal, an approach that has been recently considered in the tunnel ventilation literature.
Materials and Methods: A discovery set of 52 patients with advanced lung cancer who underwent T2-weighted MR imaging at 3 Tesla in a single center study from August 2017 to May 2019 was used. Findings were then validated using a validation set of 19 additional patients included from May to October 2019. Tumor type was obtained from the pathology report after trans-thoracic needle biopsy, metastatic lymph node or metastasis samples, or surgical excisions. MR images were preprocessed using N4ITK bias field correction and by normalizing voxel intensities with fat as a reference region. Segmentation and extraction of radiomic features were performed with LIFEx software on the raw images, on the N4ITK-corrected images and on the fully preprocessed images. Two analyses were conducted where radiomic features were extracted: (1) from the whole tumor volume (3D analysis); (2) from all slices encompassing the tumor (2D analysis).
Receiver operating characteristic (ROC) analysis was used to identify features that could distinguish between adenocarcinoma and other histological types. Sham experiments were also designed to control the number of false positive findings. Results: There were 31 (12) adenocarcinomas and 21 (7) other histological types in the discovery (validation) set. In 2D, preprocessing increased the number of discriminant radiomic features from 8 without preprocessing to 22 with preprocessing. 2D analysis yielded more features able to identify adenocarcinoma than 3D analysis (12 discriminant radiomic features after preprocessing in 3D). Preprocessing did not increase false positive findings as no discriminant features were identified in any of the sham experiments. The greatest sensitivity of the 2D analysis applied to preprocessed data was confirmed in the validation set.
Population Patient imaging, pathology, and clinical data were selected from the single-center MRI-omics database built as part of a retrospective study approved by the Institutional Review Board (protocol 32-2016, study number: 2016-A00813-48). All patients gave their written informed consents. From August 2017 to May 2019, patients with advanced lung cancer referred for brain MR imaging to detect cerebral metastases were proposed to undergo additional lung MRI sequences. Among 83 eligible patients, 8 were not included because the pathology of the tumor was not available, and 23 were excluded because of motion artifacts or incomplete MR protocol (Figure  1 ). The final population thus included 52 patients (34 men and 18 women) from 44 to 89 years old (mean age 66 years; SD 11. 3 years). Among these patients, 42 subjects (80%) were active smokers and 2 (4%) were exposed to asbestos.
Correction of Magnetic Field Inhomogeneity Magnetic field inhomogeneity artifacts were corrected based on the estimation of a bias field constrained to be spatially smooth  (32). The bias field was estimated with the publicly available N4ITK algorithm using ANTs software (http://stnava. github. io/ ANTs) with the standard setting of hyper-parameters. Each voxel value in the raw image was then modified by dividing its value by the corresponding voxel value in the bias field. This approach is widely used for brain studies  (26) , but not for other organs. It reduces variations of the mean intensity between similar tissues located at different positions within the field of view.
Segmentation An expert radiologist with 4 years experience in thoracic imaging segmented the tumors using the LIFEx software (www. lifexsoft. org)  (33). A coarse region surrounding the tumor was manually defined and then refined using an intensity threshold manually set for each patient to delineate the tumor from the lung tissue. The borders between the tumor, the mediastinum and the chest wall were manually delineated. The tumor volume was defined as a single 3D connected component. To further investigate the impact of the segmentation on the results, the original ROIs were modified by automatically shrinking the contours by two pixels.
Radiomic Feature Extraction Radiomic features were computed in the tumor region for both the raw, the N4ITK-corrected and the normalized N4ITKcorrected MR images using the LIFEx software compliant with the Image Biomarker Standardization Initiative guidelines (https://arxiv. org/abs/1612. 07003). Features included shape features, first-order features that do not account for the spatial arrangement of voxel values, and second-order (textural) features that reflect how voxel values are spatially arranged. The definition of the matrices needed for textural feature calculations requires gray level quantization. For raw images, N4ITK corrected images, and normalized N4ITK images, fixed bin sizes were used for that gray level quantization step. The bin size was chosen so that 256 bins always encompassed all voxel values observed in the tumors, yielding a bin size of 15 units for raw images, 10 units for N4ITK corrected images, and 0.
To investigate the impact of the tumor delineation on our results, all the segmented tumors were automatically eroded by an element of size 1. 5 mm and results were similar, confirming the greater sensitivity of 2D analysis on normalized N4ITK corrected data to identify discriminant features (see Supplemental Table  5 ). This suggests that for tumors with large volumes as in our study, significant variations in results due to small changes in tumor contour delineation are unlikely. Histology was mostly determined by trans-thoracic core needle biopsy, which might not be representative of the whole tumor volume. This is a definite limitation as lung tumors may have heterogeneous histological types depending on the location in the lesion  (53). Our task was to distinguish between ADK and all other tumor types, so this second tumor group was quite heterogeneous in itself.
ACKNOWLEDGMENTS The authors thank  Dr. M. Kambouchner  and  Prof. A. Martin  pathologists,  Drs. B. Duchemann  and  K. Chouahnia  clinical oncologists. ML thanks the  Agence Régionale de Santé Ile de France  for funding his research grant. All authors thank  GE Healthcare  for funding a technician to collect clinical data and  Institut National de la Santé et de la Recherche Médicale  for funding open access publication fees.
Hybridizations between species and subspecies represented major steps in the history of many crop species. Such events generally lead to genomes with mosaic patterns of chromosomal segments of various origins that may be assessed by local ancestry inference methods. However, these methods have mainly been developed in the context of human population genetics with implicit assumptions that may not always fit plant models. The purpose of this study was to evaluate the suitability of three state-of-the-art inference methods (SABER, ELAI and WINPOP) for local ancestry inference under scenarios that can be encountered in plant species. For this, we developed an R package to simulate genotyping data under such scenarios. The tested inference methods performed similarly well as far as representatives of source populations were available.
2017)  or some contributors may not be represented in available germplasm  (Sardos et al. 2016). Moreover, it remains unclear how some features, regarding reproduction modes (e. g. , selfing or vegetative reproduction) that may be encountered in plant models, may affect the performance of LAI methods. Hence, in fruit crops such as citruses or banana, individuals resulting from inter(sub)specific hybridizations were further multiplied by vegetative propagation (also termed clonal propagation). Thus, they do not form a population but rather a collection of individuals sometimes of different origins with ancestry mosaics of relatively large blocs depending on the number of sexual generations they may have undergone. Datasets of vegetatively propagated individuals may thus be heterogeneous in terms of ancestry structure and in terms of time in generations since admixture events.
Given the lack of available methods dealing with polyploids, we only considered diploid individuals. Among the 22 LAI approaches recently reviewed by  Geza et al. (2019) , we chose to evaluate three methods -SABER  (Tang et al. 2006) ; ELAI  (Guan 2014 ) and WINPOP  (Pas xaniuc et al. 2009 ) because they do not require prior phasing of the data and they could cope with more than two ancestries. We developed an R package to perform simulations and we focused our evaluation on the influence of i) the level of divergence between the source populations; ii) the number of generations since admixture for the admixed populations; iii) the number of contributing ancestries and their representation in the analyzed data sets; and iv) the mode of reproduction such as selfing (in a source representative population) or vegetative propagation (in the admixed population).
Simulation tool We developed an R package (named plmgg for plant-like mosaic genome generator) to simulate individual chromosome-wide genotyping data from an arbitrary number of populations P deriving from S differentiated source populations under scenarios that may include hybridization events and modes of reproduction representative of plant model evolution (i. e. , selfing or vegetative propagation). The simulation approach is depicted in Figure  1  and consisted of the three following successive steps i) coalescent simulation of a sample of founder chromosomes from S differentiated source populations; ii) forward in-time simulation of P populations deriving from the S source populations with complex demographic scenarios involving various modes of reproduction and admixture; and iii) sampling of individuals from the P populations to generate the genotyping data sets.
For coalescent simulations, we relied on the scrm algorithm  (Staab et al. 2015)  implemented in the R package coala  (Staab and Metzler 2016)   is the number of chromosomes from source population s). Sources were assumed to derive from a single ancestral population under a pure-drift model of divergence with a star-shaped history. The divergence scenario of the source populations was specified with three parameters: i) the divergence time t measured in units of 4N e (i. e. , t ¼ t 4Ne where t is the number of generations since the ancestral population and N e is the haploid effective source population size assumed to be the same for the S source populations); ii) the scaled mutation rate u ¼ 4N e m (where m is the mutation rate per site and per generation); and iii) the scaled recombination rate r ¼ 4N e r (where r is the recombination rate per site and per generation).
Model fitting was carried out with the default Expectation-Maximization (EM) algorithm. WINPOP, included in LAMP. =2. 3  (Sankararaman et al. 2008) , is a model-based LD-free method that focuses on ancestry informative markers (AIM) to assign local haplotype blocks to their originating source populations  (Pas xaniuc et al. 2009). WINPOP works with variable-size overlapping windows along the chromosomes, and uses a clustering method to assign ancestries in each window, based on estimates of global ancestry proportions. WINPOP was used with the simulated recombination rate and default parameters for the configuration files including a LD pruning cutoff of r 2 = 0. 1 and a fraction of sliding window overlap of 20%. Both WINPOP and SABER required estimates of global ancestry proportion. These were obtained by running the default unsupervised hierarchical clustering algorithm implemented in the ADMIXTURE software  (Alexander et al.
2009; Guan 2014 ). We developed an R package (plmgg) to simulate genotyping data under a wider range of scenarios and sample designs that include plantlike features. Even if this simulator has some limitations (it does not simulate recombination hotspots, multiple recombination per chromosomes nor selection), it allowed us to assess the influence on LAI accuracy of the level of differentiation, of multiway admixture with up to six ancestries and of limited sampling of source populations. In addition, the impact of two plant reproduction modes was also evaluated: selfing (in a source representative population) and vegetative propagation (in the admixed population). Overall, the two main factors that contributed to improve accuracy of all the three tested LAI approaches were the level of divergence between source populations (the higher, the better) and the number of generations since admixture (the smaller, the better) which was not surprising given their expected influence on the complexity of genome mosaics.
ACKNOWLEDGMENTS This work was supported by funding from the  Agropolis Fondation  ' GenomeHarvest ' project (ID  1504-006 ) through the  French 'Investissements d'avenir' program  ( Labex Agro : ANR-10-LABX-0001-01 ) and by  CIRAD. We thank  Franck Curk ,  Gilles Costantino ,  Benjamin Heuclin ,  Joao D Santos ,  Guillaume Martin  for fruitful discussions and the  PIs of the 'GenomeHarvest ' project,  Manuel Ruiz  and  Angélique D'Hont. We also thank the two anonymous reviewers for their constructive comments. This work was also supported by the  CIRAD-UMR AGAP HPC Data Center of the South Green Bioinformatics platform  (http://www. southgreen. fr/).
WES, bioinformatics filtering of variants, Sanger sequencing and interactome analysis Genomic DNA was extracted from all patients' blood samples, using standard procedures. Details regarding NGS experiments and primary bioinformatics analysis have been included as Supplementary methods. The detected sequence variants were filtered in parallel using R (v3. 5. 1; www. r-project. org), Excel (Microsoft Office 365) and VarSeq (Golden Helix Inc. , Bozeman, USA) software. A subset of 487 candidate genes (named HELLP-487) was created by screening several databases and used to filter rare (MAF <0. 005) variants evoking potentially harmful effects (Supplementary methods, Supplementary Table  2 ). Figure  1  illustrates the bioinformatics pipeline used for candidate variants´ filtering. We screened these variants in the C group of women.
We identified 87 different sequence variants of different types: 60 missense, 21 indels, 5 nonsnese and 1 splice-site. The Supplementary Figure  1  shows the results of the STRING interactome analysis. The network probability for enrichement was p=1. 18 10-5. The gene ontology analysis revealed in particular KEGG and Reactome pathways linked to the coagulation cascade (FGG, F5, SERPINC1, SERPINB2). The analysis by Webgestalt against the GLAD4U disease database revealed the significant enrichments (FDR <0. 05), also focused on thrombosis defects (Supplementary Figure  2 ).
Structural modelling of mutant proteins Various protein structures were identified in the PDB database which facilitated modelling mutations identified in patients suffering HELLP syndrome (Table  2 ). Using P models (n=12) for simulating the prediction algorithm revealed that only 8% of the polymorphic changes modified a protein's local physicochemical properties (Supplementary Table  3 ). Ten out of the 17 candidate mutants (57%) found in patients affected by HELLP (for which PDB structures were available for in silico modelling) had significant changes regarding ΔE and residue interactions (Supplementary Table  3 ). Six of them (ERAP1-p. Ile915Thr, ERAP2-p. Leu837Ser, COMT-p. His192Gln, CSADp. Pro418Ser, CDH1-p. Ala298Thr and CCR2-p. Met249Lys) led to destabilising protein structure (Figures  3, 4  and 5 , Table  2 , Supplementary Figures  3, 4  and 5 ).
Interestingly, using computational filters in those studies (similar to those used in the present work) enabled identifying mutations leading to significant structural/functional disturbances, several of which were validated thereafter by in vitro assays  [32, 33]. To identify candidate variants we used specific astringent filters (non-synonymous variants, <0. 005 MAF, four bioinformatics predictors of deleterious effects, protein sequence alignments). The genomAD database was implemented as a robust source of genomic information (> 60,000 control individuals). The very low frequency of these variants in the database argued in favor of a potential causative effect. We selected 119 sequence variants, 110 of which were definitely confirmed by Sanger sequencing (92%). This corroborated our NGS experiments' technical reliability.