dataesr/scientific-paragraphs-categorization huggingface dataset:
The expression fold change was calculated using the 2 -∆∆CT method  (Livak and Schmittgen, 2001) , as PCR efficiencies had previously been found to be similar. Low and high fold changes were calculated by adding or subtracting the standard deviation from the fold change. The results are expressed as log2 of the fold changes. Kruskal-Wallis test was used to determine the presence of significant log2 fold differences in the dataset (p-value = 1. 89e-04). Then, a Wilcox test was performed to compare log2 fold changes between sample types (AP, AB, EP, EB) to identify significant differences between pairs. A p-value less than 0. 05 was considered significant. Yu, G. , Smith, D. K. , Zhu, H. , Guan, Y. , Lam, T. T. -Y. , 2017. ggtree: an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data.
Funding This work was funded by the  National Institutes of Health ,  NIAID R01AI113287  to CSP. MAY was funded by  National Science Foundation Graduate Research Fellowship Program  under grant No. DGE-1747503. LLB was funded by  National Institute of Allergy and Infectious Diseases of the National Institutes of Health  under Award No. T32-AI55391. TDM and MBO were funded by  National Science Foundation Graduate Research Fellowship Program  under grant No. DGE-1256259  with additional funding for TDM by  National Institute of Health National Research Service  Award No. T32-GM07215.
Nuclear Microsatellites Development To identify and select nuclear microsatellite markers (nSSR) specific to P. latifolius, we used non-enriched genomic libraries of two P. latifolius samples from Cameroon (POD03-MV02 sample) and Equatorial Guinea (POD04-CM186) that were previously sequenced on an NextSeq platform (Illumina, San Diego, CA, USA), paired-end 2 × 150 bp, with 1,483,618 R1-R2 reads and 1,565,092 R1-R2 reads, respectively  [16]. Each genomic library was pair-assembled with PANDASEQ  [26] , to use the software QDD with default settings  [27]  and launch SSR detection, elimination of similar sequences, and primer design steps. We detected 37,564 SSR primer combinations, before filtering them with these main criteria: (i) eliminating duplicate primers for the same microsatellite locus, (ii) having primers located at least 20 bp from the pure SSR motif, (iii) reaching at least eight dior trinucleotide repeats, (iv) providing PCR products 130-300 bp long, and (v) avoiding the formation of self-dimers (tested using MULTIPLE PRIMER ANALYZER, Thermo Fisher Scientific, https://www.
Population Sampling and Nuclear Microsatellites Genotyping Leaf or cambium samples of P. latifolius were collected in the main Afromontane blocks of the western Central African region (four sites in Cameroon CM, three sites in the Republic of the Congo CG, and one site in Angola AO), and in four East African sites (three sites in Kenya KE, one site in Rwanda RW), sampling 8 to 37 samples per population (Figure  1 , Table  S2 ). Whenever possible, GPS coordinates were taken for each sample. Populations of Podocarpus latifolius sampled for genetic analyses in Angola (AO), Cameroon (CM), Republic of the Congo (CG), Kenya (KE), and Rwanda (RW). Symbols and colors correspond to the DAPC analyses. Black dots refer to the distribution range of P. latifolius (data extracted from GBIF, field missions, herbarium specimens (BRLU, BR, P), the Conifers of the world database, see  [16] ).
Colors on the map refer to elevation, using the software QGIS (QGIS. org, 2021. QGIS Geographic Information System. QGIS Association (http://www. qgis. org, accessed on 25 January 2022). We genotyped 270 samples with the nine newly developed nSSR markers. Total genomic DNA was extracted from 15-25 mg of dried leaves or cambial tissue, using the Nucleospin 96 Plant II kit (Macherey-Nagel, Duren, Germany) following the manufacturer's instructions. PCR reactions were performed using the nine multiplexed nSSR loci in 15-µL total volumes: 0. 15 µL of the reverse and 0. 1 µL of the forward (0. 2 µM for both) microsatellite primers, 0. 15 µL of Q1-Q4 labeled primers (0. 2 µM each), 7. 5 µL of Typeit Microsatellite PCR Kit (QIAGEN), 3. 6 µL of H 2 O, and 1. 5 µL of DNA extract. PCR conditions were: 3-min initial denaturation at 94 The data generated for each specimen were scored using the microsatellite plugin 1.
To this end, we computed pairwise kinship coefficients (Fij) for all pairs of individuals from the same population (using J. Nason's estimator in software SPAGEDI  [32] ) and regressed them on the logarithm of the spatial distance, giving the slope b. Fij values were averaged over a set of spatial distance intervals to show how kinship decays with distance, and the strength of the spatial genetic structure was quantified by the statistic Sp = -b/(1 -F(1)), where F(1) is the average kinship coefficient between nearby individuals (here between 10 and 100 m).
Demographic Inference The demographic history of P. latifolius was investigated with DIYABC 2. 1. 0 beta  [37, 38]  with a focus on populations from Cameroon to assess their divergence time and populations dynamics, as well as their relationship to East African populations. Nuclear SSR datasets were simulated under different demographic scenarios (coalescent simulations) and compared to the real dataset using summary population genetics statistics (approximate Bayesian computation, ABC) in order to estimate the most likely scenario. We investigated both when the actual sampled populations diverged and their demographic history. A two-step simulation process was tested. First, we included only Cameroonian populations (CMebo in Ebo Mt, CMkup in Kupé Mt, CMngo in Ngoro Mt, CMoku in Oku Mt) to evaluate the timescale of fragmentation events (scenario 1; from the last Holocene environmental crisis to the Glacial Maximum, or scenario 2; before 250 kyrs).
One million genetic datasets were simulated per scenario. The posterior probabilities of each scenario and the respective demographic parameters were estimated using local logistic regression  [37]. Confidence for a scenario was the number of "pseudo-observed" datasets drawn from this scenario and correctly assigned to it. Confidence in parameter estimates was calculated as mean bias (difference between the estimated and the "true value" divided by the "true value") and factor 2 (proportion of estimated values falling 50-200% of the "true value"). Time parameters were estimated in generations and their conversion into years depends on the actual generation time of P. latifolius, which is difficult to assess. In South African forests, tree ring counts gave a median age close to 100 years for trees with a diameter at breast height (dbh) ranging from 5 to 100 cm and a median dbh of 23 cm  [17].
Funding: This study was financially supported by the BELMONT FORUM research program VULPES (French ANR-15-MASC-0003, coordinator: Rachid Cheddadi), the Belgian BRAIN-be BELSPO research program BR/132/A1/AFRIFORD (coordinator Olivier J. Hardy), and the Belgian Fonds de la Recherche Scientifique (F. R. S. -FNRS, grants J. 0143. 15 and J. 0292. 17). The study was initiated also during the IFORA (ANR-06-BDIV-0014, coordinator Michel Veuille) and C3A (ANR-09-PEXT-001, coordinator Anne-Marie Lézine) French research programs.
Acknowledgments: Thanks are due to  Rachid Cheddadi  ( CNRS Montpellier ),  Esra Kaymak  ( ULB-EBE ) for their constructive discussions, and to  Laurent Grumiau  ( ULB-EBE Molecular Biology platform, Belgium ),  Latifa Karim  and  Wouter Coppieters  ( GIGA Liège, Belgium ) for their advices on genomics. Finally, we are grateful to other colleagues who have kindly participated in the sampling for several years:  Thierry Desjardins ,  Vincent Droissart ,  Kévin Lemonnier ,  Alexandra Ley ,  Olivier-Valérie Séné ,  Bruno Turcq ,  Maria Cristina Duarte & Maria M. Romeiras  ( LISC herbarium, Portugal ),  Geoffrey Fadeur  ( BRLU herbarium, Belgium ), with special thanks to  Claire Micheneau  and  Rosalía Piñeiro. We also thank two anonymous reviewers for their constructive comments.
The network uses skip connections between the down-sampling and up-sampling paths to recover the spatial context loss in the encoder, performing more accurate segmentation. Different previous cardiac imagesegmentation networks have utilized the 3D U-Net  [15]  and the 3D V-Net  [16]  as their basis architectures, reaching efficient segmentation for several cardiac segmentation tasks  [17] [18] [19]. Isensee et al. [20]  introduced nnU-Net (no-new-U-Net) to automatically adjust preprocessing techniques and network architectures to a medical dataset. nnU-Net has been well applied to several segmentation tasks  [21, 22]. Despite the potential of deep learning for several fields, few deep learning-based methodologies have been proposed in the literature for infarct segmentation from LGE-MRI. Fahmy et al.
61 ± 2. 54% for LV infarct segmentation on the 3D test dataset. Recently,  Arega et al. [25]  proposed a segmentation network that, firstly, generates uncertainty estimates during its learning process using the Monte Carlo dropout method. Secondly, it integrates the uncertainty information into the loss function for better segmentation results. The proposed model showed an accurate segmentation of all myocardial regions. The major disadvantage of adopting 2D and 3D FCNs is that they are usually trained with cross-entropy, soft-Dice losses, and compound loss functions. These losses neglect high-level features that represent the implicit anatomical structures, such as their shape or topology and spatial relationships between tissues  [26]. Likewise, U-Net architecture does not influence contextual or anatomical consistencies.
Study Subjects and Data Acquisition The LV myocardial diseases LGE-MRI dataset used in our experiment is provided by the EMIDEC challenge  [50]. The data was acquired with 1. 5 and 3 T Siemens MRI scanners. Additional data (12 features), including physiological and patient medical background were also included but not used in the present study. The training dataset includes 100 subjects, among them 67 scar cases and 33 normal cases. The EMIDEC test dataset consists of 50 exams divided into 1/3 healthy cases and 2/3 unhealthy myocardial cases (Table  1 ). All subjects underwent a standardized imaging protocol of LGE-MRI. Each patient's LGE-MRI contains a series of 5-10 short-axis slices, wrapping the whole LV myocardium from the base to the apex. Manual boundaries (LV cavity, LV healthy myocardium, scar, and MVO), delineated by a biophysicist with deep competency in medical imaging, are presented for the training set.
Pre-Processing The data pre-processing is one part of the pipeline. We performed pre-processing steps to the test data similar to those that were performed on training data. It includes the following tasks. To reduce the class imbalance in medical images and eliminate unimportant anatomical structures, all images were cropped automatically to the region whose center is the centroid of the LV cavity. LGE-MRI cropping was fully automatic. Cropping results in both reducing the background class as well as the computational time for training our model. Since the EMIDEC data shape varies between various subjects, it is necessary to alleviate the shape mismatch by reshaping each patient's volume image. Thus, all MR volumes are reshaped to 96 × 96 × 16 with appending empty slices  [51]. A standard adaptive histogram equalization algorithm is then used to enhance the local image contrast  [52, 53]  and, hence, improving the efficiency of the training process.
A 3D U-Net is used to extract the volumetric spatial features. This architecture aims to restrict the impact of intra-slice heterogeneity and take into account the volumetric information for more accurate segmentation. Thus, the cross-validation outputs automatically lead to the best ensemble to be employed for the testing prediction. The anatomical network uses 2D, 3D, and cascaded U-Net to alleviate the convenient shortcoming of a 3D U-Net architecture on datasets with large-size images. In a cascaded U-Net, a 3D U-Net is first trained on 3D down-sampled images for a preliminary segmentation. Then, the resulting segmentations are up-sampled to the original input resolution and passed to a second 3D U-Net, trained on patches at full resolution for final prediction segmentation. The anatomical network was implemented on NVIDIA Tesla V100 with four embedded GPUs using the Pytorch source code (http://github.
Class Constraint All along the optimization process, we develop a binary classification module to distinguish patients with myocardial infarction from regular patients. Hence, we incorporate classification priors for the segmentation process to constrain the predicted tissue in this known category. As Figure  4  illustrates, we propose the inclusion (IC in cyan) and class constraint (CC in purple) modules, connected as an extended network and to the bottom of the 3D U-Net, respectively, for penalizing the final prediction of myocardial segmentation output. These constraints are introduced as auxiliary L IC and L CC loss terms to highlight small classes' tissue. One of the main challenges with diseased myocardial tissues' segmentation is the class imbalance (i. e. , LV healthy myocardium has way more instances than pathological regions) in the dataset.
As shown in Table  1 , the EMIDEC test dataset provides 1/3 healthy patients and 2/3 infarcted patients. The resulting segmentation of training with the cross-entropy loss function may not be effective, as the most frequent class may leverage training. That is why it is critical to optimize the appropriate loss function for accurate segmentation. We train our penalty-based pathological network with a fusion of multi-class mean intersection over union (IoU) loss L Seg  [57] , inclusion constraint loss L IC , and a class constraint loss L CC. This final loss function is given in Equation (3): L Final = L Seg + λ IC × L IC + λ CC × L CC , (3) where λ IC denotes the inclusion constraint penalty-term and L IC indicates the L2 loss function that is defined in Frobenius norm Equation (  4 ); λ CC represents the class constraint penalty-term and L CC indicates the cross-entropy loss function.
( 7 ) The HD computes the degree of mismatch between A and M by calculating the Euclidean distance of each point a i that is most distant from any point m j. The absolute volume difference (AVD) calculates the difference between our methodgenerated V A and manual V M LV volumes by the expert. In addition to the AVD, abso-lute volume difference rate according to the volume of the myocardium (AVDR) metric, Equation (  8 ) was reported: AVDR = AVD V MYO , (8) where AVD = |V A -V M | and V MYO is the myocardium volume of the manual annotation. For consistency with other publications, the metrics were based on the online evaluation platform (http://github. com/EMIDEC-Challenge/Evaluation-metrics/) (accessed on 1 April 2020). Therefore, region-and volume-based metrics were measured for each test patient, and we calculated their mean values to investigate the performance of our approach for myocardial disease segmentation.
Results Analysis and Extensive Discussions We compared the results of our proposed network to different previous methods used in the EMIDEC challenge, enclosing Feng et al. [42] , Huellebrand et al. [44] , Yang et al. [46] , Zhang  [47] , Camarasa et al. [41] , Zhou et al. [48] , and Girum et al. [43]. The LV myocardium, scar, and MVO were segmented using those methods from the same test dataset. We also compared the ICPIU-Net segmentation results to Brahim et al. [61] , which is based on only a shape prior constraint for myocardial disease segmentation and manual delineations. The results prove that our approach performs well on all substructures. A statistical test was conducted for each model to check if the difference of the results between the coronary arteries is significant. We have found that there is no statistical difference.
There are a total of 100 exams with published labels to train our algorithm. We make random five-fold cross-validations by shuffling the scan sequence and splitting the database into five folds to provide a more comprehensive evaluation of our network. In Tables  2  and 3 , the cross-validation results of our segmentation output and of two networks' segmentation output are represented. The conducted experiments' metrics on the internal cross-validation shown in Table  2  prove that our approach can accurately segment each target tissue despite using a small sample size. The standard deviations, which are relatively small, demonstrate the stability of our proposed method for the segmentation of myocardial diseases. Table  3  gives a summary of the comparison study. From the result, our approach yielded the best clinical and geometrical metrics compared with two existing networks.
LGE-MRI Figure  6  shows segmentation results of EMIDEC challengers, ground truth mask, and our proposed ICPIU-Net for all slices, chosen from one patient of the testing dataset. Visually, our proposed network correctly depicted myocardial structures and showed a good agreement with the gold standard. In comparison to Zhang's method, our approach demonstrated promising performance in segmenting the damaged myocardial areas from LGE-MRI. As shown in Figure  7 , qualitative evaluations demonstrate that our proposed network accurately segments infarcted patients, especially at the middle slices. The segmentation results achieved by our proposed ICPIU-Net approach are coincident with the expert delineations for both two volumes. Most segmentation errors appear at basal and apical short-axis slices.
The second-best DSC for fully automated segmentation of myocardial diseases was reached using the network proposed by Zhang  [47]  (71. 24% for scar and 78. 51% for MVO). In testing, our proposed method also achieved DSC, AVD, and HD of 87. 65 %, 8863. 41 mm 3 , and 13. 10 mm for LV myocardium segmentation, respectively. The publicly available test database consists of 50 exams divided into 17 cases with normal MRI after injection of a contrast agent and 33 patients with myocardial infarction. A patient-by-patient study of the proposed networkgenerated segmentation revealed that the infarct tissue could be correctly determined in 32 out of 33 pathological subjects from the test dataset. We conducted a comprehensive ablation study of prior information modules to investigate their impact on the segmentation results.
As shown in Table  5 , the combination of IC and CC regularization penalty terms provided more plausible segmentation close to the manual delineation. The results have demonstrated the effectiveness of inclusion and class constraints to the segmentation task on the EMIDEC dataset. The metrics of DSC, AVD, and AVDR applied during the MVO segmentation challenge can be inconsistent since the MVO represents only a small volume of the input LGE-MRI. Indeed, MVO's absence from all the data seemingly supplies correct results with DSC and volumes. However, the accuracy highlights the effectiveness of the proposed method to identify MVO regions. Therefore, in Table  6 , the additional metrics of MVO tissue are provided. Results reveal the pertinence of inclusion and class constraints in segmenting MVO tissues.
Bacterial Strains Analyzed We analyzed a collection of 28 STEC O139:H1 strains isolated in France from the intestinal content or lymph nodes, after necropsy, of wild boars with clinical signs and lesions consistent with ED, along with 16 STEC O139:H1 and 6 STEC O141:H4 strains isolated in France from pigs affected by ED (Appendix 1 Table  1 , https://wwwnc. cdc. gov/EID/ article/28/2/21-1491-App1. xlsx). We also included in this study an additional 168 E. coli strains isolated from pigs or other sources, whose genome sequences were retrieved from the GenBank  (18)  and Enterobase  (19)  databases (Appendix 1 Table  2 ).
Composition of the Accessory Genome, Resistance Genes, and Virulence Genes We detected virulence genes by using VirulenceFinder 2. 0. 3  (31)  with a minimum percentage identity of 90% and resistance genes by using BioNumerics 7. 6. 3 with a minimum percentage identity of 85%, both with a minimum length of 60%. We subtyped F18 fimbriae by using amino acid sequence analysis of the major FedA subunit, including positions 122 and 123 (glycine and serine for F18ab, proline and alanine for F18ac)  (2). We analyzed the relationship between strains on the basis of accessory genome composition by using a t-distributed stochastic neighbor embedding (t-SNE) machine learning algorithm with Panini v1 (https:// gitlab. com/cgps/panini/bhtsne), with a gradient accuracy (theta) of 0. 5 and an auto perplexity (p).
Using the table of genes present or absent in the strains of the entire collection outputted from the Roary pipeline, we conducted pan-GWAS analysis to measure the statistical significance of the association of certain genes with the clade of wild boar strains by using Scoary 1. 6. 16 (https://github. com/AdmiralenOla/ Scoary). We retained the annotated genes with a p value <2. 21 × 10 -12 by Fisher exact test.
Stx2e Phages, Plasmids, and Pairwise Comparison We detected phages by using Phaster  (32). We extracted the sequences corresponding to the Stx2e phage and circular contigs (plasmids) from hybrid assemblies. We retrieved the closest similar plasmid sequence available online from the National Center for Biotechnology Information nucleotide collection (nr/nt) database (accessed April 1, 2020). We then compared Stx2e phage and plasmid sequences by using blastn 2. 9. 0 (https:// blast. ncbi. nlm. nih. gov/Blast. cgi?PROGRAM=blastn& PAGE_TYPE=BlastSearch&LINK_LOC=blasthome) with default parameters, along with GenBank annotated sequences, to create pairwise comparison in EasyFigure 2. 2. 3  (33).
The sta1/stb and sepA genes were bordered by many  transposase genes and insertion sequence (IS) elements (Figure  5 ). Plasmid-encoded enterotoxins are a typical feature of porcine PWD ETEC strains, and enterotoxin genes surrounded by IS were also reported elsewhere  (12, 37, 41) , suggesting that IS may favor the acquisition of virulence genes. We did not find such a plasmid in the pig STEC O139:H1 strain, in contrast to the pig STEC O141:H4 strain, which carried a similar IncFII plasmid, pP136-3 (Table ; Figure  5 ). A BLAST search (http://blast. ncbi. nlm. nih. gov/ Blast. cgi) led to the identification of another similar plasmid (pCV839-15-p1) in a typical diarrheic pig ETEC strain of serotype O9:H21 (GenBank accession no. SAMN0804056) (Figure  5 ). Sequence comparison of plasmids pW1316-1, pP136-3, and pCV839-15-p1 showed that a highly conserved conjugation region was located downstream of the transfer origin.
Acknowledgments We thank  Laurent Cavalié  for his assistance with high throughput DNA extraction and antimicrobialsusceptibility testing. This work was supported by the  National Research Institute for Agriculture, Food, and Environment 's GISA metaprogram (https://colloque. inrae. fr/metaprograms-workshops_eng/Metaprograms/GISA)and by funding from the  Région Occitanie  (grant no. ALDOCT-000610 ) and  Ministry of Agriculture  awarded to A. P.
Note: z-stack imaging needs to be activated first by ticking the respective box at the top left corner of the software interface. 1. 2 mm. Note that these parameters are sensitive to the quality of your images. The better your immunohistochemical staining, the easier it is for the software to detect true signal and thus settings allowing a better voxel resolution (quality of three-dimensional pixels in Imaris) can be used. Choosing the proper settings for the semi-automated reconstruction pipeline is an iterative process. Once you find your ideal settings, you need to use the same settings for all your specimen within the same experiment to yield consistent results. CRITICAL: Finding the optimal settings for your pipeline is crucial. The goal is to convert the true immunohistochemical signal obtained via staining for the specific antibodies into accurate three-dimensional surface and filament reconstructions using the Imaris software.