dataesr/scientific-paragraphs-categorization huggingface dataset:
Scanners and staining methods for each center are summarized in Table  1. When testing this so-called "master model" (or "master classifier") on data from another center, termed "target center", performance appeared to be greatly decreased. To maintain a good performance level, we used the master classifier as a starting state for transfer learning with training data extracted from the target center. The obtained "calibrated model" (or "calibrated classifier") performed well on data from the target center. Below, we first explain the WSI processing pipe by describing the filtering process and the architecture of the IC classifier. We then present the various datasets used in this study, both for training the classifier and testing the IC detection algorithm. We give insights on the labeling process and on the composition of the datasets.
Datasets The datasets used in this study were built from WSI originating from two different medical centers referred to respectively as "reference center" and "target center". As described in this section, slides were first labeled, then patches were extracted and filtered to generate training and test datasets. The training datasets were used to train the networks and the test datasets were used to assess the network's performance. The complete extraction and training pipe is illustrated in Fig 2. Both datasets were annotated according to the same process: using in-house software, 25 experienced pathologists were asked to draw regions of interest (ROI) on the WSI. Each ROI was checked and validated by an expert pathologist, in case of disagreement the WSI is sent back for revision until a consensus is found.
The datasets are described in Table  2. For each center, several slides from the same patient were usually available. In order to split each dataset into a training and a test set, patient folders were divided so that ~80% of the slides lay in the training set. A given patient folder exclusively belonged to the test or the training set. This ensured that no data leakage occurred between the training and test sets. This table summarizes, for each class, the number of patient folders, slides, and patches that are contained in both datasets (training and test subsets). Note that a slide is referred to as an "IC slide" if it contains at least one region of interest with a label being one of the following: invasive ductal carcinoma, invasive lobular carcinoma or mucinous carcinoma. Dataset 1 (resp.
2) contains 135 (resp. 48) slides with ductal IC, 52 (resp. 0) slides with lobular IC, and five (resp. 1) slides with mucinous carcinoma. Note that slides come from both biopsies and mastectomies, in comparable proportions. The proportion of cases between various labels was not chosen, it is representative of the routine practice of the medical centers that provided the data. As shown in Table  2 , the reference dataset contained much more data than the target dataset; the former was used for training the master classifier whereas the target dataset was used for calibration.
The various parameters controlling augmentation functions were set using a grid search scheme. To train the ResNet50, binary cross entropy was optimized with an Adam optimizer  [23]  with an initial learning rate of 0. 001. ResNet50 weights were initialized with weights resulting from pre-training on ImageNet and no layer was frozen. An early stopping condition was set. The features learnt from this feature extractor were used as inputs to train a random forest classifier. More specifically, the random forest was fed with feature maps output by the ResNet50 backbone from which the final fully connected layer was removed. The network resulting from the training phase was the master model and could then be calibrated with data from another center in order to perform well on the latter.
Calibration step to address a new center From one medical center or laboratory to another, slide preparation methods, staining processes, and slide scanner types can differ considerably. Because of these variations, the general aspect of the scanned slides is notably different (see  Fig 3). Consequently, although the master model was shown to be robust to analyze data from the same center as the training dataset, its performance deteriorated when moving to a new acquisition center (see Results section). In order to address this problem, we set up a calibration step which made it possible to specifically adapt the master model to a new target center. The calibration step was based on transfer learning: weights from the master model were taken as the starting state for new training with labeled data from the target center.
An almost 10-fold decrease in data required during the calibration step was observed when compared to the training on the reference center (see Table  3 ). The calibration training was performed with the same hyperparameters and training strategy as the master CNN training. This table illustrates the results of both the master IC classifier model and the target IC classifier model, on both the reference and target test sets. The master model performs well on the reference test set but its performance is greatly decreased when translating to the target center. Differences in slides preparation and acquisition scanners make it challenging to generalize to a new center. The calibrated model is obtained through a transfer learning approach by taking the master model as the starting state when learning on a training dataset from the target center.
Evaluation method The performance of the master and calibrated models was measured on both reference and target centers. As described in "Material and dataset", patch test sets (i. e. , reference test set and target test set) were built from slides from both centers to assess the generalizability of our algorithm. The performance of each model was evaluated both at the patch and slide level. For patch level classification, a simple threshold of the IC score determined whether a given patch was classified as IC or Rest. This threshold was determined as the best precision/recall tradeoff by maximizing the F1-score on validation data. For slide level classification, we defined the slide score S IC as the sum of the scores of patches classified as IC within this slide normalized by the total number of patches detected on the WSI.
Discussion The algorithm developed and described in this paper aimed at detecting IC on WSI. It proved to be accurate and precise when tested at patch and slide level on the test datasets. However, to the best of our knowledge, there is no common standard either for metrics used or for datasets, and authors usually test their algorithms on their own data using different metrics. Some datasets do not even allow slide level evaluation and metrics are only computed at patch level. These reasons make any comparison complicated. For instance, Narayanan and colleagues 17  used a simple patch-based 5-layer CNN with color constancy pre-processing and achieved an AUC of 0. 935 on a breast histology image dataset from Kaggle  [24]. In another study, Cruz-Roa and colleagues introduced a 3-layer convnet classifier with a single convolutional layer  [25].
A slide level DICE coefficient was computed to evaluate the model. These authors reported a DICE of 0. 7494 ± 0. 2071, positive predictive value (PPV) of 0. 6464 ± 0. 2870, and negative predictive value (NPV) of 0. 9709 ± 0. 0350 on a dataset consisting of TCGA breast images [26]. Celik and coworkers  [8]  were able to achieve 91. 96% accuracy on the BreakHis dataset  [27]  at patch level using a pretrained ResNet-50 with no data-augmentation. Using a FusionNet encoder + softmax classifier, Brancati's group  [28]  reported 87. 76% accuracy on Janowczyk's dataset  [29]. Finally, Zeng and Zhang  [7]  used Google Cloud AutoML Vision on breast histopathology images from Mooney's dataset  [24]  and claimed 85. 26% balanced accuracy at the patch level. As described above, our technique led to a patch level accuracy of more than 90% (see section Results) and a slide level accuracy of more than 92%.
The use of a two phase-training process provided a similar performance on the target test dataset compared to the reference test set while using much less training data. The target dataset was more than 10-times smaller than the reference dataset. It should also be noted that the diversity of pathologies found in the target dataset was smaller than that observed in the target dataset. Therefore, the reference dataset contained a lot of information that was not contained in the target dataset. It is important not to forget the information learnt during the first stage of training when transferring the knowledge to the target. As can be observed from the decreased performance on the reference center when using the calibrated CNN instead of the master CNN, it can be seen that some information is lost during the calibration phase (see Table  3 ).
This problem will be addressed in a future study. Zaneta Swiderska-Chadaj et al. [19]  proposed a technique using Cycle-GAN style normalization for a multicenter algorithm with good performance. They achieved similar accuracy on their development dataset and test datasets built using data originating from different centers. However, they used a training set containing data from several centers to train their classification model. With our proposed method, the network needs only one reference center dataset and calibration can then be performed using a small amount of data from a given target center. Furthermore, in order to reduce the computation time and the expectancy on hardware capabilities at inference, the number of models used and their size is minimal. It is therefore more adapted to use a single calibrated CNN instead of a Cycle-GAN normalization step followed by a CNN classification step.
Conclusion Our IC detection pipe is efficient and could be applied to WSI from different medical centers using a limited amount of additional data. This tool may help pathologists to make a more accurate and faster diagnosis and postoperative treatment planning. Such a support can be used for quickly screening slides in high-throughput laboratories, selecting slides needed for fast immunohistochemistry, making practice more consistent, or assisting in reporting. Future studies include a parametric study on the size of the target dataset, for various medical centers, to determine the minimum volume of data necessary to maintain performance when moving from the reference to a target center, improvement of the processing time, generalization to pathologies other than IC, and organs other than the breast.
When sequences rendered double peaks (n = 1 sample of Podarcis), amplicons were cloned using the TOPO Ò TA Cloning Ò KitDNA (Invitrogen, Life Technologies, Waltham, MA, USA) and plasmids were purified using the NZYSpeedy Miniprep kit (NZYTech, Lisboa, Portugal) (see  García-del-Río et al. (2021) ). The three cloned sequences found (Pg29c2, Pg29c3, Pg29c4) were include in the phylogeny. From the GenBank database, we selected 18S rRNA sequences of at least 1,300 bp to build a phylogenetic tree of the Adeleorina. The final alignment contained 133 sequences of the 18S rRNA gene. This included the 22 sequences newly obtained in the present survey which are available at the GenBank database (Supplementary Table  S2 ). We included Adelina bambarooniae (Apicomplexa: Adeleorina), a homoxenous coccidium species, as an outgroup.
This analysis consisted of two runs of four chains each, with 2,000,000 generations per run. Data were collected each 100 generations, and the burn-in was set to 25%. We obtained a consensus tree from 30,000 trees. The final standard deviation of the split frequencies was lower than 0. 01. In addition, we analysed the alignment using maximum likelihood inference (PhyML program;  Guindon et al. , 2010)  and the same substitution model mentioned above. We selected the subtree pruning and regrafting (SPR) and the nearest neighbour interchange (NNI) tree rearrangement options, and we used a Bayesianlike transformation of aLRT (aBayes) to obtain the clade support. This is a compelling alternative to other, slower, conventional methods, offering not only speed advantages but also excellent levels of accuracy and power  (Anisimova et al.
Host-parasite co-speciation analysis We used the statistical method ParaFit  (Legendre et al. , 2002)  implemented in CopyCat software  (Meier-Kolthoff et al. , 2007)  to test the significance of a global hypothesis of host and parasite co-speciation. ParaFit tests for the congruence of the host and parasite phylogenetic trees and for individual host-parasite association links. The global null hypothesis is that evolution of the hosts and parasites has been independent. To do a co-speciation analysis between lacertid hosts and Karyolysus, we reconstructed the phylogenetic relationships of the lizards based on a recent genomic study  (García-Porta et al. , 2019) , whereas for Karyolysus we used a phylogenetic tree based on >1,700 bp of the 18S rRNA gene. We based the co-speciation test on the analysis of genetic distance matrices.
Molecular detection and phylogenetic consistency The molecular screening with primers designed to amplify short sequences indicated that 37. 9% (314/828) of the samples contained DNA of adeleorine parasites. Lacertids were the most affected with 100% of the species infected and a mean prevalence of 51. 3%. The two skink species were infected with a mean prevalence of 21. 1%. No infection was detected in the 38 samples of the gecko host Tarentola mauritanica from the Chafarinas Islands (Supplementary Table  S1 ). We obtained 78 long 18S rRNA sequences in 16 of the 20 lacertid hosts and the two skink host species investigated (Table  2 ). We found 22 different 18S rRNA parasite haplotypes. The phylogenetic analyses provided strong phylogenetic support for the monophyletic origin of 38 of the sequences analysed in a clade that contained 20 of the 22 newly detected haplotypes, two species of Karyolysus, and 16 other sequences that were labelled in the GenBank database as 'Hepatozoon' (Fig.
1  and Supplementary Table  S2 ). It also contained haplotype MK497254, a sequence deposited in the GenBank database as Karyolysus cf. lacazei  (Zechmeisterová et al. , 2019). This haplotype shared 100% nucleotide sequence identity with Ls142 (GenBank ID: OQ093598), which has been detected by us in both Timon lepidus and Lacerta schreiberi hosts (Supplementary Table  S1 ). This clade also included haplotype MK396906, that corresponds to Karyolysus galloti, detected in a lizard from the Macaronesia, Gallotia caesaris  (Tomé et al. , 2019). MK396906 was closely related to haplotypes Gg235 (OQ093613) and Gg241 (OQ093614), detected in a second species of Gallotia in another island of Macaronesia (Fig. 1  and Supplementary Table  S1 ). This well-supported clade (hereafter, Karyolysus) is closely related to the Hepatozoon lineage detected in carnivore mammal hosts, in line with previous phylogenetic hypotheses  (Haklová-Ko c ˇíková et al.
Acknowledgements We are thankful to  I. Nasri  for providing blood samples of A. boskianus and to  V. Gomes  for her collaboration. Field stations ' El Ventorrillo'  and  'Refugio Nacional de Caza de las Islas Chafarinas' (Spain)  provided logistical support. Some pictures of lizards used in the graphical abstract were taken from the image bank of  Universidad Complutense de Madrid  (http://bioimagen. bioucm. es/). The study was conducted under licenses from all involved authorities (numbers:  10/033298. 9/13 , 10/373043. 9/12 , 10/380311. 9/12 , 10/315072. 9/11 , 10/040449. 9/13 , 2012/272 ,  372/2013-VS (FAU13_038 ),  DGMEN/SEN/avp_13_025_aut ,  Biod/ MLCE-68564 ,  EP/CYL/101/2013 ,  2013/3405  ( 44 ,  1543 ;  Cabildo de Tenerife) ,  EP/SG/625/2011 ,  EP/SG/213/2013 ,  SGYB/EF/FJRH Re-9H/13 ,  INAGA/5000201/24/2013/04434 ,  CSVZ5-4ZBJN-02QA1-DYREG, EHV/24/2010/105-106 ,  LCE/mp24/2012/426 ,  276/ HCEFLCD/DLCDPN/DPRN/CFF, 500201/24/2013/5692(1098 ),  2013/025426 ( 74/CS/13 )).
6 × 10 -4 ; Supplementary Data 16). M5 did not exhibit a significant association with any of the other meningioma subgroups (Supplementary Data 14). Collectively, our RNA-Seq results provide strong evidence for activation of Hh signaling among meningiomas that harbor relevant genomic mutations in SMO or PTCH1, or events near 2q35 or 7q36. 3. We observed markedly elevated expression of IHH and SHH in the samples with SVs affecting 2q35 and 7q36. 3, respectively (Fig. 3d ). The meningioma that was identified to acquire a focal gain at 7q36. 3 using WES (MN-60924, SHH-TD in Fig. 3d ) also exhibited upregulation of SHH. It is notable that among the four genes (FEV, CRYBA2, CFAP65 and IHH) where the copy numbers were preserved or gained (see above), only IHH showed a consistent upregulation of all the samples that acquired 2q35 events (Supplementary Fig.
To confirm our transcriptional results, we examined Hh activation patterns using multiplexed sequential immunofluorescence (seqIF™) All the events identified by MANTA from our samples (n = 24) can be found in Supplementary Data 11. Among the 24 samples, 19 acquired at least one structural variation (SV). Two of the 19 samples (MN-60690 and MN-61306) not listed here were subjected to tumor-only low-coverage WGS and their targeted SVs (tandem duplication at 2q35 implicated from WES and an event near IHH implicated from RNA-Seq) were not detected. e of FFPE-preserved tumor tissues from various genomic subgroups (Fig. 4  and Supplementary Fig. 10 ). We found that meningiomas with predicted Hh signaling activating events (including recurrent SMO variants and structural events on 2q or 7q) exhibited robust staining of GAB1, a common clinical marker used to identify Hh pathway activation  23, 35.
11 ). Two of these were found to exhibit tissue-specificity, being enriched in meningiomas but relatively absent in datasets reported for other tissues (Supplementary Fig. 12 ), and both overlapped a long non-coding RNA gene, DIRC3. Other super-enhancers in this interval overlapping with IGFBP2 and TNS1 were more ubiquitously expressed across different tissues. In the HiChIP control set, these super-enhancers interacted with regions within the same TAD and their neighbors (Supplementary Fig. 13 ). The region between 219 Mb and 220. 25 Mb was relatively silent in terms of chromatin interactions, and a 50 kb interval that contained IHH (from 219. 90 Mb to 219. 95 Mb) did not show any significant contact with other intervals (at a 1% FDR, the minimum q-value = 0. 0675 for this interval). We also did not observe any peaks as well as super-enhancers around IHH in any of the meningioma samples (Supplementary Fig.
11 ). Compared with the control set, the TAD structure in the TD set was altered (Fig. 5 ) such that new TAD boundaries emerged and were dictated by the tandem duplication boundaries (Supplementary Fig. 14 ). Additionally, new significant loops emerged between DIRC3 super-enhancer region (218 Mb-218. 7 Mb) and a region around IHH (219. 8 Mb-220. 1 Mb), which were located adjacently because of the tandem duplication (Fig. 5 , Supplementary Fig. 15 , and Supplementary Data 18). Thus, a neo-TAD that includes both DIRC3 superenhancers and IHH was created (Supplementary Fig. 15 ), and the new enhancer-promoter contacts resulted in the ectopic expression of IHH as well as genes telomeric to it (Supplementary Fig. 9 ). A similar phenomenon was observed for the two cases of inter-chromosomal translocations (MN-52288 and MN-63401), whereby the chromosomal segments juxtaposed near IHH harbored super-enhancers near the breakpoints (Supplementary Figs.
Our WES cohort included 91 mutation-negative meningiomas (Supplementary Data 1). Among these, we found that 56 belong to previously uncharacterized SCNA subgroups (2q, 7q, 3p, and multiple whole-chromosomal gains) and one acquired PTCH1 mutation (confirmed an association with Hh signaling activation, Fig. 3a  and Supplementary Fig. 8 ), leaving 37% (34/91) to be mutation-negative. g Proportion of meningiomas in our H3K27ac ChIP-seq cohort (n = 37) that harbor an overlapping super-enhancer in the designated region. h Gencode basic genes (v38lift37) in this region. Genes overlapping the common super-enhancers as well as IHH are labeled. In addition, genes that exhibited ectopic expressions and were mapped upstream of IHH in tandem duplication cases are also labeled (Supplementary Fig. 9 ). Source data are provided as a Source Data file.
001). Regarding the SHH-activating meningiomas (n = 7), while one of the three complex rearrangements was found in a non-skull base meningioma, all simple SVs (n = 4) were found in skull base ones (P = 0. 43; Table  1 , Supplementary Data 1 and 10). The distinct genomic profiles of IHH-activating meningiomas in these locations suggest the existence of epigenetically unique cell populations, whereby differences in chromatin state confer variable sensitivities to specific genomic events. The complex rearrangements found in IHH-activating meningiomas result in marked chromosomal disruption and resulting changes in epigenetic regulation may induce Hh pathway sensitivity in an otherwise resistant cell population. Although we confirmed Hh signaling activation for two non-skull base meningiomas with chromothripsis on 2q (MN-52420 and MN-52336; see Fig.
Surgical specimens were pathologically confirmed and were flash frozen for genomic study. When possible, a matching blood sample was obtained from each participating patient. Genomic material was extracted using the Allprep DNA/RNA Mini Kit (Qiagen), and its quality was assessed using a Bioanalyzer 2100 (Agilent). Demographic and clinical information of each subject are provided in Supplementary Data 1 based on the availability of medical records.
4, RRID:SCR_001876)  60. Somatic copy number alteration (SCNA) calling. Depths of coverage of target intervals for each sample were calculated by using GATK and they were converted to coverage ratios between tumor and blood samples. Alternate allele frequencies (or B allele frequencies, BAFs) of tumor and blood samples were estimated at biallelic single nucleotide variant (SNV) sites for blood samples using SAMtools (RRID:SCR_002105)  61  and retained the sites with the coverage of blood sample ≥ 8 and blood BAF being between 1/3 and 2/3. We used the variation of the deviation of BAF from its mean (devBAF) within a segment to evaluate the loss of heterozygosity (LOH) or allelic imbalance. Copy number states were inferred by the ExomeCNV R package  62  (RRID:SCR_010815) using the logarithm (base 2) of the coverage ratios (logCR).
We excluded a segment if more than 70% of the bases overlapped low-complexity region(s), downloaded from https://github. com/lh3/varcmp/blob/master/scripts (ref. 63). Merging consecutive segments. As the segmentation of ExomeCNV was aggressive and sensitive to occasional noise in the coverage ratio, we sequentially concatenated two consecutive segments when the event types (deletion, amplification, or neutral) were the same, logCR was within ± 0. 05 each other, and the mean of devBAF was within +/half of the standard deviation of devBAF each other. In addition, if either or both segments contained less than three SNV sites and were inferred to have the same event type, they were always merged. Final SCNA call. We tested for the LOH or allelic imbalance of each merged segment using two methods, the Ansari-Bradley test (onesided) to evaluate if the variance (scale) of tumor devBAF is greater than that of blood, and a Z-score test for tumor sample (Z = (mean devBAF) / (standard deviation of devBAF), two-sided) to evaluate the non-random directional shift of devBAF from zero.