dataesr/scientific-paragraphs-categorization huggingface dataset:
Deep learning processing pipeline DeepMeta network architecture was designed to perform multiclass segmentation and was based on the U-Net general network structure (see Figure  1 ). Specifically, we designed our network as a tailored implementation of the U-Net 3+  (Huang et al. , 2020). The U-Net 3+ architecture uses full-scale skip connections which combine small and large feature maps from the encoder. We postulated that basing our approach on U-Net 3+ would be suitable to capture both small details and semantic features within images, and as a consequence to accurately segment both lungs and metastases. Specifically, inter-and intra-connection between the encoder E i down-sampling and decoder U j upsampling pathways enabled the U-Net 3+ to account for both fine and coarse level details: in the case of DeepMeta, low level details contain the spatial and boundary information of both lungs and metastases, while high-level details encode their location.
Modifications we made to the classical U-Net 3+ architecture are the following. TABLE 1 Summary of the dataset obtained from 186 mice. The number of annotated slices is reported alongside the final number of annotated images. Notice that the number of multiclass masks is lower than the lungs one, it is because some metastases are not annotated inside the lungs, so these slices cannot be transformed as a multiclass mask. We split this dataset into a training set and validation set with a ratio of 80/20. • Convolution blocks of the contracting path are composed of one depthwise separable convolution  (Chollet, 2017) , which speeds up the training process without losing accuracy, followed by a batch norm layer, a ReLU activation and a dropout, repeated twice. • The expanding path takes its' usual input and concatenates it with feature maps from skip connections (C i ) as in U-Net 3+.
Training During training, the cosine annealing schedule  (Loshchilov and Hutter, 2016)  was used, which prevents the network from getting stuck in local minima. Experimental procedure that imaged mice at different time points allowed us to trace the metastases development but also to train the network regardless of the size of the metastasis, as we had access to a wide range of sizes. We split the slices from the training dataset using a ratio of 80% for training and 20% for validation. Network was trained on Nvidia T4 GPUs, for 100 epochs with a learning rate of 0. 001 on a dedicated cluster with two two CPU Intel Xeon Silver 4114 and 128Go RAM. DeepMeta was implemented in Python 3. 10 and Pytorch 1. 11, for both neural network creation and training.
Evaluation To evaluate the performance of our DeepMeta model, we used two metrics: (i) Intersection over Union (IoU), which provides a coefficient indicating how well the masks overlap, (ii) F1-score, which represents the precision and recall balance. The latter metric is particularly suited for problems with class imbalance, as it is the case in our data. Moreover, an inter-observer comparison was performed to evaluate the dataset consistency. For a given slice three metrics were computed to compare annotations by the two experts: (1) IoU, (2) positive pixel difference and (3) a ratio of surface difference between the two masks. The IoU metric indicates how well the annotations are overlapping, i. e. whether the experts have annotated the same objects. The positive pixel difference measures the difference in mask sizes.
Lung segmentation The mean volume of the mouse lungs was 463. 5 μ L ± 72 μ L in the test set. The IoU index between the manual and the DeepMeta lung segmentation was 0. 86 (see Table  2 ), improving on both vanilla U-Net and vanilla U-Net 3+. The inter-observer variability of lung segmentation was low, with a mean IoU of 0. 87, leading to 8% difference (see Table  3 ). Moreover, the reproducibility of lung segmentation was assessed on the healthy dataset that was obtained by imaging two healthy mice 3 times each. The resulting lung volume for the first mouse was 484. 5 mm 3 , 474. 9 mm 3 and 494 mm 3 ; for the second mouse: 490. 4 mm 3 , 512. 1 mm 3 and 486. 2 mm 3. The coefficients of variance were 1. 9% for the first mouse, 2. 8% for the second one and 2. 3% globally, showing high reproducibility for lung segmentation.
Metastases segmentation The metastases were detected and automatically segmented for the test dataset. See Table  3 , panel B, for an example of segmentation of a slice from the same mouse imaged at day 85 and day 92. The total metastasis volume for this mouse was 30. 8 mm 3 and 47. 3 mm 3 at these time points. Moreover, the model has shown high sensitivity as it could detect metastases as small as four voxels (representing here 0. 0188 mm 3 ) in a given slice. The IoU index of 0. 72 was obtained between the manual and the DeepMeta segmentation for small metastases (see Table  2 ) defined as metastases with an area size of less than 85 pixels (approximately 0. 4 mm 3 ). This index decreased to 0. 67 when metastases were larger than 85 pixels. Notice the systematic improvement relative to the vanilla architectures.
Capacity to distinguish different growth patterns of metastases In the RENCA dataset metastases were detected and segmented by our model in the fast-growing metastases mice group as early as 19 days post-injection (see Figure  4A ). The metastases growing subsequently after the injection of the il34 cells became detectable at 22 days after injection. Also, the model was able to measure the lung volumes occupied by the pulmonary metastases over time. This enabled us to differentiate the two groups of mice. The DeepMeta model was also able to measure the total volume of metastases at different time points. This made it possible to count the number of metastases per volume range (see Figure  4B ). Figure  4B  highlights that the metastases in the fast-growing group are more homogeneous in size than the slow-growing group.
DeepMeta's use case To assess whether our DeepMeta model can be used to process pulmonary metastases from another cell line we applied the complete procedure from imaging to lung and metastases segmentation on the 4T1 dataset. The mean lung volume found by DeepMeta was 547 mm 3 ± 61. 2 which is in the 10% range of lung volume from the healthy dataset. The network managed to also segment the pulmonary metastases (see Figure  5  and Table  4 ) and found an average number of 4. 5 metastases per mouse with a volume range from 0. 01 to 6. 9 mm 3. These results show that the DeepMeta model generalizes well to other data and in particular that it can process metastases from different cell lines. As such, this indicates that the use of DeepMeta can streamline the analysis of lungs and metastases in MR images in preclinical studies on small animals.
A previous study  (Zhang et al. , 2021)  shows that classical architectures do not perform well on small objects. Thus, this U-Net 3+ based network architecture is particularly suited for the task of small object detection. DeepMeta U-Net 3+ model enables the reconstruction of lung volumes consistent with previous studies  (Heverhagen et al. , 2004; Soutiere and Mitzner, 2004; Ribot et al. , 2011) , thus ensuring the viability of this approach. Note that the chosen supervised learning approach relies on manual data annotation for both lungs and metastases. For example, lung segmentation alone was performed on 8156 slices, which is both time consuming and potentially biais-prone. A possible avenue to augment the dataset with known ground truth could be by data augmentation based on function decomposition from a template  (Tustison et al.
, 2019). This technique is promising for lung data augmentation, but not for metastases. Indeed, these lesions have their shape and volume that grow over time, and their location varies among mice. The high variability between annotators, especially for metastases, might come from partial volume effects that can greatly modify the volume of small metastases. For larger metastases, especially those growing close to the heart or the thoracic muscles, the main issue comes from the proper delineation of the metastases, as all these structures show similar signals on the bSSFP images. The method presented here is the first stone to further developments to make deep learning models more robust to variability. First, imaging with different reception coils would enrich the training dataset. Indeed, our study was conducted using a prototype coil that is not widely used in imaging laboratories.
Limitations Deep learning networks do not carry object semantics, which results in the network segmenting any object that has a similar structure to the metastases (e. g. , blood vessels). The performance of the network depends on the architecture and on the training and thus on the training dataset. In this work, the images were acquired using the bSSFP MRI sequence, which implies that the pre-trained DeepMeta models are expected to perform well on similarly acquired images, but that the performance might decrease if the images to be segmented are acquired with a different sequence. Consequently, retraining might be necessary by including images acquired with other MRI sequences in the training dataset to achieve a higher level of generalization. The bSSFP sequence has several advantages for mice imaging.
To constrain these correlations, we use multidisciplinary and complementary data sets (e. g. , topography and bathymetry, seismology and volcano volumes), and new analytical and data processing methods. The database developed by  Grosse et al. (2014)  containing volcano volume estimations has been completed by integrating caldera and underwater volcano volumes. We accurately quantify the topographic anomalies on the oceanic side, and their projection under the arc chain. In particular, we design novel methods to take into account the effects of the subduction zone geometry, the slab dip angle and the direction of these subducting features relative to the trench (section 2). In section 3, we present the results for each volcanic arc segment, with a thorough description and quantification of the subducting linear topographic features within each segment.
Arc volcanoes database Volcano selection was based on the Smithsonian Institute Global Volcanism Program (GVP) database of Holocene volcanoes  (Siebert et al. 2010). We considered all composite volcanoes (stratovolcanoes, complex volcanoes, shield volcanoes, etc. ) and collapse calderas located along the volcanic arcs surrounding the Pacific Ocean and spatially related to one or more of the selected linear oceanic features. Back-arc and monogenetic-type volcanoes and volcanic fields were excluded from our analysis. A total of 561 volcanoes were considered (Figure  1 ; Supplementary Table  1 ), distributed along 14 arc segments: Austral and Southern Andes, Central Andes, Northern Andes, Central America, Cascades, Aleutian-Alaska and Wrangell,  Honshu, New Britain, Solomon, Vanuatu,. Volcano volumes were obtained from different sources (see Supplementary Table  1 ).
The main source for composite volcanoes was the database of  Grosse et al. (2014). This database gives volume estimates using the SRTM 90 m spatial resolution digital elevation models (DEMs)  (Jarvis et al. 2008 ) and the NETVOLC (used for defining the volcano outlines;  Euillades et al. , 2013)  and MORVOLC (used to compute morphometric parameters;  Grosse et al. , 2012 Grosse et al. , , 2014) )  softwares. For composite volcanoes that stand partially or completely underwater (i. e. , seamounts, submarine volcanoes, volcano islands), we computed volumes considering manually drawn basal outlines and using MORVOLC and the SRTM30PLUS 1 km spatial resolution DEMs  (Becker et al. 2009). For other composite volcanoes not included in the database of  Grosse et al. (2014) , mostly small edifices, we included volumes given in the literature if available, or calculated rough estimates considering the edifice basal footprint and height measured in GoogleEarth imagery and the equation of a cone.
For collapse calderas, we considered volumes given in specific articles, if available, or in the database of  Geyer and Marti (2008). For calderas lacking previous volume values, we measured caldera areas with GoogleEarth imagery and roughly estimated their volumes following the bestfit regression equation in a volume vs. area plot of calderas with independently computed values. It should be noted that the data on volcano numbers and volumes suffers from several uncertainties, namely (1) volcano inclusion is based on the GVP database, which is dynamic and continually updated, with additions/exclusions depending on new data; (2) computed volumes are of the volcano edifices only, i. e. , far-reaching deposits, such as tephra fall deposits, are not included in the volume estimates; (3) eroded volumes are likewise not included in the volume estimates.
Volcano road and densities From the arc volcanoes database, we construct a "volcano road", which is a continuous smoothed trend derived from the location of the arc volcanoes (Figure  2c ). It is designed with the "median" function available in Matlab (Matlab R2016b, Mathworks®). If the trench has a dominant N-S orientation, such as along South America, the latitude of the volcano road is defined by the latitude of the volcanoes along this segment and the longitude by applying the median filter to longitudes of the arc volcanoes. In the same way, if the trench is dominantly oriented E-W, as along the Aleutian subduction zone, the longitude of the volcano road is defined by the longitude of the volcanoes and the latitude by applying the median function to the vector defined by the volcanoes latitude.
The southernmost volcano belonging to the Izu-Bonin-Mariana Arc is at latitude 13. 25 o N, whereas the Caroline Ridge intersects the trench at latitude 10. 5 o N. Therefore, there may be no connection between the subduction of the Caroline Ridge and the lack of arc volcanoes. The subduction of the Louisville hotspot is associated with a volcanism gap. This gap has been previously indicated by  Nur and Ben Avraham (1983) ,  Ballance et al. (1989)  and  Rosenbaum and Mo (2011). However, there are submarine volcanoes within the swath that have been studied by  Massoth et al. (2007)  and  Timm et al. (2013) , but that are not in our database because they are not included in the Smithsonian Institute Global Volcanism Program database of Holocene volcanoes (they are included in the Smithsonian Institute's Pleistocene database; see  Siebert et al.
, 2009; Schuth et al. , 2011). According to these authors, this signature indicates transfer of arc mantle across the subduction zone through the slab window. So even if the Woodlark Spreading Center subduction is not associated with slow seismic velocities, geochemical data and arc volcanoes densities denote the presence of a slab window. A similar scenario has been invoked to account for the subduction of the Chile Ridge under South America  (Klein and Karsten, 1995). Indeed, the basalts along the Chile Ridge have arc-like trace element and isotopic characteristics. This also indicates transfer of arc mantle across the subduction zone through a slab window. Johnston and Thorkelson (1997)  explain the presence of the Galapagos plume geochemical characteristics in lavas on the Caribbean plate through a similar flow, occurring through the slab window created by the Cocos-Nazca Ridge subduction.
Here again, several features overlap in a relatively small area (Central America Arc)  (Figures 4i, j, l). The subduction of the Tehuantepec Ridge correlates with an isolated small volcano, El Chichón. Although it has been shown that the subduction of fracture zones affects the geochemical signature of associated volcanoes (e. g. ,  Manea et al. , 2014) , no clear pattern is found between fracture zone subduction and arc volcanism output. This is probably because only a few fracture zones have been integrated in this study. As stated in the introduction, our features selection was based on bathymetry data. It is generally difficult to pick fracture zones from bathymetry only. Wessel et al. (2015)  designed a fracture zone database by integrating seafloor fabric data, magnetic lineations, and plate tectonic models.
These data are extracted from  Hughes and Mahood (2011) , who gathered data from literature review and datasets to obtain several parameters describing arc segments. Previous studies (e. g. ,  Karlstrom et al. , 2014)  show that arc magmatism is related to parameters such as the crustal thickness of the upper plate, Tc. However, we do not find any clear correlation between 𝑑 𝑉 and any of these parameters (Figure  10 ). We find a tentative correlation between dv and slab age: all the linear features for which we found a decrease in arc volcanism are associated with a slab age younger than 54 Ma (with the exception of Louisville hotspot). However, not all the features associated with a slab younger than 54 Ma are characterized with a decrease in arc volcanism. Variations of arc volcano volumes are probably controlled by the interplay of several parameters.
Conclusions We have studied the correlation between subducting linear oceanic features and arc volcanism around the Pacific basin. Our analysis considers the inland projections of the subducting features, taking into account the subduction zone geometry and integrating the latest data and models, such as an updated arc volcano database and the latest slab dip angles (provided by Slab2. 0,  Hayes et al. , 2018). The original aspects of our work are the computation of a continuous volcano road, formed by arc volcanoes, and the quantification of both the volcanoes number and volume densities along this volcano road. These regional statistics are also compared to the local patterns observed in volume densities to thoroughly assess variations in arc volcanism. The subduction of oceanic plateaus (Ogasawara Plateau, Dutton Ridge, West Torres Plateau) is associated with a volcanism increase.
The four considered active mid-oceanic ridges (Chile Ridge, Cocos-Nazca, and Juan de Fuca ridges, and the Woodlark Spreading Center) are associated with slab windows, although their signature in geophysical data is clearly supporting this scenario for only three of them (Chile Ridge, Cocos-Nazca, and Juan de Fuca ridges). The only analyzed inactive mid-oceanic ridge (the Phoenix Ridge) is located in a volcanic gap, at the extremity of an arc volcano segment, whereas the only analyzed active arc volcano ridge (the Izu-Bonin-Mariana Arc) is associated with arc volcanism increase. Out of the five inactive arc volcano ridges considered, three (Amami, south Amami, and Daito ridges) are located near a volcanic arc extremity and associated with volcanism decrease, without any clear causality. Away from volcanic arc edges, we find that the subduction of inactive arc volcano ridges is associated with enhanced arc volcanism (D'Entrecasteaux and Kyushu-Palau ridges).
No clear pattern seems to arise when studying the effect of fracture zone subduction on arc volcanism volumes, but this may be because only four fracture zone can be characterized from bathymetry data. The comparison between arc volcanism variations and other independent geophysical data (e. g. , crustal thickness, stresses in the upper plate, kinematic velocities) do not exhibit any systematic correlations. In further studies, it would be worth comparing how our projected subducted features are correlated with seismicity (if such seismic studies are available). It would also be interesting to investigate the possible correlations between arc volcano volumes and variations in the geochemical signature of arc lavas along the volcano road. This would help inferring or differing the hypotheses discussed here and in previous studies.
The ratio of precipitated methylated DNA to input DNA was calculated. Most targets showed strong MEDIP to input ratios, indicating that these targets are highly methylated. The data are shown in Fig. 1B-C. Adolescents in the high BPA group showed lower DNA methylation than those in the low BPA group for all sequences tested, and these differences were statistically signi cant -approximately 0. 5-fold -for the H19, KCNQ10T1, IGF2 and LINE1 promoters. On the other hand, since BPA exhibits oestrogenic activity, we wondered whether DNA methylation would be altered at the promoters of genes that have indications of regulation by ESR1 and have ESR1 binding sites in the vicinity. For this purpose, genes regulated by ESR1 were chosen, and we used the Chea 2022 database of ESR1 binding ChIP-seq data from Enrichr  [18].
2A ). For example, the enriched cluster 'H4 histone acetyltransferase activity' includes genes such as KAT2A, KAT8, and OG, and the enriched cluster 'telomeric DNA binding' contains 22 genes (Table  S3 ) important for telomere function, such as PURA, RPA1, TEN1, TERT, TERF2IP and ZBTB48. An example of differential peaks for TERF2IP and ZBTB48 is illustrated in Fig. 2B. The list of genes identi ed in differential regions assigned by GREAT was then compared with the Enrichr protein-protein interaction (PPI) database  [19]. This database combines the data of protein-protein interactions previously described in experimental studies. Our analysis revealed that genes encoding ESR1-interacting proteins were strongly enriched for H3K4me3 in the high BPA group (Fig. 2C ). According to the Enrichr PPI database, ESR1 could interact with 871 proteins, of which 510 were encoded by genes located within differential H3K4me3 peaks (adj.
p value = 6. 95E-53). Among these genes were the AHR, ESRRA, NCOR1, NCOR2, RARA and RXRA receptors (Table  S4 ). Furthermore, a PPI database search showed that 236 genes were located in differential peaks corresponding to genes encoding proteins that interact with BRCA1 (BRCA1 could interact with up to 418 proteins). Many BRCA1-interacting proteins encoded by genes located in differential peaks were DNA repair proteins. The AMIGO database includes 519 "DNA repair" genes. Of them, 39 genes were detected in our differential peaks, including ATM, ATR, BLM, FANCA, MDC1, MLH1, MSH2, MSH3 and RAD51 (Table  S5 ). These genes encode proteins critical for various DNA repair processes, suggesting that exposure to BPA could activate DNA repair pathways. Overall, whole-genome H3K4me3 occupancy analysis revealed that the methylation at genes involved in protecting genetic stability, such as DNA repair genes and telomeric DNA binding protein-encoding genes, was altered by BPA exposure, suggesting the potential toxicity of BPA in these crucial processes.
H3K9me3 occupancy at selected promoters in HeLa cells Next, we asked whether gene expression changes are a consequence of changes in epigenetic regulation. We chose to analyse the H3K9me3 mark as a proxy for silencing, as a decrease in the occupancy of this mark would lead to enhanced gene expression  [24]. In addition, this mark is particularly vulnerable to BPA exposure, and alterations in H3K9me3 could be inherited; e. g. , BPA exposure causes a reduction in the repressive marks H3K9me3 and H3K27me3 in whole worms and germline nuclei in third-generation offspring  [25]. To this end, we performed chromatin immunoprecipitation against H3K9me3 in cells exposed or not exposed to BPA. qPCR analysis was then performed for selected targets. In agreement with our immuno uorescence data (Fig. 3C  and D ), the analysis of individual genes showed a global tendency towards a decrease in H3K9me3 occupancy following BPA treatment.