dataesr/scientific-paragraphs-categorization huggingface dataset:
The rest of the paper is organized as follows. Section 2 briefly introduces related work. In Section 3, we present the architecture of the proposed CMF-Net, the objective function, and the coordinate decoding during inference. Then, evaluation dataset, implementation details, and evaluation criteria are described in Section 4. Section 5 compares our approach with other advanced landmark localization methods. The effectiveness of various components has been studied, and the landmarking performance against the different amounts of training data is analyzed. Some conclusions and perspectives are drawn in Section 6.
Thus, the distributionaware landmark regression method (i. e. DARK  (Zhang et al. , 2020a) ) may output final coordinates that are far away from the targets. We estimate the location of true extreme point around the maximum in the predicted heatmaps by a quadratic curve fitting method  (Bailey, 2003). Then, the coordinates in sub-pixels are rescaled back to the original space to obtain the final regressed coordinate representations. Our algorithm can thus accurately detect multiple 3D cephalometric landmarks simultaneously when inputting a down-sampled volumetric CBCT image with a variable size. 3D CBCT data from 150 subjects (from 7 to 41 years old, average age = 17. 97 years, standard deviation = 7. 05, including 44 males and 81 females) were acquired from the Affiliated Hospital of Stomatology, Nanjing Medical University.
Experimental configurations Manual marking on volumetric CBCT images at original resolution was carried out on multiplanar reconstruction (MPR, i. e. , axial, sagittal, and coronal cross-sections) views and then confirmed the accuracy of the outcome on a segmented volume-rendered hard skull. The ground truths of all datasets were obtained by a domain-specific and experienced expert who served as reference. A second one, with less experience, was asked to annotate the datasets again to estimate the inter-observer variability and the performance of our approach when compared with the competing methods. In total, 24 skeletal landmarks were identified, including 9 on midface and 15 on the mandible. All landmarks lay on a hard skull as shown in Fig. 4  and the objective is to locate these predefined significant points accurately and robustly.
Implementation details Every image is smoothed using a 3D Gaussian filtering kernel with a standard deviation of 0. 75 before entering the network. The CBCT images are down-sampled to get isotropic volumes with size of 969696 and voxel spacing of 222 mm 3 by linear interpolation. We truncate the pixels' values in each dataset to the range of  [-1024, 2048]   Our CMF-Net is fully implemented using the TensorFlow platform on a GPU (i. e. , NVIDIA GeForce RTX 2080 Ti, 11 GB), and Ubuntu 18. 04 equipped with 64 GB RAM. The Adam optimizer with an initial learning rate of 0. 0001  (Kingma and Ba, 2014)  is employed to minimize the objective function. It follows an exponential decay function as the training step increases. The number of iterations and batch size are set to 50000 and 1, respectively.
Methods for comparison We compare our method to state-of-the-art methods on the training and two test datasets. The approach described in  (Alansary et al. , 2019)  is based on learning the optimal search strategy that maximizes the reward function to localize the target anatomical landmark with multiscale deep Q-network (DQN)  (Mnih et al. , 2015)  in a coarse-to-fine fashion. We repeatedly train an individual detection model for each landmark using an officially released source code 1. To take advantage of anatomical interdependence, a collaborative multi agent landmark detection framework (Collab DQN)  (Vlontzos et al. , 2019)  is used to increase accuracy by sharing the agents' experience. We set the number of agents to 2 and train it using the official implementation 2. The U-Net approach  (Ronneberger et al.
, 2015)  is first developed for medical image segmentation and its architecture has been adopted here to fit for CMF landmark localization purpose. SCN  (Payer et al. , 2019)  suggests a spatial configuration network to encode the spatial relationships between the landmarks and optimize objective function with learnable standard deviations. It achieved promising results on both 2D and 3D medical images and the fact that its code is publicly available 3  and is used for the comparison with our method. In contrast to the conventional inference rule of finding the extreme value  (Payer et al. , 2019)  or the center of mass  (Sun et al. , 2018)  in the predicted heatmaps, DARK  (Zhang et al. , 2020a)  exploits unbiased sub-pixel centered coordinate encoding and Taylor expansion for coordinate decoding, a model-agnostic method used to refine positions on human pose estimation.
Therefore, we selected 9 images from Test1 and Test2 data to evaluate the impact of metal artifacts on the localization performance of all techniques. The results reported in Table  3  clearly show that our proposed model performs better than other competing methods in terms of MRE±SD and SDR within various error ranges. Especially given the 1. 5 mm error range, it achieves a remarkable improvement of 8% over SA-LSTM, enabling anatomical landmark detection with significant robustness.
Influence of the amount of training data The availability of large image datasets in medicine is an important issue due to strict ethical rules applied to patient privacy and the cost of expert-specific annotations. It is therefore important to understand how the performance evolves according to the amount of data used. We investigate this issue by considering 10%, 20%, 50%, and 100% of our training dataset with a comparison between the best two methods, SCN and CMF-Net. Fig. 10  shows the cumulative successful detection distribution versus discrete precision thresholds (the MRE±SD is also specified in each case). The CMF-Net consistently performs better than SCN at all different ratios of training images with an average error of 1. 44 mm using only 10% of the training images. It proves that our CMF-Net can effectively learn representative features and generalize well on unseen data using limited sets of medical images, even in the presence of patients who suffer from severe malformations.
Acknowledgements This research was supported by the  National Key Research and Development Program of China  ( 2022YFE0116700 ), the  National Natural Science Foundation of China  ( 62171125 ,  31800825 ,  31640028 ,  61876037 ,  82071143 ), the  Natural Science Foundation of Jiangsu Province  ( BE2019748 ), the  Fundamental Research Funds for the Central Universities  ( 2242020K40039 ,  2242020K30012 ), the  Short-term Recruitment Program of Foreign Experts  ( WQ20163200398 ), the  Key Medical Research Projects of Jiangsu Health Commission  ( ZDA2020003 ). We thank the  Big Data Center of Southeast University  for providing the facility support on the numerical calculations in this paper.
and they are then normalized to [-0. 5, 1] by dividing by 2048. To get rid of overfitting issues, a series of data augmentations are carried out to enrich the diversity of training data. Intensity perturbation operations are performed including shift ([-0. 25, 0. 25] pixel value) and scale ([0. 75, 1. 25] times). Spatial augmentation is also conducted to enrich the diversity of the training dataset using off-the-shelf SimpleITK (Beare et al. , 2018). It includes translation ([-30, 30] mm), rotation ([-0. 25, 0. 25] rad), scale ([0. 95, 1. 15] times) and flip (randomly flip along the z-axis with the possibility of 0. 5). All data augmentation operations follow a uniform distribution within the predefined intervals.
In this work, we investigated the oncogenic role of Streptococcus gallolyticus subsp. gallolyticus (SGG), a gut bacterium associated with colorectal cancer (CRC). We showed that SGG UCN34 accelerates colon tumor development in a chemically induced CRC murine model. Full proteome and phosphoproteome analysis of murine colons chronically colonized by SGG UCN34 revealed that 164 proteins and 725 phosphorylation sites were differentially regulated. Ingenuity Pathway Analysis (IPA) indicates a pro-tumoral shift specifically induced by SGG UCN34, as ~ 90% of proteins and phosphoproteins identified were associated with digestive cancer. Comprehensive analysis of the altered phosphoproteins using ROMA software revealed up-regulation of several cancer hallmark pathways such as MAPK, mTOR and integrin/ILK/actin, affecting epithelial and stromal colonic cells.
Most of the proteins and phosphoproteins regulated by SGG UCN34 were associated with digestive cancer. In depth bioinformatic analyses using ROMA software revealed that the prooncogenic role of SGG UCN34 is associated with activation of several cancer hallmark pathways in tumor cells and their stromal microenvironment. In line with these results, we show here for the first time that SGG UCN34 contributes to pre-cancerous transformation of the colon epithelium using an ex-vivo organoid model. Finally, an independent protein analysis of human colon tumors colonized with SGG revealed up-regulation of PI3K/ Akt/mTOR and MAPK pathways, providing clinical relevance to our findings in a murine model of CRC.
All samples were processed by label free LC-MS/MS analysis. (b) Principal component analysis (PCA) of proteome samples in 3D representation. (c) PCA of phosphoproteome samples in 3D representation. Proteome and phosphoproteome analyses were done using myProMS web server  65. (d) Top disease and bio functions (IPA) of proteome and phosphoproteome changes between SGG UCN34 and SGM. The diseases and functions analysis identified biological functions and/or diseases that were most significant from the data set. Data sets used in this analysis were proteins or phosphoproteins differentially expressed between SGG UCN34 and SGM detected in macroscopically tumor-free colonic tissue. Molecules indicate the number of proteins or phosphoproteins associated with indicated diseases and disorders. A right-tailed Fisher's Exact Test was used to calculate a p value determining the probability that each biological function and/or disease assigned to that data set is due to chance alone.
tumor-free, SGG tumor). As expected, the protein content of the tumoral tissue (SGG tumor) differs from that of the adjacent macroscopically healthy tissue (SGG tumor-free). Importantly, SGG UCN34 induced specific proteomic and phosphoproteomic changes in macroscopically tumor-free tissue compared to control SGM-treated tumor-free tissue indicating that SGG UCN34 modulates the proteome and phosphoproteome landscapes of the murine colon epithelium before any macroscopically visible changes. These results suggest that SGG UCN34 is not a silent colon inhabitant in sharp contrast with SGM. To investigate the colonic alterations induced by SGG UCN34, Ingenuity Pathway Analysis (IPA, Qiagen) was applied to our datasets. Most of the differentially expressed proteins and phosphosites in the tumor-free colonic tissue exposed to SGG vs SGM are linked to cancer development (Fig.
SGG UCN34 activates MAPK, mTOR and integrin/ILK/actin cytoskeleton pathways in vivo To identify the specific signaling pathways altered by SGG UCN34, we first processed our phosphoproteomic data sets with the in-house built ROMA (Representation and quantification Of Module Activities) software, a gene-set-based quantification algorithm 41 integrating several databases. As shown in Fig. 3a , the control SGM group clearly differs from SGG group. Secondly, the pro-tumoral shift induced by SGG in the tumor-free region matches that found the tumoral part. Thirdly, 83 pathways were found significantly altered when comparing SGM and SGG groups (evidenced by two-tailed t-tests of activity scores; p value ≤ 0. 05) (Fig. 3a , Sup Table  S3 ). Among these 83 pathways, 34 were up-regulated by SGG UCN34 and 49 pathways were down-regulated.
These stromal cells are the main producers of bone morphogenetic protein (BMP) antagonists such as Gremlin 1 (Grem1) involved in stem cells maintenance and tumorigenesis  [44] [45] [46]  (Fig. S6b ). This result shows for the first time that SGG can alter the stromal microenvironment. The top 10 down-regulated pathways were as following: adherens junction, insulin, vascular smooth muscle contraction, protein tyrosine phosphatase 1B (PTP1B), protein tyrosine phosphatase-2 (SHP2), AUF1 (hnRNP D0) which binds and destabilizes mRNA, Feline McDonough Sarcoma-like tyrosine kinase (FLT3), RUNX2 which regulates bone development, TP53, Fc epsilon receptor 1 (FCER1) (Table  S3 ). Secondly, the same data sets were re-analysed using the commercially available Ingenuity Pathway Analysis software. The top 10 signalling pathways selected by activation prediction score (positive z-score) were: Integrinlinked kinase (ILK), BRCA1, Dilated Cardiomyopathy Signaling Pathway, Protein Kinase A, Actin Cytoskeleton, AMPK signaling, Epithelial Adherens Junction, Oxytocin Signaling Pathway, Integrin Signaling and CNTF Signaling (Fig.
Most altered proteins were associated with cancer disease. Bioinformatic analyses using IPA and ROMA software revealed significant up-regulation of multiple signaling pathways previously linked to cancer cells and their stromal microenvironment, including the three major MAPKs (ERK, JNK, p38), the PI3K/AKT/mTOR and integrin/ILK/actin cytoskeleton. These data suggest that SGG UCN34 affects multiple proteins in these pathways altering growth factors signaling cascades such as EGF and FGF receptors, ubiquitously expressed, and PDGFA receptors, which are mostly expressed in the colonic stromal compartment. Importantly, an independent analysis of the protein and phosphoprotein content from human colon tumors enriched for SGG revealed up-regulation of PI3K/Akt/ mTOR and MAPK pathways, providing clinical relevance to our findings in the murine model.
Proteome and phosphoproteome analyses For identification, the data were compared with the Mus musculus UP000000589 SwissProt database (downloaded 01/2019 and containing 22,266 entries) and a common database of contaminants (245 entries) using Sequest HT through proteome discoverer (version 2. 2). Enzyme specificity was set to trypsin and a maximum of two-missed cleavage sites were allowed. Carbamidomethylation of cysteines, oxidized methionine and N-terminal acetylation were set as variable modifications for proteomes. Phospho-serines, -threonines and -tyrosines were also set as variable modifications in phosphoproteome analyses. Maximum allowed mass deviation was set to 10 ppm for monoisotopic precursor ions and 0. 02 Da for MS/MS peaks or 0. 6 Da for phosphoproteome analyses. FDR calculation used Percolator  64  and was set to 1% at the peptide level for the whole study.
The resulting files were further processed using myProMS 65 v3. 9. 3 (https:// github. com/ bioin fo-pf-curie/ mypro ms). Label free quantification was performed by peptide Extracted Ion Chromatograms (XICs), reextracted across all conditions (healthy/tumor-free, tumor, polyp and mix samples, see PXD038272, PXD038270, PXD038268 and PXD038267) and computed with MassChroQ version 2. 2. 1  66. Polyp samples were not used in this study since histological analysis demonstrated that they correspond to lymphoid aggregates (LA, Fig. S1E ). For differential analyses, XICs from proteotypic peptides (shared between compared conditions for proteomes, no matching constraints for phosphoproteomes) with at most two-missed cleavages were used. Replicate SGM m8-H which had more than 76% missing values was excluded from the proteome differential analyses.
Quantification of pathway activity with ROMA ROMA (representation and quantification of module activities) is a gene-set-based quantification algorithm  41  with the MSigDB C2 Canonical pathways sub-collection as pathway database composed of 2232 pathways from Reactome (http:// www. react ome. org), KEGG (http:// www. pathw ay. jp), Pathway Interaction Database (http:// pid. nci. nih. gov), BioCarta (http:// cgap. nci. nih. gov/ Pathw ays/ BioCa rta_ Pathw ays) and others. ROMA was used to investigate potential changes in pathway activity between the 3 groups (SGG UCN34 tumor-free, SGM tumorfree and SGG UCN34 tumor) based on phosphoproteomic data. In this study, we used an R implementation of ROMA available at https:// github. com/ Alblu ca/ rRoma. Only the 16 most representative samples of the 3 groups were used for the analysis (samples SGM healthy m6, SGM healthy m9, SGG healthy m10 and SGG tumor m14 were removed due to their outlier behavior as illustrated in Fig.
2C ). A data reduction to a single phosphosite per protein/gene was performed to switch from a site-centric to a gene-centric dataset. This reduction was achieved with an ANOVA across groups and selection of the site with the lowest p value ("most informative" site) when multiple sites were quantified for the same protein. 2784 of the 3188 mouse genes implicated were then converted into their human counterparts with the Biomart resource (https://m. ensem bl. org/ bioma rt, database version 100) prior to ROMA analysis against the MSigDB (http:// www. gsea-msigdb. org) human C2 Canonical pathways subcollection (version 7. 1) as pathway database. 1752 genes from the dataset matched 666 of the 2232 pathways.
Ingenuity pathway analysis (IPA) We used IPA (QIAGEN, http:// www. ingen uity. com/) to characterize two sets of proteins: (1) one corresponding to the list of proteome changes between the tumor-free SGG UCN34 group and the tumor-free SGM group (164 proteins of which 163 IDs were found in the IPA database) and (2) and one to the list of phosphoproteome changes between the tumor-free SGG UCN34 group and the tumor-free SGM group (597 proteins of which all IDs were found in the IPA database). For the list of phosphoproteins, we first removed phosphosites that were found in conjunction with other phosphosites on the same protein, and in that case, we retained only the site with the lowest p value (in total 725 phosphosites mapped to 597 proteins). Lists containing differentially expressed proteins with their corresponding log2 ratio of expression values for both data sets (proteome and phosphoproteome) were uploaded and analyzed individually.
Histology and immunohistochemistry Colon fragments fixed for 48 h in 10% neutral-buffered formalin were embedded in paraffin. Four-µm-thick sections were cut and stained with hematoxylin and eosin (H&E) staining. Histological evaluation was performed by an histopathologist in a blind fashion. Characterization of LA was done by immunostaining performed on Leica Bond RX using anti-CD3 (Dako, Ref. A0452) antibody, and hematoxylin staining. Slides were then scanned using Axioscan Z1 Zeiss slide scanner and images were analyzed with the Zen 2. 6 software.
Acknowledgements This work was supported by the  Institut National contre le Cancer (INCA , Grant  PLBIO16-025 ) and from the  French Government 's  Investissement d' Avenir program ,  Laboratoire d'Excellence   Integrative Biology of Emerging Infectious Diseases  (Grant no. ANR-10-LABX-62-IBEID ). EPK received a 2-year  Roux-Cantarini  post-doctoral fellowship. We are grateful to  Johan Bedel  and  Magali Tichit  of the  Institut Pasteur Histological Platform  for paraffin embedding, sectioning, histochemistry and immunohistochemistry,  Karim Sebastien  and  Marion Bérard  for technical help with animal experimentation and all the zootechnicians from the  Animal Breeding Facility from Pasteur Institute. We thank  Dmitry Ershov  for image analysis for DNA damage (yH2AX) quantifications,  Adriana Mihalache  for her expertise and generous help in tumor grades scoring,  Bruno Périchon  and  Thomas Cokelaer  for SGG isolate sequencing,  Laurence du Merle  for technical assistance with bacterial cultures and CFU counts as well as  Valentin Sabatet  from the  Institut Curie Proteomics Platform  for protein quantification and  Olivier Mirabeau  for bioinformatic help.
Data availability All data generated or analyzed during this study are included in this published article and its supplementary information files. In addition, the raw datasets generated and/or analyzed during the mass spectrometry proteomic and phosphoproteomic study were deposited in the ProteomeXchange Consortium via the PRIDE  76  partner repository with the dataset identifiers PXD038272 (reviewer_pxd038272@ebi. ac. uk; Zc74Ei5B); PXD038270 (reviewer_pxd038270@ebi. ac. uk; Ctnt4Ao0); PXD038268 (reviewer_pxd038268@ebi. ac. uk; FW8rInt) and PXD038267 (reviewer_pxd038267@ebi. ac. uk; blfbVkViU). For RPPA analysis, the raw datasets are available from the corresponding author on reasonable request.
ESR spin-trapping (ESR-ST) experiments An X-Band spectrometer (Bruker EMX-plus) was used in order to realize the ESR-ST experiments. The experiments were performed out at room temperature (RT) under N2 with 405 nm LED illumination within the cavity of the ESR spectrometer. Radicals were trapped by XM phenyl-N-tert-butylnitrone (PBN) obtained from TCI-Europe in tert-butylbenzene according to a procedure described in refs. 36 and 37. The PEST WINSIM program was used to obtain the ESR spectrum simulations.
Acknowledgment This work received funding by the  CY Initiative of Excellence  (grant " Investissements d'Avenir "  ANR-16-IDEX-0008 ) and developed during Frédéric Dumur's stay at the  CY Advanced Studies  whose support is gratefully acknowledged. The authors want to thank also the  Foundation Pierre et Jeanne SPIEGEL  for the funding of this work ( Adel Noon ).
The procedures for endodontic treatment and coronal preparation are detailed in Table  1. Resin simulators (RSs): One first lower left molar was created using 3D modeling using Blender software (v3. 2. 1, Blender Foundation) with one mesial root with two independent canals and one distal root with one central canal. Four versions of this model were then created, differing in the presence or absence of an endodontic access cavity and the type of crown preparation (Figure  2 ): 10 duplicates of the model without coronal preparation or an access cavity (Figure  2a ), 20 duplicates with an access cavity (Figure  2b ), 40 duplicates with flat peripherical coronal preparation (Figure  2c ), and 40 duplicates with a 2 mm peripherical ferrule (Figure  2d ). The simulators were manufactured using a STRATASYS™ Objet30 Prime 3D printer (Polyjet technology, Eden Pairie, MN, USA) with RGD525 resin, which provides the best mechanical characteristics.
Statistical Analysis The data were statistically analyzed with SPSS software (28. 0. 1. 1, IBM Corp, USA). The distribution of each group was tested using a Shapiro-Wilk test. In the case where all groups had a normal distribution, the groups were compared using a one-way analysis of variance (ANOVA) with a Student-Newman-Keuls post hoc test. If one group showed an abnormal distribution, the samples were compared using a Kruskal-Wallis test. The distribution of the fracture type was compared with a chi-square test. The human teeth and resin simulator strength values were compared for each condition with a Mann-Whitney test. The equality of variance between the RS groups and HT groups was compared with Levene's test.
Acknowledgments: The authors would like to thank Jean-Luc Veyrune and Thierry Orliaguet for their financial and administrative support, respectively. The authors would also thank Caroline Eschevins for her technical assistance. The authors would like to thank Geoffrey Lefebvre, Annaëlle Saintilan, Cyril Claise, and Charles Kolbach for their help in the data collection. This study was supported by GC (Tokyo, Japan), who donated the materials used in this study.