dataesr/scientific-paragraphs-categorization huggingface dataset:
e. sets of strongly similar segments from at least 2 protein sequences aligned in PLMA blocks by Paloma-D); 3) Inference of the module composition of ancestral genes in the reference tree (through Module-Gene-Species reconciliation by SEADOG-MD using the phylogenetic tree of each module inferred with PhyML and TreeFix); 4) Annotation of proteins with known phenotypic traits of interest (here Protein-Protein Interactions); 5) Reconstruction of the ancestral scenario of phenotype evolution across the reference gene tree (PastML); 6) Merging module and phenotype evolutionary information: each ancestral gene of the reference gene tree is then characterized by a module composition and a set of phenotypic traits (protein interactants here). The final result is the prediction of functional signatures by identification of module(s) and phenotypic trait(s) co-appearance.
https://doi. org/10. 1371/journal. pcbi. 1011404. g002 is defined as a group of sequences from different species descending from a common ancestor gene. We identified 22 orthogroups containing at least one of the 159 RefSeq sequences of known human ADAMTS (102) and ADAMTSL (57) proteins, 17 orthogroups corresponding to ADAMTS proteins and 5 to ADAMTSL proteins. We also selected the 2 orthogroups corresponding to 7 RefSeq sequences of 4 human ADAM genes to use them as an outgroup enabling to root the ADAMTS-TSL phylogeny. Like ADAMTS-TSL, the ADAM (for A Disintegrin And Metalloprotease) proteins belong to the adamalysin family. The resulting dataset, called dataset-708, is composed of 708 protein sequences (386 ADAMTS, 226 ADAMTSL, 96 ADAM) from 24 selected orthogroups and is detailed in Table  1.
Inference of the reference gene tree (step 1) The gene phylogeny, starting point of our method, was inferred from the 214 representative protein sequences in dataset-214. As a first step, we used the multiple sequence aligner PASTA  [26]  (v1. 8. 5, default settings) to compute the multiple alignment of the 214 proteins and the maximum likelihood phylogeny inference software RAxML  [27]  (version provided with PASTA, default settings) to infer a first initial phylogeny. This initial tree was corrected using TreeFix  [28]  (v1. 1. 10, 100 iterations with the PROTGAMMAJTT model) taking into account the species tree defined in the NCBI taxonomy, thus possibly changing the topology of the initial tree. The branch lengths (lost by TreeFix) were then recomputed using PhyML  [29]  (v3. 3. 20190909, optimizing only branch lengths and substitution rate parameters with amino acid default settings) and the tree was rooted using ADAM proteins as the outgroup, resulting in the final phylogeny that we named the reference gene tree.
Identification of conserved sequence modules (step 2) The strongly conserved sequence modules in ADAMTS-TSL proteins were identified by partial local multiple alignment of the 214 sequences in dataset-214. Introduced by the Protomata suite  [13, 14] , a partial local multiple alignment (PLMA) consists of a set of alignment blocks containing each a set of locally aligned segments from some, but not necessarily all, of the given sequences (as in partial order alignments  [30] , but with the addition of a locality property, see  The PLMA blocks align each a set of segments conserved specifically in a sequence subset. This set of segments, provided that they are long enough, is said to be a conserved sequence module. Let us assume here that all the blocks, except B6, align segments of 5 or more residues.
1, M4. 3} specifically conserved in {S1, S3} and M5 = {M5. 1, M5. 3, M5. 2} specifically conserved in {S1, S3, S2}. Similarly, the block B6 aligns the segments S6. 3 and S6. 2 but, in contrast with the segments aligned by B4, these segments are shorter than 5 residues and do not define a module. https://doi. org/10. 1371/journal. pcbi. 1011404. g003 The PLMA of the 214 sequences contained in the representatives. fasta file was computed with the command line paloma-d -i representatives. fasta -M 20 -t 10. The alignment procedure is detailed in S2 Appendix. With these parameters, the candidate pairwise local alignments were all the diagonals (i. e. gap-free pairwise local alignments) computed by the program dialign2-2  [31] , of length smaller or equal to 20 residues and with a strong similarity weight of 10 or more (corresponding to similarities of probabilities lower than 5 × 10 -5 in random sequences).
The multiple sequence alignment was built from this set of pairwise alignments of interest as follows. First, the candidate diagonals were sorted by the default heuristic of Paloma-D, putting forward those with the stronger evidence of conservation across all the sequences. Then, they were iteratively added in this order to the PLMA, with transitive alignment and local extension of diagonals, if and only if the consistency of the resulting induced alignment  [32]  was preserved. The conserved sequence modules were identified from the alignment blocks of the resulting PLMA. Alignment blocks are the maximal alignment of segment units verifying that each segment of a block is aligned by the PLMA over its full length to, and only to, all the other segments of the block. Let us remark that block segments can thus be shorter than the pairwise local alignments used to build the PLMA.
Inference of the module composition of ancestral genes (step 3) The step 3 consists in building the evolutionary history of the modules, according to the reference gene tree. First, the phylogenetic tree of each module was inferred from the gapless alignment of its segments (as in the corresponding alignment blocks from step 2) by Maximum Likelihood software PhyML  [29]  (v3. 3. 20190909, amino acid default settings). Then, each module tree was corrected by TreeFix  [28]  (v1. 1. 10, 100 iterations with the PROTGAMMAJTT model) with respect to the reference gene tree. Of note, the final topology of the module tree might still be different and incompatible with the one of the reference gene tree. Then the module tree was mapped on the reference gene tree using the phylogenetic reconciliation software SEADOG-MD  [24]  (default options) with the help of the species tree (the evolutionary tree according to NCBI Taxonomy  [33] ).
Collecting Protein-Protein Interactions as phenotypic traits of interest (step 4) The step 4 consists in collecting functional traits associated with sequences. Because of their importance in the functions of proteins, we chose Protein-Protein Interactions (PPIs) as the phenotypic trait of interest. The conversion of the RefSeq ID of the 386 ADAMTS and 226 ADAMTSL sequences from dataset-708 enabled us to retrieve 494 UniprotKB ID that were used to query molecular interaction databases using the Proteomics Standard Initiative Common QUery InterfaCe (PSICQUIC)  [34]. The retrieved file contained 1985 lines (S4 Table) including interactions between proteins of the same species (e. g. 1685 for H. sapiens, 28 for M. musculus and 106 for C. elegans) and interactions between proteins from different species (e.
In conclusion, sequence queries of the nine species yielded very little information about the non-human species and we chose to retain only the PPIs from H. sapiens in the PSICQUIC results. Importantly, we observed that many recently identified substrates of ADAMTS were not described in molecular interaction databases and we decided to complete the list of PPIs by manual curation of the literature. Our review of the literature enabled us to identify an additional set of 303 PPIs (S6 Table ). In this dataset, we included high throughput data from the iTRAQ-TAILS method that was used to identify novel substrates for the procollagenases ADAMTS2,-3 and -4  [35, 36]  and the ADAMTS7 protein  [37]. We also included four PPIs involving LRP1 which is not a substrate but interacts with ADAMTS4,-5,-9,-10 to mediate ADAMTS internalization and thus regulate their activities  [38, 39].
Visualization of the labeled phylogenetic tree An important contribution of this work is the availability of all the data with an interactive tree (automatically generated at the end of the pipeline), using the Interactive Tree Of Life software, Itol  [40]. The ADAMTS-TSL Itol tree is available at https://itol. embl. de/tree/ 13125419158431781652196295. An HTML popup window provides detailed information about each gene node, including the name of the gene, the nature of the gene (ancestor or leaf), the PPIs associated with the gene and PPI gain/loss with respect to the ancestor, the module composition of the gene and module gain/loss with respect to the ancestor. The Itol tree also provides annotations including the number, the composition and the transfer of modules, the domain composition and the presence of PPIs.
Using Paloma-D, the partial local alignment of the 214 sequences identified 1059 conserved sequence modules (S9 Table ). As expected, some of these conserved sequence modules are shared by all sequences, while others are shared by different subsets of sequences. In particular, we identified two modules (covering a total length of 33 amino-acids) that were shared by all 26 human ADAMTS-TSL sequences and localized in the cysteine-rich and spacer domains, which are shared domains of all ADAMTS-TSL. In addition, we found 11 other modules (covering a total length of 111 amino-acids) that were specifically shared by the 19 human ADAMTS sequences, located in the catalytic domain, disintegrin, central TSP1, and cysteine-rich domains. These domains were already identified as shared among ADAMTS, which support our Paloma-D module decomposition at the ADAMTS-TSL family level.
The module composition of the 26 H. sapiens ADAMTS-TSL sequences is detailed in Fig 5. In the inferred ancestral module composition, 186 ancestral gene nodes of the ADAMTS-TSL tree were associated with the gain of one or more modules, defining as many module signatures. In parallel, we investigated the evolution of PPIs involving the ADAMTS-TSL proteins and other proteins. A first result is the identification 662 PPIs involving at least one H. sapiens ADAMTS-TSL and one of their 471 H. sapiens partners. Among the 471 partners, 119 have interactions with at least 2 ADAMTS-TSL (Fig  6 ). Using the PastML tool, we inferred 471 ancestral scenarios representing the PPI evolution given the ADAMTS-TSL gene tree. We identified 48 ancestral genes in which one or several PPIs most likely appeared. These nodes indicate potential gain of function.
As shown in Fig  8  and consistent with the PPIs inference mediated by PastML, the interaction with CCN2 or COMP most likely occurred twice during the evolution of ADAMTS-TSL. On the one hand, both PPIs (CCN2 and COMP) appeared in G161, the last common ancestral gene of ADAMTS-7 and ADAMTS-12. On the other hand, the COMP PPI was gained at G15, the last common ancestral gene of H. sapiens, B. taurus, M. musculus ADAMTS-4 and the CCN2 PPI was gained at G315, the last common ancestral gene of the H. sapiens, B. taurus, M. musculus, G. gallus ADAMTS-3. To characterize the PPI changes associated with the ancestral genes G161, G15 and G315, we focused on the modules predicted to appear in those ancestors. We first investigated the module signature at G161 and we observed that the 25 modules gained were primarily localized in the TSP1-repeat of the ancillary region (Fig  9A ).
Acknowledgments The authors thank the excellent support of the  GenOuest bioinformatics core facility ,  Nicolas Buton  ( Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France ) for predicting protein structures using AlphaFold 2 method and  Dr Catherine Lavau  ( Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France ) for proofreading the manuscript.
This is a new contribution and differs from recent research trend of extracting the features from the liver lesions only and not paying much attention to the HU which represents the relation between lesion and surrounding area. In this study, the contributions of our work are summarized as follows: 1) Collect liver CT images of all four phases before and after contrast injection as an input dataset; 2) Consider Hounsfield Unit variations between lesion and surrounding area for partitioning and locating accurately to label datasets and assist doctors in diagnosis and treatment of common liver lesions; 3) Design and implement the multi-phase model of liver lesions classification. It is based on the prior knowledge of the medical background in the analysis of multi-phase CT images before and after contrast injection to get a better and clear understanding of the liver lesion characteristics in medical CT images.
Therefore, we propose a multi-phase model for liver lesions detection and classification as shown in Figure  10. Figure  7  illustrates the general proposed model for liver lesions detection and classification. It consists of three stages: (1) preprocessing to generate the input dataset for deep neural network models; (2) feature extraction; (3) detection and classification of liver lesions. The input dataset is the abdominal CT scans in standard DICOM format, collected from patients with types of common focal lesions such as liver cyst, hemangioma, and hepatocellular carcinoma. This dataset will be pre-processed to fit into the models which are designed based on currently well-performing deep learning networks to extract features and classify liver lesions. The stages of the proposed method are described in more detail as follows.
Stage 1: Data preprocessing and labeling In Figure  8 , we perform the data preprocessing stage to generate an input dataset for experiments. This stage is performed using the intensity window WL = 160 and WW = 400 (in HU units) for abdominal CT images. The images after opening with the intensity window will be normalized to the range from 0 to 1. We then identify the region of interest (ROI) as the liver. In order to fit the proposed models, we convert CT images in DICOM format into jpg extension images, normalize these images with the size of 512x512 corresponding to each slice, and then we are just interested in the slices containing lesions. Lesions will be more clearly differentiated based on Hounsfield values on CT images for better feature extraction and more efficient training of the experimental models.
After data preprocessing, we locate and label liver lesions on the input dataset of CT images. This work is very important for training to obtain a classification accuracy. Hence, it is done with the support of radiologists to create our experimental dataset. Based on the knowledge, experience, and especially the Hounsfield values, the radiologists identify the lesion regions and classify the liver lesions. The support of the radiologists contributes to the creation of a reliable dataset of common liver lesions used in our experiments. We use the LabelImg tool to label liver lesions on the input dataset into one of three classes: Liver cyst (labeled NAN), Hemangioma (labeled HEM), and hepatocellular carcinoma (labeled HCC). LabelImg will automatically create an xml extension file representing the locations of the liver lesions.
Stage 2: Feature extraction and training The input dataset is passed through convolutional neural networks to extract the liver lesion features. The choice of the good feature extractors not only helps to build models with a high accuracy but also reduces the processing time. In this work, we use backbone networks as presented in section 2. 3. 2 to extract features efficiently by taking advantage of pre-trained networks. The extracted features are then trained on network models of the Faster R-CNN, R-FCN, SSD, and Mask R-CNN. In order to train these models effectively, the input dataset must be large enough. However, it is very difficult for medical datasets to have a large dataset. Therefore, we apply the transfer learning technique to solve the problem of a small medical dataset, shorten the training time, learn all features of liver lesions quickly, and reduce computing resources.
Specifically, we apply transfer learning for models that have been pre-trained on COCO dataset, and then use the pre-trained weights to re-train the proposed models on our experimental dataset. This stage is described in detail in Figure  9. During the training process, the input dataset includes four sub-datasets of abdominal CT images in four phases: Plain -before contrast injection, Arterial -after contrast injection in the arterial phase, Venous -the venous phase, and Delay -the delayed phase. All four sub-datasets are fed into the corresponding training models. Based on the pre-trained weights, we fine-tune them for these models to adapt the case of liver lesions by learning transfer on our dataset. The Loss values of these models are regularly monitored to identify until they are no longer improved (not reduced).
Training Model Evaluation Model evaluation is an integral part of the model building process. It helps to find the good models that fit the dataset and predict its performance for deploying in practice. In order to evaluate the performance of the models, we use metrics of the Loss value, accuracy (AP, and mAP), and training time. The following are the training results of six scenarios. 3 , scenario 1 is developed on DNN of Faster R-CNN with backbone of Inception-ResNet-V2. TotalLoss includes the classification and bounding-box Loss. The TotalLoss histogram of four phases is quite similar as illustrated in Figure  11 , where the horizontal axis represents the number of epochs and the vertical axis represents values of the Total Loss. At epochs below 80,000, the Loss value tends to decrease slightly and unevenly.
Measurement of Loss value From Table Scenario 2 is improved from DNN of R-FCN with ResNet-101 backbone. The TotalLoss value includes the classification and bounding-box Loss. The TotalLoss histogram of this scenario is presented in Figure  12. The Loss value starts to decrease dramatically from epochs of 80,000. This shows that the features have been extracted and learned more effectively making the Loss value more and more stable after a long time of training. The Loss value decreases steadily and stably when training with the Delay dataset, showing that the feature extraction at this step is quite good. The histogram with the Arterial dataset is less stable than the rest of histograms. When the Loss value no longer decreases, we stop training at epochs of 200,000 and the average TotalLoss is measured as 0.
AP metric The AP metric is used to evaluate the accuracy of the automatic detection and classification of liver lesions. We focus on three types of liver cysts (NAN), hemangioma (HEM), hepatocellular carcinoma (HCC). As can be seen Figure  18 , HCC and HEM have a fairly high prediction accuracy, whereas NAN gives a lower predictive accuracy than two remaining types. Scenarios 3 and 4 have the lowest AP values for all three types. This shows that SSD does not efficiently extract and learn features for the experimental dataset. In general, the proposed scenarios can give a high classification accuracy as shown by the AP measurement for each type of liver lesion. mAP metric The mAP measure is commonly used today for the problem of multiclass object detection and classification. Figure  19  shows the mAP measurement of the proposed scenarios.
Scenario 1 gives the average mAP of 97. 2% for all four subsets of CT images, which is the second highest mAP among the proposed scenarios. Scenario 6 provides the best detection and classification results with mAP of 97. 4%. Scenario 3 achieves the lowest mAP value of 72. 5%. In general, all scenarios have a quite high accuracy, in which scenario 6 gives the highest accuracy of liver lesions detection and classification. This shows that scenario 6 is appropriate to the experimental dataset and achieves the best classification accuracy. The lesions often have an uncertain shape and are difficult to distinguish, thus scenario 5 does not give a good classification result since it has to learn the mask of the lesions. However, the advantage of this scenario is that it helps to accurate locate and segment the liver lesions.
Test Results mAP metric for the test dataset We conduct a test on the test dataset consisting of 400 CT images. Figure  21  shows the mAP measurements of the test process. Scenario 6 has the highest mAP value of 97. 2% and scenario 3 has the lowest mAP value of 72%. The proposed models give fairly accurate detection and classification results for large and clear lesions, whereas the very small lesions having density similar with liver tissues are still not well detected.
This is because in the experimental dataset, the lesions of this type are often small thus it gives lower accuracy than the other two types of lesions. With the average Loss value of 0. 0027 and the highest mAP of 97. 4%, scenario 6 is suitable for liver lesion datasets. It is more efficient in feature extraction, achieves higher accuracy than the remaining scenarios. The experimental results show that scenario 6 obtains the best accuracy of detection and classification among the proposed scenarios. However, the prediction results are still inaccurate because it sometimes fails to detect and classify for some very small and blurred lesions having a density similar to the liver tissues. Although scenario 6 gives the best detection and classification results in most cases, we can consider scenario 4 because it is a suitable choice if we want to build applications on smart-phones or embedded systems with low computational resources.
Conclusion In fact, doctors are often interested in variations of the HU density on the four phase CT images with and without contrast to have an accurate diagnosis of liver lesions. Therefore, in this study, we propose a HU-based multi-phase model for detecting and classifying liver lesions using deep neural networks. The proposed method is based on variations of the HU density on multi-phase CT images to segment liver lesions automatically and assist in labeling our experimental dataset. Based on the variation of HU density on multi-phase CT scans and the support of radiologists, we collect a reliable dataset for liver lesions. We design and perfect the deep learning models developed on the pre-trained networks of Faster R-CNN, R-FCN, SSD, and Mask R-CNN by transfer learning approach. An improvement of the classification model is to analyze CT images in four phases of Plain, Arterial, Venous, and Delay to accurate detect and classify liver lesions.
We generate six scenarios and conduct on our experimental dataset for common liver lesion classification. In addition, we also provide a comparison of the proposed scenarios with the recent related studies. The experimental results show that the accuracy of the proposed scenarios is up to 97. 4%, of which scenario 6 achieves the highest with an average mAP of 97. 4%. Scenario 6 is a good choice for developing real systems of liver lesion classification on CT images because it is feasible and efficient in feature extraction and achieves higher accuracy than the remaining scenarios in many different cases. The proposed scenarios are very useful to assist doctors in automatic segmentation and classification of liver lesions because they save time and solve the problem of depending on the clinician's experience in the diagnose of liver lesions.
The cardiac component builds a pooled cohort of approximately 90 000 paediatric patients who underwent CFP during childhood and adolescence to investigate cancer risk following exposure to IR and explore the potential effects of conditions predisposing to cancer. HARMONIC develops software tools to allow dose reconstruction in both EBRT and CFP to enable epidemiological investigations and future optimisation of treatments. With the creation of a biobank of blood and saliva samples, HARMONIC aims to provide a mechanistic understanding of radiation-induced adverse health effects and identify potential biomarkers that can predict these effects.