dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, the data acquired on 27 and 29 December 2022 present a difference in water height of 1. 66 m (9. 75 m and 11. 41 m, respectively). The comparison could also be made with the image acquired on 11 January 2023 (8. 38 m) (Figure  13 ). Tombelaine constitutes a fixed point to the west of which a tidal channel passes. Between Tombelaine and the Grouin du Sud, the sediment accumulates in the form of a long (about 2. 2 km) and narrow (300 m on average) bank (see 'bank' in Figure  13 ) running westeast at a height of around 11. 3 m. On the image of 28 May 2020 (Figure  8 ), the elevation was only around 10. 8 m between Tombelaine and Grouin du Sud. An increase in at least +0. 5 m in this bank corresponds to the accumulation of about 330,000 m 3. The sediment in the northern part of the estuarine complex is probably trapped in the bank and corresponds to a great part of the income sedimentation.
Unfortunately, it seems impossible to evaluate the volume of sediment eroded around the Mont using this method because of the lack of data with favourable tidal conditions and the constant migration of the Tombelaine constitutes a fixed point to the west of which a tidal channel passes. Between Tombelaine and the Grouin du Sud, the sediment accumulates in the form of a long (about 2. 2 km) and narrow (300 m on average) bank (see 'bank' in Figure  13 ) running west-east at a height of around 11. 3 m. On the image of 28 May 2020 (Figure  7 ), the elevation was only around 10. 8 m between Tombelaine and Grouin du Sud. An increase in at least +0. 5 m in this bank corresponds to the accumulation of about 330,000 m 3. The sediment in the northern part of the estuarine complex is probably trapped in the bank and corresponds to a great part of the income sedimentation.
The size of the bay (about 300 km 2 , then it is impossible to cover the area in one shot and the tide is very important); 2. The regulations stipulate a maximum flight height of 150 m and a maximum horizontal distance of 100 m from the remote pilot. These conditions can only be met very occasionally, for example, close to the dam; 3. the winds are often strong and do not allow regular drone use. For all these reasons, satellite dates were used in this study. The present study of Sentinel-2 data attracted the attention of the scientific committee of the Establishment, and further discussions are planned. It will be interesting to see how optical data complements the lidar data acquired around the Mont.
Acknowledgments: The author would thank all the students who contributed to the field investigations, particularly the students from the  University of Bordeaux 3 , and  Yvette Marchand  and  Clélia Bilodeau , former students. A special thanks to  Irène Offermans  for reviewing a preliminary English version. The author warmly dedicates this paper to the memory of his friend,  Fernand Verger  (1929Verger ( -2018)), a great specialist of the  Mont-Saint-Michel Bay.
Statistical analysis of experimental data Experimental data were analyzed using a two-way ANOVA test or an unpaired Student test with GraphPad Prism 7 for Windows (GraphPad Software). Data shown are mean ± s. d. of three or more independent experiments. *p < 0. 05; **p < 0. 01; ***p < 0. 001; ****p < 0. 0001; ns: not significant.
Transcriptomic data analysis Micro-array data from HCT-116 (GSE7161,  [65] ) or A549 (GSE52931,  [14] ) cells were loaded from the GEO (https:// www. ncbi. nlm. nih. gov/ geo/) database. Differential expression analyses were performed in R 3. 4. 4 with the limma package on the log2 transformed data. A subset of genes was selected as up-regulated genes in the siTop1 condition if their RNA levels fold change (siTop1/siCtrl) > 1. 5 and the corresponding fdr adjusted p-values < 0. 05. The same number of genes which RNA levels were not significantly modified were considered as controls.
Prediction of G4 structures G4 structures were predicted for the sets of up-regulated or control genes (see transcriptomic data analysis). The TSSs of the genes were recovered in R using the biomaRt package with ensembl annotations. Fasta format files of sequences corresponding to TSS-500 bp-TSS regions were generated from these sets of genes using awk and betools. The strand information of the TSSs was considered to compute the coordinates of the TSSs-500 bp-TSS. These sequences were then used with the G4Hunter program (https:// g4hun terap ps. shiny apps. io/ G4Hun terMu ltiFa staSe eker/) with a window size of 25 nucleotides and a score threshold > 1. 7. All graphs and statistical tests were performed in R 3. 4. 4.
Acknowledgements The authors thank  M. P. Teulade-Fichou  ( I. Curie, Orsay, France ) for fruitful discussions on G4 structures and  Y. Pommier  ( NIH, NCI, Bethesda, USA ) for his generous gift of purified human DNA TOP1. We also thank  E. Dufour ,  M. Nowakowski  and  S. Petres  ( I. Pasteur ,  PF3PR ,  C2RT ,  Paris, France ) for their help in the production and purification of an N-terminal tagged human DNA TOP1 (called His-TOP1). We thank  E. Batsché  ( IBPS, Paris, France ) and  M. Ait-Said  ( I. Cochin, Paris, France ) for scientific discussions and  L. Lacroix  ( IBENS, Paris, France ) for his advice on the use of G4hunter algorithm. J-Lat  A1  and  J-Lat  10. 6 cell lines from  E. Verdin  [63] were obtained through the  NIH AIDS Reagent Program, Division of AIDS, NIAID, NIH, USA.
A more thorough traction of these aspects is available to the interested reader in  Penta and Merodio (2017). In the following section we will present the numerical solutions of the aforementioned cell problems, as well as the solution of the macroscale problem in Eqs. (  2 )-(  4 ) for a simple setting. The numerical solutions are obtained by coding the PDEs in the commercial software COMSOL Multiphysics ® (2021). We implemented the microscale cell problems in the solid mechanics module using quadratic serendipity finite elements. For the macroscale problem, we coupled the solid mechanics module to two 'Coefficient form PDE' modules. Equations were discretized using quadratic Lagrange finite elements. Time discretization was carried out via an implicit backward differentiation formula. In both the microscale cell problems and macroscale problems the discretized equations were solved as a fully coupled system, making use of a standard Newton solver.
Acknowledgements This work was supported by the  Israel Science Foundation (ISF)  (grant no. 1231/19 ), and was supported by a  Chateaubriand Senior Research fellowship  (to A. S. ), the  CNRS  (to M. O), and a joint  H2020 EU Eureka!EUROSTARS grant  (project E! 10266 , to M. O. and  Rainer Uhl ,  Till. I. D. , Munich ).
In recent years, Machine Learning approaches (ML) have shown promising results in addressing many tasks in medical image analysis. In particular, the analysis of Bone Metastases (BM) has attracted considerable interest from both the medical and computer vision communities due to its critical and challenging aspect. Despite the research efforts, the detection of BM is still an open problem, mainly due to the lack of available datasets. This is due to two main obstacles: (i) the enormous time required for data collection and annotation, and (ii) privacy constraints. To overcome these challenges, we propose BM-Seg, a new dataset for segmenting BM from CT-scans. Our BM-Seg dataset consists of 1517 CT images from 23 patients where BM and bone regions were labeled by three radiologists. BM-Seg is constructed to cover the diversity of bone metastases in terms of location, organ and severity.
We also propose a new CNN-based approach to segmentation of BM, presenting two main contributions. First, we introduce Hybrid-AttUnet++, a new Unet++ derived architecture with dual decoders that performs segmentation of BM and bone regions simultaneously. Second, we use an ensemble of trained Hybrid-AttUnet++ models (EH-AttUnet++) to optimize segmentation performance. Our experiments show that the EH-AttUnet++ architecture achieves better performance compared to state-of-the-art approaches for various evaluation metrics. The purpose of this work is to provide a benchmark dataset with new state-of-the-art performance in bone metastasis segmentation. This will facilitate further research in this area and help to put automatic detection and segmentation of bone metastases into practice.
, 2022; Lei et al. , 2021). However, automated segmentation of BM using CT-scans has received relatively little attention due to the lack of labeled data  (Chmelik et al. , 2018; Noguchi et al. , 2022). As far as we know, there is no publicly available dataset for segmenting BM. The goal of this work is to create a benchmark dataset for segmenting BM from CT-scans. In addition, we propose a new CNN-based approach for segmenting BM to further improve the segmentation performance compared to state-of-the-art architectures. The main contributions of this work can be summarized as follows: 1. We present BM-Seg dataset for segmenting BM from CT-scans. The BM-Seg dataset is publicly available at https://BMseg_short link. edu. 2. We introduce Hybrid-AttUnet++, a new Unet++ derived architecture that combines attention gates and dual decoders.
Hybrid-AttUnet++ efficiently segments both BM and bone regions. 3. To improve the segmentation of BM, we propose an ensemble approach, which we christen EH-AttUnet++. In this approach, five trained Hybrid-AttUnet++ models are used to improve the predictions of each model by averaging the predicted probabilities of five model predictors. 4. We perform extensive experiments on the BM-Seg dataset and obtain good results compared to other CNN-based segmentation architectures. Further analysis of the results is performed in the ablation study. We have made the code of the proposed CNN architecture publicly available at: 1. This paper is organized as follows. Section 2 gives a brief overview of recent work on the state-of-the-art for the analysis of BM. The proposed BM-Seg dataset is presented in Section 3.
Zhang, Huang, Deng, Cao, and Lin (2021)  developed a Unet-based approach  (Ronneberger, Fischer, & Brox, 2015)  with an attention mechanism for bone segmentation from SPECT data images. Their approach automatically identifies and localizes the BM. Moreau et al. (2020)  compared different approaches to bone segmentation and metastatic bone lesions in breast cancer. They proposed two deep learning methods to segment either both bone and bone lesions or only bone lesions on PET/CT images. Later,  (Fan, Zhang, Zhang, & Jiang, 2021)  proposed a deep learning algorithm for spine metastases in lung cancer. They proposed a dilated convolutional U-Net (DC-U-Net) to segment the energy/spectral images CT. More recently,  (Noguchi et al. , 2022)  used CT images and proposed a segmentation approach based on three convolutional neural networks (CNNs): a 2D UNet-based network to segment bones, a 3D UNet-based network to segment candidate regions, and a 3D ResNetbased network to reduce false positives.
Although the state-of-the-art methods show promising results, their impact is limited by the use of private datasets, which prevents the community from using the data and building on their findings. In addition, existing work has used various imaging modalities such as MRI, bone scan, and PET, while few papers have used CT-scan despite their clear advantages. Table  1  shows a comparison of existing BM datasets. Table  1  shows that the major limitation of the existing datasets is their small size, which can lead to overfitting. To overcome these problems, we created a new BM segmentation dataset and made it available to the research community.
BM-seg dataset This section describes the following steps to create the BM-Seg dataset in Fig. 1. First, the patient is injected with a contrast agent into the veins to highlight any bone lesions. Once the patient exits the scanner CT, the images are forwarded from CT to three radiologists for examination. After the radiologists interpret the data, the regions of interest (RoIs), which represent the bone and lesion regions for each CT-scan slice, are marked using the Apeer tool  (Zeiss, 2021). At the end of the process of creating the BM-Seg dataset, we obtain both bone and lesion masks for each BM image.
Data collection and labeling Our BM-Seg dataset consists of 23 CT-scans of 23 patients with bone metastases, including 9 female and 14 male subjects aged 18 to 83 years. All CT-scans were retrospectively collected at UHC (University Hospital Centre) Hedi Chaker, Sfax, Tunisia. Data were collected from November 2020 to June 2022. Each CT-scan was reviewed by three experienced radiologists using the Radiant Dicom Viewer tool  (Medixant, 2009). Based on the location of bone pain, the patient's health status, and any history of trauma, two physicians classified the slices of each CT scan as infected or not infected. Then, a radiologist (with more than 20 years of diagnostic imaging experience) reviewed the annotated slices that had originally been rated by the other two physicians. Finally, the radiologists selected the regions corresponding to the BM lesions in each infected slice of the CT-scan.
The examination was excluded if there was no clear agreement in diagnosis between the three radiologists. Fig. 2  shows examples of BM-Seg images with the corresponding annotated lesions. The slices that met the approval of all three radiologists were converted to JPEG format, then the bone and BM regions were manually segmented to extract ground truth masks (GT) using Apeer software. Fig. 3  shows four CT-scan images from BM-Seg and their corresponding bone and BM masks. The four examples belong to different locations of the human skeleton and have different BM lesion sizes. Since the labeling process is time-consuming, 70 infected slices were randomly selected from each CT-scan. On the other hand, for the CT-scans that had less than 70 infected slices, all infected slices were selected. A total  of 1517 slices were annotated by creating the bone metastasis and bone masks.
According to  Hammon et al. (2013) , it is difficult and time-consuming to detect bone lesions at an early stage on CT images, especially when a large number of benign bone lesions with an osteolytic appearance are present. As a result, each lesion type has a different appearance,   complicating the labeling process and making the segmentation task more difficult • Benign processes such as inflammation, degenerative activities, fractures, and injuries show hotspots. In addition, primary malignant and benign bone tumors show a similar appearance, i. e. , increased radiotracer uptake. To address the above challenges, annotation by various radiologists is essential to reduce noise in the ground truth. Figs. 4  and 5  show the main challenges in our dataset. As shown in Fig. 4 , it is difficult to distinguish between an intervertebral disc and a bone lesion in the CTscan, especially in osteolytic lesions where radiologists have to spend a lot of time to verify a CT-scan from only one patient.
EH-AttUnet++ Ensemble models are created in ML by combining the predictions of different independent models to improve the overall predictions. The goal of the ensemble method is to reduce generalization errors in ML algorithms and improve the segmentation result by taking lessons from the results of the individual models. To improve the performance and robustness of our system, we propose an ensemble method consisting of five Hybrid-AttUnet++ models as shown in Fig. 9. To ensure model diversity, which is critical for ensemble performance, we used a k-fold cross-validation technique, where k, the number of folds, was set to 5. To create the partitions, we first randomly divided the dataset into five folds. Of these folds, one was used for validation and the remaining four for training. Then we created five splits with different training/validation subsets.
Experimental results To evaluate our approach, we perform 5-fold cross-validation using 80% of the combined dataset as training data and the rest as test data. The average results of the five-fold cross-validation are then calculated and summarized in Table  3. In it, the quantitative experimental performance of Hybrid-AttUnet++ and EH-AttUnet++ is compared with the state-of-the-art models on BM-Seg. The results show that our proposed Hybrid-AttUnet++ performs better than the four Unet variants in terms of F1-score, Dice and IoU, which are commonly used to evaluate segmentation tasks. More specifically, F1score was improved by 2. 81%, 2. 86%, 2. 53% and 13. 49% compared to Unet, AttUnet, Unet++ and AttUnet++, respectively. Similarly, Dice scores improved 3. 44%, 3. 94%, 3. 71%, and 16. 96% compared to Unet, AttUnet, Unet++, and AttUnet++, respectively.
Discussion and ablation study BM detection on the CT-scan is a common but difficult task for radiologists. Moreover, diagnosis of BM by imaging is further complicated by the rarity of data sets available at BM. In this study, we created our BM-Seg dataset and used a deep-learning algorithm combining dual decoders and an ensemble method to solve these problems. As mentioned in Table  4 , the F1-score, Dice, and IoU of the segmentation of BM were 83. 67%, 77. 05%, and 71. 92%, respectively, which were higher than the results of the baseline models. The visual comparison of EH-AttUnet++ with state-of-the-art algorithms is shown in Fig. 10. The images from left to right show the raw infested CT-scan images and the segmentation results of Unet, AttUnet, Unet++, AttUnet++ with the modified BCBlock, our proposed EH-AttUnet++ and the GT-masks.
For a comprehensive analysis, we perform ablation experiments to evaluate the efficiency of the modified BCBlock, dual decoders, and ensembling approach. The AttUNet++ architecture  (Li et al. , 2020)  is considered as the baseline approach. The ablation results are shown in Table  6 , which indicates that the modified BCBlock (MB), the Dual-Decoder structure (DD), and the ensembling approach (EN) significantly improve the F1-score, Dice, and IoU metrics. In particular, the modified BCBlock can improve the F1-score, Dice, and IoU by 11. 49%, 13. 89%, and 14. 59%, respectively, compared to the original AttUnet++. This shows that the BN and the residual skip connections are very important to prevent overfitting since the dataset is small and AttUnet++ has a higher number of parameters to be optimized compared to the other variants of Unet  (Li et al.
Moreover, the model outperforms other state-of-the-art models in complex scenarios where lesions of different sizes coexist and small lesions can be easily overlooked due to their similar appearance. Although our research has shown the benefits and prospects of using deep learning to segment BM from the CT-scan, more work needs to be done to analyze BM. It should be noted that our BM-Seg dataset was collected from a single center, UHC (University hospital Center Hedi Chaker, Sfax, Tunisia). The patients included in this study were mainly from the south of Tunisia. However, pathologic data may vary greatly in clinical scenarios from different regions and races. In addition, larger datasets will help to build applications for real-world scenarios for the analysis of BM. However, due to the scarcity of patients and privacy concerns, building a large dataset is challenging.
Conclusion In this paper, we present a new dataset for bone metastasis segmentation (BM-Seg). This dataset is the first publicly available annotated dataset for automatic segmentation of metastatic lesions in bone CTscan images. The goal of this benchmark is to help the community make further progress in the segmentation of BM. In addition, we proposed an ensemble method (EH-AttUnet++) that highlights areas of high uptake in CT-scan images to improve segmentation performance. The proposed approach outperforms the popular cutting-edge models evaluated in our experiments and significantly improves the F1-score. In our future work, we plan to improve BM-Seg by expanding the dataset with a more diverse set of BM cases from multicenter sources. To this end, we will collaborate with multiple medical centers to collect and combine imaging data.
The creation process will include rigorous quality control and data pre-processing steps to ensure the robustness of the dataset and representativeness of the target population. By using the new version of BM-Seg, we can further validate the effectiveness and accuracy of our proposed system and evaluate its potential for real-world applications. In addition, this diverse dataset will be used to evaluate the generalizability of the model to fit different imaging sources, including MRI and PET/CT. In addition, the diverse dataset can be used to train and optimize machine learning algorithms. These algorithms can then better mimic real-world application scenarios, ultimately leading to improved healthcare quality. This will help the research community to comprehensively evaluate the proposed system, improve its performance, and increase its practical value in the medical field.
Acknowledgments The authors would like to express their heartfelt gratitude to  Drs. Zaineb Mnif  and  Hela Fendri  for their helpful advice on various technical issues examined in this work, and to the  Tunisian Ministry of Health  for permission to conduct this study. This work was supported by the  PhD School 'Sciences & Technologies' of the National Engineering School of Sfax (ENIS)  and the  Computer and Embedded Systems (CES) Laboratory.
, 85 1985). Twenty-five thousand events of one distinct phycoerythrin/chlorophyll-positive population (Pop1, Supplementary Fig. 6a-b ) were sorted directly on a 13 mm, 0. 4 µm pore size polycarbonate filters (MerckMillipore, Germany) using the most accurate sorting mode "Single-cell purity". The filters were prepared and observed by SEM as described below. For DNA extraction, 20 cells were sorted under the same conditions, directly into a 0. 2 mL microtube (Eppendorf) and stored at -20°C. Cytometric data were processed using the BD FACSDIVA V9. 0. 1 software  (Becton, Dickinson and Company, San Jose, 90  California, USA). Besides, we also isolated cyanobacterial cells by micromanipulation from samples collected in March 2018 using an Eppendorf PatchMan NP2 micromanipulator equipped with 6 μm-diameter microcapillaries (Eppendorf) mounted on a Leica Dlll3000 B inverted microscope.
Because of the possible presence of more than one species in the samples, we constructed 16S rRNA gene clone libraries with the PCR amplicons using the Topo TA cloning system (Invitrogen) following the instructions provided by the manufacturer. After plating, positive transformants were screened by PCR amplification using the M13R and T7 flanking vector primers. Amplicons were sequenced by Sanger sequencing (Genewiz, Essex, UK). 110 All forward and reverse Sanger sequences were quality-controlled and merged using Codon Code Aligner (http://www. codoncode. com/aligner/). Then, we utilised Mafft  (Katoh and Standley, 2013)  to produce multiple sequence alignments including our sequences and the closest blast  (Altschul et al. , 1997)  hits identified in GenBank. Gaps and ambiguously aligned regions were removed with TrimAl  (Capella-Gutiérrez et al.
Solution chemistry Orthosilicic acid (H4SiO4) concentrations were determined by continuous flow colorimetric analyses (QuAAtro Axflow) at the Institut de Physique du Globe de Paris (IPGP, Paris, France) using non-acidified, 0. 2-m-filtered La Preciosa solutions. Anion concentrations were measured using ion chromatography (ICS1100 Dionex Thermofisher) using non-acidified, 0. 2-mfiltered La Preciosa solutions. Concentrations of major cations were determined by inductively coupled plasma -optical 130 emission spectrometry (ICP-OES, iCAP6200 Thermofisher), using acidified, 0. 2-m-filtered La Preciosa solutions. The uncertainty in the concentration measurements of orthosilicic acid, anions and cations was lower than 5%. Activities of anions, cations and orthosilicic acid as well as saturation indices of the surface water solutions of the lakes were calculated using the Visual MINTEQ software.