dataesr/scientific-paragraphs-categorization huggingface dataset:
DATA SHARING Data underlying the findings described in this manuscript may be obtained in accordance with AstraZeneca's data sharing policy described at https://astrazenecagrouptrials. pharmacm. com/ST/Submission/Disclosure. Data for studies directly listed on Vivli can be requested through Vivli at www. vivli. org. Data for studies not listed on Vivli could be requested through Vivli at https://vivli. org/members/enquiries-about-studies-not-listed-on-the-vivli-platform/. AstraZeneca Vivli member page is also available outlining further details: https://vivli. org/ourmember/astrazeneca/. J o u r n a l P r e -p r o o f.
However, their actual determination being uncertain, analyses were performed in order to ascertain to which species they could belong, and all analyses on the femur were performed twice: once with these specimens considered as Asian elephant, and once without these specimens. Age determination was provided by the institutions in some cases. When no data on the age of the specimens were available, we determined the ontogenetic stage (juvenile, subadult, adult) based on the level of fusion and development of the epiphyses (juvenile: unfused epiphyses, subadult: visible epiphyseal plate line, adult: fully fused epiphyses). The sex of the specimens, as well as their exact origin and captivity state, were generally unknown. As such, we could not account for these parameters in our analyses.
| 3D imaging A large part of the sample (61 bones) was digitized using a structuredlight three-dimensional scanner (Artec Eva) and reconstructed with Artec Studio Professional software  (version 12. 1. 6. 16, Artec 3D, 2018). Complementarily, 25 bones were digitized using photogrammetry, following  Mallison & Wings (2014)  and  Fau et al. (2016). Pictures were taken with a digital camera (Nikon D5500, Nikon Inc. , 50 mm lens) all around each bone and aligned to create a 3D model  using Agisoft Photoscan software (version 1. 4. 0. 5076, Agisoft, 2017). Additionally, three bones were CT scanned for a later study; they were scanned using high-resolution computed tomography at the AST-RX platform (UMS 2700, Muséum National d'Histoire Naturelle, Paris) with reconstructions performed using X-Act (RX-Solutions).
Voxel size varies from 86 μm to 330 μm depending on specimen size. The external surface of these bones was segmented and reconstructed in VGStudio MAX Software (2016). Each mesh was decimated to reach 250,000 vertices and 500,000 faces using MeshLab software  (version 2020 (version. 07, Cignoni et al. , 2008)). Finally, 3D models from the specimen IMNH-1486 were obtained from MorphoSource (six bones); they had been created using a laser scanner (Faro Edge Arm, Idaho Virtualization Lab). Previous research on similarly sized bones has found no major differences in 3D models created using these two methods  (Díez Díaz et al. , 2021; Fau et al. , 2016; Petti et al. , 2008; Remondino et al. , 2010; Soodmand et al. , 2018; Waltenberger et al. , 2021). The right bones were symmetrized arbitrarily on the left side for the purpose of the analyses using Meshlab software.
| Landmark digitization We defined the shape of the bones using anatomical landmarks, and curve and surface sliding semi-landmarks, as described by  Gunz et al. (2005) ,  Gunz and Mitteroecker (2013), and Botton-Divet et al. (2016). We used 14 anatomical landmarks for the humerus, 12 for the radius, 15 for the ulna, 16 for the femur, 18 for the tibia and 10 for the fibula (Figure  S1 -S6; Tables  S1-S6 ). Each curve is bordered by anatomical landmarks as recommended by  Gunz and Mitteroecker (2013). All landmarks and curves were placed using the IDAV Landmark software (version 3. 0,  Wiley et al. , 2005). For some specimens, the radius and ulna could not be separated because the two bones were fused together, so that we could not access the surface of contact between them. In order to place homologous landmarks on the entire sample of radius and ulna, we placed curves around the contact zones to delimit the surface of each bone, so that semi-landmarks could not slide out of the defined area.
We placed the same curves on isolated radii and ulnae so that all surfaces considered were homologous (Figures  S2  and S3 ). These curves were removed after the sliding landmark procedure and before performing the shape analysis, so that they are not included in our analyses, following  Pintore et al. (2022). For each bone, surface semi-landmarks were manually placed on a template, created from a single specimen selected beforehand for its mean conformation with the 'findMeanSpec' function of the geomorph package  (Adams & Otárola-Castillo, 2013)  of R (R Core Team, 2020, version 4. 0. 2), using RStudio  (RStudio Team, 2020, version 1. 3. 959-1). Each bone template was then used to project the semi-landmarks onto the surface of the other specimens of the dataset using the 'placePatch' function of the Morpho package  (Schlager, 2017).
| Robustness parameters In order to assess the robustness of the bones, we measured the circumference of the diaphyses at their thinnest part (Ci), and the maximal length (MaxL) of the bones. Bones were aligned along their longitudinal axis following  Ruff (2002). Circumferences were obtained using the CloudCompare software (version 2. 12. 0, http://www. cloud compa re. org) for each bone except for the radius and ulna, which could not be separated in several specimens. Radius and ulna were thus excluded from analyses using circumference as a parameter. Bone maximal length was obtained virtually by placing reference points on the 3D models and measuring the distance between them using the Landmark software. Robustness (Rb) was defined as the ratio of minimal diaphyseal circumference to maximal length of the bones (Ci/MaxL).
A. H. and C. B. designed the study. A. D. , A. H. and C. B. did the bone data acquisition. C. B. conducted the analyses and drafted the manuscript, A. H. and C. B. contributed to the final manuscript, and all authors read it and approved it. ACK N O WLE D G E M ENTS This work was funded by the European Research Council as part of the GRAVIBONE project (ERC-2016-STG-715300), and has received financial support from the CNRS through the MITI interdisciplinary programs and from the CSTB. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We warmly thank Joséphine Lesur, Céline Bens, Aurélie Verguin (Muséum national d'Histoire naturelle (MNHN), Paris, France), Olivier Pauwels, Sébastien Bruaux (Royal Belgian Institute of Natural Sciences, Brussels, Belgium), Frank Zachos, Alexander Bibl (Naturhistorisches Museum Wien, Vienna, Austria), L.
Acknowledgments The authors acknowledge the support of the  IRT/STAE Foundation  (funding for the post-doctorate contract of Ch. P), and also L. R. M. Maas  for fruitful discussions. M. M. thanks  Lucile Planes  and  Martin Benebig  for testing computations with iTides. N. G. acknowledges the support of the  Natural Sciences and Engineering Research Council of Canada (NSERC)  [ RGPIN-2015-03684 ].
Therefore, no report has tried to evaluate the DWI-FLAIR mismatch on NCCT scans of acute ischemic stroke suspects. With the development of artificial intelligence (AI) modeling for medical imaging, it is possible to create an AI model with automated image interpretation capability equivalent to that of human experts  (16). In this study, the authors analyzed data from patients presenting with acute ischemic stroke who completed NCCT and MR images within an hour to build a deep-learning model that could predict the DWI-FLAIR mismatch using NCCT. The performance of this model was then compared to inexperienced human raters.
Development of reference standards and quantification of the diffusion-FLAIR mismatch NCCT images were initially pre-processed by skull stripping, cropping, and intensity normalization. The DWI and FLAIR images were skull-stripped and automatically co-registered onto the pre-processed NCCT images using rigid body transformation (the SimpleITK packages in Python)  (19, 20). The NCCT, DWI, and FLAIR images were, therefore, spatially aligned in the same space. An experienced neuroradiologist visually inspected the registration results and made manual adjustments using the ITK-SNAP (version 3. 8 1 ) when the co-registration was suboptimal  (21). Board-certified vascular neurologists (BJK, NS), a neuroradiologist (RM), and interventional neuroradiologists (FB and PC) manually segmented high signal lesions on every slice on DWI and FLAIR images referring to ADC images using the ITK-SNAP.
Deep learning model An automated lesion segmentation framework was built based on the nnU-net architecture  (25, 26). The proposed DL model employed a full-resolution training strategy with a deep-supervision mechanism. Specifically, a channel and spatial attention block were designed to enable the model to focus on the salient areas of images at different scales and acquire compact and conducive features  (27). To fully utilize multi-scale context information, a scale-aware pyramid fusion module with three parallel dilated convolutions with varying dilation rates of 1, 2, and 4 was employed and fused the information at different scales (Figure  2 )  (28). To train and choose the optimum model, a five-fold crossvalidation was executed. Spatial augmentations, including rotation, scaling, and low-resolution simulation, were applied in three dimensions to boost the diversity of training data.
Depending on the distribution of the dataset, Pearson and Spearman's correlations were used to compare the segmented lesion volumes. Interclass coefficients were calculated to estimate the interobserver agreement of the DWI-FLAIR mismatch rating. A confusion matrix was created to assess the classification performance, from which the sensitivity, specificity, accuracy, and AUC-ROC score were derived. All of these metrics were computed in 3D space at the level of the patient. After developing the image prediction model, we tested the performance and practicality of the model in a quasi-practice setting. Vascular neurologists with more than 10 years of practice (BJK) and vascular fellows in training (YSK and JWK) evaluated the presence of DWI-FLAIR mismatch from NCCT images without clinical information or the model's prediction (evaluation round #1).
49 without output to 0. 61 with output. The greater discrimination values imply our model's feasibility and applicability in general clinical practice with restricted resource conditions. This study has several limitations. The predictability of our model in terms of lesion volume and Dice coefficient is modest. Developing a deep learning model with greater accuracy requires large data. However, the NCCT -DWI -FLAIR image sets acquired within 1 h are scarce in real-world clinical practice, as only 5% of more than 8,000 acute stroke patients produced such sets. Our study was based on single-center data without an external cohort to validate. We excluded cases with tiny DWI lesions of less than 2 mL, considering the limited spatial resolution of NCCT images. We supposed no substantial pathologic changes during the 1-h image acquisition period from NCCT to FLAIR images.
Funding The current study was supported by the intramural research fund from  Seoul National University Bundang Hospital  [ 14-2016-0017 ], endowed to BK. This research was supported by a grant of the  Korea Health Technology R&D  Project through the  Korea Health Industry Development Institute (KHIDI) , funded by the  Ministry of Health and Welfare, Republic of Korea  (grant number:  HI22C0454 ), endowed to BK. This research was funded by  Natural Science Basic Research Plan in Shaanxi Province of China  (Program No. 2021JM-413 ), endowed to KZ.
Statistical Analysis Statistical analysis and graphical representations of the study's data were performed using SPSS software for Windows (IBM SPSS 26. 0, IBM Ireland, Dublin, Ireland) and GraphPad Prism 9. 0 (GraphPad Software Inc. , San Diego, CA, USA). Correlation plots were designed using Statistical Software Package R 4. 2. 2 (particularly xlsx and corrplot packages) and R-studio for Windows (version 2022. 12. 0+353); scatter plots for correlations of ncRNAs with glucose levels were created using SPSS 26. 0 Graph builder. To increase the power of the statistical analysis, we used log-transformed data for lncRNA and miRNA levels. The continuous distributed quantitative variables (age, biochemical, lncRNA, and miRNA data) were expressed as mean ± standard error of the mean (SEM) and analyzed using the independent Student's t-test for comparisons between study groups.
To evaluate the differences between categorical variables (gender, presence of hyperglycemia, hypertension, hyperlipidemia, diabetes, and obesity), we used Crosstabs distribution with chi-squared (χ 2 ) analysis with Mantel-Haenszel common odds ratio (OR) estimates, performed using SPSS. Pearson's parametric bivariate correlation analysis for plasma lncRNA and miRNA levels with age and biochemical parameters was performed using SPSS. To analyze the potential of plasma lncRNA and miRNA levels to discriminate vulnerable CAD patients, univariate and multivariate receiver operating characteristic (ROC) analysis (2-value) was performed, adjusted for age and gender (females as reference group), considering the SA group as the reference category and the UA group as the risk (vulnerable) category.
This research was funded by a grant of the  Romanian Ministry of Education and Research, CCCDI-UEFISCDI , project number  PN-III-P2-2. 1-PED-2019-1897 ,within  PNCDI III. FP has re-ceived support from the  French Government , managed by the  National Research Agency (ANR)  under the program " Investissements d'avenir ", reference number  ANR-16-RHUS-0003. FP is a member of FHU-CARNAVAL. This manuscript is based upon work from  EU-CardioRNA COST  Action  CA17129  (www. cardiorna. eu)supported by  COST (European Cooperation in Science and Technology ). TB received a " Short-Term Scientific Mission " grant awarded by  EU-CardioRNA COST Action   CA17129. Institutional Review Board Statement : The study was conducted in accordance with the  Declaration of Helsinki  and approved by the  Ethics Committee of the Institute of Cellular Biology and Pathology "Nicolae Simionescu " (# 1382/17 , September 2012; # 03/08 , October 2019).
Statistical analyses of data Pearson correlation analysis was conducted for each core to establish geochemical associations and behavior of the elements in sediments. A two-tailed Pearson's coefficient was used in correlation analysis. Principal component analysis (PCA) was performed to check the factors that control the geochemical origin of the elements in coastal sediments. PCA was subjected to varimax rotation with the Kaiser normalization method to maximize each factor's component loadings and eliminate invalid components. All variables were tested by Kaiser-Meyer-Olkin and Bartlett tests. Statistical processing of the data was performed using SPSS software 22. 0.
Principal component analysis (PCA) To further examine the origin of the elements in the study area, a Principal Component Analysis (PCA) was performed for each sediment core (Tab. 3, Suppl. Fig. 2 ). PCA has been applied successfully from a correlation matrix including 17 variables (OM %, TS %, TW %, Fe, Mn, Al, Na, V, Cr, Co, Ni, Cu, Zn, Pb, As, Ag, and Cd). The variables used all passed the Kaiser-Meyer-Olkin (KMO > 0. 62) and Bartlett (p < 0. 001) tests, confirming the suitability of the dataset for PCA. Four principal components (PC) with eigenvalues higher than one were extracted from the variables in sediment core A, which accounted for 88. 65 % of the total variance (Tab. 3). The first two components accounted for 68. 82 % of the total variance and reflected the most important information, with PC1 (45.
, 2015). The third component (PC3) accounted for 14,11 % of the total variance and had strong loading values on As, Na, and TW (PCA coefficients > 0,89) suggesting that PC3 presents the mobile fraction of sediment. Lastly, the fourth component (PC4) accounted for 5. 72 % of the total variance and had high loading on TS (0. 85). The detected high TS content in core A (Fig. 2 ) seems to be the consequence of the direct inputs from El Harrach river into Algiers Bay. For the sediment core B, the dataset used for PCA analyses gave two major components with eigenvalues > 1 and explained 86. 43 % of the total variance (Tab. 3). The first component extracted (PC1) explained the most variance (72. 29 %) and had high positive loading on Fe, Al, Pb, V, Cr, Co, Ni, Cu, Zn, Cd and Ag. Since Fe and Al are generally derived from weathering and erosion of rocks and soil parent materials  (Li and Li, 2017) , PC1 was considered the lithogenic source of metals bound in oxihydroxide minerals.
Acknowledgments This research work was financially supported by the  French program  METMAR-MERMEX as part of the  PHC   TASSILI  project (Code PHC:  17MDU986 ; Title: Inputs, accumulations and trace metal contaminations in Algiers and  Marseille coastal areas)  and the directorate-general for scientific research and technological development (DGRSDT). The authors are very grateful to CNRDPA, especially to our colleague Inal Ahmed for their help to carry out the sampling campaign onboard the vessel GRINE BELKACEM, as well as to  Djamel Taib Errahmani  from  CNRA  for providing us the sediment corer and for technical assistance during the sampling campaign. The authors are also grateful to our dear colleague  Noureddine Djerrai  who helped us with the preparation of the sampling map.
Study Populations In this case-control study, 16 patients with the homozygous form of FH (case group) from all over Iran and 18 individuals with the heterozygous form of FH (control group) among HOFH family members were recruited. The selection criteria were defined previously  [18]. Briefly, FH scores for patients with suspected FH were calculated according to the Dutch Lipid Clinic Network Criteria. Then, mutations in LDLR, APOB and PCSK9 genes were assessed by a next-generation sequencing technique and confirmed by the Sanger sequencing method. Pathogenic mutations were the ones previously identified and reported according to the ClinVar database, but for some novel mutations, their pathogenicity was predicted by the SIFT database and PolyPhen software. The HEFH patients were family members of the case group who are closely related to the HOFH patients in terms of lifestyle and genetics.
The most extensively studied ALK inhibitor in neuroblastoma, crizotinib, is a small-molecule competitive inhibitor of ALK and MET kinase activity and was the first FDA-approved drug for use in adult patients with ALK-translocated non-small cell lung cancer. A phase II trial involving crizotinib has been completed by the Children's Oncology Group (COG) in pediatric patients with relapsed/refractory ALKdriven neuroblastoma (NCT00939770). Only 3 of 20 patients showed objective responses  (17) , mainly explained by the intrinsic resistance of F1174 and F1245 hotspot mutations to crizotinib  (16). Preclinical studies showed that crizotinib resistance can be overcome when combined with chemotherapy  (18) , and was the rationale for the COG phase I trial combining crizotinib with topotecan and cyclophosphamide in children with relapsed and refractory solid tumors (NCT01606878; ref.
19). The second-generation ALK inhibitor ceritinib resulted in objective responses in patients with neuroblastoma harboring ALK F1275 mutations whereas patients with F1174 mutations did not benefit  (20). Later generation ALK inhibitors have been developed to overcome both intrinsic and de novo resistance observed with crizotinib and ceritinib. Lorlatinib, a third-generation ROS1 and ALK inhibitor has been shown to exert preclinical activity against cell line xenografts, patient-derived xenografts (PDX), and genetically engineered mouse models (GEMM) harboring all hotspot ALK mutations  (21, 22). These findings led to a phase I trial of lorlatinib for patients with ALK-driven neuroblastoma that is currently ongoing through the New Approaches to Neuroblastoma Therapy (NANT) consortium (NCT03107988), but even following initial response, relapse can occur.
Idasanutlin synergizes with ALK inhibitors in TP53 wild-type and ALK-aberrant neuroblastoma cell lines While the combination of lorlatinib with chemotherapy will soon be introduced into upfront therapy for high-risk ALK-aberrant neuroblastoma, other combinations that might be suitable for this subgroup of patients following relapse and chemotherapy resistance are still warranted. To this end, we also investigated combinations of ALK inhibitors with MDM2 inhibitors, which have previously been suggested as potential strategies to overcome resistance to ALK inhibitors  (25, 26). Because none of the PDXs had RAS-MAPK mutations, this was not a mechanism of resistance to ALK inhibitors in these models and all of them were TP53 wild-type (Supplementary Table  S2 ). The MDM2 antagonist, idasanutlin, is being evaluated in combination with venetoclax or chemotherapy for patients with relapsed and/or refractory neuroblastoma in a phase I clinical trial (NCT04029688).
Discussion The goal of this study was to provide robust preclinical insights into the activity of ALK inhibitors in combination with chemotherapy or targeted MDM2 inhibition in neuroblastomas carrying mutations or amplifications of the ALK gene. The ongoing International Society of Paediatric Oncology European Neuroblastoma trial HRNBL2 (NCT04221035) for first-line treatment of high-risk neuroblastoma will soon be amended to introduce lorlatinib in upfront treatment of patients with ALKaberrant high-risk neuroblastoma. Concurrently, the COG is amending the ANBL1531 phase III trial to replace crizotinib with lorlatinib. Since 2008, when the role of ALK in neuroblastoma was first described  (10, 11, 47) , the study of ALK inhibitors in neuroblastoma has grown as a result of progress in the development of these drugs for adult malignancies and advances made by the scientific neuroblastoma community.
Acknowledgments In the United Kingdom,  E. R. Tucker ,  E. Poon ,  H. Che ,  B. Martins Da Costa ,  K. Barker ,  S. Shrestha , and L. Chesler were supported by a  Cancer Research UK (CRUK)   Programme Grant  ( A28278 ). E. Calton was supported by a  Children with Cancer Clinical PhD Fellowship  ( CWL022X ). Q. Gao was supported by a  Cancer Research UK  ( A24566 ). Y. Jamin was supported by a  Children  with.
Potential maps For each time step, ECGI and recorded potential maps were first compared using the CC at each point in time. The center of the negative region of epicardial potential maps at a time point early in the QRS has previously been used with ECGI to estimate the epicardial breakthrough sites  (Oster et al. , 1997). In order to enhance these negative potentials, the broken electrodes identified in recorded potential maps were first interpolated using a Laplacian spatial filter  (Oostendorp et al. , 1989; Bergquist et al. , 2019)  implemented in the open-source SCIRun package (https:// www. sci. utah. edu/cibc-software/scirun. html). All of the positive potentials were set to zero with a threshold of > -5% of Pk-Pk magnitude to avoid noise. These maps were defined as negative potential enhanced maps (NPEMs).
Statistical methods The accuracy of ECGI was first evaluated using PVCs and pacing data at baseline. The impact of physiological condition on ECGI accuracy was then evaluated by comparing the previous baseline ectopy data  (PVC and stimulated)  to the ectopies measured during high K + perfusion. In addition, reconstruction accuracy of PVCs occurring on the T-wave (R-on-T) at baseline and at high K + perfusion was assessed. Statistical analysis was conducted using GraphPad Prism 9. 5. 1. A two-way ANOVA was used to compare accuracy between beat types (PVC, sub-epicardial, or subendocardial pacing) and physiological condition (baseline or high K + ) with p < 0. 05 defined as significant. Data in tables and error bars in figures are expressed as mean ± standard deviation unless otherwise stated.