dataesr/scientific-paragraphs-categorization huggingface dataset:
Research in First BCP-ALL Relapse Despite a strong desire by paediatric haemato-oncologists, it has not yet been possible to set up a study with tisagenlecleucel for patients in first relapse. In the 2010 European IntReALL SR protocol (NCT01802814), patients with standard-risk first relapse were stratified to HSCT only if they responded insufficiently to re-induction treatment. In the IntReALL HR protocol (NCT03590171) patients with high-risk characteristics of first relapse were eligible for HSCT provided they entered remission on chemotherapy +/-blinatumomab. Only patients truly refractory to relapse therapy are within the current indication of commercial tisagenlecleucel. However, studies are being conducted with CAR-T products other than tisagenlecleucel in which patients with a first relapse qualify for CD19 CAR-T.
The ZUMA-4 trial (NCT02625480), which includes patients with a first relapse within 18 months of diagnosis, may broaden the CAR-T indication in the future. However, current long-term followup data on the brexucabtagene autoleucel product used in ZUMA-4 (CD28. CD3ζ CAR) indicate that consolidative HSCT is mandatory in all responding patients  (27). The TRANSCEND PEDALL study (NCT03743246), after establishing the recommended Phase II dose of the CAR-T product JCAR017, will also include patients with a first relapse and MRD positivity after re-induction therapy. A recent update of the PLAT-02 Phase I/II trial (JCAR017 in R/R BCP-ALL, NCT02028455) demonstrated an advantage of consolidative HSCT vs. watchful waiting with this CAR construct  (181). To answer the question of whether HSCT can be avoided in a first relapse setting, studies are needed with CAR-T products persisting for a sufficiently long time to serve as a stand-alone therapy.
CD3ζ+EGFR Auto T Lentiviral (39) Research Centre N) cells Guangdong Provincial NCT02822326 (B) P/yA/A CD19 FMC63 NR CD28 CD28. CD3ζ+TLR2 Auto T Lentiviral (78) People's Hospital cells Hebei Senlang NCT02963038 (B, N) P/yA/A CD19 FMC63 NR NR CD28. Auto T Lentiviral (79) Biotechnology 4-1BB. CD3ζ+EGFR cells Kite, A Gilead Company NCT02614066 (B) yA/A CD19 FMC63 CD28 CD28 CD28. CD3ζ Auto T Retroviral (80) cells MD Anderson Cancer NCT01497184 (B, C, P/yA/A CD19 FMC63 NR NR CD28. CD3ζ Auto T Electro- - Centre N) cells poration Memorial Sloan Kettering NCT01044069 (B-, C) yA/A CD19 SJ25C1 CD28 CD28 CD28. CD3ζ Auto T Retroviral (41) Cancer Centre cells Memorial Sloan Kettering NCT01860937 (B) P/yA CD19 SJ25C1 CD28 CD28 CD28. CD3ζ Auto T Retroviral (16) Cancer Centre cells National Cancer Institute NCT00924326 (N) yA/A CD19 FMC63 CD28 CD28 CD28.
CD3ζ Auto T Retroviral (52, 81) cells National Cancer Institute NCT01593696 (B, N) P/yA CD19 FMC63 CD28 CD28 CD28. CD3ζ Auto T Retroviral (18) cells Seattle Children's Hospital NCT02028455 (B) P/yA CD19 FMC63 NR NR 4-1BB. CD3ζ+EGFR Auto T Lentiviral (82) cells Sheba Medical Centre NCT02772198 (B, N) P/yA/A CD19 FMC63 CD28 CD28 CD28. CD3ζ Auto T Retroviral (83) cells Southwest Hospital NCT02349698 (B, C, P/yA/A CD19 Humanised CD8 CD8 4-1BB. CD3ζ Auto T Lentiviral (84, 85) N, H) cells University College London NCT02443831 (B, N) P/yA CD19 CAT CD8 CD8 4-1BB. CD3ζ Auto T Lentiviral (25) cells University of Pennsylvania NCT01029366 (B, C, yA/A CD19 FMC63 CD8 CD8 4-1BB. CD3ζ Auto T Lentiviral (22, 86) N) cells University of Pennsylvania NCT01626495 (B, C, P/yA CD19 FMC63 CD8 CD8 4-1BB. CD3ζ Auto T Lentiviral (13) N, H) cells (Continued) A, Adult; Allo, allogeneic; Auto, autologous; B, B-cell precursor acute lymphoblastic leukaemia; CAR, chimeric antigen receptor; C, chronic lymphoblastic leukaemia; H, Hodgkin lymphoma; IgG4, immunoglobulin 4; iPSC, induced pluripotent stem cells; MM, multiple myeloma; N, non-Hodgkin lymphoma; NK, natural killer; NR, not reported; P, Paediatric; yA: young adult.
Herbarium Data A new dataset was created from a selection of specimens located in different ecosystems of the French Guiana territory, scanned and hosted by the Herbarium of French Guiana (http://herbier-guyane. ird. fr/ accessed on 10 May 2021, see Section 4. 1 for more details on the various steps involved in assembling and enriching the dataset). Each specimen was annotated with taxonomic and contextual information (species, genus, family, date, name of locality, and GPS), with tags on the presence of fertile organs, but initially without information on the presence or absence of vegetative growths. A great effort was made by two experts to manually and meticulously select relevant specimens for our experiments by checking for the presence or absence of new growing shoots. In the end, the resulting dataset consists of 408 herbarium specimens belonging to 57 species, 40 genera, and 25 families, with a total of 77 specimens showing the presence of recent shoots.
Two complementary tags specifying the type of growing shoots-"Rhythmic" or "Continuous", corresponding to two fundamentally different plant growth behaviors-were added by the experts to those 77 specimens. Finally, the scans of each specimen were manually annotated to provide bounding boxes and masks that precisely localize the growth shoots in the image. These were used for the training of deep model approaches evaluated in the experiments. The resulting dataset and its additional annotations of growing vegetative shoots (type of shoot, bounding boxes, and masks) are an important first contribution of this paper by providing a new annotated dataset, opening perspectives to phenological analyses based on the observation of this type of botanical structure.
92 and an FPR of 0. 46. To give a more condensed view of the results and to facilitate the comparison of the three methods, Table  2  reports the true positive rates given at three regular intervals of false negative rates (0. 10, 0. 20, and 0. 30). As an example, this table indicates that if 20% of the images without growing shoots are incorrectly predicted as possessing growing shoots, by using the Faster R-CNN model, we can expect to find 64% of all the specimens really containing a growing shoot. Table  1. Summary of the statistics of the dataset with the number of families (Fam. ), genera (Gen. ), species (Sp. ), and observations (Obs. ) as well as the total number of images (Total), of images with new growing shoots (w/Shoots), and of images by type of growing pattern (w/Cont. for "Continuous", w/Rhyt.
Discussion This study confirms the fact that the proportion of specimens showing new growing shoots is extremely low in herbarium collections. During the preparation of the dataset, the two experts went through 1109 herbarium sheets to certify that 104 pictures (9. 46%) contain growing shoots. When we compare this proportion to that of flowering plants on all the specimens of the herbarium on which  [49]  have worked (ranging from 79. 4 to 92. 7%), we see a highly significant difference. This difference can be interpreted by the combined facts that (i) the elongation and maturation of new shoots usually occur over a relatively short period of time when growth is rhythmic and that (ii) botanists preferentially collect fertile (i. e. , flowering or fruiting) specimens in order to have maximum evidence for their botanical determination.
Assembling Herbarium Records and Manual Annotations A specific dataset was created from a large corpus already made available to the scientific community by the Herbarium of French Guiana  [54]  (http://herbier-guyane. ird. fr/ -CAY -accessed on 10 May 2021) of the French national institute for sustainable development (IRD). All digitized specimens of this herbarium are accessible online (http: //publish. plantnet-project. org/project/caypub_en accessed in 10 May 2021). Most of them were collected in the tropical rainforests of French Guiana, with the remaining specimens coming from Suriname, Guyana, and Brazil (Amapá and Pará states). The new dedicated dataset consists of a selection of specimens located in different ecosystems of the French Guyana territory  [55]. Each specimen is annotated with taxonomic and contextual information-species, genus, family, date, locality name, and GPS-and tags about the presence of fertile organs.
46%. However, this initial estimate is probably an overestimation of the true ratio, as the two experts focused on taxonomic groups that were likely to contain growing shoots. Moreover, for this first selection, we decided to exclude a large number of taxa from the final dataset-about 150 species, 80 genera, and 13 families-that were associated with a single specimen. Indeed, retaining too many species (and a wide taxonomic coverage) would have incurred the risk of introducing many isolated and potentially atypical vegetative growing patterns, which would have strongly affected the performances of the automatic detection methods. Furthermore, by excluding these isolated specimens, it was assured that any herbarium containing a growing shoot that would be used for the evaluation of automated approaches would have at least one training example from the same taxonomic group.
In the end, the resulting dataset was composed of 408 herbarium specimens belonging to 57 species, 40 genera, and 25 families, with a total of 77 specimens showing the presence of recent growing shoots. These additional annotations of growing vegetative shoots represent the first important contribution of this paper by proposing a new annotated dataset, opening perspectives in phenological analyses based on the observation of this type of botanical structure. During the identification of the growing shoots, we tried to distinguish two categories of shoot extension modalities: "rhythmic" or "continuous" growth. "Rhythmic growth" is manifested by an alternation of periods of rest and periods of active shoot extension or "growth flushes". A "growth unit" is defined as the portion of an axis that develops during an uninterrupted period of extension  [56].
Note that this last category can include species with rhythmic growth without any morphological markers. These two growing patterns are illustrated in Figure  5. Because of the ambiguity between continuous and rhythmic patterns (due to the potential overlap of some structures with the basis of the new shoot, among others), some of the specimens initially annotated as having a recent growing shoot were finally removed from the dataset. From the perspective of deep learning approaches, there are several ways to automatically detect recent growing shoot and eventually specify their type ("Rhythmic" or "Continuous"). In this study, we first considered a global approach that tried to detect the presence or not of growing shoots in an image without precisely indicating their locations. This first approach was then compared to local approaches that tried to more finely detect the growing shoots through bounding boxes or even masks that locate the vegetative structures at the pixel level.
To evaluate the performance of such different approaches, the herbarium dataset was manually annotated at three levels of granularity related to the presence of growing shoots: • "FullImage" level. All herbarium sheets used in this study were associated with a tag (Yes or No) indicating if the sheet had at least one recent growing shoot visible or not. No information on the location, number, or size of recent growing shoots was recorded. • "Mask" level. In each herbarium sheet containing one or several recent growing shoots, each shoot was manually annotated and cross-validated by two co-authors using COCO Annotator (https://github. com/jsbroks/coco-annotator) accessed on 10 May 2021) a web-based image annotation tool designed to efficiently label images and create training data for object detection tasks (see Figure  6 ).
Global model (ResNet50). The first deep learning model that was trained is the Convolutional Neural Network (CNN) ResNet50  [59]. It was pretrained on the ImageNet dataset  [60]  and fine-tuned on our training dataset. ResNet50 is widely used in image classification tasks and research works for its good compromise between performance, memory use, and training time. Moreover, ResNet50 is often preferred to other recent architectures for a wide range of application studies because its architecture is rather simple, and it is relatively easy to find training hyperparameters that produce good and stable results. A CNN produces as outputs a list of classification scores (probabilities) related to the considered categories, but without any information about the sub-parts of the image that contributed to the prediction.
However, with such an approach, the number of parameters remains the same while being able to exploit finer details in the images. Moreover, this allows for the performance of transfer learning by fine-tuning the model, reusing parameters from a pre-trained model on another dataset. This method dramatically reduces the training time and enables the achievement of good performances despite the limited number of herbarium images in our dataset. Two separate models were trained: one for the growing shoot detection task and one for the reproductive organ detection task. Both were fine-tuned on our herbarium dataset from a pre-trained ResNet50 model on tens of millions of images from the ImageNet dataset  [60]  and available from PyTorch's model zoo (https://pytorch. org/vision/stable/ models. html accessed on 10 May 2021).
The models were fine-tuned on our dataset from pre-trained models on the COCO dataset 2017  [62]. The Detectron2 model zoo (https://github. com/facebookresearch/detectron2 /blob/main/MODEL_ZOO. md accessed on 10 May 2021) provides numerous pretrained models. We selected the pretrained models combining a ResNet50 backbone (initially itself pre-trained on an ImageNet classification task) with a Feature Pyramid Network. According to the Detectron2 documentation, these baseline models obtain the best speed/accuracy trade-off. The pre-trained models are named "R50-FPN" on the Detectron2 model zoo and are associated with the configuration file named faster_rcnn_R_50_FPN_3x. yaml for Faster R-CNN and mask_rcnn_R_50_FPN_3x. yaml for Mask R-CNN. Then, we used the following hyperparameters, which are, for the most part, those from the default configuration of Detectron2:.
Funding: This work is supported by an " Investissement d'Avenir " grant managed by the  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01 ). CAY specimens were digitized thanks to an " Investissement d'Avenir " grants managed by the  Agence Nationale de la Recherche  (eRECOLNAT, ref. ANR-11-INBS-0004 )". Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Multimodal neuroimaging grants a powerful window into the structure and function of the human brain at multiple scales. Recent methodological and conceptual advances have enabled investigations of the interplay between large-scale spatial trends (also referred to as gradients) in brain microstructure and connectivity, offering an integrative framework to study multiscale brain organization. Here, we share a multimodal MRI dataset for Microstructure-Informed Connectomics (MICA-MICs) acquired in 50 healthy adults (23 women; 29. 54±5. 62 years) who underwent high-resolution T1-weighted MRI, myelin-sensitive quantitative T1 relaxometry, diffusionweighted MRI, and resting-state functional MRI at 3 Tesla. In addition to raw anonymized MRI data, this release includes brain-wide connectomes derived from i) resting-state functional imaging, ii) diffusion tractography, iii) microstructure covariance analysis, and iv) geodesic cortical distance, gathered across multiple parcellation scales.
In addition to advancing our understanding of brain organization, these repositories have supported increased exchange and collaboration, boosting transparency and reproducibility  50. In line with this perspective, this work presents a ready-to-use multimodal MRI dataset for Microstructure-Informed Connectomics (MICA-MICs). MICA-MICs provides connectomes based on i) task-free functional MRI, ii) diffusion tractography, iii) microstructure covariance analysis based on qT1 mapping, and iv) geodesic cortical distance, each built across multiple parcellation schemes and spatial scales. We furthermore provide anonymized raw data adhering to Brain Imaging Data Structure (BIDS) standards  51. Processing has been carried out using an open access pipeline (https://micapipe. readthedocs. io/). This resource promises to deepen our understanding of the human brain at multiple scales and augment assessments of generalizability and replicability.
MRI data pre-processing Raw DICOMS were sorted by sequence, converted to NIfTI format using dcm2niix (v1. 0. 20200427; https://github. com/rordenlab/dcm2niix)  54 , renamed, and assigned to their respective subject-specific directories according to BIDS  51. Agreement between the resulting data structure and BIDS standards was ascertained using the BIDS-validator (v1. 5. 10; DOI: 10. 5281/zenodo. 3762221)  55. All further processing was performed via micapipe, an openly accessible processing pipeline for multimodal MRI data (https://micapipe. readthedocs. io/), and BrainSpace, a toolbox for macroscale gradient mapping (https://brainspace. readthedocs. io/)  56. T1w pre-processing. Native structural images were anonymized and de-identified by defacing all structural volumes using custom scripts (https://github.
Generating individual and group-level connectome matrices The following sections describe the construction of feature matrices, derived from each imaging sequence included in this data release (Figure  1B ). Cortical connectomes are provided according to anatomical  20  , intrinsic functional  24  , and multimodal parcellation schemes 26 at different resolutions, for a total of 18 distinct cortical parcellations. Anatomical atlases available in this dataset include Desikan-Killiany (aparc)  20  and Destrieux (aparc. a2009s)  21 arcellations provided by FreeSurfer, as well as an in vivo approximation of the cytoarchitectonic parcellation studies of Von Economo and Koskinas  73. We additionally include similarly sized subparcellations, constrained within the boundaries of the Desikan-Killany atlas  20  , providing matrices with 100 to 400 cortical parcels following major sulco-gyral landmarks.
Parcellations based on intrinsic functional activity (Schaefer atlases based on 7network parcellation) are also included in this release according to a wide range of resolutions (100-1000 nodes)  24. Lastly, we also provide connectome matrices generated from a multimodal atlas with 360 nodes derived from the Human Connectome Project dataset, known as the Glasser parcellation  26. All atlases are available on Conte69 56 and fsaverage5 surface templates (see parcellations in https://github. com/MICA-LAB/micapipe), and were resampled to each participant's native surface to generate modality-and subject-specific matrices. In addition, structural and functional connectome matrices include data for each subcortical structure (nucleus accumbens, amygdala, caudate nucleus, pallidum, putamen, and thalamus) and the hippocampus appended before entries for cortical parcels (see USAGE NOTES).
DATA RECORDS All files are organized according to the Brain Imaging Directory Structure (BIDS)  51  and are hosted on the Canadian Open Neuroscience Platform's data portal (CONP; https://portal. conp. ca/dataset?id=projects/micamics). Group and subject-level gradients (/derivatives/gradients) were derived from averaged and individual connectivity matrices computed from several parcellation schemes using micapipe (/derivatives/micapipe). Matrices and gradients are organized into modality-specific directories for structural (/anat/micro_profiles for MPC, /anat/geo_dist for geodesic distance), functional (/func), and diffusion-weighted (/dwi) imaging. We additionally provide detailed image quality reports for T1w and rsfMRI raw data generated using MRIQC  80.
Quality control. Reports of image quality metrics computed by MRIQC v0. 15. 2 (https://github. com/poldracklab/mriqc/)  80 are included in the /mriqc branch of MICA-MICs processing derivatives. For each subject, /mriqc directories contain /anat and /func subdirectories, which include image quality metric reports for T1w and resting-state functional scans in. html and. json formats. These reports provide a number of metrics evaluating the quality of the input data, including estimates of motion, signal-to-noise, and intensity non-uniformities  80.
Data hosting. MICA-MICs is made openly available via the CONP portal (https://portal. conp. ca/dataset?id=projects/mica-mics). Matrix ordering. Rows and columns of GD and MPC matrices follow the order defined by annotation labels associated with their parcellation (see parcellations in https://github. com/MICA-LAB/micapipe), including unique entries for the left and right medial walls. For example, row and column entries of the Schaefer-100 matrices are ordered according to: Left hemisphere cortical parcels (1 medial wall followed by 50 cortical regions), and right hemisphere cortical parcels (1 medial wall followed by 50 cortical regions). FC and SC matrices follow the same ordering, although entries for subcortical structures are appended before cortical parcels. As such, row and column entries of the Schaefer-100 FC and SC matrices are ordered according to: Subcortical structures and hippocampus (7 left, 7 right), left hemisphere cortical parcels (1 medial wall followed by 50 cortical regions), and right hemisphere cortical parcels (1 medial wall followed by 50 cortical regions).
2A ). Importantly, our strategy also pointed out TFs more seldomly or never previously reported to be important in MFs, including the poorly studied zincfinger TFs called Zinc Finger Homeobox 4 (ZFHX4) and Basonuclin 2 (BNC2). Further supporting these predictions, we found BNC2 expression levels to be high in MFs in general regardless of their tissue of origin (Fig. 2B ). In line, BNC2 expressing cells also showed higher expression of the MF signature genes Actin Alpha 2, Smooth Muscle (ACTA2 also known as α-SMA) and Collagen Type I Alpha 1 Chain (COL1A1) (Fig. 2B ). Moreover, Gene-Module Association Determination annotation (G-MAD), which predicts gene functions by analyzing coexpressed genes across large-scale transcriptomic data from human samples  28  , pointed out genes linked to ECM-related functions when interrogated using BNC2 (Fig.
BNC2 integrates pro-fibrotic stimuli in MF-HSCs In order to define the functional role exerted by BNC2 in MFs, a combination of cistromic and epigenomic analyses were performed in the human MF-HSC cell line LX2. ChIP-seq was used to define the genome-wide binding sites of BNC2 using an antibody qualified through rapid immunoprecipitation mass spectrometry of endogenous protein (RIME) experiments  38  as being able to immunoprecipitate cross-linked BNC2 (Supplementary Data File 6). We identified 1479 BNC2 binding sites, which occurred in active transcriptional regulatory regions as judged through measurement of chromatin accessibility using Column Purified chromatin (CoP)-seq  39  and histone acetylation using H3K27ac ChIP-seq (Fig. 5A  and Supplementary Data File 7). Enrichment for H3K27ac at identified BNC2 binding sites also specifically extended to primary human MF-HSCs (Supplementary Fig.
9A ). De novo motif discovery retrieved the previously proposed BNC2 consensus binding motif  40  as the top enriched motif (Supplementary Fig. 9B ). To further mine the BNC2 cistrome, we next used the Cis-tromeDB ChIP-seq database toolkit  41  to monitor overlaps with known cistromes. Interestingly, transcriptional regulators involved in the fibrogenic TGFβ (SMAD) and Hippo/YAP1 (TEAD/YAP1) signaling pathways were identified in these analyses (Fig. 5B ). Accordingly, DNA motifs used by the SMAD or TEAD TF families are enriched within the BNC2 cistrome (Supplementary Fig. 9B, C ). This is in line with previous observations indicating that the TGFβ and Hippo/YAP1 signaling pathways converge in the regulation of myofibroblastic activation  8, 42  and shows this occurs at BNC2-bound regulatory regions.