dataesr/scientific-paragraphs-categorization huggingface dataset:
Definition of anatomical classes We created a highly detailed hierarchical parcellation scheme of the brainstem from a literature review including macroscopic and histologic textbooks  (Brodal, 2010; Kamina, 2008; Naidich and Duvernoy, 2009; Nieuwenhuys et al. , 2008; Paxinos and Huang, 1995). Brainstem nuclei and sub-nuclei found in the literature were included in a database without knowing a priori if they would be visible on the MR data. Some structures not belonging to the brainstem but partially included at the border of the segmented specimen (cerebellar and diencephalic nuclei) were added to this database. A five-digit hierarchical index was assigned to each anatomical class and was stored in the voxels of the segmented volume (Table  1 ): -The first digit designated the right (1), left (2) or median location of the structure/substructure.
Manual segmentation -Definition of segmentation rules The manual segmentation of the obtained 800 slices was performed using an iMac computer (3,5 GHz Intel Core i7) running High Sierra connected to a combined 27-inch LCD monitor and graphic tablet and stylus (Wacom Cintiq 22HD). We used the Freeview software, part of the Freesurfer package (https://surfer. nmr. mgh. harvard. edu/), which simultaneously displayed coronal, axial, and sagittal planes, plus a 3D rendering of the segmented classes; co-registered anatomical (T2 100µm and T2 185µm) and diffusion (ADC, FA and CED) maps were superimposed with a user-defined transparency (offset) to obtain an optimal delineation for each anatomical class. Anatomical classes were sequentially segmented, beginning with the biggest and easily delineable ones (for instance, principal nucleus of the inferior olive or red nucleus) before moving to smaller or ill-limited nuclei (reticular nuclei).
WIKIBrainStem interface WIKIBrainStem (https://fibratlas. univ-tours. fr/brainstems/index. html) is a publicly available WebApp specifically developed by ILIAD3 for this project. It is composed of two components: a 3D viewer and a media wiki. The first one (Figure  1 ) is based on an open source, webGL-based javascript library (https://github. com/xtk/X). This graphical user interface allows for real-time 3D navigation through the segmented results, represented as vtk files (https://vtk. org/). The proposed anatomical hierarchy is displayed using a tree-like menu on the right panel of the application. While the user browses this hierarchical menu, the alpha translucency of the selected structures is modulated to highlight them in a 3D view on the middle panel of the application. This 3D view can be zoomed in and out, and the position of the camera can be interactively changed.
Data availability The NIFTI T2-weighted 100 and 185µm isotropic anatomic images, the scalar maps derived from diffusion-weighted imaging (FA, CED), and the segmented volume will be freely available under Creative Commons CC BY-NC-SA 4. 0 license on the Fibratlas project website (https://fibratlas. univ-tours. fr/), along with the LUT table, as well as on the Human Brain Project atlas webpage (https://ebrains. eu/services/atlases/brain-atlases/). This format allows the use of the data by any modern imaging software, voxels of the segmented volumes containing the index value of the corresponding anatomical class.
11. 7T MRI acquisition and data processing Anatomical 100µm T2 3D Multi Slice Multi Echo series (MSME3D) sequence (Figure  3a  and d ) provided highly detailed images on which very small nuclei were possibly detected, for instance the trochlear nucleus or the accessory nuclei of the inferior olive. However, the cost of such a high resolution was a loss of contrast, which sometimes decreased our ability to distinguish two neighboring nuclei. For this reason, we sometimes were required to use anatomical images with a lower resolution (185µm) but a higher signal/contrast-to-noise ratio. Major tracts were easily visible on T2 anatomical sequences thanks to their lower signal related to their important myelin content associated with a shorter T2 relaxation time, whereas nuclei appeared in hypersignal, with various shades of gray.
Additionally, 8 structures of the diencephalo-mesencephalic junction and 8 structures of the cerebellum, located at the border of the anatomical specimen, were partially segmented and included in the LUT but not described in the WIKIBrainStem pages. The hierarchical tree of these structures is presented in Table  2  and the volume of each fully segmented anatomical class is given in Table  3  (supplementary material). All the anatomical classes (structures and substructures) initially retained from the literature are displayed. The ones currently segmented from the dataset are displayed with the color used to display them within the atlas. Structures displayed with a dotted pattern were not directly segmented, but can easily be reconstructed from their segmented substructures. The name of each segmented class is followed by the corresponding abbreviation used in the atlas as defined by Paxinos  (Paxinos and Huang, 1995) , and by a letter showing if median (M), right (R) or left (L) structure is available.
Moreover, a comparison to histology would not completely solve the problem, because ambiguities remain even for the segmentation of immuno-histochemical stained slices  (Brodal, 2010; Kamina, 2008; Naidich and Duvernoy, 2009; Nieuwenhuys et al. , 2008; Paxinos and Huang, 1995). The structure of WIKIBrainStem will allow its future extension to non-MRI data that may arise from additional studies to be very feasible. In the same vein, the segmentation rules described in this study were developed for the field strength and imaging protocols detailed in the Materials and Methods section. Different acquisition methods, more or less sensitive to the underlying microstructure, as well as the impact of different techniques for specimen preparation, may thus lead to different results. For instance, the volume of the red nucleus, still considered as easy to segment, was measured by several authors  (Eapen et al.
Consequently, even if the same region is slightly variable in other specimens, the anatomical rules will be usable. One of the next applications of WIKIBrainStem will be the segmentation of several anatomical specimens by different experts. This will produce probability maps of the brainstem inner structures across subjects, which will be the starting point to propose an automated segmentation protocol, as this has already been performed at lower resolution for the brain  (Destrieux et al. , 2010). Such a probabilistic atlas of the brainstem defined at very high resolution will later be used for an automated parcellation of lower resolution images obtained from clinical MRI systems (1,5 or 3T). This would lead to a probabilistic localization of the nuclei from in vivo data at a resolution at which they are obviously not visible.
which was labeled 1/23850 It was best seen on T2-wieghted MRI dataset at 100µm, on which it appears as a juxtaposition of white and black dots, resulting from an artifact induced by its neuromelanin content (García-Lorenzo et al. , 2013). It was almost not visible at a lower resolution. The proposed segmentation rules include a localization step (Figure8) followed by a definition of the limits, which vary depending on the rostro-caudal level (Figure9, Figure10, Figure11).
fr/brainstems/index. html). Depending on the user internet connection speed, the first download of the 3D objects may require a few seconds to 1 minute. Subsequent downloads are considerably faster thanks to the browser cache. WIKIBrainStem is hosted by Tours University (http://fibratlas. univ- Original page available at: https://fibratlas. univ- tours. fr/mediawiki/index. php/Red_nucleus Each page gives the following information: A, anatomical details; B, used datasets; C, segmentation rules; D, difficulties; E, 3D model. tours.
Observations are often less than perfect and care has to be taken to choose the most reliable dataset. As an example, Fig. 1  shows four estimates of the 3-day averaged precipitation from Hurricane Harvey around Houston. The radar analysis is probably the most reliable one, but is only available since 2005. The CPC gridded analysis only uses stations that report in real time and therefore underestimates the event. The satellite analysis diverges strongly from the others. Here, we chose the station data as the most reliable dataset with a long time series, more than 100 years for many stations. Due to the relatively small number of observed data points, some assumptions have to be made to extract the maximum information from the time series. These assumptions can later be checked in model data.
An extreme example for this is given in Fig. 3 , where the trend in May maximum temperature in India 1975-2016 in the CRU TS 4. 01 dataset is shown to be based on hardly any station data at all and therefore unreliable. In contrast, reanalyses can produce extremes between stations and are based on many other sources of information. These are in principle therefore less susceptible to this problem than statistical analyses of observations. However, the quality of reanalyses varies greatly by variable and region  (Angélil et al. 2016). We found in this case that the trends from a reanalysis in Fig. 3c  are more reliable and in better agreement with statistical analyses of the Indian Meteorological Department  (van Oldenborgh et al. 2018). Similar problems in depressed extremes and spurious trends were found in an interpolated precipitation analysis of Australia  (King et al.
Our analyses therefore include drivers that have changed exposure as well as vulnerability (which includes the capacity to manage the risk). Due to the complexity of assessing changes in vulnerability and exposure as well as the persistent lack of standardised, granular (local or aggregated) data  (Field et al. 2012) , usually only a qualitative approach is feasible (cf. Cramer et al. , 2014). This includes (1) a review and categorisation of the people, assets and systems that affected or were affected by the physical hazard extreme (done partly already in the event definition stage), (2) a review of existing peer-reviewed and select grey literature that describes changes to the vulnerability or exposure of the key categories of people, assets or systems over the past few decades and (3) key informant interviews with local experts.
In presenting results to the scientific community, a focus on allowing full reproducibility is key. We always publish a scientific report that documents the attribution study in sufficient detail for another scientist to be able to reproduce or replicate the study's results. If there are novel elements to the analysis, the paper should also undergo peer review. We have found that this documentation is also essential to ensure consistency within the team on numbers and conclusions. We recommend that all datasets used are also made publicly available (if not prohibited by the restrictive data policies of some national meteorological services). We have found it very useful to summarise the main findings and graphs of the attribution study into a two-page scientific summary aimed at science-literate audiences who prefer a snapshot of the results.
Epigenetic alterations, such as promoter hypermethylation, may drive cancer through tumor suppressor gene inactivation. However, we have limited ability to differentiate driver DNA methylation (DNAme) changes from passenger events. We developed DNAme driver inference-MethSig-accounting for the varying stochastic hypermethylation rate across the genome and between samples. We applied MethSig to bisulfite sequencing data of chronic lymphocytic leukemia (CLL), multiple myeloma, ductal carcinoma in situ, glioblastoma, and to methylation array data across 18 tumor types in TCGA. MethSig resulted in well-calibrated quantile-quantile plots and reproducible inference of likely DNAme drivers with increased sensitivity/specificity compared with benchmarked methods. CRISPR/Cas9 knockout of selected candidate CLL DNAme drivers provided a fitness advantage with and without therapeutic intervention.
pdf by guest on 26 June 2022 inference  (12). MethSig estimates expected tumor promoter hypermethylation with an inference model that includes biological features known to affect the stochastic rate of DNAme changes  (5). We applied MethSig to reduced representation bisulfite sequencing (RRBS; ref. 14)  data across blood and solid tumor malignancies. Compared with benchmarked methods, MethSig delivers well-calibrated quantile-quantile (Q-Q) plots and more reproducible identification of DNAme drivers in independent cohorts. Importantly, Meth-Sig achieved higher sensitivity and specificity in the inference of likely DNAme drivers compared with extant methods. Finally, the MethSig framework was extended to methylation array data (MethSig-array) and demonstrated the ability to identify candidate DNAme drivers, enriched in TSGs and associated with gene silencing, as well as disease outcomes.
We performed extensive model optimization to ensure the robustness of DNAme driver inference by MethSig, confirming that MethSig included informative covariates, parameters, and methodology (Supplementary Figs. S2A-S2H and S3A-S3G; see Supplementary Data for details). Notably, model optimization was also performed for benchmarked methods (e. g. , using overdispersion correction option in methylKit); however, improvements of Q-Q plots were subtle (Supplementary Fig. S3H ).
2B ; Supplementary Fig. S4A ). Next, we tested whether DNAme drivers nominated by MethSig are more frequently linked to gene silencing compared with other methods (see Supplementary Data for details). Area under the receiver operating characteristic (AUROC) showed that MethSig achieved higher performance compared with benchmarked methods in identifying DNAme drivers associated with gene silencing (Fig. 2C ; Supplementary Fig. S4B ). Indeed, candidate DNAme drivers identified by MethSig were significantly more enriched in silenced genes compared with benchmarked methods or randomly selected genes (Fig. 2C ). Similar findings were observed in the DCIS and GBM cohorts, where matched DNAme and RNA-sequencing (RNA-seq) data are available (Supplementary Fig. S4C  and S4D ). Integrating data across the two CLL cohorts, MethSig nominated 189 candidate DNAme drivers out of 9,661 promoters captured by RRBS and with available input covariates (Supplementary Table  S2 ; see Supplementary Fig.
MethSig identifies Relapse-Specific DNAme Drivers in CLL Our data demonstrated that MethSig is an effective tool to nominate cancer DNAme drivers through the comparison of primary malignant (T1) versus controls. We sought to extend the application of MethSig to identify DNAme drivers of relapsed disease after fludarabine-based chemotherapy through the comparison of relapse (T2) versus control samples (Fig. 1E ; CLL8). Notably, the application of MethSig within the context of relapsed CLL resulted in an equally well-calibrated Q-Q plot (Fig. 4G ), consistent with its ability to identify the infrequent DNAme changes that likely contribute to the relapse phenotype. We identified T2-specific (n = 32), T1 and T2 shared (n = 88), and T1-specific DNAme drivers (n = 101; Fig. 4H  and I ; Supplementary Table  S2 ).
MethSigarray t test GlobalTest (Supplementary Data). RASSF1 is a microtubule-associated and multitasking scaffold protein communicating with the RAS pathway, estrogen receptor signaling, and Hippo pathway  (36). RASSF1 methylation is proposed as a candidate maker in many cancer types  (36). Other identified important TSGs included genes in a plethora of cancer-related cellular pathways, such as DNA repair (MLH1, RBBP8), apoptosis (WIF1, SFRP1), and tyrosine kinase cascades (SOCS3; refs. 7, 8). Collectively, these data confirm that MethSig can accurately infer likely DNAme drivers across cancer with both array and next-generation sequencing (NGS)-based methylation assays.
To address this challenge, we developed a statistical inference framework accounting for varying stochastic hypermethylation rate across the genome and between samples-MethSig. The model provides the expected promoter DNAme changes between tumor and control samples, allowing the identification of loci where the observed hypermethylation significantly exceeds expectation, potentially reflecting positive selection of fitness-enhancing candidate DNAme drivers. We applied MethSig to methylation-sequencing data of two CLL cohorts, including 304 CLLs from a prospective clinical trial, as well as to other malignancies with available DNAme data (multiple myeloma, DCIS, and GBM), and benchmarked against state-of-the-art methods. Compared with benchmarked methods, MethSig resulted in wellcalibrated Q-Q plots, higher reproducibility in DNAme driver inference across independent cohorts, and increased sensitivity/ specificity in the inference of likely DNAme drivers.
These observations confirm that MethSig allows separation of specific cancer-related DNAme drivers, which cause gene downregulation and phenotypic changes associated with tumoral progression, from stochastic passenger DNAme changes. Notably, the performance of MethSig was maintained across both hematologic malignancies (CLL and multiple myeloma) and solid tumors (DCIS and GBM), suggesting broad applicability for creating catalogs of candidate DNAme drivers of cancer genesis and relapse. Moreover, while MethSig was extended to array data and provided a nonincremental improvement in DNAme driver inference, we anticipate that future shift toward NGS data will leverage the even higher performance of MethSig with read-level data. Our data showed that MethSig can also account for broad phenomena that alter DNAme profiles in identifying gene-specific DNAme drivers.
Algorithmic Procedure The superscripts n, t, e, are shorthand for normal, tumor, and expected. The subscripts i and j represent gene i and sample j. The model was processed as following steps: (i) Estimate expected hypermethylation of tumor samples (DHcR e ): The independent variable matrix (X) has number of genes times the number of patients rows (Eq. A). The beta regression model was implemented by R package betareg (ref. 42 ; Eq. B). Next, predicted distribution of DHcR t (used as distribution of DHcR e in the following analysis) was estimated using α ^ and β ^ derived from the above beta regression model (Eq. C).
Cell Lines HG3 (DSMZ #ACC-765, RRID: CVCL_Y547), PGA1 (DSMZ #ACC-766, RRID: CVCL_Y545), and MEC1 (DSMZ #ACC-497, RRID: CVCL_1870) cells were provided by Leibniz Institute DSMZ in August 2018. HEK293T cells (ATCC #CRL-3216, RRID: CVCL_0063) were provided by the ATCC in January 2017. Cells were routinely tested for Mycoplasma using the MycoAlert Mycoplasma Detection Kit (Lonza #LT07-318). All cell lines used for described experiments came from early frozen batches between 3 and 8 passages after cell receipt. HG3, PGA1, and MEC1 cells were used for qRT-PCR (Supplementary Data). HEK293T cells were used for the production of lentivirus and transduction (Supplementary Data). HG3 cells were used for all the other described experiments.
Risk Model of Patients with CLL The regression model was implemented by R package glmnet (RRID: SCR_015505; refs. 47, 48). When evaluating the performance of the model in the training and test set, CLL cases were divided into two subgroups based on their predicted risk scores (patients with high vs. low risk, median risk score of the cohort was used as the cutoff) and the FFS difference between groups was evaluated using log-rank test.
Accession Codes RRBS data of CLL8 are available via GEO accession number GSE143673. RRBS data of CLL-DFCI are available via dbGap accession number phs000435. v3. p1. RRBS data of DCIS-MDACC are available via GEO accession number GSE69994. RRBS data of GBM-MUV are available via EGA accession number EGAS00001002538.
0001; ***, P < 0. 001. The median, upper, and lower quartiles are shown. Whiskers represent upper quartile + 1. 5 IQR and lower quartile -1. 5 IQR. In B and C, only tumor types with a minimum of 10 likely DNAme drivers were included into the analysis. In each AUROC analysis, the same list of genes was used among all methods. One of the benchmarked methods-methylKit-could not be applied to TCGA data set due to lack of count-based data.
Acknowledgments R. Chaligne is supported by the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement no. 750345. D. A. Landau  is supported by the  Burroughs Wellcome Fund   Career Award for Medical Scientists, Vallee Scholar Award ,  Pershing Square Sohn Prize for Young Investigators in Cancer Research , the  Sontag Foundation Distinguished Scientist award , and the  NIH   Director's New Innovator Award  ( DP2-CA239065 ). J. Moreaux is supported by  Institut Universitaire de France (IUF) , by grants from  INCa (Institut National du Cancer ;  PLBIO2018-160   PIT-MM ),  ANR  ( PLASMADIFF-3D ),  ANR  ( TIE-Skip ;  2017-CE15-0024-01 ), and  SIRIC Montpellier Cancer INCa_ Inserm_DGOS_12553. S. Stilgenbauer  is supported by the  DFG ,  SFB1074  subprojects  B1  and  B2.
5% HC rate E 7% HC rate Low Expression Variable 2,397 false positives Cancer type Dataset 0 Step 2: Evaluate if DHcR t (observed) is significantly 1 DHcR t 1 DHcR t DHcR e P = 0. 02 P = 0. 50 higher than DHcR e (expected) Multiple sampling (sample size = K, times = S) Cohort P(1) P(2). P(s). P(S) Quartile of combined P-values Wilkinson P-value combination Step 3: Determine if promoter hypermethylation is overrepresented in patients (DNAme driver) Q-Q plot List of DNAme drivers DUSP22 SASH1 RPRM. Outputs K CLL DCIS MM CNRS DFCI 1 CLL8 MDACC 2 GBM P MUV 3 Downloaded from http://aacrjournals. org/cancerdiscovery/article-pdf/11/9/2266/3103891/2266. pdf by guest on 26 June 2022 Low r = -0. 76 (P < 2. 2 × 10 -16 ) High Tumor samples 304 103 44 24 235 High Late RRBS 451* 103 44 24 494 Low Promoter DHcR r = 0.
jupiter. no/apex/f?p=101:1). Reported patients mostly originate from Europe, with only 13 patients from the Arab world including Algeria, Morocco, Palestine, United Arab Emirates, Kuwait, and Saudi Arabia  [12]. To the best of our knowledge, clinical and genetic investigations of AM have never been carried out on Tunisian patients. The Alpha-mannosidosis Mutation Database referred to one Tunisian case for which the clinical and genetic data are not available (https://apex. jupiter. no/ apex/f?p=101:1). The current work reports the first clinical and genetic study of AM in Tunisia.
Patient recruitment Two Tunisian families (TNDF617 and TNDF182), including six affected members, were recruited at the Oto-Rhino-Laryngology Department at La Rabta Hospital in Tunis and the Congenital and Hereditary Diseases Department at Charles Nicolle Hospital in Tunis. The two families originate from Northern Tunisia. Consenting and available family members were interviewed following a questionnaire that allowed the collection of data on genealogy, geographical origin, history of miscarriages and neonatal deaths, course of pregnancy, perinatal suffering as well as family history of diseases.
Raw sequence files were aligned to the human genome reference sequence (version GRCh38) using the Burrows-Wheeler Aligner-MEM (BWA-MEM version 1. 1. 1; https://bio-bwa. sourceforge. net). Duplicates were removed from BAM files using PICARD tool (www. picard. sorceforge. net). The Genome Analysis Tool Kit (GATK, www. broadinstitute. org/gatk/) was used for Indels realignment by the Realigner Target Creator tool. GATK was also used for base quality score recalibration. Variant Calling was performed by the GATK package Haplotype Caller. Genetic variants were annotated and prioritized using the VarAFT software version 2. 16 (http://varaft. eu/). SNPs and INDELs located in functionally relevant genomic regions (exonic, splicing) were selected. Variants identified as synonymous or non-coding were filtered out.