dataesr/scientific-paragraphs-categorization huggingface dataset:
To select the best calculation interval, we compared the MethScore for intervals from two to eight neighboring nucleotides for naturally modified yeast rRNA and for unmodified synthetic rRNA transcripts. The MethScore values were expected to reach maximum for naturally modified rRNA and minimum for the unmodified counterpart. Figure  2B  shows the difference between the MethScore values calculated for variable intervals for rRNA and synthetic transcript. For MethScore6 (+/-6 nt), we also tested different weight contributions of neighboring nucleotides. The MethScore demonstrates the maximal cumulative difference between rRNA and synthetic transcript for the shortest interval of +/-2 nt; the other tested intervals gave roughly the same results. On the basis of this observation, we suggest calculating the MethScore for two neighboring nucleotides (referred to as MethScore2).
Absolute values of the 2′-O-methylation The bold font highlights the number of uncovered positions in the different datasets. cannot be directly measured using the RiboMethSeq approach; however, for a limited subset of sites the values of MethScore2 are comparable with independent measurements of 2′-O-Me rates assessed by LC-MS/MS  (Buchhaupt et al. , 2014; Taoka et al. , 2016).
Calculation of the max MCC value demonstrated that ScoreMAX-MAX is less performant than the original ScoreMAX2, while ScoreMEAN2 shows better discrimination of FP hits. For the same dataset, Sample 3 -HeLa application of the ScoreMEAN2 allows attainment of the maximal MCC value of 0. 7954 and reduces the FDR from 27% to 4%. A similar tendency was observed for two other datasets (Supplementary Table  S3 ). An important source of FP hits in RiboMethSeq analysis is the reduced ligation efficiency observed when modified RNA nucleotides are present at the 5'-end extremity of the fragment. This was experimentally observed for pseudouridine and other rare RNA modifications  (Birkedal et al. , 2015; Marchand et al. , 2016). Since this reduced ligation efficiency also generates a   for pseudouridines (red) partially overlaps with that of 2′-Omethylation.
ACKNOWLEDGMENTS We thank  Team 05 of IMoPA UMR7365  for generous gift of HUVEC and bone marrow stem cells used for RNA extraction. This work was supported by  FRCR EpiARN project from Lorraine Region  to YM and  SPP1784 ,  HE 3397/13-2 ,  HE 3397/ 14-1  from  DFG  to MH. This work was performed in the framework of EPITRAN  COST initiative  ( CA16120 ).
DATA AVAILABILITY STATEMENT We used previously published datasets obtained for wild type yeast S. cerevisiae and human HeLa cells rRNA 2′-O-methylation analysis as well additional samples  (Marchand et al. , 2016; Erales et al. , 2017; Sharma et al. , 2017) , accession numbers PRJEB43738, PRJEB35565 and PRJEB34951.
NucleusJ 1. 0, an ImageJ plugin, is a useful tool to analyze nuclear morphology and chromatin organization in plant and animal cells. NucleusJ 2. 0 is a new release of NucleusJ, in which image processing is achieved more quickly using a command-lineuser interface. Starting with large collection of 3D nuclei, segmentation can be performed by the previously developed Otsumodified method or by a new 3D gift-wrapping method, taking better account of nuclear indentations and unstained nucleoli. These two complementary methods are compared for their accuracy by using three types of datasets available to the community at https://www. brookes. ac. uk/indepth/images/. Finally, NucleusJ 2. 0 was evaluated using original plant genetic material by assessing its efficiency on nuclei stained with DNA dyes or after 3D-DNA Fluorescence in situ hybridization.
The leaf area of the 21-day-old plants was determined with the ImageJ software using the SIOX (Simple Interaction Object eXtraction) plugin  [30]. 13 dag cotyledons were used to determine the number of stomates, guard cell, and pavement cell nuclei. To this aim a maximum Z-projection of a wide-field stack stained with Hoechst 33,258 and a single plane image under transmission light using Differential Interference Contrast (DIC) were combined (overlay) as described in Figure  1.
Sample preparation, DNA staining and 3D DNA-FISH 3D images were obtained from whole mount preparations of 13 dag cotyledons as previously described  [29, 31]. Whole mount preparations were then used either for DNA staining using Hoechst-staining procedure (Hoechst 33 258 overnight at 1 µg/ml final) or for 3D-DNA Fluorescence In Situ Hybridization (3D DNA   Six types of image datasets (Dataset names) gained from wide-field or confocal microscopy (Acquisition systems) were stored as six main directories at OMERO-FSU under the name IDP3006_Dubos-Desset _2020. For each dataset, images were organized in acquisition, autocrop, raw and segmented sub-directories and can be directly accessed using the web link included into Table  1  (Link). OMERO allows to screen for key-value pairs (Key and value filters).
Number of images are indicated in the last column. The six datasets and their image processing represent a total of 7,313 images. FISH). 3D DNA FISH experiments were performed as previously described  [32]. 5S rDNA probe was produced from pCT4. 2 vector  [33]  amplified with 5 CY5-dUTP and directlylabeled oligonucleotides (/CY5/ CCCAAATTTTGACCTTTAAG) and (/CY5/ GTCGACAAAAAGTCAATGGA) or with 5'CY3-dUTP and the same oligonucleotides without fluorescent labels. 180pb satellite repeat probe was designed as an LNA-oligonucleotide (/5TYE563/ GTATGATTGAGTATAAGAACTTAAACCG -Qiagen)  [34].
Microscope and image acquisition Microscopic observations were performed by structured illumination microscopy to produce wide-field stacks using an Optigrid module (Leicamicrosystems MAAF DM 16000B). All stacks were acquired using an X63 oil N. A. 1. 4 objective and a digital CMOS Camera (ORCA-Flash4. 0 V2 C11440-22 CU -Hamamatsu) at an optimal resolution such that lateral and axial resolution were respectively XY = 0. 103 µm and Z = 0. 2 µm. For better resolution, some confocal images were acquired with a Zeiss LSM 800 with an X63 oil N. A. 1. 4 objective and a voxel size XY = 0. 60 µm and Z = 0. 2 µm. Final image numbers are given for each dataset in Table  1.
Datasets storage and availability Datasets were stored at OMERO-Florida State University (OMERO-FSU), a public repository under the accession number IDP3006 Dubos-Desset Nucleus 2020 that can be accessed through the INDEPTH COST-Action (CA16212) website at https://www. brookes. ac. uk/indepth/images/. The INDEPTH image webpage provides a guideline to access and download the datasets that are freely available for research purposes. More tutorials to use OMERO are available at the OMERO webpage at https://www. openmicro scopy. org/. Six types of datasets were produced for this study (#1 to #6) (Table  1 ). Each dataset was stored at OMERO-FSU under the accession number IDP3006_Dubos-Desset_2020 where it was organized in four folders: acquisition (wide-field stacks or confocal images), autocrop (results of autocrop processing), raw (3D images containing a single nucleus per stack) and segmented (raw image after segmentation) except for dataset#1, which was generated in silico and for that reason no acquisition nor autocrop was performed.
NucleusJ 2. 0 algorithm development Documentation: NucleusJ 2. 0 is an ImageJ plugin in Java language released as a jar file for the ImageJ platform. Installation and usage guides are available at https://gitlab. com/DesTristus/NucleusJ2. 0. The software contains a 3D autocrop module and three independent segmentation methods hereafter referred to as the modified Otsu, 3D giftwrapping and 3D watershed methods. NucleusJ was described in a previous publication  [23]. New functionalities or improvements are detailed below. Autocrop: From any given 2D or 3D wide-field image a simple Otsu threshold  [35]  was applied to obtain a binary image i. e. transforming all the voxels of the image to a value of 0 for the background voxels and 1 for each voxel from the object (nucleus). Volume of each connected voxel (i.
Results Automatic selection of 3D nuclei from wide-field stacks using a 3D autocrop process Any kind of 3D bio-image analysis starts with the capture of images of best quality. During this initial step, 3D stacks containing the objects of interest, such as a cell nucleus, are collected either one by one when using a confocal microscope or by collecting multiple nuclei at a time using widefield microscopy. In the latter, bounding boxes surrounding the 3D nuclei have to be defined to delimit and extract (crop) the appropriate volume containing each nucleus. Automatic tracking of segmented objects would strongly reduce this tedious manual step. Setting-up such middle to high-throughput 3D tracking process is a timely objective when using wide-field stacks to rapidly buildup large image datasets  [42].
Then, a dataset of 502 nuclei from WT plants stained with Hoechst was used to evaluate the appropriateness of the method (Supplemental table 4). Shape artifacts due to the nucleolus or to low staining intensity were efficiently removed (Figure  2 (a)). The limit of this method is its accuracy, as the measured nuclear volume is significantly increased especially for small objects such as guard cell nuclei (Figure  2  (c)). This over-segmentation also leads to the loss of 0. 02% of the nuclei (Supplemental table  4 ) located at the boundaries of the 3D images that are then considered as incomplete by the software. Although the gift-wrapping method increases the size of segmented objects, PCA analysis confirmed that it does not introduce bias in the analysis and that the two nuclei populations of different cellular origin are still nicely distinguished (Figure  2(d) ).
0001. (b) Fluorescent Microspheres of diameter of 1, 2. 5, and 4 µm (n = 28, 24, and 15, respectively) were imaged using the wide-field microscope with an optigrid module, subjected to autocrop and segmented by the two NucleusJ 2. 0 segmentation methods. Surface area gained from Otsu and gift-wrapping segmentation was then computed with the initial and new method of calculation (Supplemental table2). Red: theoretical surface area; gray: Otsu method; green: gift-wrapping method; triangle: new method; square: initial method. (c) Plant nuclei. Surface area of nuclei from cotyledon epidermis of WT plants were segmented by the Otsu or gift-wrapping methods. Nuclei from guard cells (GC, n = 375) and pavement cells (PC, n = 127) (Supplemental table  4 ). Student t-test P-value: *** <0. 0001. small-segmented objects consisting of only a few voxels.
068 and 1. 084 instead of 1. 608 and 1. 636, respectively, for the Otsu and gift-wrapping segmentation methods (Figure  3(a) ). The accuracy of our surface calculation was also assessed using standardized microspheres of three distinct diameters ranging from 1 to 4 µm. For these, the new surface area calculation was closer to the theoretical values (Figure  3  (b)). Surface area was also assessed for a dataset of 502 nuclei from WT plants stained with Hoechst indicating that the surface area was over-estimated when the surface area was calculated with NucleusJ 1. 0 (Figure  3(c)). Taken together, a new surface area calculation is now introduced in NucleusJ 2. 0 to obtain a more realistic value of this key parameter.
The dataset was then used to assess nuclear morphology and chromocenter organization. Results gained from our new gift-wrapping method are presented in Figure  4. The nuclei of the k4c1c4 mutant are strongly affected for most of the nuclear parameters and show reduced nuclear size, reduced elongation (Figure  4 (a)) and fusion of chromocenters leading to an increased chromocenter volume (Figure  4(b) ). This is very similar to previous results obtained with crwn1 crwn2 mutant  [23]. NucleusJ can also be used to quantify 3D DNA FISH signals. To illustrate this application, two new datasets of 3D-DNA-FISH images were used to investigate the effect of the triple k4c1c4 mutant. The distinct fluorescent channels were collected corresponding to signals from the DNA dye and the probes for 180bp and 5S rDNA repeats (Supplemental tables 7-8).
0 to use more powerful servers at computing centers. Taken together, the two methods are considered complementary to each other and present two possible alternatives to generate segmented datasets. The autocrop process will contribute to middle throughput 3D image analysis. It automatically produces an average of 21 isolated 3D nuclei in about 6 minutes reducing substantially the time-intensive manual process. Starting from these large populations of nuclei, NucleusJ 2. 0 then describes automatically the 3D nuclear morphology. The second step in NucleusJ 2. 0 was to describe chromatin organization and a triple kaku4-2 crwn 1-2 crwn 4-1 mutant was used to illustrate the potential of our new software. In the triple mutant some nuclear morphology parameters are altered: guard cells show reduced nuclear volume, while pavement cells show an increased surface area and both types of nuclei revealed reduced flatness in the mutant background.
We illustrated the use of NucleusJ 2. 0 to measure FISH signals in a quantitative manner in order to estimate the volume occupied by the FISH signals. Then, the border or the barycenter of the signal are used to compute the positions of the FISH signal in respect to the nuclear periphery. This offers an original method to quantify chromatin compaction while most softwares often define FISH signals as standardized spots that can only be used to compute distances. Using this method we observed that the 180bp signals are more dispersed while the 5S rDNA loci seem more condensed in the mutant background and preferentially locate at the nuclear periphery. All these parameters were detected successfully by the new software update and are consistent with previous observations  [8]. Segmentation of nuclear domains remains challenging to characterize nuclear architecture and NucleusJ 2.
0 can provide a solution. In future, further optimization of nuclear domain detection and quantification are required as this still relies on a 3D watershed process, which is timeconsuming and user-dependent. Recent progress in Artificial Intelligence is opening up new opportunities to improve 3D bio-imaging. Convolutional Neural Networks (CNN) such as those developed for U-Net, Ilastik, or Star dist  [45] [46] [47]  have been successfully applied for 3D bioimaging. For the 3D watershed process, preliminary tests using a U-Net CNN  [46]  currently under development are promising. We expect that the present study, which provides several wellannotated training datasets, will help to pave the way for the development of CNN methods, which we expect will transform the way we acquire and analyze 3D images creating an automated highperformance tool well designed for big-data analysis.
Acknowledgments The work of CG, TD, SD, AVP and CT was supported by  CNRS ,  INSERM ,  Université Clermont Auvergne (UCA) ,  16-IDEX-0001 CAP 20-25 challenge 1 ,  Temporary Assignment  to the  CNRS (CT) ,  Pack Ambition Recherche  project  Noyau-HD  from the  Region Auvergne Rhone Alpes  and the  COST-Action INDEPTH  ( CA16212 ). All pictures were acquired at the  CLIC microscopy facility (CLermont Imagerie Confocale). We would like to acknowledge  Eric Richards  and  Kentaro Tamura  who made available initial mutant seeds,  Rémy Malgouyres  for his advices in discrete geometry and  Pierre Pouchin  for his assistance for using ImageJ and OMERO. Finally, we thank  David Evans  for critical reading of the manuscript.
The PA is simulated using standard simulation test benches for performance characterization. Thus, we employed a small signal test bench for characterizing S-parameters, large signal test benches for P sat , P AE and compression point, and a DC test bench for I DC characterization. The MC simulation of the DUT in the 55 nm CMOS technology includes more than 500 independent process parameters. As described in the previous section, we explore the space of MC parameters to find the root causes of performance degradation. For performing this search, we rely on the proposed Brownian distance correlation-directed search. A simple perceptron Neural Network implemented in Matlab is used as the machine learning regression algorithm for predicting the PA performances during the search. As an application example, the procedure is fully detailed for the small signal gain (i.
As it was presented in section II-A, this requires training a regression function f that maps the signatures to the target specifications of the PA under test as described in equation  (1). In this case study we use a machine learning regression model, namely a perceptron Neural Network implemented in Matlab, for building the regression function f. In order to validate this indirect test strategy, we present two sets of experiments using the available data obtained from the fabricated samples. Thus, our first set of experiments is aimed at evaluating the performance (that is, the accuracy) of the proposed indirect test, while the second set of experiments shows the feasibility of the approach in a practical application scenario.
ACKNOWLEDGEMENTS Circuits fabrication by STMicroelectronics has been funded by the  TARANTO  project. In this regard, the authors acknowledge the contribution of  P. Ferrari  from  RFIC-Lab laboratory ,  D. Gloria , and  C. Durand  from  STMicroelectronics. The authors also thank  N. Corrao  from  IMEP-LAHC laboratory  for his help with high frequency and power measurements.
Background: Facing the diversity of omic data and the difficulty of selecting one result over all those produced by several methods, consensus strategies have the potential to reconcile multiple inputs and to produce robust results. Results: Here, we introduce ClustOmics, a generic consensus clustering tool that we use in the context of cancer subtyping. ClustOmics relies on a non-relational graph database, which allows for the simultaneous integration of both multiple omic data and results from various clustering methods. This new tool conciliates input clusterings, regardless of their origin, their number, their size or their shape. ClustOmics implements an intuitive and flexible strategy, based upon the idea of evidence accumulation clustering. It computes co-occurrences of pairs of samples in input clusters and uses this score as a similarity measure to reorganise data into consensus clusters.
Conclusion: We applied ClustOmics to multi-omic disease subtyping on real TCGA cancer data from ten different cancer types. We showed that ClustOmics is robust to heterogeneous qualities of input partitions, smoothing and reconciling preliminary predictions into high quality consensus clusters, both from a computational and a biological point of view. The comparison to a state-of-the-art consensus based integration tool, COCA, further corroborated this statement. However, the main interest of ClustOmics is not to compete with other tools, but rather to make profit from their various predictions when no gold-standard metric is available to assess their significance.
As these mechanisms are not circumscribed to a single molecular level, the detection of groups of patients showing similar characteristics across different omics is a key issue to enable personalised medicine, which aims to offer patients a treatment adapted to the characteristics of their tumors. This motivated the development of new computational methods implementing different strategies to analyse simultaneously several omic datasets (for detailed reviews see  [1, 2] ). According to the classification proposed in  [1] , the early integration strategy consists in concatenating omic datasets in a large matrix and applying a clustering method conceived for single-omic data  [3, 4]. On the other hand, late integration approaches first cluster each omic dataset independently and fuse single-omic clusterings into one multi-omic clustering  [5, 6].
Other approaches perform intermediate integration, fusing sample similarities across omics  [7, 8, 9] , using dimension reduction strategies  [10, 11] , or statistical modelling with Bayesian frameworks  [12, 13, 14]. To tackle both issues mentioned above, i. e. , multi-omic and multi-strategy integration, one may want to apply a particular type of late integration strategy by taking multiple clustering results (using different data, methods and parameters) and fuse all of them into one consensus clustering. Such a consensus clustering should benefit from the complementary information carried by various omics data and capitalise upon each method's strengths while fading their weaknesses. Note that with respect to classical late integration strategies that start from the raw omic datasets (e.
g. , PINS  [6]  uses perturbations of raw omic data to generate the most stable multi-omic clustering), consensus clustering methods rely solely on clustering results. This is an essential property, as it allows for any clustering algorithm and any clustering result to be used, regardless of the availability of the raw omic dataset and of its type. A naive way to compute a consensus clustering would be to perform the intersection of the clustering results, i. e. by simply taking the associations on which all methods agree. However, the greater the number of clusterings to fuse, the smaller the intersection. Moreover, when clusterings show different numbers of clusters, the question of the intersection is not trivial. Therefore, the issue of consensus clustering requires further methodological developments.
Here, we present ClustOmics, a new graph-based multi-method and multi-source consensus clustering strategy. ClustOmics can be used to fuse multiple input clustering results, obtained with existing clustering methods that were applied on diverse omic datasets, into one consensus clustering, whatever the number of input clusters, the number of individuals clustered, the omics and the methods used to generate the input clusterings. The co-occurrence strategy implemented in ClustOmics (detailed in the Methods section) is based on Evidence Accumulation Clustering (EAC), first introduced by Fred A. and Jain A. [16]. The idea is to consider each partition as an independent evidence of data organisation and to combine them using a voting mechanism. Similarly to clustering methods that use a distance or a similarity measure to compare objects, EAC considers the co-occurrences of pairs of objects in the same cluster as a vote for their association.
The underlying assumption is that objects belonging to a natural cluster are more likely to be partitioned in the same groups for different data partitions. Thus, one can use the counting of the objects' co-occurrences in clusters as a pairwise similarity measure. We further refer to these co-occurrence counts as number of supports. This measure, summarising the results from the input clusterings, is a good indicator of the agreement between the partitions and allows to produce a new partitioning that can be qualified as consensual. Although computationally expensive, this strategy allows exploiting all clustering results, regardless of the number of clusters, their size and shape. We designed ClustOmics as an exploratory tool to investigate clustering results with the objective to increase the robustness of predictions, taking advantage of accumulating evidences.
In order to allow the user to tackle a specific question and to explore relationship patterns within input clusterings and generated consensus, we store the data in a non-relational graph based database implemented with the Neo4j graph platform  [17]. The use of a graph native database facilitates the storage, the query and the visualisation of heterogeneous data, hence allowing to develop a solution that is flexible to various integration strategies. Indeed, by fusing clusterings from different clustering methods, different data types, different experimental conditions, or several options at the same time, through the use of what we call integration scenarios, ClustOmics can address a wide range of biological questions. ClustOmics was applied in the context of multi-omic cancer subtyping, with TCGA data from different cancer types and multiple omic datasets.