dataesr/scientific-paragraphs-categorization huggingface dataset:
Accessibility and downloads The ACSN maps can be browsed online via NaviCell platform. The maps components are clickable, making it interactive. The extended annotations of the map components contain a rich tagging system converted to links. This allows tracing the involvement of molecules into different maps and sub-structures as modules. The tagging system also allows to use the map as a source of annotated signatures. The semantic zooming feature of NaviCell simplifies navigation throughout the large collection of molecular interactions, revealing a readable amount of details at each zoom level. A gradual exclusion of details allows the exploration of map contents, going from the detailed towards the top-level view. The hierarchical structure of the ACSN atlas facilitated the generation of several zoom levels for web-based navigation of the map.
The ACSN maps can be downloaded from the homepage in several exchange formats as xml, SBGN-ML, BioPax (https://acsn. curie. fr/ACSN2/downloads. html). In addition, the content of the ACSN maps grouped per modules is available and downloadable in the form of GMT files suitable for further functional data analysis. Finally, ACSN2 binary relations between proteins are also available and provided in SIF format.
Concluding remarks In this chapter we demonstrated several examples on ACSN applications for systems medicine. This has been possible thanks to the systematic representations of signaling pathways implicated in the disease, in the form of comprehensive network maps and tools for network-based analysis, visualization and interpretation of cancer omics data. The aforementioned resource and analytical tools can serve as basis for a more general approach to understand signaling regulation in human disorders, to develop network-based models underlining drug resistance and to suggest intervention sets  (Barillot et al. , 2013). A workflow that is applicable not only to cancer-related studies, but also to study other pathologies has been depicted. With the aim of revealing perturbations of molecular mechanisms in different human diseases, to predict drug sensitivity, and to find optimal intervention schemes, one could combine the approaches represented in the examples previously described, into a workflow that contains various steps: (i) construction of a comprehensive intra-and intercellular signaling network of a disease; (ii) integration of omics data and retrieval of network-based signatures, characterizing the disease; (iii) upon data availability, the resistance to treatment can be exploited to elucidate the mechanisms associated with the resistance, being highlighted in a form of deregulated functional modules, pathways or key players; (iv) modelling mechanisms governing the disease and drug resistance and computing intervention gene sets to interfere with the disease and drug resistance.
, 2015). There is an ongoing effort to include ACSN content into various open source platforms and pathway databases, such as (a) MINERVA  (Gawron et al. , 2016)  which is a web-based platform hosting molecular interaction networks encoded as process diagrams in SBGN  (Novère et al. , 2009)  and CellDesigner  (Funahashi et al. , 2003)  formats. MINERVA enables highlighting targets of drugs, and upload and visualization of user-provided experimental datasets, including multiomics entries and custom color coding, (b) Pathway Commons  (Cerami et al. , 2011)  which is a collection of publicly available pathway data from multiple organisms. It provides a web-based interface that enables to browse and search a comprehensive collection of pathways from multiple sources represented in a common language, a download site that provides integrated bulk sets of pathway information in standard or convenient formats and a web service that software developers can use to conveniently query and access all data, (c) WikiPathways  (Slenter et al.
, 2018)  which is an open, collaborative platform for capturing and disseminating models of biological pathways for data visualization and analysis. Built on the same MediaWiki software that powers Wikipedia, WikiPathways contains a custom graphical pathway editing tool and integrated databases covering major gene, protein, and small-molecule systems such that it can be used in many projects through standard interfaces as Cytoscape  (Shannon et al. , 2003) , and (d) NDEx  (Pratt et al. , 2015) , the Network Data Exchange, which is an open-source software framework that facilitates the sharing of networks of many types and formats, the publication of networks as data, and the use of networks in modular software. NDEx exists also as a Cytoscape Plugin and can be coupled with various tools available for network analysis or manipulation.
A conventional approach is that in an electrical circuit simulator (e. g. , Spice), designers import device compact model (self developed or from device manufacturer) and lumped model for parasitic inductance and resistance, which are extracted by using 3D simulation software based on finite element method (FEM) or partial element equivalent circuit (PEEC). This approach was applied by Liu et al. to model a cascode GaN transistor  [7] , by  Endruschat et al. to model a GaN-HEMT  [8]  and by  Liang et al. to model a SiC-MOSFET  [9] , where the extracted parasitic inductance value varies from sub-nanoHenry to a fews tens of nano-Henry. However, one drawback of this approach is its crude representation of electromagnetic (EM) physics and the accuracy for the required values is in the range of nano-Henry.
The presented GaN-HEMT model will then be implemented into our developed PEVP software. I ch = a log(1 + exp(b1(V gs -c))) • V ds 1 + max(d + e • (V gs + f 1), 0. 2) • V ds V ds ≥ 0 I ch = a log(1 + exp(b2(V gs -V ds -c))) • V ds 1 -max(d + e • (V gs -V ds + f 2), 0. 2) • V ds V ds < 0 (1) V GS = 0V V GS = 6V V GS = -2V V GS = 2V V GS = 3V V GS = 4V V GS = 5V V GS = 6V C gs = a 1 -b 1 (1 + tanh(c 1 (V gs + d 1 ))) -e 1 (1 + tanh( f 1 (V gs + g 1 ))) + h 1 (1 + tanh(i 1 (V gs + j 1 ))) C gd = a 2 -b 2 1 + tanh c 2 V gd + d 2 + e 2 1 + tanh f 2 V gd + g 2 -h 2 1 + tanh i 2 V gd + j 2 C ds = a 3 -b 3 (1 + tanh(c 3 (V ds + d 3 ))) -e 3 (1 + tanh( f 3 (V ds + g 3 ))) -h 3 (1 + tanh(i 3 (V ds + j 3 ))) (2) Voltage V DS (V).
Model Implementation into Power Electronics Virtual Prototyping Software The used simulation techniques in PEVP software are summarised in Figure  4. There are in general two models that can be used for different components in power electronics converters. For example, for the represented half-bridge leg, it is formed by two power semiconductor devices S1 and S2 with their gate drivers, decoupling capacitor C decp , power supply V and PCB tracks for interconnections. For components such as power semiconductor devices, gate driver, C decp and V, power electronics engineers are interested in their characteristics. Therefore, the compact model described by equivalent circuits is suitable in simulation to obtain results quickly. For components like the PCB track or other interconnections, their layout and material properties are important for the design, as they directly influence power electronics converters parasitic inductance (L para ) values.
It can be seen that both L gate and L power slightly decrease with frequency, illustrating the impact of skin effect and proximity effect. The obtained average L gate and L power between 10 and 100 MHz are then compared with another commercial software ADS  [10]  in Table  3 , where it validates the accuracy of PEVP for electromagnetic simulation. Note that the simulation time to obtain these results in PEVP (60 points/decade from 1 kHz to 100 MHz) is within 1 min (25 s for meshing and 17 s for MOR), which is much reduced compared to ADS. We understand that full EM simulation in ADS give comprehensive results including capacitive coupling between each node, and this comparison in Table  3  is only to demonstrate the advantage of using MOR in PEVP, which meets our objective to obtain device switching waveforms quickly.
Conclusions A GaN-HEMT compact model including dynamic R DSon effect is proposed in the paper, with the objective to finely model GaN-HEMT switching transients and power losses. Device static characteristics and capacitance-voltage characteristics are accurately represented by mathematical equations. The model is then implemented into our developed Power Electronics Virtual Prototyping (PEVP) software, where the parasitic inductance of the power loop and gate driver loop is accurately extracted by PEEC method. A model order reduction technique is also used in PEVP software to reduce the numbers of equations, so as to obtain the above parasitic inductance within 1 min. By comparing with experimental measurement, the model represents well turn-ON and turn-OFF switching waveforms in both time and frequency domain.
One of the most recent and potentially most powerful SMLM techniques is Point Accumulation for Imaging in Nanoscale Topography (PAINT) microscopy  [64]. In PAINT, a structure of interest is labeled with a non-fluorescent binding target, against which fluorescently labeled ligands can reversibly bind with high specificity. The ligand bound to the target site can be individually seen in a widefield image and can be localized as in conventional PALM/STORM. The ligand affinity determines the "ontime", which is the time the molecule is bound to the target site before being released into solution again. The concentration of ligands in solution determines the ligand "off-time", which is related to the probability with which a ligand finds its target protein. Binding/unbinding events are recorded in a raw data movie and appear similar to blinking events in PALM/STORM, so that the same data analysis procedure as in PALM/STORM can be applied.
Instead, the detection probability is defined by the angular distribution of radiation (radiation pattern) and its overlap with the collection aperture of the optics, and finally by the quantum efficiency of the detector. This picture leads to the following list of observables: There is an almost three orders of magnitude variation over a 100 nm distance. For this frequency (chosen in between two resonances for illustration purposes), the LDOS is due to the superposition of two antenna modes, each with a very different radiation pattern. The radiation patterns in (e) show strong position dependencies and asymmetries, due to the far-field interference of the cube modes (three choice antenna positions indicated lozenges in (c)). Results obtained by COMSOL Multiphysics, using the RETOP package  [88].
As an example, ref. [193]  demonstrated dSTORM imaging of microtubules in human mesenchymal stem cells (hMSC), see Figure  11c-d , and DNA-PAINT imaging of mitochondria in HeLa cells, see Figure  11e-f. Interestingly, the LINCam data output consists of an array of photon arrival times and coordinates, while the division into virtual pixels is done only after data acquisition. Therefore, pixel size in the final image can be adjusted and optimized by means of signal-to-noise ratio of a specific data set and pixelation artifacts can be reduced to a minimum. Besides the obvious advantage of FL-SMLM for multiplexed imaging via the different lifetimes of different fluorophores, the most important advantage in the context of SMLM super-resolution is that FL-SMLM offers the possibility to do high-precision co-localization measurements between different fluorophores without any chromatic aberration artifacts.
We also confirmed that the use of an autoencoder is a safe and robust way to extract essential features from complex and high dimensional fluid fields. Details about this test case and the code used to reproduce these results can be found on the GitHub repository: https://github. com/DonsetPG/ fenics-DRL. We recall that this code uses different library: FEniCS  (Alnaes et al. , 2015)  for the CFD solver, Gym  (Brockman et al. , 2016)  for the DRL environment, Stable-baselines  (Hill et al. , 2018)  for the DRL algorithms. Future work on this library will focus on generalizing the coupling of DRL with different open source CFD code.
Image processing for the study of the spheroid size In order to characterize the time evolution of the sizes of the spheroids, we developed a Matlab image analysis code. It works automatically with a few user-defined parameters. In particu-lar, we manually defined the regions of interest (ROIs) for each spheroid. Then an ellipsoid is fitted on the binarized image. The section of the original image passing through the ellipse center is selected in the axial and radial directions. We stored the width of the 70% thresholded curves of the X and Y sections (See movieImageProcessing. mp4). For the whole image sequence, ROIs were defined automatically from the ellipsoid characteristics of the previous image. We obtained the time evolution of the axial and radial dimensions of the spheroid with a one-minute time step.
Evaluation of MSC surface markers and potential of differentiation After 24h of acoustic levitation, MSC spheroids were collected and reseeded in 75cm 2 for expanding in MSC culture medium. About a week later, cells were detached and evaluated by flow cytometry using a panel of MSC positive and negative markers including CD31, CD34, CD29, CD73, CD90 and CD105. Data were acquired and analyzed with an Attune flow cytometer (Ther-mofisher™). The potential of differentiation of the aggregates into adipocytes and osteoblasts was also evaluated. For this purpose, the spheroids were collected and reseeded in a 24 wells plate at a density of one spheroid / well in MSC expansion medium. A 2D culture of cells at the same passage that had not undergone levitation was also used as control. After allowing the cells to adhere and expand for three days, the cell culture medium was replaced by adipocyte differentiation medium or osteoblast differentiation medium (StemX-Vivo™, R&D systems).
Funding This work was supported by grants from  Région Ile-de-France (DIM ELICIT)  and  Recherche Hospitalo-Universitaire iLite (Agence Nationale de la Recherche ). The authors benefited from access to the microfluidic plateform of " Institut Pierre-Gilles de Gennes"  supported by the program " Investissements d'Avenir "  ANR-10-EQPX-34.
The detection of copy number variations (CNVs) in whole-exome sequencing (WES) data is important, as CNVs may underlie a number of human genetic disorders. The recently developed HMZDelFinder algorithm can detect rare homozygous and hemizygous (HMZ) deletions in WES data more effectively than other widely used tools. Here, we present HMZDelFinder_opt, an approach that outperforms HMZDelFinder for the detection of HMZ deletions, including partial exon deletions in particular, in typical laboratory cohorts that are generated over time under different experimental conditions. We show that using an optimized reference control set of WES data, based on a PCA-derived Euclidean distance for coverage, strongly improves the detection of HMZ deletions both in real patients carrying validated disease-causing deletions and in simulated data.
Common WGS-based methods use breakpoints, the regions in which the rearrangements occur, to detect CNVs. By contrast, WES focuses on noncontiguous genomic targets (the exons), and most breakpoints are not sequenced. Hence, current WESbased approaches for detecting CNVs use the read depth (or coverage information) as a proxy for copy number information. The HMZDelFinder algorithm is a recently developed coverage-based method for detecting rare homozygous and hemizygous (HMZ) deletions  (14). This subset of CNVs may result in null alleles and a complete loss of gene function. Their identification may, therefore, lead to the discovery of novel genes or variations underlying Mendelian diseases. HMZDelFinder jointly evaluates the normalized per-interval coverage of all the samples of the entire dataset, making it possible to detect rare exonic HMZ deletions while minimizing the number of falsepositive calls due to low-coverage regions.
HMZDelFinder outperformed other CNV-calling tools, such as CONIFER  (15) , CoNVex  (16) , XHMM  (17) , ExonDel  (18) , CANOES  (19) , CLAMMS  (20)  and CODEX  (21) , particularly for the detection of single-exon deletions (i. e. deletions spanning only one exon)  (14). However, two major limitations remain to be addressed. First, HMZDelFinder has been optimized to detect HMZ deletions from an entire dataset (>500) of homogeneous exome data. Its performance for typical laboratory cohort, which include exome data generated over time, often under different conditions, is, therefore, not optimal. Second, HMZDelFinder was not designed for the systematic detection of partial exon deletions (i. e. deletions spanning less than one exon). Here, we provide HMZDelFinder_opt, a method that extends the scope of HMZDelFinder by improving the performance of the algorithm for the calling of HMZ deletions in typical laboratory cohorts, which are generated over time, and by allowing the systematic detection of partial exon deletions.
In addition, the FASTQC tool kit (www. bioinformatics. babraham. ac. uk/projects/fastqc/) was used to review base quality distribution, representation of the four nucleotides of particular k-mer sequences (adaptor contamination). We used the Genome Analysis Software Kit (GATK) (version 3. 2. 2 or 3. 4-46) best-practice pipeline to analyze our WES data  (23). Reads were aligned with the human reference genome (hg19), using the maximum exact matches algorithm in Burrows-Wheeler Aligner (BWA)  (24). PCR duplicates were removed with Picard tools (picard. sourceforge. net/). The GATK base quality score recalibrator was applied to correct sequencing artifacts.
HMZDelFinder-opt The general workflow used in HMZDelFinder-opt is depicted in SI Figure  1. First, HMZDelFinder_opt computes coverage profiles from the BAM files of the entire dataset. Second, the Principal component analysis (PCA) is calculated from a covariance matrix based on standardized coverage profiles and a k nearest neighbors algorithm is used to select the reference control set. Third, the BAM file of a given sample and the BAM files of the reference control set are used as input of HMZDelFinder to detect HMZ deletions. Fourth, when HMZDelFinder_opt is provided with the parameter -sliding_window_size and the related size, it will employ a sliding window approach for identification of partial deletions of exons. Each of these steps is described in the following paragraphs.
HMZDelFinder We used the HMZDelFinder algorithm as described  (14). In brief, HMZDelFinder calculates per-exon read depth (reads per thousand base pairs per million reads; RPKM) to detect HMZ deletions. For our purpose of covering all the coding regions, we employed an interval file containing all coding sequences from Gencode. For a given interval, the criteria to call a deletion are as follows: 1) RPKM < 0. 65 and 2) frequency of the deletion within the dataset £ 0. 5%. Filtering criteria at the interval and sample levels include removal of low quality intervals (RPKM median < 7 across all samples) and removal of low quality samples (2% with highest number of calls). When using the optional absence of heterozygosity (AOH) step, HMZDelFinder uses VCF files to filter out deletions not falling in AOH regions, assuming that rare and pathogenic homozygous deletions are likely to be located within larger AOH regions due to the inheritance of a shared haplotype block from both parents.
Finally, to prioritize deletions, z-scores are computed. The z-score of a deletion measures the number of standard deviations between the coverage of the deleted interval in a given sample compared to the mean coverage of the same interval in the rest of the dataset. A very low z-score indicates high mean coverage with low variance in the dataset and very low (or no coverage at all) in a given sample. Hence, lower z-scores denote higher confidence in a given deletion.
Analysis of common deletions To determine whether some of the called deletions were previously reported as common deletions, we utilized the CNVs from the Gold Standard track (hg19 version dated 2016-05-15) of the Database of Genomic Variants (DGV), a highly curated resource that collects CNVs in the human genome  (29). We retained only entries with field 'variant_sub_type' equal to 'Loss' and frequency greater than 1%. We then crossed the retained entries with the deletions called by HMZDelFinder and HMZDelFinder_opt in the positive controls. Deletions were considered common in the DGV database when they overlapped at least 50% with the retained entries from the DGV database.
We then compared the performances of HMZDelFinder_opt and HMZDelFinder, using five WES samples carrying validated rare HMZ disease-causing deletions of different lengths as positive controls (SI Table  1 , methods). Specifically, we tested the ability of HMZDelFinder_opt and HMZDelFinder to detect the validated deletions, and we also compared the total numbers of deletions called and their z-scores (see Methods). In HMZDelFinder_opt, we compared reference control sets of different size (ranging from 50 to 500, SI Figure  3 ), selected for each sample as described above. In HMZDelFinder, we used the entire dataset, consisting of 3,954 WES samples. For both approaches, the final set of called deletions for each sample was narrowed down to the capture kit corresponding to the patient WES data.
Finally, we studied the HMZ deletions called by both approaches, in addition to the validated ones, to determine whether some of the deletions identified were reported as common deletions. We used the CNVs from the gold standard track of the Database of Genomic Variants (DGV), a highly curated resource containing CNVs from the human genome  (29). We focused on the positive controls with high data quality (P2, P3, P4 and P5), and found that the HMZ deletions called by HMZDelFinder_opt were more enriched in common deletions (frequency > 1%) than those called by HMZDelFinder (SI Table  3 ). Among the 6 and 303 additional HMZ deletions called by HMZDelFinder-opt (with the reference control set of 100 exomes) and HMZDelFinder, 50% and 1%, respectively, were present in the DGV database (SI Table  3 ), suggesting that the deletions called by HMZDelFinder_opt were enriched in true deletions.
Detection of HMZ partial exon deletions by HMZDelFinder_opt In HMZDelFinder, individual exome BAM files are transformed into per-exon read depths, facilitating a more efficient detection of single-exon HMZ deletions than can be achieved with other classical CNV-calling algorithms  (14). Here, we aimed to address the need for the identification of even smaller HMZ deletions, spanning less than an exon (partial exon deletions). To this end, we used HMZDelFinder_opt with a sliding window approach, in which each exon was divided into 100 bp windows, with 50 bp overlaps, and BAM files for individual exomes were transformed into per-window read depths. We tested this approach by simulating deletions in two exons of similar size (~400 bp) but with different mean coverages in a randomly selected dataset of 200 WES samples from our in-house cohort.
The deletions spanned 100%, 75%, 50% or 25% of either exon 11 of LIMCH1 (409 bp, ~85x mean coverage) or exon 4 of RPL15 (406 bp, ~15x mean coverage). We used these datasets to compare the performances of HMZDelFinder_opt with sliding windows of 100 bp (HMZDelFinder_opt+sw100), HMZDelFinder_opt without sliding windows (HMZDelFinder_opt), and the original HMZDelFinder. For HMZDelFinder_opt+sw100 and HMZDelFinder_opt ,we used reference control sets of size 100. For deletions spanning the full exon (100%), we confirmed that HMZDelFinder_opt had a detection rate (98% and 93% for exons with higher and lower coverage, respectively; Figure  3 ) similar to that of HMZDelFinder (98% and 93% for exons with higher and lower coverage, respectively). However, the total number of HMZ deletions called by HMZDelFinder_opt was only one eighth the total number of HMZ deletions called by HMZDelFinder (median number of HMZ deletions: 2 vs.