dataesr/scientific-paragraphs-categorization huggingface dataset:
Cell culture, grid preparation and cryo-EM imaging Mycoplasma pneumoniae sample preparation and cryo-EM imaging were carried out as described previously  (O'Reilly et al. , 2020; Tichelaar et al. , 2020). In brief, M. pneumoniae cells were grown on Quantifoil gold grids in modified Hayflick medium and the grid was quickly washed with PBS buffer with 10 nm gold fiducial beads (Aurion, Germany) before plungefreezing. The grids were imaged in a 300 keV Krios TEM (ThermoFisher) equipped with a direct detector (Gatan) and a quantum post-column energy filter (Gatan). Data collection was done using SerialEM  (Mastronarde, 2005). For the 2D images paired with tilt-series data, 19 cells were first imaged following the SPA standard, with the magnification of 215,000 (pixel size 0. 65 Å) and exposure time of 2 seconds (20 frames, total exposure dose 32 e -/Å 2 ).
The target defocus of 0. 2 μm to 0. 5 μm. Tilt-series were collected using the dose-symmetric scheme  (Hagen et al. , 2017)  with the following settings: magnification 81,000 (pixel size 1. 7 Å), tilt range -60° to 60° with 3° increment, exposure time per tilt 1 second, total exposure dose ~129 e -/Å 2. The target defocus remained the same within tilt-series, and ranged from 1. 5 μm to 3. 5 µm between tilt-series. A dataset of 220 2D images of M. pneumoniae were collected on a K3 camera (Gatan) and at a magnification of 81,000 (pixel size 1. 503 Å). The K3 camera was run in non-CDS mode with the following settings: exposure time 1. 678 second, 24 frames, total exposure dose ~31 e -/Å 2.
2D template generation To generate templates for 2DTM, we used the computer program pdb2mrc  (Tang et al. , 2007)  to convert PDB-formatted atomic coordinates into 3D density. To reduce potential aliasing, we initially generated an over-sampled density map at half the pixel size of the image to be searched. The sampling rate was then halved to the final value pixel size by Fourier cropping using the program resample from the cisTEM software package  (Grant et al. , 2018). The resulting 3D density was low-pass filtered with the standalone program bfactor (Grigorieff, 2021a) using a B-factor of 85 Å 2 and placed into a 256 x 256 x 256 voxel box, about twice the size of the 50S ribosomal subunit it contained. The final pixel size for searching the 19-image dataset of M. pneumoniae that was compared with tomograms was of 1.
27 Å. For computational efficiency, we used a final pixel size of 1. 5 Å to search the 220-image dataset. As template models, we used the M. pneumoniae ribosome (PDB: in progress; EMDB 11999) and the B. subtilis ribosome (PDB: 3J9W). We separated the small and large ribosomal subunits and, for the B. subtilis ribosome, removed the mRNA, tRNAs and MifM protein from the coordinate file using custom Perl scripts. The resulting coordinates were aligned with USCF Chimera  (Pettersen & Goddard, 2004)  to the 50S template used for 3DTM to place them in a common coordinate system. This ensured that the 3D positions and Euler angles found by 2DTM and 3DTM referred to the same coordinate system and could be directly compared. For local 30S searches, the atomic coordinates corresponding to the body of the M.
Detection of 30S by local search using 50S 2DTM coordinates To detect 30S subunits bound to 50S subunits, we wrote refine_template, a computer program that is accessible through cisTEM's GUI, or as a command line tool. refine_template reads the 3D template and the output files of a template search, including the MIP and alignment parameters for detected targets. Using these data, it calculates template projections and performs a local refinement of the projection parameters. This refinement can be used, for example, to find the defocus values that maximize the cross-correlation coefficient between projection and image. It can also be used to detect molecules and complexes bound to already detected targets. In this case, refine_template is used with the search results obtained for the detected targets, but with the bound complex to be detected as the input template.
3D reconstruction using 50S 2DTM coordinates We wrote a new computer program called make_particle_stack, as part of the cisTEM image processing package. It reads the results of a template search, including the MIP with the peaks indicating detected targets, as well as the Euler angles of the detected targets, the original image that was searched, and other imaging parameters (defocus, amplitude contrast, lens aberrations, beam energy and pixel size). On output, it will generate a stack of boxed-out targets and a list of alignment parameters that can be used for 3D reconstruction with cisTEM. Using make_particle_stack and cisTEM, we generated a reconstructions from 5,080 and 1,172 50S targets detected in 220 images of M. pneumoniae cells, using the Euler angles and x,y locations assigned by the M.
A total of 10. 5x speedup is achieved by optimizing data throughput using the vectorized FP16 format for storing results. (d) Figure legends The code scales nearly linearly with the number of GPUs and tracks with the total memory bandwidth of a given model. All timing was obtained using a padded K3 image with 4096 x 5832 pixels, and searching one defocus plane with 2. 5°/1. 5° angular steps.
Kernel-fusion using cuFFT callbacks and custom data structures combined with flexible kernel launch parameters ensure the GPUs stay saturated, enabling an 8x speedup. A total of 10. 5x speedup is achieved by optimizing data throughput using the vectorized FP16 format for storing results. (d) The code scales nearly linearly with the number of GPUs and tracks with the total memory bandwidth of a given model. All timing was obtained using a padded K3 image with 4096 x 5832 pixels, and searching one defocus plane with 2. 5°/1. 5° angular steps.
Characteristics of the study samples In ALSPAC, a total of 358 boys and 374 girls with DNA methylation data and epidemiological information were available for our analysis. Children were included in the analysis if they had DNAm data available for at least two time points so that their longitudinal methylation profile could be estimated. The distribution of DNA samples by age and sex is given in Table  1. We found a higher proportion of girls vs. boys with EBF ≥ 3 months, 50. 5% vs. 44. 8% (Table  1 ). Other than that, the distribution of confounding variables is similar in boys and girls (Table  1 ).
Functional annotation We conducted a functional analysis of the top CpG sites significant at the 1 × 10 -4 level with the software DAVID  [22]. For girls, the top KEGG biological pathways include the "AMPK signaling pathway" (P = 0. 034), the "Insulin signaling pathway" (P = 0. 046), and "Endocytosis" (P = 0. 050); and the top GO terms entail the ATP binding (P = 0. 019) and multicellular organism development (P = 0. 049) (Additional file 1: Table  S4 ). In boys, "Pathways in cancer" is the only significant KEGG biological pathway at the 5% level and the top GO terms include "Negative regulation of transcription DNA-templated" (P = 1 × 10 -4 ), "Positive regulation of tyrosine phosphorylation of Stat3 protein" (P = 0. 020), "Negative regulation of transcription from RNA polymerase II promoter" (P = 0.
CpG sites located in miRNAs We found significant enrichment of CpG sites located within miRNAs among the top CpG sites selected at the 10 -4 level, i. e. , 4/113 in girls (P = 9. 7 × 10 -3 , Fisher's exact test) and 4/93 in boys (P = 5. 1 × 10 -3 ) for 3439 out of 485,512 CpG sites annotated to miRNAs. Among the 8 CpG sites significant at the 10 -4 level (Additional file 1: Table  S5 ), 6 of them have been previously found in breast milk (liqDB database). EBF from 3 to 5 months leads to lower methylation of 6 out of 8 of the associated miRNA   S1  for girls and Additional file 1: Table  S2  for boys). The whiskers correspond to ± 1. 5 IQR and the points outside this range are outliers     S2 ). This region contains binding sites for multiple transcription factors and a TSS in the lymphoblastoid cell lines and embryonic stem cells.
Underlying genetic variants Among the top 15 EWAS hits, 8 are associated in cis-or trans-with an underlying genetic variant or with one or several genomic regions based on the mQTLdb database   S1  for girls and Additional file 1: Table  S2  for boys). The whiskers correspond to ± 1. 5 IQR and the points outside this range are outliers  [25]  (Additional file 1: Table  S6 ). Among the 206 CpG sites significant at the 10 -4 level (girls and boys combined), 74 are located in genes previously associated with BMI-related traits through genome-wide significant SNPs (Additional file 1: Tables  S1  and S2 ). Adjusting our analyses for a 94-SNPs BMI-related polygenic risk score that we previously derived  [26]  did not change the significance of the individual CpG sites (Additional file 1: Tables  S1  and S2 ) nor the overall mediation effect of all these CpG sites (Additional file 1: Fig.
In ALSPAC boys, we identified a general KEGG pathway related to cancer and several GO terms related to the "Negative regulation of transcription" and to development mechanisms. In terms of key genes, we were able to replicate the cut-like homeobox 1 (CUX1) gene, although a different probe was found in ALSPAC and Gen3G studies. It has previously been shown that CUX1 could regulate the expression of the FTO and the RPGRIP1L genes and be involved in the coordination of the leptin receptor signaling pathway and its association with eating behavior  [41]. CUX1 and its related pathways such as the AMPK pathway could therefore be an important target of breastfeeding and might potentially play a role in modulating the risk of obesity in individuals carrying variants in the FTO gene  [42]. Our study is based on one of the largest child cohorts worldwide with blood DNAm data and extensive epidemiological data available.
Study sample Our discovery cohort is the Avon Longitudinal Study of Parents and Children (ALSPAC)  [45, 46]. Pregnant women resident in Avon, UK with expected dates of delivery 1st April 1991 to 31st December 1992 were invited to take part in the study. The core ALSPAC sample consists of 14,541 pregnancies. An additional 542 eligible pregnancies not in the core sample, who were invited to participate at age 7 and for whom research data were available in November 2004, were included in this study. These 15,454 pregnancies resulted in 15,589 known fetuses of which 14,901 were alive at 1 year of age. For reasons of confidentiality data on the 13 triplet and quadruplet children were not available for analysis. After removing twins (n = 201) and children without anthropometric measures (height/length or weight) or age information (n = 2245), a total of 12,761 children were available for analysis.
The children of these women have been followed for over two decades. Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the local research ethics committees. Informed consent for the use of data collected via questionnaires and clinics was obtained from all participants following the recommendations of the ALSPAC Ethics and Law Committee at the time. Please note that the study website contains details of all the data that is available through a fully searchable data dictionary and variable search tool  [47].
DNA methylation In addition, 1018 ALSPAC mother-offspring pairs participated to the Accessible Resource for Integrated Epigenomics Studies (ARIES), which used both Illumina Infinium 450 k methylation arrays and Bisulfite-seq approaches to generate epigenetic data  [48]. The ARIES participants were selected based on availability of DNA samples at two time points for the mother (antenatal and at follow-up when children were in adolescence) and at three time points for the infants/children (at birth, during childhood at age 7 and during adolescence at age 17). In this study, we only used the child methylation data. ARIES children provided a DNA sample extracted from cord blood drawn from the umbilical cord upon delivery (n = 1127 newborn babies). A second DNA sample was extracted from peripheral blood drawn at the 7-year clinic visit (n = 68 from white cells from buffy coats and n = 1018 from whole blood), and a third at a clinic held either at age 15 or 17 years, leading to three measurements of DNA methylation per child (n = 1073 from white cells from buffy coats on 1073).
Pre-processing of the methylation data and normalization were performed by the ARIES team (see below). After preprocessing and accounting for missing epidemiological information, our dataset was restricted to 358 boys and 374 girls (Table  1 ). Beta-values, that measure the ratio of the methylated probe intensity over the overall intensity (sum of methylated and unmethylated probe intensities)  [49] , were derived on 481,166 probes. We transformed them into M-values to facilitate batch effect correction and the statistical analyses. To remove batch effects and adjust for cell type heterogeneity (i. e. , whole blood vs. white blood cells), we used the Empirical Bayes method ComBat  [50]. Finally, M-values were imputed between birth and age 7 and between age 7 and the third timepoint available (between 15 and 17 years) based on simple linear regression.
For the probes on the X and Y chromosomes, males and females are normalized separately using the sex information. For the Y chromosome, standard quantile normalization is used due to the small number of probes, which results in instability for functional normalization. Principal Component Analysis was used for post-normalization quality controls, 2 outliers were found and removed. ANOVA tests and t-tests were then performed to determine the association between the principal components and several batch variables (slide, plate, sample type, time code, time point), 11 samples were removed. A cell count normalization has also been performed for each sample individually based on a reference blood count dataset (gse35069 cell type methylation reference). The team noticed that it might be useful to remove the plate and slide effect since normalization did not efficiently removed this technical variation.
Functional annotation and enrichment analyses We also examined CpG sites significant at the 5 × 10 -4 level for a pathway-based analysis with the software DAVID  [22]. The top enriched gene ontology (GO) terms and KEGG pathways were selected at the 5% significance level. To help understand possible regulatory roles of DNAm in biological pathways, we examined the genomic landscape of the EWAS-significant CpGs using the Ensembl Genome browser  [57]. Finally, we built genegene and gene-miRNA interaction networks from our discovered genes and miRNAs using the software Omic-sNet  [24].
DNAm data were obtained in children at 5 years of age, using the Infinium MethylationE-PIC BeadChip Arrays (Illumina Inc, USA). Processing was performed including normalization and bias correction using funnorm  [59]  and RCP  [60] , respectively. After pre-processing and accounting for missing epidemiological information, the Gen3G dataset was restricted to 104 boys and 89 girls (Additional file 1: Table  S7  for a description of the data). DNAm was analyzed as M-value. For replication, we have included all CpG sites from Gen3G present in our top genes (Table  2 ) and all the CpG sites present in the top miRNAs (Additional file 1: Table  S5 ). This led to 170 CpG sites to replicate and we used 0. 05/170 = 2. 9 × 10 -4 for the replication p-value. Similarly to our analyses in ALSPAC, we performed in Gen3G an ANOVA test for the components β E.
Medical ultrasound imaging requires recurring use of elementary principles, namely, the choice of the transducer, the design of the emitted waves and their wavefronts (e. g. focused or wide), the demodulation of the received signals, their beamforming, and their post-processing to generate B-mode or flow images. The ultrasound signals used for research purposes can be synthetic or acquired. To make the whole pipeline easily accessible to many researchers and students, the objective was to provide an openaccess toolbox, widely documented, and adapted to ultrasound imaging research involving experimental or simulation methods. The MUST Matlab UltraSound Toolbox contains algorithms that focus on the development, simulation, and analysis of ultrasound signals for medical imaging. The user can design various ultrasound-imaging scenarios and analyze their performance using simulated or acquired data.
The MUST functions allow studying the characteristics of transducers and waveforms, analyze signals, and construct ultrasound images. The many examples provide a starting point for students and researchers to quickly gain an understanding of the essentials of ultrasound imaging. The simulators integrated into MUST provide very realistic acoustic pressure fields and ultrasound images. The Matlab MUST toolbox is freely available at https://www. biomecardio. com/MUST. Before engaging in advanced ultrasound techniques and comparing them with so-called standard methods, it is necessary to have a good understanding of the latter and their advantages and limitations. With this in mind, the MUST toolbox includes everything needed for comprehensive ultrasound imaging: simulators of acoustic pressure fields and backscattered RF signals, delayand-sum beamforming, B-mode imaging, wall filtering, color or vector Doppler, speckle tracking.
I. INTRODUCTION INCE the commercialization of research ultrasound scanners, researchers and engineers working in ultrasound imaging have access to digitized radio frequency (RF) signals. Software analysis has overtaken hardware, which has strongly contributed to the development of innovative ultrasound methods. These relate to the transmission of the waves, as well as the T his work was supported in part by the LABEX CeLyA (ANR-10-LABX-0060) of Université de Lyon, within the program "Investissements d'Avenir" (ANR-16-IDEX-0005) operated by the French National Research Agency. D. G. (e-mail: damien. garcia@inserm. fr; garcia. damien@gmail. com) is with INSERM at CREATIS (Centre de Recherche en Acquisition et Traitement processing of the recorded echoes. Ultrasound research systems, such as Verasonics scanners, offer great flexibility for biomedical research and non-destructive testing.
The first wide-scope simulator (Field II) was introduced by J. A. Jensen  [1]. Its free access and ease of use democratized the practice of ultrasound simulations and accelerated the development of new processing methods. Subsequently, B. E. Treeby and B. T. Cox introduced the software k-Wave  [2] , which is also widely used by the ultrasound community. Field II, k-Wave, and the Verasonics scanners work in a MATLAB environment because of its rich repertoire of built-in functions for data analysis and processing, and image display. Once RF signals are acquired or simulated, they need to be analyzed to reconstruct interpretable images of the insonified medium, whether stationary or in motion. The processing steps typically include demodulation, beamforming, filtering, and motion detection techniques such as Doppler and speckle tracking.
III. IS MUST A MUST-HAVE? In this IUS proceeding was presented the Matlab UltraSound Toolbox MUST. It allows students, researchers, and other ultrasound imaging enthusiasts to post-process experimental RF and I/Q ultrasound data, and to simulate acoustic pressure fields and ultrasound signals emitted and received by ultrasound transducers. The MUST toolbox contains user-friendly and well-documented Matlab functions. It is 100% open-source, fast, and easy to use. Examples of MUST applications include experimental design, demodulation and beamforming, B-mode imaging, color and vector Doppler, speckle tracking, realistic simulations, etc. MUST also offers the possibility to make wave propagation and backscattering movies. The reader is invited to visit the website (www. biomecardio. com/MUST) and run the examples that illustrate the diversity of MUST.
The functions of MUST have all been the subject of published scientific articles, for example with ultrafast Doppler echocardiography in volunteers with a Verasonics scanner  [6] ,  [9] ,  [14] , or for the study of vector Doppler in silico, in vitro and in vivo  [8] ,  [15]. MUST has its own simulator SIMUS, which is very easy to use. For users with the MATLAB Parallel Computing Toolbox, SIMUS can be run on a parallel pool of workers. Unlike Field II, its code is open, allowing the advanced user to make modifications or implement it with a different programming language. The MUST toolbox will evolve according to the users' requests. It will probably include functionalities for volume ultrasound imaging with matrix ultrasound probes. The current version of MUST contains only popular techniques (e.
| Rare variant association test A number of methods have been proposed to test for the association between rare variants and a phenotype which are based on the aggregation of rare variants within a genetic unit, for example, protein-coding genes, and we focused on two classical methods: (i) the "cohort allelic sum test" (CAST)  (Morgenthaler & Thilly, 2007) , in which rare variants within a genetic unit are collapsed into a binary score taking the value of 0 in the absence of rare variant and 1 in the presence of at least one rare variant and (ii) the variance-component "sequence kernel association test" (SKAT) approach  (Wu et al. , 2011). We implemented the CAST approach in the logistic regression framework in R and used the Likelihood Ratio test (LRT) statistic. For SKAT, we used the SKAT_Null_Model and SKAT functions implemented in the SKAT R package with the default parameters including the small sample adjustment.
| Simulation study For the simulation study, we used two real NGS datasets (public and in-house), to have realistic site frequency spectrum and LD structure. The in-house data set, referred as HGID (Human Genetic of Infectious Diseases Database), is composed of 3104 WES data generated with the exome capture kit SureSelect Human All Exon V4+UTRs (https:// agilent. com). All study participants provided written informed consent for the use of their DNA in studies aiming to identify genetic risk variants for disease. IRB approval was obtained from The Rockefeller University and Necker Hospital for Sick Children, along with a number of collaborating institutions. The public data set is composed of 2504 wholegenomes from the 1000 genome phase 3 (http://www. internationalgenome. org/). We first merged the two datasets and extracted only the exonic regions captured by the Agilent V4+UTRs capture kit.
We performed genotype and variant level quality control to focus only on high-quality coding variants defined as having a depth of coverage (DP) >8, a genotype quality (GQ) >20, a minor read ratio (MRR) >0. 2, and a call-rate >95%  (Belkadi et al. , 2015). We then excluded all related individuals up to the second degree based on the kinship coefficient (King's kinship K > 0. 09375;  Anderson et al. , 2010; Manichaikul et al. , 2010)  leading to a total of 4887 unrelated samples. From the whole data set, we selected samples of European ancestry including all individuals with a reported European ancestry from 1000 genomes (i. e. , CEU, TSI, FIN, GBR, and IBS) and HGID cohorts. In addition, we included samples with unknown recorded ancestry from HGID but with a genetic distance to the randomly selected sample HG00146 form the 1000 genomes GBR population lower than the maximum genetic distance observed between HG00146 and other samples with known European ancestry.
ACKNOWLEDGMENTS The author thanks both branches of the  Laboratory of Human Genetics of Infectious Diseases  for helpful discussions and support. The  Laboratory of Human Genetics of Infectious Diseases  was supported in part by grants from the  French National Agency for Research (ANR)  under the " Investissement d'avenir" program  (grant number  ANR-10-IAHU-01 ), the  TBPATHGEN  project ( ANR-14-CE14-0007 -.