dataesr/scientific-paragraphs-categorization huggingface dataset:
For knocking out the YIPF5 gene in H1 hESCs, the third exon was deleted using 2 CRISPR/ Cas9 guides that were designed with Benchling (Biology Software, 2019) (G1. 1 GGCTATGACTATTCGCAGCA and G1. 2 GATGAGC-CACCTTTATTAGA). The ribonucleoprotein (RNP) components (HiFi Cas9 protein, crRNA and tracrRNA) were purchased from Integrated DNA Technologies (IDT) and prepared based on the manufacturer's protocol. Two million cells were electroporated with the RNP complex using Neon Transfection System (1100 V, 20 milliseconds, 2 pulses, Thermo Fisher Scientific) and single-cell-cloned using limiting dilution. The sequence of the primers used to screen the formed colonies is provided in Supplemental Table  5. The KO cell line was characterized for pluripotency using immunocytochemistry for OCT4, TRA1-60, and SSEA4, and for gene expression levels of OCT4, SOX2, and NANOG by qPCR.
Samples were sequenced on an Illumina HiSeq 2500 with a mean read depth of 38. 3 for patient I and 33. 6 for patient II. The sequencing data were analyzed using an approach based on the GATK best practice guidelines. GATK HaplotypeCaller (https://gatk. broadinstitute. org/hc/en-us/ articles/360037225632-HaplotypeCaller) was used to identify variants that were annotated using Alamut batch version 1. 8 (Interactive Biosoftware), and variants that failed the QD2 VCF filter or had less than 5 reads supporting the variant allele were excluded. Copy number variants were called by SavvyCNV, which uses read depth to judge copy number states. SavvyVcfHomozygosity was used to identify large (>3 Mb) homozygous regions in the genome sequencing data (https:// github. com/rdemolgen/SavvySuite). Replication studies were performed in a cohort of 187 patients diagnosed with diabetes before age 12 months in whom the known genetic causes of neonatal diabetes had been excluded.
Patients were analyzed using a targeted next-generation sequencing assay  (50) , which includes baits for known neonatal diabetes genes and additional candidate genes followed up from gene discovery, such as YIPF5, or by independent exome sequencing analysis. Variant confirmation and cosegregation in family members were performed by Sanger sequencing (see Supplemental Table  4  for primer sequence). The bioinformatics tools SIFT, PolyPhen-2, MutationTaster, and Align GVGD were accessed through the Alamut software (Interactive Biosoftware) to predict the effect of variants on the YIPF5 protein. Human β cell and islet culture and treatment. Human insulin-producing EndoC-βH1 cells, provided by R. Scharfmann (Institut Cochin, Université Paris Descartes, Paris, France), were cultured as previously described  (51, 52).
Statistics. Statistical analyses were performed with GraphPad Prism (version 7. 0c, GraphPad Software). Data points represent independent experiments. In secretion experiments, data points represent the average of biological duplicates. In the box plots, the median is shown by a horizontal line; 25th and 75th percentiles are at the bottom and top of the boxes; whiskers represent minimum and maximum values. In time course experiments, data are shown as mean ± SEM. For the EndoC-β1 and iPSC experiments, comparisons between groups were performed by paired 2-way ANOVA or mixed-model analysis (in case of a missing value), followed by 2-tailed t tests with the Bonferroni correction for multiple comparisons. For hESC experiments, the parametric unpaired 2-tailed t test and 1-way and 2-way ANOVA tests with the Bonferroni multiple-comparisons test were used to compare the sum of ranks.
jci. org Volume 130 Number 12 December 2020 of gratitude to  Jarkko Ustinov  and  Solja Eurola  ( Biomedicum Stem Cell Center, University of Helsinki ) for excellent technical and experimental support. We thank  Diego Balboa Alonso  ( Centre for Genomic Regulation, Barcelona ) for professional guidance and support. EDF is a  Diabetes UK RD Lawrence Fellow  ( 19/005971 ). ATH and SE are the recipients of a  Wellcome Trust Senior Investigator award  (grant  WT098395/Z/12/Z ), and ATH is employed as a core member of staff within the  National Institute for Health Research-funded Exeter Clinical Research Facility  and is an  NIHR  senior investigator. SEF has a  Sir Henry Dale Fellowship jointly  funded by the  Wellcome Trust  and the  Royal Society  (grant  105636/Z/14/Z ). This work was supported by grants from the  Fonds Erasme  to ML and MC; grants from the  Fondation ULB , the  Fonds National de la Recherche Scientifique (FNRS) , the  Brussels Capital Region Innoviris project DiaType , and the  Francophone Foundation for Diabetes Research  (sponsored by the  French Diabetes Federation, Abbott ,  Eli Lilly ,  Merck Sharp & Dohme , and  Novo Nordisk)  to MC; and the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement  115797  (INNODIA) and 945268 (INNODIA HARVEST).
Acknowledgments We thank the families for participating in the study. We are also grateful to  Rebecca Ward  and  Richard Caswell  ( University of Exeter Medical School ),  Isabelle Millard  and  Anyishaï Musuaya  ( ULB Center for Diabetes Research ), and  Anne Degrave (Faculté de Médecine Paris-Diderot ,  Université de Paris). Special thanks jci. org Volume 130 Number 12 December 2020.
Introduction Heart failure with preserved ejection fraction (HFpEF) presents an important challenge for clinical practice given its rising incidence, poor prognosis and lack of clear evidence from randomized clinical trials of treatment that can reduce mortality  (1) (2) (3) (4). Consistent epidemiological data demonstrate that women constitute the majority of patients with HFpEF  (5). Although to some extent explained by sexdifferences in cardiovascular structure and function, immune system biology and the myocardial response to comorbidities  (6) , the underlying pathophysiological mechanisms of HFpEF remain incompletely understood. Given the sex-differences in HFpEF, we sought differences in biomarker profiles possibly linked to underlying pathophysiology. In this study, we therefore assessed sex-differences in 415 circulating proteins in a prospective observational cohort of HFpEF patients (The Metabolic Road to Diastolic Heart Failure (MEDIA-DHF) study; NCT02446327).
Study population In MEDIA-DHF, a multicenter, multinational, observational study, a total of 626 HFpEF patients were enrolled between 2012 and 2014 in 10 centers (listed in Supplemental table  1). A description of this study population and methods has been published previously  (7). To summarize, after standardized echocardiography (Supplemental data) and/or local natriuretic peptide measurements, eligible patients with a diagnosis of diastolic dysfunction as established by the 2007 ESC recommendations  (8)  were included. Three clinical modes of presentations were considered: i) acute decompensated HF patients, ii) patients recently discharged after admission for an acute HF episode (<60 days), or iii) ambulatory chronic disease patients. Data on demographics, clinical parameters, laboratory values (including extensive biomarker measurements), electrocardiography and echocardiography were obtained at inclusion in the study.
Biomarker measurements Plasma samples taken at inclusion in the study were analyzed for circulating protein (including natriuretic peptides) using the Olink Proseek Multiplex cardiovascular disease (CVD) II, III, Inflammation, Cardiometabolic and Organ damage panels (Olink Proteomics, Uppsala, Sweden). The assay uses a proximity extension assay (PEA) technology where 92 oligonucleotide-labelled antibody probe pairs per panel are allowed to bind to their respective targets in 1 µL plasma sample. The PEA technology has been described previously  (9). In brief, when binding to their correct targets, they give rise to new DNA amplicons with each ID barcoding their respective antigens. The amplicons are subsequently quantified using a Fluidigm BioMark HD real-time PCR platform. Data is quality controlled and normalized using an internal extension control and an inter-plate control, to adjust for intra-and inter-run variation.
Network analyses The FHF-GKB (Fight Heart Failure -Graph Knowledge Box) resource is a customized upgrade of the EdgeBox provided by the EdgeLeap company (available from: https://www. edgeleap. com/edgebox). It extracts data from public data sources hence providing most available public knowledge about human protein-disease, protein-protein and protein-pathway relationships. The FHF-GKB resource enables the study of a total of 20,386 protein nodes imported from Uniprot  (10)  (including all circulating proteins involved in this study), 28,176 disease nodes from Disease Ontology  (11)  and DisGenet  (12)  and 2,222 pathway nodes from Reactome (v65)  (13). Protein -protein relationships were retrieved from STRING (v10. 5) (14) or Reactome or WikiPathways  (15)  or Mentha  (16)  or BioGrid  (17) , proteindisease associations from  DisGenet (2018-08-24)  and proteinpathway relationships from Reactome.
The discrimination of the final multivariate model for estimation of clinical end-points was assessed by calculating the area under the curve (AUC). Similar beta-estimates of variables in the multivariate model derived from the multiple imputation datasets were obtained when the pooled mean hemoglobin (N=46), heart rate (N=12) and total cholesterol levels (N=67) were imputed in the original datasets (data not shown). Further analyses were therefore performed in the original dataset with pooled mean levels for hemoglobin, heart rate and total cholesterol. To identify differences in proteins between sexes, we studied which proteins were significantly associated with female sex on top of the clinical model. Therefore, all proteins were tested individually on top of the clinical model (i. e. , fully adjusted on the clinical variables) in a bivariate logistic regression model, correcting for multiple comparisons using a Bonferroni adjusted p-value of <0.
The association between sex and the composite endpoint of CV death or CV hospitalizations within 1 year after inclusion in the study was studied using Kaplan-Meier estimates and Cox proportional hazards models. In multivariate analyses, adjustment was performed for clinical variables previously found to be independently associated with CV death or CV hospitalizations in MEDIA-DHF  (19). These clinical variables were: a history of CAD, pulmonary rales at baseline and age. The proportional hazard assumption was not met and a landmark analysis was performed excluding those patients with an endpoint or being censored within 120 days after inclusion in the study. Statistical analyses were performed using SPSS 24 (IBM inc. , Armonk, New York) and R (The R Foundation for Statistical Computing, Vienna, Austria).
Ethics approval and consent to participate The study protocol complied with the Declaration of Helsinki and was approved by the respective Ethics Committees of the participating institutions. Written informed consent was provided by all patients. (ClinicalTrials. gov Identifier: NCT02446327)    The proportional hazard assumption was not met and a landmark analysis was performed excluding those patients with an endpoint or being censored within 120 days after inclusion in the study. processes  (50)  Conflicting data: High levels have been described in atherosclerotic plaques  (51)  and associated with poor outcome after MI  (54). However, a common mutation in the MMP-3 promoter (which results in decreased MMP-3 expression) was associated with atherosclerosis development  (52).
Right ventricular function was estimated by measuring TAPSE (mm) and pulmonary arterial systolic pressure (PASP) was calculated using the peak velocity of tricuspid regurgitation (TR) and the maximum IVC diameter (IVC baseline) and respiratory variation (Ratio IVC inspiration / IVC baseline) measured 3 cm before merger with the right atrium. Overall, echocardiographic data were complete in >75% of patients, except for the following variables: A (and E/A ratio), S/D, Ard-Ad, E/Vp (which were available in 70%, 61%, 44% and 62% of the population, respectively).
Competing interest and funding This study was supported by a grant from the  European Union  ( FP7-HEALTH-2010-MEDIA ), by the  French Programme Hospitalier de Recherche Clinique (PHRC)  and by the  RHU Fight-HF , a public grant overseen by the  French National Research Agency (ANR)  as part of the second " Investissements d'Avenir" program  (reference:  ANR-15-RHU-0004 ), the  GEENAGE  ( ANR-15-IDEX-04-LUE) program , by the  Contrat de Plan Etat Région Lorraine  and  FEDER IT2MP. )N-LA was supported by a Miguel Servet contract  CP13/00221  from the  "Instituto de Salud Carlos III-FEDER ". WJP and LvH were supported by  CVON ,  Dutch Heart Foundation , The Hague, The Netherlands (RECONNECT and EARLY-HFpEF projects). AM received speaker's honoraria from  Orion ,  Otsuka ,  Philips ,  Roche  and  Servier.
Jacobson's team shows the usage of this humanoid structure into animation applications, to control a virtual avatar by binding its elemental structures to the physical model. Glauser also created a deformation capture technique using a foldable sensors array with a customized sensors placement and training its neural network with a dataset  [5]. Even if these interfaces show promising results, they both use wired connectivity, do not provide automatic pairing, and thus, are not suitable for common protein manipulation. Wireless interfaces, especially for bio-electronics  [6, 7] , are still in development. Even if the solution is not wired, it often remains non-modular and non-portable. Once implemented, the interface is usually not designed to be connected/reconnected quickly and easily. To improve interacting with molecular simulations, we need a physical structure that can represent each protein using different parts that can be easily connected to each others.
Bootloader The bootloader is a start-up program that is called with each reset of the ATMega328p chip before the main program. The default bootloader for this chip provided by Arduino  [18]  is set up to listen to the serial port for 4 s and intercept the incoming bytes, if there is any, it locks itself into update mode. The intercepted byte-stream consists of a compiled code for the new main program to be overwritten in the flash memory and then executed. To perform an OTA update, we use the MySensors bootloader  [17, 19]  which changes the default bootloader and adds the interception of the incoming bytes from the Network Routing Facility (NRF). If any data was available, the bootloader locks itself in update mode. It can retrieve the updated program by requesting it from the gateway. The data coherence is ensured by the bootloader requesting the new firmware page by page, 16 bytes per page and will act in burst mode trying to get as much page as possible in succession.
296. pusmol files by recording every valid command that was entered. This mode is not recommended for users who do not have programming capabilities in C#, object-oriented or/and Unity3D because it requires the implementation of a script in the scene of the game and instantiate the chain in a procedural manner. The chain shown in Figure  18  shows two generated animated chains thanks to a script in C#. The main interest of this method is to be able to create very large molecules. The created molecules will not be made by hand but described via scripts or loops.
Selected sample In this work, we analyse two data samples of DGS galaxies and LBGs for which the SED fitting has been performed in  Burgarella et al. (2020)  by means of the code cigale  (Boquien et al. 2019). In cigale the synthetic spectra are calculated from the energy balance between the photons absorbed and re-emitted by dust. Emission and absorption lines of gas are also included. The physical properties of the galaxies are estimated through the analysis of the likelihood distribution. The DGS galaxies form a low-metallicity (12 + log(O/H) < 8. 5) sub-sample of the galaxies presented in  Rémy-Ruyer et al. (2015)  for which the photometric data from the infrared to the sub-mm range are available. The SED is then built by adding the photometry at shorter wavelengths (UV, B and R) taken from the NASA Extragalactic Database (NED).
We take care of using the most recent data corresponding to the integrated photometry of the galaxy. Employing data at short wavelengths allows us to better constrain the stellar mass and the SFR of the selected galaxies. LBGs were selected in the rest-frame UV and observed by ALMA. Burgarella et al. (2020)  used the detections to build an infrared SED template that is later used to re-fit all the objects individually. The SED fitting has been performed employing a Chabrier initial mass function (IMF) in cigale with stellar mass in the range 0. 1 ≤ M * ≤ 100 M  (Bruzual & Charlot 2003). A top-heavy IMF is not available in the current version of the cigale and will be a future improvement in the code. For the galaxies selected, the code cigale provides a good fit between the synthetic and observed photometry (reduced χ 2 ≤ 5) for 31 out of 48 DGS galaxies and for 18 LBGs which represents the two samples studied here.
Method The observed photometry for galaxies is fitted with the code cigale. The features of the code are explained in  Boquien et al. (2019)  while the assumptions adopted for computing the synthetic spectra are discussed in  Burgarella et al. (2020). The SED fitting procedure of the sources described in Section 2 allows us to derive the best value for the parameter τ in Eq. 1 for each of the galaxies, as well as the total mass of stars and dust, and the SFR. Furthermore, throughout this paper we use the specific mass of dust (sMdust), i. e. the mass of dust divided by the mass of stars, specific SFR (sSFR), i. e. the SFR divided by the mass of stars, age. For DGS galaxies other properties are available from the literature: metallicity, gas fraction, dust-to-gas ratio, circumgalactic dust fraction.
The metallicity and the gas mass are taken from  Rémy-Ruyer et al. (2013 , 2014)  for DGS galaxies. We compute the total mass of gas by selecting A&A proofs: manuscript no. aanda 10 -12 10 -11 10 -10 10 -9 10 -8 10 -7 sSFR [ r -1 ] 10 -5 10 -4 10 -3 10 -2 10 -1 sMdust dwarf galaxies LBGs Fig. 1. sMdust against sSFR derived from the best fit performed with the code cigale for DGS galaxies (red dots) and LBGs (black triangles). See also  Burgarella et al. (2020)  for all the details. from Rémy-Ruyer et al. (  2014 ) the atomic and molecular hydrogen mass. The total hydrogen gas is then multiplied by the mean atomic weight of the galaxy (µ gal ). The molecular hydrogen mass is derived from a metallicity-dependent conversion factor between the CO and H 2 masses (M H2,z ). All the data adopted are provided in Table  A.
(2020). In Fig. 2  we show the comparison between the mass of dust, stellar content and SFR derived in  Burgarella et al. (2020)  and the ones in  Rémy-Ruyer et al. (2015). We find that the stellar mass and SFR derived by  Rémy-Ruyer et al. (2015)  are systematically larger than the one derived in this work, while the mass of dust, constrained by the infrared emission is comparable. In Fig. 3  we compare the sMdust against the sSFR of the work by  Rémy-Ruyer et al. (2015)  with the ones derived from the SED fitting with cigale for the same galaxies. Our data points appear to be shifted at higher sMdust and a few of them present high sSFR. We will discuss in the following sections the consequences for our analysis. The trend shown has been derived through the SED fitting performed with a Chabrier IMF since a top-heavy one is not yet available in the SED fitting code cigale.
Also in this case, a larger uncertainty on the values of the condensation fraction is found for LBGs. We also keep in mind that there is some degeneracy between the metal yields provided in the literature and the assumed dust condensation fraction. The dust-to-gas ratio as a function of the metallicity is fairly well reproduced for DGS galaxies (Fig. 16 ), even though the observed distribution of data points is more tilted than the one we recovered. This discrepancy between our model predictions and observations might be partially due to the large uncertainties affecting the determination of the gas mass from the observations, especially in low-metallicity galaxies. The trend between the total mass of dust and stellar mass is naturally reproduced (Fig. 17 ). -Dust chemical composition and condensation fraction.
Acknowledgements. AN acknowledges the support of the  Centre National d'Etudes Spatiale (CNES)  through a post-doctoral fellowship. PT work was supported by the  Programme National "Physique et Chimie du Milieu Interstellaire" (PCMI)  of  CNRS/INSU  with  INC/INP  co-funded by  CEA  and  CNES. BC was supported by the  ERC Consolidator Grant (Hungary)  funding scheme (Project RADIOSTAR, G. A. n. 724560). HH thanks the  Ministry of Science and Technology  for support through grant  MOST   107-2923-M-001-003-MY3  and  MOST 108-2112-M-001-007-MY3 , and the  Academia   Sinica for Investigator Award   AS-IA-109-M02. We are grateful to the anonymous referee for carefully reading the manuscript and for helping in improving it.
Scanning Electron Microscopy (SEM) of PCL/cPVA-ASP Microspheres Freeze-dried PCL-cPVA-ASP microspheres loaded with GM-AOT or CF were dispersed in ethanol and 40 µL was deposited on scanning electron microscopy (SEM) specimen mounts covered with an adhesive tape. After evaporation of the ethanol, a 10 nm layer of Au/Pd was deposited on the samples by sputter-coating. SEM imaging was performed immediately after on a field emission scanning electron microscope (S-4700, Hitachi, Zürich, Switzerland) with a beam voltage of 1. 0 keV. At least 100 microspheres' diameters were measured from the SEM images using standard Axiovision4 software (version 4. 9. 1. 0). The size polydispersity index (PDI) of the microspheres was calculated by Equation  (1). PDI = (Average diameter/Standard deviation) 2  (1) 2.
Antibiotic Release from PCL/cPVA-ASP Microspheres. Data on the release of CF and GM-AOT from PCL/cPVA-ASP was collected and subjected to various drug release models including the Higuchi model and the Korsmeyer-Peppas model  [30]. Both models can be considered as important tools to understand which type of release kinetics take place during the elution of drug from the polymeric microspheres. Figure  4  presents the cumulative release of CF and GM-AOT (Figure  5A, D ) and the same data presented in Higuchi (Figure  5B, E ) and Korsmeyer-Peppas plots (Figure  5C, F ). Linear trendlines were fitted to the Higuchi and Korsmeyer-Peppas datasets by means of linear regression with the fit equation and R 2 reported for each of the graphs and are additionally presented in Table  2. Data on the release of CF and GM-AOT from PCL/cPVA-ASP was collected and subjected to various drug release models including the Higuchi model and the Korsmeyer-Peppas model  [30].
Both models can be considered as important tools to understand which type of release kinetics take place during the elution of drug from the polymeric microspheres. Figure  4  presents the cumulative release of CF and GM-AOT (Figure  5A, D ) and the same data presented in Higuchi (Figure  5B, E ) and Korsmeyer-Peppas plots (Figure  5C, F ). Linear trendlines were fitted to the Higuchi and Korsmeyer-Peppas datasets by means of linear regression with the fit equation and R 2 reported for each of the graphs and are additionally presented in Table  2. Figure  5A ,D shows the cumulative release of GM-AOT and CF, respectively. The release of GM-AOT nearly reached its complete drug load after 2 weeks. CF release appeared to plateau after 1 week of antibiotic release. In Figure  5B, E , the antibiotic release data was plotted on a Higuchi axis system (square root of time vs.