dataesr/scientific-paragraphs-categorization huggingface dataset:
miRNAs from the DLK1/DIO3 Locus Impact the Expression of Receptors from the Transforming Growth Factor-β (TGFβ) Superfamily Controlling Osteogenesis To determine the mechanisms by which the miRNAs can influence the cell fate of human pluripotent stem cells toward the osteogenic lineage, we identified the putative targets of each of the 52 microRNAs from this locus using Targetscan  [34]  and investigated the molecular pathways enriched within this gene list with PANTHER software tools  [35]. Interestingly, the TGFβ signaling pathway, known to be involved in the osteogenic specification process, appeared as the main pathway targeted by the miRNAs from the DLK1/DIO3 locus (Figure  3A  and Figure  S3 ). In this pathway, we focused on ACVR2B and its coreceptor ACVR1 (ALK2), both described as negative regulators of osteogenesis.
Conceptualization, S. B. and C. M. ; methodology, C. M. , T. Q. , and S. B. ; software, S. B. ; validation, T. Q. , L. B. , J. G. , L. L. , J. T. , and S. B. ; formal analysis, S. B. ; investigation, S. B. and C. M. ; resources, M. C. , L. A. , S. B. , and C. M. ; data curation, S. B. and C. M. ; writing-original draft preparation, S. B. and C. M. ; writing-review and editing, J. A. D. , S. B. , and C. M. ; visualization, S. B. and C. M. ; supervision, S. B. and C. M. ; project administration, M. P. , S. B. , and C. M. ; funding acquisition, M. P. and C. M. Funding: I-Stem is part of the Biotherapies Institute for Rare Diseases (BIRD) supported by the Association Française contre les Myopathies (AFM-Téléthon). This project was supported by grants from Laboratoire d'Excellence Revive (Investissement d'Avenir; ANR-10-LABX-73) and the program "investissement d'avenir" INGESTEM.
Author Contributions: Conceptualization, S. B. and C. M. ; methodology, C. M. , T. Q. , and S. B. ; software, S. B. ; validation, T. Q. , L. B. , J. G. , L. L. , J. T. , and S. B. ; formal analysis, S. B. ; investigation, S. B. and C. M. ; resources, M. C. , L. A. , S. B. , and C. M. ; data curation, S. B. and C. M. ; writing-original draft preparation, S. B. and C. M. ; writing-review and editing, J. A. D. , S. B. , and C. M. ; visualization, S. B. and C. M. ; supervision, S. B. and C. M. ; project administration, M. P. , S. B. , and C. M. ; funding acquisition, M. P. and C. M. Funding: I-Stem is part of the Biotherapies Institute for Rare Diseases (BIRD) supported by the Association Française contre les Myopathies (AFM-Téléthon). This project was supported by grants from Laboratoire d'Excellence Revive (Investissement d'Avenir; ANR-10-LABX-73) and the program "investissement d'avenir" INGESTEM.
The reflected light is collected by a primary mirror and a secondary mirror, respectively with a diameter of 60 cm and 8 cm and a focal length of 102 cm and 10 cm. The reflectivity of the mirrors is 80% between 300 and 600 nm. A dichroic filter, located at the focal plane, separates the 355 and 532 nm components and photo-multiplicator tubes are used to measure the return signal. Capable of detecting signals up to ~ 15 km, the lidar sends 1200 light pulses over a period of 3 minutes before each H. E. S. S. observation run. The acquisition is then recorded on a server, connected to that of the H. E. S. S. data acquisition. A detailed technical description of the H. E. S. S. lidar and its performance can be found in  [2].
Crab Nebula spectral studies We used data from two distinct periods (in 2012 and 2013), for which we have exploitable lidar profiles (for a total of 22 observation runs). The atmospheric conditions and the number of participating telescopes are different in the dataset from 2012 and from 2013. Data taken in 2012 include all 4 HESS-I telescopes and the large HESS-II telescope in the central trigger, and have mostly better atmospheric conditions, while data taken in 2013 include only the 4 HESS-I telescopes in the central trigger and have generally a strong presence of clouds and aerosols. Part of these runs did not pass the standard quality selection criteria defined in the H. E. S. S. collaboration. The total extinction of the atmosphere is due to both Rayleigh and Mie scattering. The optical depth (OD) of the atmosphere corresponds to the total extinction integrated over altitude.
We performed the analysis from 1 to 10 TeV: in this conservative energy range, the IRFs are well described (far from the low-energy threshold) and the Crab Nebula spectrum is well approximated by a simple power law. Since the two datasets (from 2012 and from 2013) have different trigger configurations, we study the normalization dispersion separately. The values of the normalized differential flux at 1 TeV, obtained for each observation run, with the lidar and standard profiles, are given in Figure  5 , for the two periods (2012 and 2013). To quantify the dispersion, we calculated the reduced χ 2 (= χ 2 / d. o. f. , where d. o. f. is the number of degrees of freedom). The differential fluxes are normalized to the one minimizing the χ 2 using lidar data (dotted blue curve). As shown in Figure  5 , the spectra from the 11 first runs are not compatible with a constant flux from the Crab Nebula at more than 3 sigma, both using the lidar and standard profiles.
Conclusions We studied the impact of the lidar profiles on Crab Nebula data. Using a run-wise simulation chain, we derived the IRFs for 22 runs, with the corresponding lidar profiles and the standard profile currently used by the H. E. S. S. collaboration. We observed a notable impact on the IRFs, demonstrating the possibility to have a better knowledge of the low-energy threshold and the photon energy thanks to lidar data. Spectral differences are visible in a run-by-run analysis but are not significant when considering the 22 runs, which was not unexpected given the average OD value of these runs. We then studied the normalization dispersion dividing the dataset between runs taken in 2012 and in 2013. The normalization dispersion is notably reduced with lidar data only for the 11 last runs.
Chemistry All synthetic compounds mentioned in this study were visualised under UV light at 254 nm or stained by corresponding reagents, and purified by column chromatography (CC) on a Reveleris X2 (Grace) automated flash unit. 1  H and  13  C NMR spectral data were obtained from a Varian Mercury 300/75 MHz spectrometer at 300 K using TMS as an internal standard. Structural assignment was confirmed with the assistance of COSY, HSQC and HMBC. High-resolution mass spectrometry was performed on a Waters LCT Premier XETM time of flight (TOF) mass spectrometer equipped with a standard electrospray ionisation (ESI) and modular LockSpray TM interface. The purity of the tested compounds was determined by LC-MS analysis (Waters Alliance 2695 XE separation module). 3-((benzyloxy)methyl)-1-(1-(3-iodobenzoyl)piperidin-3-yl)-5-methylpyrimidine-2,4(1H,3H)-dione (6) Intermediate 8 was synthesised as previously reported  25.
Molecular modelling The modelling was conducted using AutoDock vina and AutodockTools-1. 5. 6  26  with publicly available X-ray structure of the MtbTMPK (PDB entry 1G3U 9 and 5NQ5  27  ). All PDB files of ligand were generated in ChemDraw 3 D 16. 0 after being minimised the energy. Centred on MtbTMPK active site PHE70 CE2, the prepared PDBQT files of ligands and receptors were docked using a grid spacing of 0. 375 and 60 Â 60 Â 60 number of grid points. Each ligand was docked three times in autodock vina by Lamarckian 4. 2 method, affording total 60 possible conformations. All vinadock results were viewed in Chimaera, further analysed and validated through the predicted free energy in combination with possible interactions in LigPlus.
Docking To gain insight into their putative binding mode, putative analogues were docked in the target enzyme using the crystal structure of MtbTMK in complex with (S)-1 (PDB code 5NQ5)  27. Docking indicated that the (R)-3 is better accommodated than its (S)-enantiomer (Figure  3 ). The thymine moiety of (R)-3 adopts an identical pose as that of (S)-1 and is involved in two hydrogen bonds between its NH and C(4)O groups and the side chains of Asn100 and Arg74, as well as a p-p stacking interactions with Phe-70. By protruding out of the active site, the meta-piperidine ring forms a p-alkyl interaction with the aromatic side chain of Tyr 103. Importantly, in the (R)-enantiomer the oxygen atom of the carbonyl that connects ring B and ring C is expected to form a hydrogen bond with Arg-95 (Figure  3(A) ), which justifies our choice to connect ring C to the piperidine ring B through an amide bond.
Furthermore, through various hydrophobic interactions ring C and ring D contribute to the overall affinity. The putative binding mode of the (S)-enantiomer is quite different (Figure  3(B) ). Although a hydrogen bond may be formed between the oxygen atom of the carbonyl connecting ring B and ring C and Arg-107, the loss of the hydrogen bonds between N 3 and C 4 groups of thymine ring and the side chains of Asn100 and Arg7, as well as the p-p stacking interaction between pyrimidine ring and Phe-70 results in a less promising docking score. (R)-3 was also docked starting from the co-crystal structure of the natural substrate TMP (PDB code 1G3U)  9. The resulting docking pose of (R)-3 in MtbTMPK is superimposed with the one described above (Figure  4 ). The MtbTMPK conformation is highly similar except for the flexible Lid region, leading to slightly more encouraging docking score due to extra pronounced hydrophobic interactions exhibited by the C and D ring of (R)-3.
Sampling selection method The sample was a probability-based, multi-stage cluster sample of 390 household elderly. They were set in 28 clusters. Sampling was at several levels: (i) city or village, (ii) neighborhood, (iii) household, and (iv) individual. A fixed sample size of exactly 40 households was selected from each cluster in urban areas, and 10-12 households in each cluster in rural areas. In the absence of reliable census data in the various communities, the random sampling of households was done according to the "itinerary method. " Overall, we selected 117 (30. 0%) individuals in urban and 273 (70. 0%) individuals in rural areas, taking into consideration the proportion of urban to rural residents in Nabatieh area. For each individual, we allocated a substitute individual, in case of refusal to participate.
Data collection methods A predesigned structured interview questionnaire was developed to collect the following data: sociodemographic data including demographic characteristics (age, gender, marital status, education, and residency location), living conditions (living alone or with others, having a housekeeper in the house), health insurance, and the financial perception (poor, fair, good, and very good). Health status and lifestyle characteristics were assessed by self-rated health status (SRH), global quality of life (QoL) assessment, and declared morbidity. SRH and the perception of the QoL were showed to be reliable methods for measuring, respectively, the health status  [18]  and the quality of life concept  [19]. SRH was assessed by one question, and the response was rated from 1 (very poor) to 6 (excellent); also, the overall QoL status was rated from 1 (very poor) to 5 (very good).
Morbidities were recorded by asking participants (using a recall period of 12 months) if they suffered from chronic diseases (such as hypertension, diabetes, and insomnia). All self-reported morbidity data were checked for its accuracy from the responsible physician. In addition, the level of physical exercise was assessed by estimating the frequency and duration of exercise (excluding housework) by "a minimum of 30 min daily," "occasionally," or "never. " Information about the pastime activities and social club membership of the aged persons was also collected. The Arabic version of Vulnerable Elders Survey (VES-13) is a tool used to assess the risk of health deterioration in community-dwelling older adults  [20]. It consists of the following factors: age (0-3), self-reported health status (0-1), physical disability (0-2), and functional disability (0-4).
Statistical analysis Mean and standard deviations were calculated for the total GFI scores and separate GFI domains since the data were normally distributed. Elderly persons were grouped according to a high (≥ 4) and low (< 4) GFI score. The differences between groups were tested with chi-square test, with a p value of < 0. 05 to be considered significant. The feasibility of the GFI was assessed by determining the proportion of missing values per item  [12, 19, 25]. The reliability of the GFI and VES-13 scale scores was estimated using internal consistency methods (Cronbach's alpha), whereas an alpha above 0. 70 was acceptable for group comparisons  [26] [27] [28]. The construct validity of the GFI was assessed in terms of known-groups validity and convergent and discriminant validity. For known-groups validity, we hypothesized that the GFI total scores differ among elderly subgroups as older people, women and those with comorbidities.
We selected 48 amyloid (+) and 48 amyloid (-) subjects and performed extensive omics analyses: transcriptomics using RNAseq, metabolomics, and lipidomics. Besides univariate and multivariate methods, we applied multi-block approaches for integrating amyloid imaging to metabolomics, lipidomics, and transcriptomics datasets to unravel new panels of biomarkers predicting amyloid deposition in elderly asymptomatic individuals.
SUVr values ranged from 0•538 to 0•681 for the amyloid (-) subjects and from 0•797 to 1•577 for the amyloid (+) subjects. The time interval between the 18 F-florbetapir PET scan and blood sampling was 11•1+/-2•8 months. Additionally, data on age, sex, weight, body mass index, APOE genotype, treatments, education and place of residence were collected for all selected individuals. For the longitudinal study we used available fasting blood samples obtained 36 months after inclusion from a subset of 22 subjects included in the initial analysis (9 amyloid (+) and 13 amyloid (-)). The ethics committee of the Pitie-Salpetriere University Hospital approved the study protocol. All participants signed an informed consent form, given and explained to them 2 weeks before enrolment. Neither the participants nor the investigators were aware of participants' amyloid β status.
Metabolomics and lipidomics analyses and data processing Three LC-MS experiments were performed using HILIC, Sequant ZIC-pHILIC column 5 μm, 2•1 × 150 mm at 15 °C (Merck, Darmstadt, Germany), and pFPP, Discovery HS F5-PFPP column, 5 μm, 2•1 × 150 mm (Sigma, Saint Quentin Fallavier, France), chromatographic columns for metabolomics analyses, and using a reverse phase chromatographic column, kinetex C8 2•6 μm, 2•1 × 150 mm (Phenomenex, Sydney, NSW, Australia) for lipidomics analyses. Q-Exactive and Fussion mass spectrometers (Thermo Scientific, San Jose, CA) were used for metabolomics and lipidomics respectively. Experimental settings for metabolomics and lipidomics global approaches by LC-HRMS were carried out as detailed in Garali et al. and Seyer et al. 12 , respectively. All processing steps were carried out using the R software  [13].
LC-MS raw data were first converted into mzXML format using MSconvert tool  [14]. Peak detection, correction, alignment and integration were processed using XCMS R package with CentWave algorithm  [15, 16]  and workflow4metabolomics platforms  [17]. The resulted datasets in both approaches were Log-10 normalised, filtered and cleaned based on quality control (QC) samples  [18]. The remaining features were annotated based on their mass over charge ratio (m/z) and retention time using an "in house" database as described previously  [19] , and also putatively annotated based solely on their m/z using public databases such as the human metabolome database HMDB  [20]  and the Kyoto Encyclopedia of Genes and Genomes database, KEGG  [21]. Lipidomic features were then annotated thanks to an in silico lipid database and based on accurate measured masses, retention time windows and relative isotopic abundance (RIA) of lipid species as described previously  [12].
Univariate and multivariate omics data analyses The large and heterogeneous datasets generated from these highthroughput technologies were analysed using appropriate computational solutions  [22, 23]. For the RNA-Seq dataset, all genes with fewer than 10 raw read counts across 75% of the samples (low expressed genes) were filtered out from the analysis. Expression data was then normalised using the rlog (regularised logarithm) function in the R package DESeq2  [24]. Analyses were carried out using the R software version 3•4•4. The Student's t-test and the chi-square test were respectively used to compare age and sex differences between the amyloid (+) and amyloid (-) groups. The distribution of both APOE ε2 and ε4 carriers was compared between amyloid groups using Fisher's exact tests. The Wilcoxon rank-sum test was performed to assess whether there were age differences between APOE ε4 carriers from the amyloid (+) and amyloid (-) groups.
The metabolomics and lipidomics data used for analysis were log10-transformed intensity values. To control for potential effects of sex and APOE ε4 status, an additional normalization step was applied to the omics datasets using the ComBat method  [25]. Omics datasets were first investigated separately to identify features (among genes, metabolites and lipids) that best discriminate between amyloid (+) and amyloid (-) groups. This was done by combining the results of different feature selection techniques in an attempt to overcome the limitation of individual methods and thus retain the most consistent features  [26]. Therefore, only features selected by at least two methods were considered. Therefor we used more permissive p value threshold of 0•05 thus minimizing false negatives. Two methods were applied to all datasets: 1) non-parametric Wilcoxon rank-sum test (WT) using the Benjamini-Hochberg (BH) correction for multiple testing and 2) a parametric selection using parametric selection based on correlation-adjusted t-scores (CAT scores)  [27] , as implemented in the R package sda.
The CAT score, which is a multivariate modification of the Student t-statistic, has the advantage of taking into account the correlation structure among features. Following a procedure described previously  [28] , selection from CAT scores was optimised using a 5-fold cross validation with 20 repeats to assess the feature selection stability over a total of 100 rounds. In each round, 1/5 of the training dataset was left out and the CAT scores were calculated from the remaining 4/5 of the subjects with a Local False Discovery Rate (LFDR) (indicating the posterior probability of a feature being non-informative given its CAT score) for each tested feature. Only features that had a LFDR smaller than 0•2 over N50 rounds were retained. Additionally, as this is the most commonly used approach for transcriptomics data, the selected genes were also overlapped with the most differentially expressed genes identified by DESeq2  [24]  with the BH adjustment.
Multi-block analysis Beside univariate and multivariate common statistical methods, we used regularised generalised canonical correlation analysis (RGCCA)  [30]  that provides a common framework to access a variety of multiblock data analysis methods. The term 'multi-block' refers to the datasets organised in blocks of variables for the same group of individuals, preserving the block structure of each dataset (typically, one or more blocks per data type). To address variable selection, sparse GCCA (SGCCA) was proposed to extend the RGCCA model using the Lasso method  [31]  as successfully applied to a case study of spinocerebellar ataxia (SCA)  [32]. Multi-omics data integration was conducted on the 78 participants (41 amyloid (+) and 37 amyloid (-)) for which data were available on the three complete omics datasets.
In doing this, the objective was to simultaneously examine in a unified framework the complex relationship between the different sources of omics data (transcriptomics, metabolomics and lipidomics) and the amyloid status (amyloid (+) and amyloid (-)) both at the block and the variable levels. Analyses were performed using RGCCA and SGCCA for multi-block data  [32]  as implemented in the block. splsda function of the R package mixOmics  [34]. Using a component-based analysis strategy, RGCCA and SGCCA offer an effective solution for dimension reduction by searching a small set of orthogonal latent variables (constructed as linear combinations of the manifest variables) to summarise each high-dimensional dataset. sPLS-DA (sparse partial least squares-discriminant analysis) is a special case of SGCCA that combines Partial Least Squares (PLS) regression for discriminant analysis, and Lasso penalization for selecting the most discriminative variables.
Supervised integration of multi-omics data, involving one block per omics dataset and a dummy block matrix indicating the levels of amyloid (+) or (-), was determined by a weighted design matrix to define a trade-off between the double-objective of maximising the covariance between the blocks of omics, and maximising the covariance with the dummy block (i. e. discriminating between the amyloid groups). In order to prioritise this second objective, the weights were set to 0•1 between the blocks of omics and to 1 between the blocks of omics and the block amyloid. The optimal number of components was determined by the balanced error rate based on centroids distance, averaged over 50 repeats of a 5-fold cross-validation, for each tested number of components up to 10 units per function. An optimal number of variables to be kept for each component was chosen over a grid of candidate values, ranging from 1 to 12, using the lowest average balanced classification error rate based on centroids distance as calculated by the function tune.
Principal component analysis In an attempt to derive a global molecular signature of early amyloid deposition, a 'superblock' was built in grouping the 9 block-wise sparse components (3 components for each dataset: transcriptomic, lipidomic, and metabolomics). A principal component analysis (PCA) was then performed on the superblock to extract two so-called 'superscores', defined by the two first principal components with all the selected omics variables. For data visualization, the superscores were used to generate a common space to observe the distribution of subjects in amyloid groups; and which were the omics variables that most influenced this distribution.
Discriminant power of selected omics features Finally, the discriminant potential of the selected omics features was examined through a Receiver Operating Characteristic (ROC) curve analysis using the R package cvAUC  [35]. With this aim, sPLS-DA was repeated for the blocks of omics restricted to the top ten most stable features as determined by the bootstrap procedure and the addition of 3 blocks for the pairwise combination of the omics data (no feature selection; 1 component per block). The prediction of the amyloid status was then calculated in a leave-one-out analysis using the function perf for the first component of each block. The derived ROC curves and area under the curve (AUC) estimates were used to rank the tested classifiers and to assess the potential of a multi-omics signature for better characterising the earlier stages of amyloid deposition.