dataesr/scientific-paragraphs-categorization huggingface dataset:
org/10. 1016/j. cell. 2020. 08. 002. Pro# 3165017B Pp65/PNFkB Fluidigm Pro# 3166006A CD38 Fluidigm Pro# 3167001B CD71 Fluidigm Pro# 3168014B CD40 BioLegend Cat# 334302 CX3CR1 Fluidigm Pro# 3172017B Granzyme B Fluidigm Pro# 3173006C HLA-DR Fluidigm Pro# 3174001B CD56 Fluidigm Pro# 3176008B CD11b Fluidigm Pro# 3209003B Biological Samples Human blood samples APHP-Paris Tables S1-S3, S4, and S5-S7 Chemicals, Peptides, and Recombinant Proteins Cytodelics Cytodelics hC001-1000 Cytofix/Cytoperm BD 554714 BSA GE Healthcare Hyclone SH30574. 02 EDTA SIGMA 3690 PBS ThermoFisher 20012068 RNase OUT Recombinant Ribonuclease Inhibitor, Invitrogen 10777019 40U/mL Chromium Single Cell 3 0 Solution v3 10x Genomics PN-1000075 RNeasy Mini Kit QIAGEN 74104 reverse transcribed with SuperScript IV VILO Invitrogen 11766050 Master Mix with ezDNase Enzyme Power SYBR Green Applied Biosystems 4368577 Foxp3/Transcription Factor Staining Buffer kit eBiosciences 00-5523-00 Maxpar Cell Acquisition Solution Fluidigm 107950 EQ Beads Fluidigm 201078 Bio-Plex Pro TM Human Chemokine Panel Bio-rad 171AK99MR2 40-plex Assay R-plex Human Calprotectin Antibody Set Meso Scale Discovery F21YB-3 ultra-sensitive assay S-plex Human IFNa2a kit Meso Scale Discovery K151P3S-1 20-Plex Pd barcoding kit Fluidigm 201060 Deposited Data COVID Blood cells ArrayExpress Archive of Functional E-MTAB-9221 Genomics Data COVID BALF cells (Liao et al.
These optimization problems include dynamic and stochastic control system design, sub-communities in machine learning reproducing and replicating of our study's findings have been shared publicly in the Zenodo repository via DOI and URL as below: DOI: 10. 5281/zenodo. 4266126 URL: https://doi. org/10. 5281/zenodo. 4266126. problems, discrete event systems (e. g. queues, operations, and networks), manufacturing, medicine and biology, engineering, computer science, electronics, transportation, and logistics, see  [3, 5, 38, [40] [41] [42]. However, several studies have systematically illustrated the applications of surrogate-based optimization algorithms  [38, 39, [43] [44] [45]. In this paper, a new outline of robust real-time optimization in the CPS control model under the effect of environmental factors (also known as noise factors or uncertainty, see  [4, 5] ), and variability in feedback loop due to sensor's anomaly is studied.
The authors in  [77]  have used the bootstrapping technique to obtain perdition error for a GP surrogate using resampling to refit surrogate and obtain prediction error. However, resampling imposes extra computational cost due to the additional number of required simulation experiments (function evaluations). Here, to estimate the surrogate prediction error for each candidate point, we apply the jackknife leave-one-out approach. This approach applied an available set of I/O data and doesn't need resampling and extra simulation experiments. 3. 1. 5 Jackknife leave-one-out approach. Jackknife was first introduced by Quenouille (1949)  [78]  and named by  Tukey (1958)   [79]. The application of the jackknife method involves a leave-one-out strategy for the estimation of a parameter (e. g. the variance) in a dataset  [80].
Simulation and algorithm setup Here, the main goal is to obtain a robust optimal design of FOPID controller in such a CPS control model as elucidated in Section 2. We simulate the five-bar linkage robot manipulator using Eq  (16)  in Matlab 1 /Simulink environment. Simulink does not have a library for the FOPID. Therefore, the controller from the library of FOMCON: a Matlab 1 toolbox for fractional-order system identification and control  [91] , which allows for the computation of the fractional-order derivative and integration is used. Numeric values of the parameters of the five-bar manipulator dynamics are taken from  [54, 92]  as shown in Table  2. From the data that are driven in Table  2 , it is revealed that m 3 l 2 l c3 = m 4 l 1 l c4 , thus we can perform robust optimal design of controller for one motor, where the same results are also valid for the second motor.
First, we design a set of experiments with the size of l = 15 samples using LHS. Another sampling design is constructed for   2 ), (  4 ) and (  6 ) respectively. We set both M 1 and M 2 parameters equal to 10 used in Eqs (  2 ) and (  4 ). Regarding m uncertainty scenarios, we repeat each input combination m times, and compute the relevant mean and Std of OF using Eqs (  11 ) and  (12). Then, we calculate SNR s for each input combination s = (1,2,. , l = 15) using Eq (13) while assume ω = 3. Afterwards, we fit a GP surrogate over set of input combinations and set of SNR s outputs. The DACE  [93] , Matlab 1 toolbox has been employed to construct GP surrogate. In the current study, first-order polynomial regression and Gaussian correlation functions are adjusted to fit GP surrogate. The correlation parameter is fixed on 0.
99, 95. 91, and 96. 08% of the exome for the patient, mother, and father, respectively. In our variant analysis, we prioritized variants that were rare in the healthy population according to GnomAD v3 database (< 1%), the variants predicted to be deleterious on protein function according to SIFT and PolyPhen tools, and transmitted as compound heterozygous or arose de novo, consistent with the non-consanguineous and healthy parent context (Table  S1 ). Among these pertinent variants, the only one that could explain the patient's phenotype was the de novo synonymous variant c. 702G > T (transcript NM_ 004247. 4) in the exon 9 of EFTUD2 at position chr17: 42956924 (GRCh37/hg19) in the patient (Fig. 1a ). This variant replaces a GGG codon to GGT, resulting in the retention of glycine at amino acid residue 234 (p.
G234G). According to ACMG 2015 guidelines  [8] , this variant is classified as having unknown significance. Sanger sequencing confirmed that neither parents carried the variant (Fig. 1b ). The variant is located in the G-domain of the protein, which is known to bind and hydrolyze GTP and a site of other variants of EFTUD2 gene that are associated with MFDM (Fig. 1c ). As MFDM disease patterns seem to correspond closely to the symptoms of the patient (Table  1 ), we decided to investigate the potential impact of this synonymous variant on EFTUD2 function. The T allele at this position is novel in all public databases, including the NHLBI Exome Sequencing Project, the 1000 Genomes Project, and GnomAD v3, suggesting very high conservation of the G allele in the population. The mutated residue is the last nucleotide of exon 9, localized at the exon/intron junction adjacent to the splice donor site GT (c.
Bioinformatic analysis FastQ data were aligned to the GRCh37 (hg19) reference genome with bwa-0. 7. 12  [9] , sorted and indexed with samtools-1. 2  [10] , deduplicated with PICARD-1. 110, and base corrected and indel realigned with GATK-3. 8  [11, 12]. Variant calling was done with GATK-3. 8 Haplotype-Caller in GVCF ERC mode. Variants were called individually for each sample and then combined with GATK-3. 8 GenotypeVCFs to produce a combined VCF. The combined VCF was then uploaded and analyzed with Ingenuity Variant Analysis software. Alignments were visualized with GenomeBrowse (Golden Helix -Massachusetts). FastQC-0. 11. 5 was used to calculate quality metrics for FastQ files and Qualimap-2. 2. 1  [13]  was used to calculate coverage statistics using the truseq-exome-targeted-regions-manifest-v1-2.
Discussion and conclusions The increased access to next-generation sequencing for clinical purposes has allowed the identification of thousands of novel pathogenic variants in different individuals. One of the main challenges in clinical genetics is the interpretation of pathogenicity from a sea of variants that remain largely of unknown significance. Synonymous variants are often interpreted by default as being silent and benign given their predicted null impact on the protein sequence. However, there is evidence for some synonymous SNVs to affect RNA splicing, expression, folding and ultimately function, and, in doing so, contribute to the pathophysiology of many diseases  [15] [16] [17]. In this case study, we report a synonymous c. 702G > T variant in the EFTUD2 gene. This variant has not previously been reported in the literature and is absent from large population databases (GnomAD, 1000 Genomes); without further analysis, our initial classification would have been of uncertain significance.
Funding This study was funded by grants from the  Agence Nationale de la Recherche (ANR)  ( ANR-11-LABX-0070_TRANSPLANTEX ), the  INSERM  ( UMR_S 1109 ), the  Institut Universitaire de France (IUF) , all the  University of Strasbourg (IDEX UNISTRA) , the  European regional development fund (European Union )  INTE RREG V program  (project n°3. 2 TRIDIAG) and  MSD-Avenir  grant  AUTOGEN. No funders participated in the design of the study; the collection, analysis, and interpretation of data; or the writing of the manuscript.
Availability of data and materials The hg19/GRCh37 human reference genome (https://genome. ucsc. edu/cgibin/hgTracks?db=hg19&position=lastDbPos) was used as the reference dataset in this study. The reference sequence used for the validation of the G234G variant in EFTUD2 was obtained from NCBI Nucleotide using the accession number NM_004247. 4. The variant reported in here is available in the Clinvar repository, with accession ID: SCV001251173. The datasets generated during the current study are not publicly available because it is possible that individual privacy could be compromised and the participants did not provide consent to make the data public.
1 ). In contrast to the study of Lebsir and colleagues in which targeted genes were considered  8  , this study proposes a large-scale gene screening in order to explore, in a more exhaustive manner, putative mechanistic interactions between genes of the cortex and thyroid. From these DE genes, correlations between them were inferred using two different statistical methods. In addition, to determine a putative mode of action, a Process Descriptive (PD) diagram has been constructed using the identified DE genes and their correlation data enriched with data obtained from the PubMed database. The PD diagram represents molecular mechanisms  14  and identifies genes belonging to key events that may be associated with the AO. In addition, identification of causal relationships between genes from the thyroid and the cortex was explored.
A diagram depicting the workflow that has been followed in this study. Two groups consisting of pregnant rats received either KI or saline solution for eight days starting at nineth gestational day until the 17th gestational day. Transcriptomics was performed on tissue from the cortex (brain) and the thyroid from the progeny, 51 days after birth. After analysis of differentially expressed (DE) genes networks are statistically inferred using either the ShrinkNet R-package or Machine Learning method using the MixOmics R-package. Based on both networks, a process descriptive (PD) network is manually constructed using literature and the above-mentioned networks, to elucidate putative molecular mechanisms that are associated with the AO of the CNS of the progeny during repetitive administration of KI by pregnant mother rats.
Results Gene expression profile for the thyroid and cortex. Gene expression analysis was performed on brain cortex and thyroid of the progeny from the two groups of pregnant rats, (KI-treated group and control group (saline-treated), respectively, 51 days post-natal. Compared to the saline-treated group, 60 and 94 genes were found to be differently expressed for the thyroid and cortex from the KI-treated group, respectively (t-test, Q-value < 0. 05) (Fig. 2 ). A number of biological functions associated with these genes have been retrieved by querying the DAVID database  15  , including site-specific DNA binding, ATP binding, and transcription for the thyroid (Fig. 2A  right-hand panel and Supplementary data Table  1 ), and transcription regulation, DNA, RNA and ATP binding, nucleotide binding and neurogenesis for the cortex (Fig.
Genes used for constructing the PD network were prioritised by applying the sPLS-DA method to obtain genes that contributed the most to the difference in phenotypes (control and KI exposed groups). Notably, all genes selected by sPLS-DA except for tomm70a and kcnj15 were under-expressed in the KI-exposed group (Fig. 3B ). The combination of the results of Bayesian statistics and sPLS-DA was used as input for constructing the PD network. For example, the gene gas7 was identified using sPLS-DA as a contributor that distinguishes the control and treated groups. This gene was used as a starting point for creating the PD network. Database search in PubMed linked this gene or its corresponding protein to p53 and cancer invasion. The former was    53. The nodes (closed circles and squares) represent genes and edges represent correlation between genes.
The square nodes are genes that can be found in the process descriptive network as well (C). (B) shows the results of sPLS-DA: genes that contribute most to either the control group or the KI exposed group, respectively. These genes will be used as a starting point for constructing the PD network (C). (C) shows a process descriptive network that represents molecular mechanism  14. Squares that are colour coded linked to SP1 whose gene was found in the inferred Bayesian network. Systematically all genes that have been identified by sPLS-DA (gas7, kcnj15, eef1a2, c17h10orf113, pzf709l, and tomm70a) were used as key words for PubMed database search. The PD network contained 45 chemical species including genes, proteins, RNAs, anti-sense RNA molecules and 37 edges that have been annotated with references to the original scientific articles (Fig.
3C ). The annotations can be found back in the "reaction notes" using the CellDesigner software. The network was based on 24 unique articles retrieved from the PubMed database. The PD network displayed genes and their corresponding proteins (coloured-code boxes) associated with functions including ATP and DNA-binding, transcription, as well as cellular locations such as cell membrane and mitochondrion (Fig. 2A ). The grey-coloured molecules including genes and proteins were retrieved from the literature and formed together with the DE genes and their products a biochemical reaction network that represented molecular mechanisms in the thyroid. Gene expression data was applied (colour coded genes and their products) to this network which allowed better understanding of the effect of KI on gene expression in the network.
The PD network displayed genes and their corresponding protein (coloured-code boxes) associated with biological functions including ATP-and DNA-binding, transcription, neurogenesis, as well as cellular locations such as cell membrane, mitochondrion (Fig. 4A ). The grey-coloured genes and proteins were retrieved from the literature and together with the DE genes and their gene products they formed a biochemical reaction network that represented molecular mechanisms in the cortex. Gene expression data was applied to this network (colour coded genes and their products) which allowed better understanding the effect of KI on gene expression in the network. Certain DE genes including katnal1, hmgb1, cacna1a and ralgapa1 modulate the biological processes (Ochre coloured hexagons) associated with memory, intelligence, learning, neurite outgrowth and branching, transmitter release and brain development (Fig.
Indeed, the statistical inferred Bayesian and Diablo networks are by nature dedicated to highlight correlations and not causalities. Furthermore, these networks are undirected networks solely based on gene expression. In contrast to the previous networks, the PD diagram shows a directed network suggesting a putative mechanism based on empirical data. Indeed, the different components of this network are extracted from publications, available on PubMed, that support interactions between the unmeasured molecules from both organs and the candidate genes provided by the statistical analyses. This study demonstrates the dimensional reduction of the data obtained by the proposed approach: starting from large-scale of measured genes in thyroid and cortex towards a reduced number of candidates potentially involved in the interconnection between cortex and thyroid (Fig.
Only edges with an FDR lower than 0. 05 were kept to construct the network. sPLS-DA and DIABLO framework. Besides the previous gene network inference approach, a supervised multivariate analysis was conducted by means of the R package mixOmics (version 6. 6. 1) in R studio  54  to specifically target inter-group differences (KI and Controls). Two sparse partial least square discriminant analysis (sPLS-DA) allows the selection of the most predictive or discriminative features in the data to classify the samples accordingly  55. sPLS-DA helps to prioritise genes of interest for the construction of the PD network. Firstly, sPLS-DA models were conducted separately on cortex and thyroid transcriptomics' result. After this intra-organ analysis, an inter-organ transcriptomics integration was conducted via the DIABLO framework  56.
PD Network construction. The manual construction of a biochemical reaction network commenced from the collection of common genes, highlighted simultaneously by the methods described in the network inference section above (Shrinknet, sPLS-DA and DIABLO), assuming that it constitutes the most robust significant gene candidates. The construction of the PD networks was performed as described before  58  ; to enrich the inferred networks with relevant information from scientific articles, articles were retrieved from the PubMed database using the molecule(s) of interest as key word(s), e. g. "gene A" was used as a query or the combination either "thyroid" or "cortex", respectively, and "gene A" was used as query. "Gene B" was used as separate query either in combination with key words or not. No data-mining tools were used.
The CellDesigner software (version 4. 4)  59  was used to represent molecular biological mechanisms, derived from the literature, resulting in a structured network representation (PD diagram) compliant with Systems Biology Markup Language (SBML) level 2 that is suitable for further computational analysis  60. Each reaction has been annotated in the "reaction note" at least once with a corresponding scientific article. The CellDesigner's graphical notation  61  can be converted into the Systems Biology Graphical Notation (SBGN) standard  62  by using the CellDesigner's internal convertor. For each PD diagram, the CellDesigner software creates an xml-file that can be retrieved from the supplementary material section. In addition, a svg-file format from a PD network can be downloaded instead which can be opened by any internet browser and the diagram can viewed with different zoom levels.
Background: Goats were domesticated 10,500 years ago to supply humans with useful resources. Since then, specialized breeds that are adapted to their local environment have been developed and display specific genetic profiles. The VarGoats project is a 1000 genomes resequencing program designed to cover the genetic diversity of the Capra genus. In this study, our main objective was to assess the use of sequence data to detect genomic regions associated with traits of interest in French Alpine and Saanen breeds. Results: Direct imputation from the GoatSNP50 BeadChip genotypes to sequence level was investigated in these breeds using FImpute and different reference panels: within-breed, all Capra hircus sequenced individuals, European goats and French mainland goats. The best results were obtained with the French goat panel with allele and genotype concordance rates reaching 0.
Data available No animal experiments were necessary for this study, therefore no ethics committee approval was required. Sequence data were obtained from the VarGoats project using Illumina HiSeq or Illumina NovaSeq technologies, the first step towards a 1000 goat genome project (http://www. goatgenome. org/vargoats. html). The current data bank comprises 808 individuals from Capra hircus of various breeds and geographical origins, as well as 21 wild goat individuals. Forty-four French Alpine and thirty-seven French Saanen individuals were sequenced at Genoscope (Evry, France) with an average coverage of 12X. The individuals sequenced were selected to best represent the genetic structure of the current French population: AI bucks from the largest families, maximized haplotype coverage from the population by picking unrelated individuals following the approach described by Druet et al.
[14]. However for some research purposes (milk flow trait, specific casein profiles), a few closely related individuals were sequenced and added to the overall dataset. Thus, sequence data include 13 pairs of cousins (G jk > 0. 12) and 7 parent/descendant pairs (G jk > 0. 40). All selected animals except 3 had previously been genotyped using the Illumina GoatSNP50 BeadChip. A total of 2455 French Alpine individuals (994 males, 1461 females) and 1570 French Saanen individuals (757 males, 813 females) genotyped using the Illumina GoatSNP50 BeadChip were available for imputation. Pedigree information was available and used as the genotyped individuals were closely related to the reference panel of sequences. Data were cleaned using an in-house pipeline as described in  Martin et al. (2018). In brief, all individuals with a call rate below 95% or showing pedigree inconsistency (i.
Among the 829 sequenced individuals, 16 had a mean coverage below 5 (4 French Alpine and 4 French Saanen) and were removed from the data set for subsequent analyses. The sequence dataset therefore consisted of 815 sequenced individuals including 40 French Alpine goats (36 males and 4 females) and 33 French Saanen goats (31 males and 2 females). Quality checks were applied to the sequence variants using the indicators listed in Fig. 1. The thresholds for individual genotype quality (GQ) and individual depth (DP) were set to 8 and 7 respectively by comparing the genotypes of the GoatSNP50 BeadChip SNPs with the sequence variants. The mean GQ and mean DP were 6. 7 (± 2. 8) and 7. 6 (± 4. 8) respectively for mismatching SNPs compared with 48. 0 (± 16. 7) and 11. 7 (± 5. 1) respectively for the matching genotypes.
Imputation of missing genotypes in the sequence reference panel It should be emphasized that missing genotypes represented on average 4. 63% of all the sequence variants for an individual of French Alpine and Saanen breeds. This percentage could attain 66% if sequencing was of low coverage. A within-breed imputation was therefore applied to fill in the gaps. Using a combination of AlphaImpute (v 1. 9)  [19]  and FImpute (v 3. 0)  [20]  gave higher concordance rates than using solely one software while minimizing computation time. We hence imputed filtered sequences using AlphaImpute and FImpute consecutively for French Alpine and Saanen breeds separately. The mean concordance rate between 50 k genotypes and sequence data was 98. 62% (± 1. 19) after imputation and no missing genotypes remained.
Various reference populations were tested based on their proximity to French Alpine and Saanen goats. To accurately build the different reference panels, a Principal Component Analysis (PCA) was performed on chromosome 1 filtered data using PLINK software  [24]. Groups were then formed based on the origin of an individual and on the PCA results (Fig. 2 ). The number of individuals per group is given in Table  2. All sequenced wild individuals were removed from reference populations as they are genetically different from the Capra hircus species. We also tried to impute sequences without pedigree while using all sequenced French goats available (excluding Angora and Creole breeds).