dataesr/scientific-paragraphs-categorization huggingface dataset:
miRNAs Potentially Targeting HIF-2α, VEGFR1 and/or VEGFR2 Sequencing data of 454 miRNAs were available in all tumor samples. As the biological function of miRNA is to inhibit mRNA levels or translation, only miRNAs correlated with lower mRNA expression were considered for further study resulting in 94 miRNA-mRNA correlations. Full correlation results are reported in Table  S2. These miRNA-mRNA interactions were then searched in the miRDB, miRabel and DIANA Tarbase v. 8 miRNA-mRNA interaction databases (Table  2  and Table  S3 )  [22] [23] [24]. miRNAs with predicted target sequences in HIF-2α, VEGFR1 and/or VEGFR2 are visualized in Figure  S1. For five of the miRNAs correlated with HIF-2α, the interaction with HIF-2α was listed by miRNA-mRNA interaction databases. For miR-142-5p, miR-185-5p and miR-223-3p, this interaction was also experimentally validated  [26] [27] [28] [29] [30].
We identified nine miRNAs correlated with VEGFR1 for which this potential interaction was also listed by miRNA-mRNA databases. Of these, miR-142-3p and miR-378a-3p had experimental validation of the interaction with VEGFR1 [31  -33]. For VEGFR2, we identified five miRNAs correlated with lower expression that were also mentioned as targeting VEGFR2 in the aforementioned databases. Studies providing experimental validation were found for miR-21-3p, miR-221-3p and miR-370-Cancers 2021, 13, 3099 6 of 15 3p  [15, [34] [35] [36] [37]. In summary, we selected 14 miRNAs with 19 miRNA-mRNA interactions (Figure  1 ). lower expression that were also mentioned as targeting VEGFR2 in the aforementioned databases. Studies providing experimental validation were found for miR-21-3p, miR-221-3p and miR-370-3p  [15, [34] [35] [36] [37].
Abbreviations: miRNA = microRNA, HIF-2α = hypoxia-induced factor 2α, VEGFR1 = vascular endothelial growth factor receptor 1; VEGFR2 = vascular endothelial growth factor receptor 2, miR = microRNA. Note: Correlation coefficient rho as calculated by Spearman correlation. As mRNA expression is reported in Ct values, for which higher values indicate lower mRNA expression, positive correlation coefficients indicate that miRNA expression is correlated with lower mRNA expression. Reported p-values are FDR-corrected. Predicted correlations are highlighted in bold. 1 : Interaction listed by miRDB [22] ; 2 : interaction listed by miRabel database with RRA score < 0. 05 [23] ; 3 : interaction listed by DIANA Tarbase v8 [24].
[37]  Abbreviations: miRNA = microRNA, HIF-2α = hypoxia-induced factor 2α, VEGFR1 = vascular endothelial growth factor receptor 1; VEGFR2 = vascular endothelial growth factor receptor 2, miR = microRNA. Note: Correlation coefficient rho as calculated by Spearman correlation. As mRNA expression is reported in Ct values, for which higher values indicate lower mRNA expression, positive correlation coefficients indicate that miRNA expression is correlated with lower mRNA expression. Reported p-values are FDR-corrected. Predicted correlations are highlighted in bold. 1 : Interaction listed by miRDB [22] ; 2 : interaction listed by miRabel database with RRA score < 0. 05 [23] ; 3 : interaction listed by DIANA Tarbase v8 [24].
IMDC risk group at start of first-line therapy Favorable (n) 13 11. 9% Intermediate (n) 69 63. 3% Poor (n) 27 24. 8% First-line targeted therapy Sunitinib (n) 68 62. 4% Pazopanib (n) 30 10. 1% Sorafenib (n) 11 27. 5% Molecular subtypes ccrcc1 19/76 25% ccrcc2 33/76 43. 4% ccrcc3 4/76 5. 2% ccrcc4 20/76 26. 3% Abbreviations: IQR = interquartile range, OS = overall survival, IMDC = International Metastatic RCC Database Consortium. Note: Molecular subtype proportions are relative to the total number of patients for whom mRNA expression data were available (n = 76).
No difference in binding was observed between 20 mg/day and 40 mg/ day and it is plausible that the lack of a within-subject design reduced the power to detect dos-related effects, given the inherent between-subject variability of 5-HTT binding, and effects of season and sex on this measure  (Ruhe, Booij, Reitsma, et al. 2009). A major leap forward in neuroimaging occurred in the year 2000 with the advent of [ 11 C]DASB. Across two datasets [ 11 C]DASB PET was applied in a withinsubject design, measuring 5-HTT BP ND (binding potential BP ND , ND¼ non-displaceable) before and after 4 weeks of citalopram treatment in 18 subjects  (Meyer, Wilson, et al. 2001; Meyer et al. 2004 ). These studies reported an occupancy of 81% in striatum with similar occupancies across the prefrontal cortex, anterior cingulate cortex, thalamus and midbrain after treatment with the minimum therapeutic dose (20-40mg).
Both enantiomers of fluoxetine and the active metabolite S-norfluoxetine are substrates of CYP2D6 with PMs displaying a significant increase of AUCs as compared to EMs in single-dose studies  (Zhou 2009). Dose-adjusted plasma concentrations of fluoxetine have been associated with a number of CYP2D6 active genes  (Llerena et al. 2004 ) and, despite conflicting results  (Serretti et al. 2009 ), CYP2D6 polymorphism may contribute to variability of fluoxetine pharmacokinetics  (Blazquez et al. 2012). Based on a scarce dataset, a dose adjustment of 50 to 70% for PMs and 110 to 120% for EMs has been proposed  (Kirchheiner, Nickchen, et al. 2004). No data is available, however, to support the influence of CYP2D6 genotype on clinical outcome. Several small studies failed to demonstrate a correlation between CYP2D6 genotype and the risk for side effects.
2014). With the ER form, the rate of absorption of venlafaxine is slower than its elimination rate. Therefore, the apparent t1 =2 of venlafaxine of 15 ± 6 h actually represents the absorption half-life instead of the true disposition half-life (Government of Canada, Drug Product Database online. Product monograph venlafaxine XR, extended release capsules. 2012). The clearance of venlafaxine is reduced in cases of hepatic and renal dysfunction and dose adjustments are recommended. Venlafaxine and its metabolites are PGP substrates, which can influence their absorption, distribution to the brain and elimination  (Magalhaes et al. 2014). Only a limited number of studies including a small number of patients have evaluated the relationship between venlafaxine and O-desmethylvenlafaxine plasma concentrations and antidepressant response, with little concordance.
8 mg/day and an EC 50 of 3. 4 ng/mL with considerable plateauing of occupancy at plasma concentrations above 40-60 ng/mL, although a theoretical maximum occupancy of 92% was suggested by the fitting. However, a limitation of this plasma-occupancy relationship is that the SERT is occupied both by venlafaxine and its active metabolite, O-desmethylvenlafaxine. Since the concentration of Odesmethylvenlafaxine is on average twofold higher than that of the parent compound  (Shams et al. 2006 ), the active moiety (venlafaxine þ O-desmethylvenlafaxine) concentration associated with 80% SERT occupancy can be calculated as approximately 60 ng/mL. This value is in line with TDM findings  (Reis et al. 2009)  which showed that, according to a very large database, treatment with 75 mg venlafaxine daily is associated with a median active moiety concentration of 135 ng/mL (10th percentile 68 ng/mL)  (Reis et al.
2012; Stenkrona et al. 2013 ). In the first study, SERT occupancy was assessed in a large group (n ¼ 46) of healthy volunteers in the midbrain. After single doses, 2. 5 mg vortioxetine was associated with 29% occupancy of the SERT, 10 mg with 44%, and 60 mg with 70%  (Areberg, Luntang-Jensen, et al. 2012). Occupancy values after multiple dosing (9 or 13 days, respectively) were higher, being 35-49% at 2. 5 mg, 51% at 5 mg, 63% at 10 mg, 90% at 20 mg and above 95% at 60 mg. From those data, an EC 50 value of 4. 8 ng/mL was calculated. The oral dose needed to occupy 50% of the SERT was calculated to be 8. 5 mg  (Stenkrona et al. 2013 ). The 5-HT 1A receptor was not measurably occupied by 30 mg of vortioxetine  (Stenkrona et al. 2013). Taken together, the available data suggest that the SERT is occupied to a lower extent by vortioxetine than by the SSRIs at the lower end of the clinically used dose range (5 À 20 mg).
2012; Ramsey et al. 2013; Xu Q et al. 2016 ), however, no pharmacogenetic studies of antidepressant activity have been published so far. There are no genotypebased dose recommendations for quetiapine available. It is notable that the highest level of evidence in the PharmGKB database for quetiapine was the rs489693 located in the MC4R gene, and associated with weight gain. The latter was annotated as level 2B evidence indicating that this genetic marker needed to be further replicated before implementation in clinical practice (Variant annotations 2020) Quetiapine's binding to dopamine D 2 receptors has been extensively characterised with PET, and a detailed review of these binding properties is reviewed elsewhere  (Gr€ under et al. 2011). Briefly, quetiapine shows a D 2 receptor binding profile most similar to clozapine.
All participants had a Clinical Dementia Rating scale  (19)  score ≤0. 5. Main exclusion criteria have been described elsewhere  (22). All examinations (including neuropsychological battery administration, clinical examinations, brain MRI, CSF samples and fluorodeoxyglucose [FDG] and amyloid PET) followed standardized procedures  (18). Among the 2,323 participants included in the MEMENTO cohort, 2,288 participants from 26 study centers were included in this analysis after exclusion of participants with missing data on diabetes status (N = 35).
Standard protocol approvals, registrations, and patient consents This study was performed in accordance with the Declaration of Helsinki. All participants provided written informed consent. The MEMENTO cohort protocol has been approved by the local ethics committee ("Comité de Protection des Personnes Sud-Ouest et Outre Mer III"; approval number 2010-A01394-35) and was registered in ClinicalTrials. gov (Identifier: NCT01926249).
FDG-PET 18F-FDG-PET was offered to all participants but was not mandatory. PET images were acquired after a standardization of the acquisition and reconstruction imaging parameters, coordinated by the CATI  (31). After a centralized quality check and postprocessing performed by the CATI, the following measures were obtained: mean FDG-PET uptake for the regions of interest (ROIs) of the Automated Anatomical Labeling atlas relative to the pons reference region  (32) , including partial volume correction, and mean FDG-PET uptake for a set of AD-specific ROIs inferred from the Alzheimer's Disease Neuroimaging Initiative database  (33) , expressed as standard uptake value ratios (SUVRs).
Finally, we also compared the results with those obtained when considering interactions between diabetes and each mediator in the main adjusted model, as recommended for mediation analysis  (41). We also explored the mediating pathways in the association of diabetes with specific cognitive domains in separate models: a latent variable for memory (indicators: total free recall score and verbal fluency) and a latent variable for executive functioning (indicators: TMT A and TMT B scores). Analyses were conducted using SAS v9. 3 (SAS Institute Inc, Cary, NC, USA), and R version 3. 5. 1  (42)  with the lavaan package for SEM analysis  (38).
Several exploratory clinical trials have reported a beneficial effect on cognition of intranasal insulin for healthy participants, participants with diabetes, mild cognitive impairment or AD  (46) , and longer-term trials are currently ongoing. The MEMENTO study has several strengths to answer the current objectives. First, a wide range of biomarkers was acquired in a highly standardized setting on more than 2,000 participants allowing a multi-dimensional assessment of brain ageing and pathology biomarkers. Indeed, we were able to include simultaneously brain MRI, brain FDG-PET, amyloid-PET and CSF data in a mediation analysis of the diabetescognition association, offering a unique insight on underlying mechanisms. Second, we were able to model brain biomarkers as latent variables in a SEM framework, accounting for measurement error of the indicators, and we were able to estimate direct and indirect effects of diabetes on several domains of cognition.
The MEMENTO cohort is sponsored by  Bordeaux University Hospital  (coordination:  CIC1401-EC , Bordeaux) and was funded through research grants from the  Fondation Plan Alzheimer  (Alzheimer Plan  2008-2012 ), the  French ministry of research and higher education (Plan Malandies Neurodégénératives  ( 2016-2020 )). The MEMENTO cohort has received funding support from  AVID, GE Healthcare , and FUJIREBIO through private-public partnerships. The Insight-PreAD substudy was promoted by  INSERM  in collaboration with the  Institut du Cerveau et de la Moelle épinière, Institut Hospitalo-Universitaire , and  Pfizer  and has received support within the  "Investissement d'Avenir"  ( ANR-10-AIHU-06 ) program. Sponsor and funders were not involved in the study conduct, analysis and interpretation of data.
No homologs of any queried ChOx could be found in the genome of E. coprostanoligenes or among almost six million non-redundant complete genes assembled from the metagenomic sequencing of more than three thousand human fecal samples from various countries of the world  [74]. In contrast, within the same study  [74] , a 3β-HSD was found in E. coprostanoligenes ATCC 5122. The enzyme, named IsmA for "Intestinal steroid metabolism A," was overexpressed in E. coli, purified using HisPur™ Ni-NTA Resin, and partially characterized  [74]. It is an NADP+ dependent, oxygen independent, and cholesterol inducible HSD that oxidizes cholesterol to 4-cholesten-3-one and coprostanol to coprostanone, but does not accept the 3α-hydroxy bile acids cholic and chenodeoxycholic acids as substrates. Based on massive amount of correlations combining huge metagenomics and metabolomics datasets from several cohorts in the world, six homologs of IsmA were also tracked from uncultivated members of the human gut microbiota  [74].
They were overexpressed in E. coli, and all six lysates were confirmed to oxidize cholesterol to 4-cholesten-3-one as well as coprostanol to coprostanone. When binning co-abundant genes into metagenomic species (MSPs) across more than 3000 metagenomics datasets, 20 different MSPs containing ismA genes were identified. They formed a coherent clade in the phylogenetic neighbourhood of Clostridium cluster IV, which contains species linked to host health, including short-chain fatty acid producers. IsmA-encoding MSPs had a high relative abundance (average 1. 4%) in human metagenomes, and the percentage of metagenomes containing at least one IsmA-encoding MSP varied from 37% to 92%, which supports the idea that IsmA-encoding bacteria are prevalent constituents of the human gut microbiome  [74].
We could not find any recent proof of 3-oxo-∆ 4 -steroid 5β-reductase activity in the gut microbiota, and no bacterial 3-oxo-∆ 4 -steroid 5β-reductase; EC:1. 3. 1. 3, has never been purified nor can be found in the databases (Figure  3 ). However, three genes predicted to encode cholestenone 5β-reductase, EC 1. 3. 1. 3, were found to be upregulated when B. bifidum PRL2010 was grown in presence of cholesterol  [83]  (Figure  3 ). One of them had a sequence that was found to be highly conserved (identity greater than 80 % and coverage 100 %) in the genomes of ten other bifidobacteria, all isolated from the mammalian gut  [83]. When a large daily inoculum is administered (10 9 cells per day of overnight cultures over 20 days) to the hypercholesterolemic apoE-KO mice, B. bifidum PRL2010 tended to lower plasma cholesterol in this small-sized experiment (5 mice treated and 5 untreated).
Related Work An approach to modelling the COVID-19 pandemic using self-exciting branching processes has been suggested by  [40]. These authors employ a continuous-time Hawkes model with a nonparametric estimate of the reproduction number, R(t), the average number of secondary cases produced by a single case of the virus. Both death counts and the number of confirmed cases in the early stage of the epidemic, before April 1st, are modelled in three states of the U. S. , several European countries and China. Compared to SIR and SEIR models with a fixed reproduction number, their Hawkes model with a dynamic parameter leads to lower estimates of the basic reproduction number, R 0. In the same line of work,  [41]  consider several datasets for the state of Indiana in the early stage of the epidemic.
) and D (. ) are the gradients of log L with respect to ↵ and respectively, G is a pre-conditioning matrix accounting for covariance between parameters and ✏ is the step size in the Metropolis-adjusted Langevin algorithm. The MCMC chain was run for 60,000 iterations discarding the first 20,000. The pre-conditioning matrix G was taken as the covariance matrix from an implementation of the standard Metropolis-Hastings algorithm for each country. The R code and data required to replicate this study are available on Github (https://github. com/RaihaTuiTaura/covid-hawkes-paper). Diagnostic plots, including MCMC trace plots, autocorrelation between the MCMC samples and pairwise correlation between parameters were examined and suggest the algorithm has converged. Further details on the posterior distributions of the model parameters, convergence and model diagnostics are provided in the supplementary material.
Model Fit Several measures are used to assess model fit. First, the model's capability to interpolate missing data is evaluated. Then in-sample and out-of-sample posterior predictive checks are considered. The purpose of prediction in this study is to assess model fit and to discover what can be learned about the process retrospectively. The first measure of model fit considers how accurately the model can recover missing data. We randomly remove 10% of observations across the entire time series and treat the missing data as parameters in the model to estimate. Table  4  describes the number of interpolated data points for which the observed value lies within both the 95% and 80% credible intervals (CrI) of the posterior distributions for the missing data. The proportion of data points correctly interpolated is generally high when considering the 95% credible intervals.
For instance, a recent MAKER-based optimized pipeline tested on 39 budding yeast genomes missed only 3. 9% of genes and 4. 8% of exons, on average  (Shen et al. 2018). However, the results were poorer in intron-rich species, for which gene annotation is challenging. Even when RNAseq data are available, it is still very difficult to correctly predict the exon-intron structure primarily because fungal exons can be extremely short  (Janbon et al. 2014) , but also because these genomes are compact. Thus, when tested on fungal data sets, de novo transcriptome assemblers like Trinity  (Grabherr et al. 2011)  or Cufflinks  (Trapnell et al. 2010)  tend to predict very large transcripts with no biological relevance. Nevertheless, several pipelines have been published and sequencing centers like the Joint Genome Institute (JGI) and the Broad Institute have developed specialized pipelines to produce annotation drafts, which are very useful in large-scale comparison analyses  (Hass et al.
Pipeline selection The RNA-seq mapped reads from C. neoformans H99 and C. deneoformans JEC21 from the EXPO30 condition (exponential growth at 30 C) were tested in the three pipelines for gene prediction. BRAKER1  (Hoff et al. 2016)  was performed with the default parameters plus the exclusion of alternative transcripts (-alternativesfrom-evidence¼false) using the three replicates (A, B, and C) as RNA-seq source. Cuff-CQ  (Cufflinks v2. 1. 1 ((Trapnell et al. 2010 ))/ Coding Quarry v2. 0  (Testa et al. 2015) ) and C3Q (Cufflinks v2. 1. 1/ Cuffmerge/Coding Quarry v2. 0) were tested with the basic parameters: minimum intron length (30); maximum intron length (4000); minimum isoform fraction (0. 9); and overlap radius (10). The merged BAM file generated by the three replicates (A, B, and C) and used in the Cuff-CQ pipeline was obtained with Samtools merge.
C3Q was performed separately for the three BAM files; the GTF files generated by the three predictions (for replicates A, B, and C) were then combined by Cuffmerge and the resulting transcripts were processed by CodingQuarry. The evaluation of the pipeline sensitivity and precision for gene prediction was performed by comparing the predicted annotations against the H99 and JEC21 reference annotations  (Wallace et al. 2020)  with the GFFCompare program  (Pertea and Pertea 2020). For a better understanding, the C3Q pipeline with the basic Cufflinks parameters is named as C3Q1 protocol in the results section.
Cufflinks parameters selection The selection of the best Cufflinks parameter combination was also performed with EXPO30 RNA-seq libraries from C. neoformans H99 and C. deneoformans JEC21 according to the C3Q pipeline. For this, the Cufflinks transcript assembly generated for each EXPO30 replicate (A, B, and C) was tested with fixed and variable parameter combinations (Table  1 ). Subsequently, as established for the C3Q pipeline, the predicted GTFs were merged and processed by CodingQuarry. All combinations include minimum intron length 30; maximum intron length 4000; and minimum isoform fraction 0. 9; since we want to remove all isoforms. The variable parameters include: pre-mRNA fraction 0. 15 to 1. 0; small anchor fraction 0. 0; minimum fragments per transfag 1; overlap radius 1, 10 and 100; 3' trimming (-trim-3-avgcov-thresh andtrim-3-dropoff-frac) 0.
Evaluation of the effect of the sequencing depth on gene prediction quality The evaluation of the effect of the sequencing depth on gene prediction was performed by down sampling the three RNA-seq libraries from the EXPO30 condition (replicates A, B, and C) with the tool PositionBasedDownsampleSam from Picard package (https://broadinstitute. github. io/picard/). In this analysis, C. neoformans H99 and C. deneoformans JEC21 were used. According to a random algorithm that downsamples BAM files, we used defined fractions of  1, 5, 7. 5, 10, 15, 20, 30 , and 40 million reads for each replicate. Subsequently, the predictions were performed according to the C3Q pipeline with the Cufflinks "Q" parameter combination using the downsampled files. Evaluation of the sensitivity and specificity of gene prediction was performed by comparison of the predicted annotations against H99 and JEC21 reference annotations with the GFFCompare program.
Characterization of novel and missed loci The identification of novel and missed loci was performed with the GFFCompare program using the reference annotations from C. neoformans H99 and C. deneoformans JEC21 and the predicted C3Q gene annotations. Evaluation of the functional annotation (function, presence of domain signatures) of these sequences was performed by Blastp and Interproscan search from Blast2GO  (Conesa et al. 2005). The expression quantification of these sequences was performed with HTSeq-count  (Anders et al. 2015)  with the following parameters -stranded yes -f bam -r pos -t CDS.
Deletion of dubious novel loci from predictions Deletion of dubious novel sequences was tested with predicted transcripts of 100, 150, 200, and 300 nt, as well as intronless sequences of 300 nt from C. neoformans H99 and C. deneoformans JEC21 C3Q predictions. The sequence deletion and evaluation of the results was performed with an in-house AWK script and the GFFCompare program. Deletion of genome-predicted sequences without supporting reads and those with low FPKM values were performed and evaluated with an in-house AWK script combined with the HTSeq-count and the GFFCompare program. Deletion of alternative transcripts from multi-transcript loci was also performed with an in-house AWK script and GFFCompare. In this process, we selected for transcripts predicted by Cufflinks with supporting RNA-seq evidence.