dataesr/scientific-paragraphs-categorization huggingface dataset:
2019) , we did not detect an obvious influence of genome size on RADseq data assembly and downstream phylogenetic inference, not even in newts with extremely large genomes. RADseq approaches have several advantages, including relatively low sequencing costs facilitating the generation of population-level data sets, and applicability to species without prior genomic knowledge. Hence, our data confirm RADseq as a promising as tool for accurate inference of evolutionary history and species limits in poorly known taxa, as observed in numerous recent studies (e. g. ,  Razkin et al. 2016; Rodríguez et al. 2017; Paetzold et al. 2019; Rancilhac et al. 2019; Appelhans et al. 2020; Arrigoni et al. 2020; Dufresnes et al. 2020a Dufresnes et al. , 2020b;; Terraneo et al. 2021 ). However, it is of relevance that especially the bCT had a decisive impact on the estimation of branch lengths in concatenation trees, and that long and short branches were apparently affected differently.
Acknowledgments This study was supported by the  Deutsche Forschungsgemeinschaft  (grant  VE247/16-1 -HO 3492/6-1 ) in the framework of the ' TaxonOmics' priority program  ( SPP1991 ). We are gratefuk to  M, Hofreiter  ( University of Potsdam ) for valuable advice, and thank  Glasgow Polyomics  for assistance with RAD sequencing.
Data availability statement The supplementary material and appendix 1 are available at https://datadryad. org/stash/share/lyFID1l-7ZlgZNSNeFZWq_wYzYA0OnGFUrfCTgVzgjY. The raw RAD sequencing reads are available in the Short Read Archive under Bioproject ###### (to be added upon manuscript acceptance). The alignments of the Hybrid-Enrichment markers, output of Ipyrad assemblies (assembly metrics, assembled loci and SNPs), as well as the concatenation and tetrad trees inferred from the RADseq data were uploaded to https://zenodo. org/record/7829243 (DOI:10. 5281/zenodo. 7829243). The code used to assemble the RADseq data, analyse the assemblies and produce plots is available at https://github. com/rancilhac/optiRADCT.
(1997). For each specimen, four simple parameters were analysed: the length and width of the coccoliths were measured on parallel polar images, where the outline is more easily detected; the length and width of the coccolith central area were measured on polarized-light images. Measurements were made using the software "Scion Image" (beta version 4. 0. 2), which is a modified Windows equivalent of the "NIH Image" technology developed for Macintosh. The accuracy of measurements was estimated by multiple-scale calibration (conversion of pixels into micrometres using "Scion Image") and by multiple dimensional measurements on the same specimen for the four considered parameters. The scale is 1 µm = 9. 34 ± 0. 02 pixels; the accuracy of scale calibration is ±0. 002 µm. Mean error on dimensional measurements is ±0.
088 µm. By adding these two types of error, the accuracy of dimensional measurements is ±0. 09 µm, which is very close to the error of ±0. 1 µm published by  Young et al. (1996)  for similar biometric methods. At first, mixture analysis was applied to coccolith length and width of the entire population of measured specimens using the PAST 3. 01 software  (Hammer et al. , 2001). The data were then treated with the software "StatView" (version 5. 0). Simple parameters were considered for the morphologic description of the three species, such as the coccolith and central-area dimensions (length and width), the relative proportion of the central area with respect to the coccolith size (calculated as the ratios "central-area length over coccolith length" and "central-area width over coccolith width"), and the coccolith ellipticity (calculated as the ratio "coccolith length over coccolith width").
Mixture analysis A comparison of one-and two-group models was performed using the whole dataset for coccolith length and width by comparing their associated Akaike information criterion (AIC;  Akaike, 1973)  according to the statistical procedure developed by  Favre et al. (2008)  that allows the optimized separation of two sets of normally distributed values. This method is thus based on two a priori-defined sets of values -namely, one with the smallest mean and another with the largest mean -obtained for a given, normally distributed variable  (Suchéras-Marx et al. , 2010). The number of size classes or bins was chosen in order to obtain each bin at 0. 5 µm. The results of mixture analysis show two sets of samples with a mode at 4. 0-5. 0 and 7. 5-8. 5 µm for coccolith length and a mode at 3.
5-4. 5 and 6. 5-7. 5 µm for coccolith width (Fig. 1 ). According to mixture analysis, the dataset has a bimodal nature with a larger morphotype (herein named Similiscutum giganteum sp. nov. ; Plate 1) and two smaller morphotypes, namely S. finchii and S. novum (Plate 2). In the following, we present boxplot analysis of coccolith geometry separately for the three species. Mixture analysis applied to the length and width of all the analysed coccoliths using PAST 3. 01 software  (Hammer et al. , 2001). The lowest Akaike information criterion (AIC,  Akaike, 1973)  is obtained with two populations with modes at 4. 0-5. 0 and 7. 5-8. 5 µm for coccolith length and at 3. 5-4. 5 and 6. 5-7. 5 µm for coccolith width. The number of size classes or bins was chosen in order to obtain each bin at 0. 5 µm.
Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/jm-42-1-2023-supplement. Acknowledgements. The authors warmly thank  Fabienne Giraud  and  Serge Elmi  for the constructive discussions and review of an earlier version of this paper. Financial support. Samuel Mailliot  was funded by  MRT of the French Ministry of Research. Financial support was provided by the  Projet ATIP  ( CNRS PG/MR/No. 2002. 053 ). Review statement. This paper was edited by  Kirsty Edgar  and reviewed by  Angela Fraguas  and  Jeremy Young.
inputs are used to estimate flow or resistance, and computational fluid dynamics (CFD) techniques or mathematical formulas are used to analyze the pressure drop across a lesion  [4, 5]. Several angiography-derived FFR software packages have been developed. Quantitative flow ratio (QFR)  [6] [7] [8] [9] [10] , cardiovascular angiographic analysis systems for vessel fractional flow reserve (CAAS vFFR)  [11] [12] [13] , and the FFRangio system  [14] [15] [16] [17] [18] [19]  are angiographically derived estimates of FFR with comparable performances  [20]. The FlashAngio caFFR System is another coronary angiography-derived fractional flow reserve measurement system. The system calculates FFR values based on a three-dimensional (3D) model of the coronary arterial system restored from two angiograms.
It differs from other systems in that it uses real-time aortic pressure measurements taken by a specific device (FlashPressure, Rainmed Ltd. ). The recorded aortic pressure is averaged over five cardiac cycles and converted to hyperemic aortic pressure using a simple formula. The resting flow velocity is obtained from frame counting, followed by auto-calculation of the hyperemic flow velocity. Finally, a proprietary computational pressure-flow dynamics (CPFD) method is applied to compute the pressure drop across the lesion by solving the Navier-Stokes equation  [21]. It is important to note that in order to adapt to the conditions of a Cath-lab, most of the available software do not use CFD to completely solve the Navier-Stokes equations and continuity equations and produce unknown variable pressure.
This is because physical properties of the blood and definition of boundary conditions, such as flow velocity in the inlet and outlet of the 3D geometry model, are required for CFD analysis, which is time consuming. Indeed, data analysis initially took ≈24 h  [22] , and despite subsequent improvements, vFFR data analysis remains an offline process  [23]. A fast CDF was developed for QFR, which uses different mathematical equations based upon the laws of Bernoulli and Poiseuille. The boundary conditions include the following assumptions: blood is a homogeneous and Newtonian fluid, coronary pressure is constant in the absence of stenosis, coronary flow velocity is maintained along the coronary, and steady flow is specified as a boundary condition at the outlet. Flow velocity is frame-counted under resting conditions and an assumption/prediction is then made about what the velocity would be if hyperemia was induced.
Currently, the quantitative flow ratio (QFR) is the angiography-based index with the largest amount of evidence. The FAVOR III China is a prospective study that included 3825 patients from China in which QFR-guided PCI was used with a cut off ≤0. 89 and compared with angiography-guided PCI. The composite endpoint including all-cause death, MI, and ischemia-driven revascularization occurred in 5. 8% (11/1913) of patients in the QFR group compared to 8. 8% (167/1912) in the angiography group (HR 0. 65, 95% CI 0. 51-0. 83, p = 0. 004) at 1 year  [26]. Further non-inferiority studies against FFR are expected. The FAVOR III EJ study (NCT03729739) is investigating whether QFR-guided PCI will be non-inferior at 12 months compared to an FFR-guided strategy. The FAST III study (NCT0493177) is a randomized controlled, open-label, multicenter, international, non-inferiority strategy trial of a vFFR-guided strategy compared to an FFR-guided strategy to guide coronary revascularization in 2228 subjects with intermediate coronary artery lesions.
The endpoint is the rate of the composite of all-cause death, any myocardial infarction, or any revascularization  [27]. The Flash FFR II Study (NCT04575207) is a prospective, multicenter, blinded, randomized, noninferiority clinical trial of coronary angiography fractional flow reserve (caFFR) versus fractional flow reserve (FFR) to guide percutaneous coronary intervention with 2132 participants. The endpoint is a composite of all-cause death, myocardial infarction (MI), and unplanned revascularization at 1 year. The PIONEER-IV study (NCT04923191) is a prospective, multicenter, blinded, randomized, non-inferiority clinical trial of coronary angiography fractional flow reserve (caFFR or QFR) versus fractional flow reserve (FFR) to guide percutaneous coronary intervention with 2540 participants.
For STEMI patients, in the RESTORE-AMI study (NCT03998319), only patients with an IMR > 32 will be eligible for randomization between intracoronary tenecteplase and placebo. Similar studies will need to be designed to demonstrate that the use of software to measure angiography-derived IMR can improve prognosis with stratified medicine. Several studies are underway using IMR as a theragnostic biomarker to develop stratified medicine  [65]. For example, for chronic coronary syndromes, in a DECI-SIONING study (NCT05178914), patients will benefit from therapeutic escalation or deescalation according to IMR in comparison with the sham procedure. For STEMI patients, in the RESTORE-AMI study (NCT03998319), only patients with an IMR > 32 will be eligible for randomization between intracoronary tenecteplase and placebo.
The qPCR reactions were performed in technical triplicates using independent cDNA reactions for each biological replicate and 300 nM gene-specific primer pairs. Specific primers for all candidate genes were designed using Primer3 software (version 0. 4. 0) and are listed in Supplementary Table  S1. The thermal cycler protocol included 98 • C for 3 min, 40 cycles of 98 • C for 15 s, 60 • C for 30 s, 72 • C for 15 s, and a final 3 min extension at 72 • C. The expression of all the candidate genes was normalized against four rapeseed reference genes, namely BnaEf1alpha, BnaACT7, BnaTIP41 and BnaPP2A. The selection of the four reference genes was based on the published literature comparing reference gene stability in stress conditions in Brassica species  [68, 69]. We analysed the four reference genes in both the roots and the leaves of the experimental samples and used the inbuilt Reference Gene Selection tool provided with CFX Maestro software version 1.
Acknowledgments: We thank  Mathis Presse ,  Sandrine Jaguelin ,  Laetitia Dauve  and  Sibylle Gremaud  for their excellent technical assistance. We are very grateful to  Yudelsy Antonia Tandrón Moya  from the  Leibniz Institute of Plant Genetics and Crop Plant Research (IPK ) for the analysis of 10 B and 11 B. We also thank  Lun Jing , in charge of Metabolomic Division, and  Frank Jamois , the director of the  Phys-Chem and Bio-Analytics R&D Department, Centre Mondial de l'Innovation of Roullier Group.
Functional Near Infrared Spectroscopy (fNIRS) is a useful tool for measuring hemoglobin concentration. Linear theory of the hemodynamic response function supports low frequency analysis (<0. 2 Hz). However, we hypothesized that nonlinearities, arising from the complex neurovascular interactions sustaining vasomotor tone, may be revealed in higher frequency components of fNIRS signals. To test this hypothesis, we simulated nonlinear hemodynamic models to explore how blood flow autoregulation changes may alter evoked neurovascular signals in high frequencies. Next, we analyzed experimental fNIRS data to compare neural representations between fast (0. 2-0. 6 Hz) and slow (<0. 2 Hz) waves, demonstrating that only nonlinear representations quantified by sample entropy are distinct between these frequency bands.
This study uses sample entropy (SampEn)  (Richman and Moorman, 2000)  to assess fNIRS signals irregularity (and so predictability) based on embeddings of the fNIRS signals  (Sauer et al. , 1991). First, we simulated plausible modulations of autoregulatory feedback on blood flow control with stochastic dynamics in the hemodynamic model  (Friston et al. , 2000)  to motivate fast wave nonlinearity analysis. Then, using a mental arithmetic paradigm and a control motor imagery paradigm  (Berntson et al. , 1996) , we investigated the additional information that high frequency components may provide in experimental data when a subject is under cognitive stress. We performed a representational similarity analysis (RSA)  (Kriegeskorte et al. , 2008; Carlin et al. , 2011)  on data obtained from a public database  (Shin et al.
Experiment data A publicly available dataset was used to obtain fNIRS signals with the desired experimental protocol for this study, as reported in  (Shin et al. , 2017). In summary, the experiment recruited twenty-nine healthy subjects (aged 28. 5 ± 3. 7), fifteen of which were female and fourteen male. Of these twenty-nine healthy subjects, all were right-handed expect for one. As according to  Shin et al. (2017) , all participants were free from neurological, psychiatric, or any brain-related disorders. They were fully informed about the experimental process, and written consent was obtained from each volunteer. After the experiment, they received financial compensation. The study adhered to the guidelines of the declaration of Helsinki and received approval from the Ethics Committee of the Institute of Psychology and Ergonomics, Technical University of Berlin (approval number: SH_01_20150330).
Three trials were performed with ten repetitions of mental arithmetic and baseline events, and three trials were performed with ten repetitions of right-hand and left-hand motor imagery for each subject. We note that the motor imagery tasks and mental arithmetic tasks were done independently, not concurrently. Thirty-six fNIRS series were acquired for each subject with 10 Hz sampling rate. The experiment design had sixty seconds of resting state to start data acquisition from a subject, after which an instruction was shown on the screen indicating which task was to be performed-either an arithmetic problem, a " -" for a baseline, or a left or right arrow for motor imagery. The subject performed the indicated task for ten seconds, with a subsequent fifteen second resting phase before the next instruction.
, 2020). A delay-time τ and embedding dimension m are needed to reconstruct manifolds using delay-coordinates. τ was selected as the first zero of the autocorrelation, while m was found using the false nearest neighbors approach  (Abarbanel et al. , 1993). For calculating SampEn, radius R = 0. 2 × σ x was used as the threshold to determine whether states were neighbors, where σ x is the standard deviation of the fNIRS time series (Delgado-Bonal and Marshak, 2019a). The particular equation describing how to calculate SampEn is:  SampEn = -log ⎛ ⎜ ⎝ ∑ N-m i=1,i∕ =j 1 N-m-1 number of |X m+1 i -X m+1 j | 〈 R ∑ N-m i=1,i∕ =j 1 N-m-1 number of |X m i -X m j | 〈 R ⎞ ⎟ ⎠ (6) X in this equation is a state space reconstructed using a time series. The superscript denotes the embedding dimension while the subscript denotes the state index, for which there exists N states.
Discussion and conclusion In this study, we compared neural activity representations between  fast wave (0. 2-0. 6 Hz) fNIRS and the standard slow wave ( < 0. 2 Hz) fNIRS signal using both linear measures of mean value and nonlinear measures derived from sample entropy. The fNIRS signals were obtained from a publicly available dataset of 29 subjects performing mental arithmetic and left-/right-hand motor imagery tasks  (Shin et al. , 2017). Our hypothesis was that mental arithmetic tasks, which induce cognitive stress, would provide more variation in areas of cortical activation than motor imagery when comparing slow and fast wave fNIRS using nonlinear analysis. This hypothesis was motivated by literature suggesting the nonlinear modulation of fNIRS dynamics sustained by various factors, including those induced by autonomic nervous system activity  (Friston et al.
Prioritization and Annotation of Putative Regulatory SNPs in the Non-Coding FCGR Gene Cluster The SNP selection procedure, summarized in Figure  1 , involved several steps. Initially, we identified 773 SNPs with eQTL annotations for FCGR2A, FCGR2B, FCGR2C, FCGR3A, and FCGR3B genes in the GTEX version 8 database  [43]. Subsequently, we prioritized 636 SNPs using the RegVar tool  [44] , which is based on the GTEX version 7 database (Supplemental Table  S1 ). Among these, we further annotated the 20 SNPs with the best RegVar scores, which were higher than the 0. 8 threshold (Supplemental Table  S2 ). Out of the selected SNPs, 13 were found to be eQTLs in immune cells, as indicated by both the ImmuNexUT database  [45]  and the ebi eQTL catalog  [46]. Among these, only 2 intergenic eQTLs (rs1771575 and rs2099684) were located in open genomic regions binding transcription factors in immune cells, as shown by the ATAC-seq Database  [47]  and the ReMap ChIP-seq catalog  [48].
These immune cells include neutrophils, monocytes, macrophages, dendritic, and NK cells, as stated in Table  1. Additionally, we examined two coding SNPs (rs1801274 and rs1050501) previously associated with malaria phenotypes and provided their structural annotations in Table  1. While rs1050501 was not identified as an expression quantitative trait locus (eQTL), the coding variant rs1801274 was found to be an eQTL for FCGR genes in whole blood (Supplemental Table  S2 ). We also cross-referenced the positions of eQTL candidates with enhancer regions identified in the ENCODE database (Table  1 )  [49]. However, this eQTL was not located within an open genomic region in immune cells (Table  1  and Figure  1 ). as an expression quantitative trait locus (eQTL), the coding variant rs1801274 was found to be an eQTL for FCGR genes in whole blood (Supplemental Table  S2 ).
We also crossreferenced the positions of eQTL candidates with enhancer regions identified in the EN-CODE database (Table  1 )  [49]. However, this eQTL was not located within an open genomic region in immune cells (Table  1  and Figure  1 ). A total of 773 SNPs carrying an eQTL annotation in whole blood for FCGR2A, FCGR2B, FCGR2C, FCGR3A, and FCGR3B genes were searched by querying the current release of GTEX (V8) database. The RegVar tool further allowed gene-specific annotation and tissue-specific annotation to prioritize 636 SNPs. The 20 SNPs that displayed the best scores were selected using RegVar; eQTL annotations were retained to select variants that have annotation in 2 independent immune cell eQTL databases, ImmuNexUT and ebi eQTL catalog. The position of eQTL candidates was cross-referenced with the position of ATAC-seq regions in immune cells and positioning in regions that exhibit transcription factor binding activity in immune cells, using the ReMap catalog.
ReMap data were filtered on primary NK, T and B lymphocytes, monocytes, macrophages, neutrophils, peripheral blood cells, and monocytic and lymphocytic cell lines to allow selection of 2 FCGR regulatory variants further used for association studies. In contrast, the rs1771575 variant was situated in open genomic regions in various immune cells, including monocytes, macrophages, neutrophils, and dendritic cells (Table  1 ). It was also identified as an eQTL for FCGR2B and FCGR3A, as well as FCGR3B target genes in neutrophils, using data from the ImmuNexUT database (Table  2 ). However, its effects on gene expression were found to be less pronounced in naive monocytes when eQTL data from the EBI catalog were utilized. Individuals with the rs1771575-TT genotype exhibited significantly enhanced gene expression of FCGR3A and FCGR2B in neutrophils compared to those with the CC/CT genotype.
However, this variant did not show any significant impact on FCGR3A expression in NK cells or FCGR2B gene expression in B cells. Furthermore, the rs1771575 mutation did not have a substantial effect on FCGR3B or FCGR2A transcripts, which encode activating receptors, in neutrophils. Similarly, the rs2099684 variant was found within open regions in B lymphocytes, dendritic cells, monocytes, and NK cells (Table  1 ), and it was identified as an eQTL for FCGR2A, FCGR2B, FCGR2C, and FCGR3B in neutrophils, monocytes, macrophages (Table  2 ), and NK cells. The rs2099684 GG genotype was highly associated with enhanced expression of FCGR2A transcripts in neutrophils and monocytes and was also significantly associated with enhanced FCGR2C and FCGR3B transcript expression in NK cells, according to the ImmuNexUT database.
To gain deeper insights, we further investigated the presence of epigenetic markers (ATAC-seq, H3K4me1, H3K4me3, H3K27ac, and H3K27me3) and the transcription factor binding profile of CCCTC-binding factor (CTCF) for rs1771575, rs2099684, rs1801274, and rs1050501 in blood monocytes and neutrophils (Figure  2 ) as well as in B and NK lymphocytes (Supplemental Figure  S1 ). These cell-specific profiles were visualized using the CistromeDB database with the WashU epigenome browser  [37]. To gain deeper insights, we further investigated the presence of epigenetic markers (ATAC-seq, H3K4me1, H3K4me3, H3K27ac, and H3K27me3) and the transcription factor binding profile of CCCTC-binding factor (CTCF) for rs1771575, rs2099684, rs1801274, and rs1050501 in blood monocytes and neutrophils (Figure  2 ) as well as in B and NK lymphocytes (Supplemental Figure  S1 ).
These cell-specific profiles were visualized using the CistromeDB database with the WashU epigenome browser  [37]. Interestingly, rs1771575 coincided with a peak of CTCF binding in monocytes, neutrophils, and NK cells, while rs2099684 was situated within peaks of H3K27ac, particularly in neutrophils. In contrast, rs1801274 and rs1050501 displayed weak associations with epigenomic marks linked to gene expression regulation. ) and CTCF transcription factor binding profiles for rs1771575, rs2099684, rs1801274, and rs1050501 in blood monocytes and neutrophils were visualized using the CistromeDB database in conjunction with the WashU epigenome browser. The genomic location of the FCGR locus on chromosome 1q23 is depicted as a red line. For both monocytes (upper panel) and neutrophils (lower panel), the following data is displayed: ATAC-Seq peaks, CTCF (CCCTC-binding factor) binding sites, ChIP-seq peaks, and Histone modifications associated with active regulatory regions: H3K4me1, H3K4me3, H3K27ac, and H3K27me3.