dataesr/scientific-paragraphs-categorization huggingface dataset:
Tier 0 exit: Step 4 → Step 8 Selection of a systemic toxicity point of departure (POD) for caffeine using RAX. At the end of Tier 0, we considered whether we had enough information to move to step 8 and derive a POD. Several systemic toxicity animal data were gathered for the three analogues theophylline, theobromine and paraxanthine, with more study data available for theophylline than for the other two analogues. The studies were judged to be of sufficient quality (scored as Klimisch 1 or 2;  Klimisch et al. , 1997). Overall, the most sensitive toxic effect seen for theophylline, theobromine and paraxanthine was developmental toxicity such as reduction in foetal body weight gain and ossification as well as increased occurrence of supernumerary ribs. However, the foetal toxicity always occurred at maternally toxic doses and may therefore also be secondary to maternal toxicity  (Khera, 1985).
Tier 2 -steps 7 to 10 Step 7a: Targeted Testing using NAM assaysin this case exploring the possibility of endocrine-mediated activity involved in the observed developmental toxicity. In Tier 1, the non-selective inhibition of the A1-adenosine receptor was considered to contribute to developmental toxicity effects secondary to maternal toxicity most probably caused by exaggerated pharmacological effects. However, further data are needed to exclude the possible involvement of endocrine-mediated mechanisms in this kind of toxicity. Several in silico screening tools were used in order to look for alerts of potential endocrine activity related to estrogenic, androgenic, thyroidal and steroidogenic activities (EATS) activities (EFSA/ECHA Guidance, 2018). The results from the OECD QSAR toolbox v4.
vegahub. eu/download/), predicted that caffeine has no estrogenic activity. Overall, the available in silico data for caffeine and the analogues theobromine, theophylline and paraxanthine, indicated only very low EATS receptor-mediated activities (probability of acting as NON-BINDER = >0. 75), if any. Because of the absence of relevant in silico alerts, no hypothesis could be derived from these results. Therefore, additional generation of experimental in vitro data on potential EATSrelated endocrine activity was not considered necessary. Indeed, some tests in the ToxCast database showed weak in vitro EATS-related endocrine activity for caffeine. However, these tests are screening assays, at the most OECD Conceptual Framework Level 2 studies, and not conducted according to GLP or OECD guidelines.
A local sensitivity analysis for model parameters was conducted using the built-in tool in the Berkeley Madonna software. The sensitivity coefficients were then normalised by the output and input parameter values according to the following equation: Normalised Sensitivity Coefficient = ΔY/Y ΔX/X where Y was the output (i. e. , C max or AUC), X was the input parameter (e. g. , Ka, Kp), ΔX the change in the parameter value, and ΔY the resulting change in the output value. Normalization of the sensitivity coefficients was necessary to make comparisons across parameters of different scales  (Clewell et al. , 1994).
Estimation of internal exposure using PBK modelling A qualitative evaluation of the agreement between experimental plasma concentration and simulations was conducted through visual inspection of the time-course concentration curves (USEPA, 2006). Good agreement was obtained for both the oral and dermal route, with model predictions generally within a factor of two of the data (WHO 2010) (Figs. 6 7 8 9 ). Only the hepatocyte intrinsic clearance (CLint), and oral (Ka) and dermal (Kp) absorption coefficients were fitted to experimental data, while all remaining parameters were obtained from the literature or data collected by Cosmetics Europe  (Géniès et al. , 2019; Hewitt et al. , 2020). The values of CL int obtained from fitting to the repeated oral  (Denaro et al. , 1991)  and dermal data  (Otberg et al.
A default body weight of 60 kg was used as recommended in the SCCS Notes of Guidance (SCCS, 2021). The simulated internal dose metrics (Cmax and AUC) are shown in Table  9. The dosing simulation was run for 3 (simulated) days to achieve steady periodicity, as shown in Fig. 9. The maximum concentration in Fig. 6. Caffeine PBK simulations compared to measured repeated oral exposure data in human volunteers  (Denaro et al. , 1991). Caffeine plasma concentrations (black stars, green squares) were determined following ingestion of 6 cups of caffeinated coffee (0. 7 and 2 mg/kg caffeine) over 5 days. Since the body weight (BW) was not reported, a default value of 70 kg was used. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.
) Fig. 7. Caffeine PBK simulations compared to measured oral exposure data in human volunteers  (Lelo et al. , 1986). Caffeine plasma concentrations (black crosses) were determined following ingestion of 3. 25 mg/kg bw caffeine in a gelatin capsule. BW = 83 kg. blood (Cmax) was 9. 4 mg/L, the daily area under the concentration curve (AUC, calculated over the time period from 48 h to 72 h) was 150 mg*h/L, and the average daily concentration (Cavg) was 6. 4 mg/L. From sensitivity analyses (data not shown), the model is generally positively affected (increased Cmax) by increases in parameters driving absorption (FracAvail and Ka), and negatively (decreased Cmax) by increases in parameters driving clearance (e. g. , liver parameters and intrinsic clearance). Table  9  shows the calculation of Cmax and AUC from the PBK model.
8. Caffeine PBK simulations compared to measured dermal exposure data in human volunteers  (Otberg et al. , 2008). Caffeine plasma concentrations (black crosses) were determined following dermal application of 50 mg (0. 05 mL) of an ethanol/propylene glycol formulation containing 2. 5% caffeine to 25 cm 2 of the chest of 6 male volunteers (1. 25 mg caffeine). Since the body weight (BW) was not reported, a default value of 70 kg was used. In this case study, a margin of internal exposure (MOIE) has been defined for caffeine using a PBK model which estimated blood concentrations derived from external caffeine exposure data in humans. A MOIE differs from a traditional margin of safety (MOS) in that it is calculated as the ratio of a measure of internal exposure, such as blood/ plasma concentration or target-tissue dose, rather than a measure of external exposure concentration, total bolus dose or ingested dose  (Bessems et al.
Since adenosine may exert multiple actions in the CNS, but also on the cardiovascular and other systems, this MOA may also be responsible for developmental toxicity, e. g. methylxanthines may affect neuronal growth and neuron interconnections during gestation and the neonatal period. Similarity of other supportive data such as ToxCast ++ The enzymatic and receptor signaling assays from the US EPA ToxCast database identified adequate biological similarities of caffeine with the selected analogues, with theophylline having the greatest mechanistic similarity in terms of interaction with adenosine receptors. Number of analogues used for the read across ++ The number of analogues selected for the read-across was considered sufficient. Quality of the toxicity endpoint data used for the read across +++ Animal studies of acceptable quality (Klimisch score 1 and 2) were available for the selected analogues.
As a result of the available NAM data (in silico and in vitro), we regarded the caffeine analogues theophylline, theobromine and paraxanthine forming a robust analogue category for the application of the RAX approach for the following reasons: a) very high degree of structural similarity, i. e. based on the Tanimoto score (≥0. 9) as well as on the same xanthine scaffold with at least one N-methyl substituent as functional group; b) sufficiently similar physicochemical properties; c) caffeine is metabolised in vivo to those three analogues with paraxanthine being the major metabolite; d) the enzymatic and receptor signaling assays from the US EPA Tox-Cast database identified adequate biological similarities of caffeine with its analogues, with theophylline having the greatest mechanistic similarity; e) an in silico safety alert tool, the OECD QSAR toolbox v4.
3, predicted largely similar toxicological characteristics for caffeine and those three analogues; Systemic toxicity animal data of sufficient quality were gathered for two caffeine analogues, i. e. theophylline and theobromine. Overall, the most sensitive toxic effect seen for theophylline and theobromine was foetal toxicity which may also be related to the maternal toxicity occurring at the same doses  (Khera, 1985). The selected POD of 30 mg/kg bw/day was derived from a developmental toxicity study with theophylline. This POD was considered to be highly conservative and consequently protective for human health, as the route of exposure was via IV infusion preventing oral or dermal metabolism and resulting in 100% bioavailability by definition. Given their close structural and metabolic similarity, our hypothesis was that caffeine and its analogues may have a common MOA but with a different potency.
Data analysis Recordings were acquired and analyzed using Spike2 v. 7 (CED, Cambridge, UK). Data were collected and analyzed using Pandas v. 1. 0. 0  (McKinney, 2011 ), SciPy Python v. 1. 4. 1 (Virtanen et al. , 2020 ) and DABEST v. 0. 3. 0 (Ho et al. , 2019). Figures were generated using matplotlib v. 3. 1. 3 (Hunter, 2007)  and seaborn v. 0. 10. 0  (Waskom et al. , 2020).
Data Processing Sequencing reads were processed and aligned against the horse (EquCab3. 0 (Kalbfleisch et al. , 2018) ) and donkey  (Renaud et al. , 2018)  reference genomes using the PALEOMIX pipeline  (Schubert, Ermini, et al. , 2014)  with default parameters, except that we followed the recommendations from  (Schubert et al. , 2012)  and disabled seeding. Briefly, paired-end (PE) reads longer than 25 nucleotides were trimmed with AdapterRemoval v2. 2  (Schubert, Lindgreen, & Orlando, 2016)  and aligned against the reference genomes using BWA  (Li & Durbin, 2009) , retaining alignments with mapping qualities superior to 25. PCR duplicates were then removed using Picard MarkDuplicates (http://broadinstitute. github. io/picard/). Finally, all ancient and modern reads were locally realigned around indels using GATK  (McKenna et al.
Principal component analysis (PCA) The genotype likelhood framework implemented in ANGSD helped mitigate various error rates in ancient and modern genomes. Using EquCab3  (Kalbfleisch et al. , 2018)  as the reference genome, ANGSD was run using the following options: "-only_proper_pairs 1 -uniqueOnly 1 -remove_bads 1 -minQ 20 -minMapQ 25 -C 50 -baq 1 -skipTriallelic 1 -GL 2 -SNP_pval 1e-6 -rmTrans 1". This provided a dataset consisting of a total of 16,293,825 transversions when the horse was included, and 10,094,431 transversions when the horse was excluded (i. e. when analyses were restricted to non-caballine genomes only). In these analyses, only specimens sequenced to an average depth of coverage ≥ 1× were retained. Principal Component Analyses were carried out using the PCAngsd package  (Meisner & Albrechtsen, 2018)  (Figure  2A ).
Mitochondrial phylogeny Cleaned reads were mapped against the mitochondrial genome (Genbank accession no. NC_001640), following the same procedure as when mapping against the nuclear genome. Samples showing an average depth-of-coverage <1× were disregarded, leaving a total of 17 individuals for further analyses. After removing duplicates, consensus mitochondrial sequences were generated using ANGSD (-doFasta 2 -doCounts 1 -setMinDepth 3 -uniqueOnly 1 -remove_bads 1 -minQ 25 -minMapQ 25). Multiple alignment was performed together with the comparative mtDNA sequences downloaded from GenBank (Table  S4 ) using MUSCLE v3. 8. 31  (Edgar, 2004) , with default parameters. The alignments were then split into six partitions (1 st , 2 nd and 3 rd codon positions, rRNA, tRNA, and control region). Two Maximum Likelihood (ML) trees based on all 6 partitions and excluding the control region (positions 15,469-16,660 of the horse reference mitochondrial genome) were both reconstructed using RAxML-NG v.
Additionally, we extracted biallelic single nucleotide polymorphisms (SNPs) from the dataset generated in Section 5 using bcftools v1. 9  (Li et al. , 2009). Both variant datasets obtained following mapping against the horse and donkey reference genomes were used in this analysis to rule out reference bias. We applied filters composed of minimum phred-scaled quality score quality (QUAL) = 20, sites for all individuals below 2 or twice the mean coverage, and allowed up to three individuals with missing data per site. After disregarding transitions, a total of 18,803,101 (mapping against horse genome) and 19,459,070 (mapping against donkey genome) transversions were finally used as input for TreeMix  (Pickrell & Pritchard, 2012)  with parameters "-k 500 -root TWI", and considering an increasing number of migrations edges (0≤m≤3; Figure  2 -figure supplements 8 and 9, Table  S5 ).
Heterozygosity Inference and Inbreeding Global heterozygosity rates and inbreeding levels were inferred for high coverage individuals (>10×) using ROHan  (Renaud, Hanghoj, Korneliussen, Willerslev, & Orlando, 2019)  with default parameters, except that transitions were excluded (--tvonly) (Figure  5 -figure supplement 1). To limit the impact of remnant mis-incorporations, we used the attached estimateDamage. pl script to estimate damage for all ancient samples prior to heterozygosity computation. Inbreeding was co-estimated together with genome-wide heterozygosity levels from the total ROH length (Figure  5 -figure supplement 2). excluding the outgroup individual underlying the horse reference genome (TWI)  (Kalbfleisch et al. , 2018). Sequence data were aligned against the horse reference genome  (Kalbfleisch et al.
Funding Funder Grant reference number Author Major Projects of the  National Social Science Foundation of China 17ZDA221 Dawei Cai European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme 681605 Ludovic Orlando National Natural Science Foundation of China 31822052 Yu Jiang The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Data format raw Parameters for data collection The in silico DW-MRI was simulated for 360 measurements, using four b-values ( b = 10 0 0 , 1925, 3094, 13,191 s/mm 2 ). Additionally, four non-diffusion-weighted images are included. The echo time was set to 0. 0535 s. The DW-MRI dataset was then corrupted with various levels of Rician noise.
Value of the Data • A novel DW-MRI in silico dataset with both microstructure and macrostructure complexity, obtained from Monte Carlo simulations of spins dynamics, to improve the estimation of the quantitative structural connectivity. • The dataset can benefit researchers targeting method development for quantitative structural connectivity estimation from diffusion-weighted MRI. • This novel numerical phantom, with a ground truth quantitative connectivity, was designed for the testing and validation of the DW-MRI processing pipeline, from signal pre-processing, algorithms for fibre orientation distributions estimation and tractography algorithms, to tractography filtering and algorithms for the calculation of connectivity strength. • This phantom has increased complexity from previously available dataset, and may help improving the reconstruction methods of the quantitative structural connectome.
Data Description The four-dimensional (4D) DW-MRI signal obtained from Monte Carlo simulation is available in the file 'DiSCo_DWI. nii. gz' in the standard Nifti format. The image is of dimension 40 × 40 × 40 × 364 , where the 4th dimension corresponds to the DW-MRI gradient parameters ( b -values and b -vectors). The files 'DiSCo_DWI_ RicianNoise-snr *. nii. gz' are the same DW-MRI images but corrupted with Rician noise at a signal to noise ratio (SNR) of 10, 20, 30, 40 and 50. The file 'DiSCo_gradients. bvals' lists the b -values (s/mm 2 ) of the 364 DW-MRI simulated measurements. The files 'DiSCo_gradients_ dipy. bvecs', 'DiSCo_gradients_fsl. bvecs' and 'DiSCo_gradients_mrtrix. b' list the corresponding gradient orientation formatted for input to the DIPY  [1]  , FSL  [2]  and MRtrix3  [3]  software library, respectively.
Experimental Design, Materials and Methods In this work, we have designed a novel dataset of simulated Diffusion-Weighted MRI (DW-MRI) images from a numerical phantom to foster the development of diffusion tractography and connectivity methods. The DiSCo (Diffusion-Simulated Connectivity) phantom is composed of 12,196 tubular fibres (strands), with gamma-distributed inner diameters ranging from 1. 4 μm to 4. 2 μm, connecting 16 distant Regions of Interest (ROIs). The strands form different white matter configurations (e. g. , kissing, branching) intersect at different crossing angles, split and group after leaving and ending in the ROIs. The simulation substrate has a micrometric resolution and an unprecedented size of 1 cubic millimetre to mimic an image acquisition matrix of 40 × 40 × 40 voxels (64,0 0 0 voxels).
7 was reached (12,196 strands, see Fig. 1 f ). Sampled cylinders not fitting within the surface were discarded. The Numerical Fiber Generator optimisation procedure was performed for an additional iteration to further reduce overlapping strands and use the space left by the sampling procedure. The final set of strands (trajectories and diameters) were used to generate a mesh of the substrate with the Blender software (see Fig. 2  ). For each strand, a tubular mesh was generated to represent the outer surface of the axon-like structure. An additional inner tubular mesh representing the inner surface of the axon-like structure was generated following the same trajectory, but with a diameter of 0. 7 times the outer diameter. The mesh was used as input to the opensource Monte Carlo Diffusion and Collision Simulator (MC/DC) proposed by Rafael-Patino et al.
Those correspond to the 3 b-shells of the ActiveAx protocol  [8]  with an additional shell at b = 10 0 0 s/mm 2. The echo time was set to 0. 0535 s. Each shell is sampled using 90 uniformly distributed gradient directions on the sphere. The resulting DW-MRI signal was corrupted using various levels of Rician noise using the Diffusion Imaging in Python (DIPY) library  [1]. For each voxel of the image, the 1-dimensional diffusion signal S was corrupted following S = (S + N 1 ) 2 + N 2 2 , ( 1 ) where N 1 and N 2 are 1-dimensional vectors with value sampled from a zero-mean Gaussian distribution. The standard deviation was set to the non-diffusion-weighted ( b = 0 ) signal intensity divided by the target SNR. of the MMINCARAV Inria associate team program between Empenn (Inria Rennes Bretagne Atlantique) and LTS5 (École Polytechnique Fédérale de Lausanne -EPFL) started in 2019.
Acknowledgments We acknowledge access to the facilities and expertise of the  CIBM Center for Biomedical Imaging  , a Swiss research center of excellence founded and supported by  Lausanne University Hospital (CHUV), University of Lausanne (UNIL) ,  Ecole polytechnique fédérale de Lausanne (EPFL) ,  University of Geneva (UNIGE)  and  Geneva University Hospitals (HUG). The calculations have been performed using the facilities of the  Scientific IT and Application Support Center of EPFL. We gratefully acknowledge the support of  NVIDIA Corporation  with the donation of the Titan Xp GPU used for this research. This project has received funding from the  Swiss National Science Foundation  under grant number  205320_175974  and Spark grant number  190297. Marco Pizzolato  acknowledges the European Union's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement no.
It allows full core depletion calculations. The new APOLLO3 ®  [8]  code in development at CEA brings advanced options for deterministic calculations. New solvers are available, such as the unstructured conform MINARET  [9]  Sn solver, a 2D/3D transport solver based on the discrete ordinates method (Sn) whose spatial discretization is relying on a Discontinuous Galerkin Finite Element Method (DGFEM). A subgroup method for calculating resonance self-shielding is implemented as it was done in the ECCO code. The method is coupled with the TDT flux calculation. It is also possible to create complex core geometry thanks to the SALOME platform  [10]. The goal of this work is to design a new full reference deterministic scheme based on APOLLO3 ® to perform neutronics calculation on the Jules Horowitz Reactor.
NEW 3-STEP NEUTRONIC SCHEME Today, the only way to compute JHR core in 3D in depletion, is a 2-step calculation (figure  3 ). The first step solves the Boltzmann neutrons transport equation for each type of fuel assembly with specular boundary conditions. Space and energy are finely described, cross-sections are taken from the nuclear data library and a resonance self-shielding treatment is implemented to take into account the deeply resonant structure of cross-sections. The neutron flux calculated at each time step creates homogenized/condensed cross-sections stored into Multi-Parameters-Outputs (MPO) files. During the second step, a flux calculation on a coarser energy mesh is now performed on the 3D homogenized geometry for different burn up steps thank to the information stored in MPO.
Graph coverings for proteins 2. 1. The D614G variant (minus RBD) of the SARS-CoV-2 spike protein. As a first example of the application of our approach, let us consider the D614G variant (minus RBD: the receptor binding domain) of the SARS-CoV-2 spike protein. In the Protein Data Bank in Europe the name of the sequence is 6XS6  [8]. D614G is a missense mutation (a nonsynonymous substitution where a single nucleotide results in a codon that codes for a different amino acid). The mutation occurs at position 614 where G (glycine) has replaced D (aspartic acid) in many countries. It is found that G increases the transmission rate and correlates with the prevalence of loss of smell as a symptom of COVID-19, possibly related to a higher binding of the RBD to the ACE2 receptor: an enzyme attached to the membrane of heart cells.
The bold numbers means that the cardinality structure of cc of subgroups of G fits the one of the free group F r-1 when the encoding makes use of r letters. In the last column, r is the first Betti number of the generating group f p. One observes that the cardinality structure of cc of subgroups of the finitely presented groups f p = H, E, C, G, I, T |rel ,. , f p = H, E, C|rel fits the one of the free group F r-1 when the encoding makes use of r = 6, 5, 4, 3 letters. This in line with our results found in  [1]  on several kinds of proteins. 2. 2. The β-2-glycoprotein 1 or apolipoprotein-H. Our second example deals about a protein playing an important role in the immune system  [12]. In the protein data bank, the name of the sequence is 6V06  [13]. It contains 326 aa. All models predict secondary structures mainly comprising β-pleated sheets and random coils and sometimes short segments of α-helices.
One observes in Table  3  that the cardinality structure of cc of subgroups of the finitely presented groups f p = H, E, C|rel fits reasonably well the one of the free group F 2 on two letters for the first three models but not for the RAPTORX model. In one case (with the PORTER model  [14] ), all first Table  3. Group analysis of apolipoprotein-H (PDB 6V06). The bold numbers means that the cardinality structure of cc of subgroups of f p fits the one of the free group F 3 when the encoding makes use of 2 letters. The first model is the one used in the previous subsection  [11]  where we took 4 = H and T = C. The other models of secondary structures with segments E, H and C are from softwares PORTER, PHYRE2 and RAPTORX. The references to these softwares may be found in our recent paper  [1].