dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation of insertion recall with long read simulated data For each of these short-read simulated datasets, we also simulated a corresponding PacBio long read dataset, with 40 X coverage and 16% error rate. We then applied a state-of-the-art long-read SV caller, Sniffles  [17] , on each of them to assess whether the previously identified difficulty factors for short read data have also an impact on the recall with long read data (see Supplementary Table  S5 ). For most insertion scenarios, Sniffles reported accurately 100% of the insertions sites, except for the tandem duplication type and for the insertions with large junctional homologies (recall below 20%). In these cases, insertions were in fact reported but at more than 10 bp from the simulated insertion site. This is probably due to imprecise sequence resolution preventing the correct left normalization of breakpoint positions.
Another difficulty factor was the close proximity of insertion locations, for which Sniffles reported one complex event instead of several close insertions. This mainly explained the low recall of 58% for the dataset with the real chromosome Fig. 5  Intersections of true positive insertion callsets between different SV callers. Intersections of true positive insertion callsets between GRIDSS, SvABA and Manta on the scenario 5 simulation (real insertions at real locations). In this scenario, the 889 insertions located on the chromosome 3 from the NA19240 callset were simulated as described in the vcf file. Insertion calls were validated and compared based solely on the insertion site prediction 3 insertions at their real locations. Concerning sequence resolution, although Sniffles calls contained systematically a full inserted sequence, the latter was imprecise and contained sequencing errors leading to sequence-resolved recalls around only 20% when requiring at least 90% of sequence identity.
Discussion The discovery of genomic variants is an important step towards the understanding of genetic diseases and species evolution  [21, 22]. The detection of insertions too small (<1kb) to be detected using comparative genomic hybridization array (CGH array) but larger than indel size (>50 bp) to be detected by the gold standard small variant discovery pipeline (GATK), remained a challenge with short read technology  [4]. Thus these variations were poorly characterised in databases as compared to other SVs such as deletions. Numerous variant callers have been developed to overcome this issue but without resolving it  [7]. Long read technologies or the crossing of various sequencing technologies overcome these limitations but are not affordable for many applications such as routine diagnosis of genetic diseases  [18].
Thus, to improve current and future SR based SV callers, a better understanding of the actual insertion variants present in human populations is required. We have presented here one of the most detailed and comprehensive analyses of actual insertion variants in the human genome looking for factors impacting their detection with short read re-sequencing data. This could be possible thanks to the publication of two exceptional SV callsets by Chaisson et al. [18]  and Zook et al. (GiaB)  [19]. These catalogs of insertions are considered as the most exhaustive for a given human individual and are qualified as gold standards thanks to their extensive validation by extensive and cross technology sequencing datasets. Unlike in the Chaisson et al study, the GiaB callset contained two categories of variants : 7640 insertions that were reported with a higher confidence (PASS in the FILTER field) and 6210 other insertions.
As mentioned by the authors, the first category is likely to be biased towards easier to discover variants. Because we did not want to introduce this potential bias, and after checking that these two categories showed similar insertion feature distributions (see Supplementary Figure  S1 ), we decided to conduct our analyses on the whole callset. Not only, these catalogs of insertion variants are considered as the most exhaustive for a given human individual, but they are also the first sets with sequence-resolved events for any size and type of insertions. The fine resolution of the inserted sequences, present in these datasets, enabled us to propose a refined classification of insertion variants. In the two datasets, insertion types were not formally defined and the classifications differed between the datasets.
Our classification allowed to normalize these heterogeneous annotations and was a direct application of variant definitions from the dbVar database which is based on the sequence ontology (SO)  [12]. We based our insertion type annotation on a minimal sequence coverage threshold, that was set to a relatively high value, 80%, in order to ensure a good specificity of our annotation. Increasing this value led to many more unassigned insertions, as the annotations were based on sequence alignments that were affected by potential remaining sequencing errors in the inserted sequences, polymorphism with the reference genome and the usage of alignment heuristics. If the amount of unassigned insertions decreased with the coverage threshold value, proportions of the different insertion types remained quite stable (Supplementary Table  S2 ).
The difference in SR-based recalls between the two studies can certainly be explained by the difference in the read depths of sequencing datasets (77X vs 300X for Chaisson et al and GiaB studies respectively), by the different SR-based tool sets used and by the different callset filtering and merging methodologies. The two studies used roughly the same number of SV-callers (13 and 15), but with a poor intersection: only one SV-caller (Manta) was common to both studies. Additionally, the method annotation of each variant is highly dependant on the study methodology to filter and merge the numerous callsets obtained for the same individual with different sequencing technologies and SV callers. For instance, it is not clear if the presence of an SRbased tag for a given variant does necessarily mean in both studies that the latter can be sequence-resolved solely using short reads.
We based our selection of SV callers on a recent and comprehensive benchmark study by Kosugui et al. [7]. SV callers selected in our study were chosen for their good performance in this benchmark, for their diversity of algorithms and for their ease of installation and usage. MindTheGap was not among the best insertion callers identified by Kosugui et al but was the only one not based on read mapping and using intensively de novo assembly with the whole read dataset. Simulations remain a powerful approach to identify the strengths and weaknesses of SV callers but they were not meant to reflect perfectly real situations. In our simulations, several features may be far from the real complexity of human genome re-sequencing, such as some sequencing technology biases, the use of one chromosome instead of the whole genome, and the absence of other polymorphisms than insertion variants (SNPs, small indels and other SVs).
As these tasks are the basis for association studies and medical diagnosis, efforts should be directed towards a better resolution of the sequence of these variants  [8, 23]. Results obtained with the local assembly tool MindTheGap showed that the use of the whole read dataset allowed many insertions and even large ones to be assembled. The restriction to a small subset of reads to perform local assembly may therefore be the shortcoming of the other tested SV callers. Resolving the inserted sequence is possible to some extent, but tandem repeats larger than the read size will remain difficult to resolve with short reads technology. Interestingly, sequence resolution appeared also to be an issue with long read sequencing data. In this case, the tested long read SV caller did report full inserted sequences but with a poor sequence precision, due the higher sequencing error rate.
Insertion type annotation TandemRepeatFinder (TRF) was used to annotate tandem repeats within each inserted sequence  [32]. Recommended parameters were used, except for the maximum expected TR length (-l) which was set to 6 millions. In order to annotate mobile elements in inserted sequences, we used dfam, one of the annotation tools of Repeat-Masker  [33]. Each inserted sequence was scanned by dfam with the standard HMM profile database of human mobile elements provided by the tool. For the annotation of dispersed duplications and the occurrence count of their copies in the reference genome, each inserted sequence was locally aligned against the Hg38 genome using Blat with default parameters  [34]. Only the alignments with at least 90% of sequence identity were kept. For the annotation of tandem duplications, the two sequences on either side of the insertion site and of the same size as the insertion were aligned against the inserted sequence using Blat.
Scenario 4 : varying the genomic context of insertion The 250 bp insertions from the baseline simulation were alternatively inserted in specific genomic contexts : either inside different types of mobile elements, namely SINEs and LINEs, in small (<300 bp) and large (>300 bp) simple repeats or in other regions not annotated by Repeat-Masker (non repeated). A dataset with closely located variants was simulated by adding insertions closed to the insertions simulated in the baseline scenario. The distance between insertions varied uniformly from 5 to 150 bp.
Long read simulation and benchmark For each short read simulated dataset, a corresponding PacBio long read simulated dataset was produced, using Simlord at 40 X coverage with probabilities of deletion, insertion and substitution equal to 11%, 4% and 1% respectively  [42]. Reads were aligned with Minimap2, alignments were sorted with samtools and variants were called with Sniffles  [17, 43]. The evaluation of insertion site recalls followed the same process than for short readbased variant callers. For the sequence-resolved recall, two sequence identity thresholds, 90 and 80%, were used to validate the inserted sequences. We also used the evaluation tool from GiaB, SVBenchmark module from SVanalyzer tools suite, with parameters similarly set as our benchmark method: -minsize set to 50 bp and -maxdist to 10 bp.
6/HG002_SVs_Tier1_v0. 6. vcf. gz. Reference Genome,GRCh38/hg38: http://hgdownload. soe. ucsc. edu/ goldenPath/hg38/bigZips/hg38. fa. gz Custom scripts used in this study are freely available at https://github. com/ WesDe/DeepAn and at https://github. com/WesDe/InserSim.
Acknowledgements We are grateful to  Stefano Baroni  for useful discussions. High-performance computing resources have been provided by the Arctur company (https://hpc. arctur. si)and by CINECA super computing center (www. cineca. it,grant number  ISCRA C HP10CQGOFM ). N. S. , L. G. , and M. V. acknowledge the financial support from the  Slovenian Research Agency  (research core funding no. P2-0412 ). N. S. , L. M. -S. , S. d. G. , L. G. , and A. H. are active members of the Multiscale and  Multi-Model Approach  for  Materials in Applied Science consortium (MAMMASMIAS consortium) , and acknowledge the efforts of the consortium in fostering scientific collaboration.
Data availability The software used for the present study (Quantum ESPRESSO 6. 0) is released under GPL and is freely available at https://github. com/QEF/q-e. The pseudopotentials (O. pz-mt. UPF, Si. pz-vbc. UPF, Mg. pz-n-vbc. UPF) are available in the pseudopotential table at www. quantum-espresso. org under the sub-category "original QE pp table. " Source data that support the findings of this study, including Quantum ESPRESSO inputs and outputs, and the post-processing script (python notebook) that computes BECs, displacements, and polarization changes, are available as a zip file as Supplementary information. Source data are provided with this paper.
Subjects, data acquisition procedure and fMRI paradigms Sixteen healthy adult volunteers (seven males and nine females, mean age 25 ± 2 years) with normal or corrected vision participated in the study. The experiment was approved by the regional ethical committee (Hôpital de Bicêtre, France) and undertaken with the understanding and written consent of each subject. A SIEMENS MAGNETOM 7T scanner with head gradient insert (Gmax 80mT/m and slew rate 333T/m/s) and adapted 32-channel head coil (Nova Medical, Wilmington, MA, USA) was used to collect functional images as T2 * -weighted Fat-Saturation echo-planar image (EPI) volumes with 1. 3 mm isotropic voxels using a multi-band sequence  ( Moeller et al. , 2010 )   ] = 1516 Hz/px, phase-encode direction anterior to posterior). Calibration preparation was done using Gradient Recalled Echo (GRE) data.
Fifty transversal slices covering the parietal and frontal cortex were obtained in ascending interleaved order. At the beginning of the scanning session, two single volumes were acquired with the parameters listed above but with opposite phase encode directions. The single-band reference images of these two initial volumes were used for distortion correction (see Data Analysis). Anatomical images (T1-weighted) were acquired at 1 mm isotropic resolution using an MP2RAGE sequence (GRAPPA acceleration with A radiofrequency absorbent jacket (Accusorb MRI, MWT Materials Inc. , Passaic, NJ, USA) was used to minimize the socalled "third-arm " or "shoulder " artifacts due to regions where the head gradient is unable to unambiguously spatially encode the image  ( Wald et al. , 2005 ). The participants' head was stabilized by padding and tape to prevent excessive movements.
Stimuli were presented under Matlab 9. 0 using Cogent ( http://www. vislab. ucl. ac. uk/cogent. php ) or using E-Prime software. (A) In a delayed number comparison task, sample and comparison stimuli were briefly shown (200 ms) in either non-symbolic (sets of items) or symbolic (Arabic digit) format. Participants were instructed to keep in memory the number seen in a given trial until the following trial appeared (after 10. 5 s), and to perform a numerical comparison on occasional match trials, marked by a change in the fixation color. Participants were asked to judge whether the number displayed in the match trial was smaller or larger than the one seen in the previous sample trial.
Data analysis Statistical parametric mapping software (SPM12, https://www. fil. ion. ucl. ac. uk/spm/software/spm12/ ) was used to motion-correct the EPI images and to co-register them to the first single-band reference image. EPI images were corrected for distortions in FSL ( https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/FSL ) in two steps: first we estimated a set of field coefficients with the topup function from the single-band reference images of the two initial volumes acquired with opposite phase encoding directions, and then we applied these to all the EPI images with the apply_topup function. Freesurfer 6. 0 ( https://surfer. nmr. mgh. harvard. edu/ ) was used to perform cortical The preprocessed EPI images (in subjects' native space) were entered into GLMs using SPM, for the delayed comparison task modeling separately 12 sample stimulus conditions (3 numbers x 2 formats x 2 stimulus sets [shapes/fonts in case of non-symbolic/symbolic format]) within each run and 4 match stimulus conditions (2 formats x 2 magnitudes [smaller vs larger than sample]) as stick functions (using the default of 0 duration for events) convolved with the standard hemodynamic response function.
Acknowledgments This work was supported by the  French National Research Agency  (grant number  ANR-14-CE13-0020-01  to E. E. ) and by the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sk ł odowska-Curie  grant agreement No  885672 -DYSC-EYE-7T  to E. C. We thank  F. De Martino  and  V. Kemper  for advice on fMRI acquisition parameters and procedures.
Here, we did not model the C-peptide concentration nor the liver production or absorption of glucose unlike in the more detailed model of  [29]. The parameters p 1 , p 2 , p 3 , n, γ, G b , I b , and h are original parameters of the MINMOD model. However, they were refitted in our experimental context. We refer to Section 2. 2 for a more detailed introduction to the original MINMOD model. In addition to the parameters of the original MINMOD model, we added, for our purpose of modeling OGTT and cadmium impact, three additional parameters. The flux associated with glucose intake is modeled by G RA (t) in Equation  (2). The efficiency of the rate of glucose withdrawal X(t) is modeled by r Cd. This parameter r Cd was always taken equal to 1. 0 in the simulations corresponding to the data of the control group.
Finally, the parameter p 4 models the secretion rate of the readily releasable pool of insulin modeled by the variable I s (t). The datasets were obtained from three groups of pups  [18] , namely control, Cd1, and Cd2 born from female rats with background, medium, and relatively high cadmium burden, respectively, still all corresponding to low exposure doses  [18]. OGTT were performed on pups at weaning (21 days after birth, that is PND21), a few days later after shifting on a regular non contaminated chow (PND26), and pcorfive weeks later (PND60). To estimate the goodness of fit of a given simulation compared to the experimental data, we used the root of the weighted least squares error: ε(k) = ∑ i W i (x exp,i -x simu (t i , k)) 2 , ( 5 ) where k is a parameter set, x exp,i are the mean values associated to an experimental dataset (see Table  1  for its corresponding population), and x simu (t, k) its associated simulation of the OGTT.
Here, the uncertainty on the experimental results not only comes from the precision of the measurements, but also, and mainly, from the individual variations within groups. It is important to note that the error is specific to a dataset, and that errors associated to two different datasets cannot be compared. Only comparing the effect of two parameter sets to mean data points (and associated variances) corresponding to a given group (control, Cd1, or Cd2) at a given period (PND21, -26, or -60) makes sense. In Figure  3 , we provide a schematic representation of our method to test the possible target of cadmium at PND21, PND26, and PND60, with respect to both our OGTT modeling and the experimental data.
Parameter Analysis The parameters were searched in the intervals proposed in  [21] , bloated by one order of magnitude, when possible. The initial value of the concentration of readily releasable insulin was fitted for the control group at PND21 and kept the same for the other experiments. The initial concentration of fasting glucose and insulin in plasma were taken from  [18]. We chose to take X(0) = 0 as initial condition of the withdrawal rate of glucose: this implies a lack of regulation effect at t = 0. To relax the parameter search, when fitting the parameters to the datasets corresponding to groups Cd1 and Cd2, we only considered a few hypotheses on the evolution of the parameters, starting from the ones fitting the control group, by only altering one parameter at a time. We considered the following hypotheses for the possible effects of cadmium of plasma glucose regulation: Hypothesis 1.
Results at PND21 (Weaning) For 21-day-old pups at weaning, the goodness of fit for each hypothesis and associated to group Cd1 dataset are shown in Table  3. Considering the initial conditions from Table  4 , the associated best parameter sets are given in Table  5. The simulation corresponding to the best parameter fits are shown in Figure  4. It should be reminded here that the experimental points reported in Figure  4  showed significant differences between the animal groups for the areas under the arbitrary drawn curves (AUC) in previously published data  [17, 18]. Here, the goodness of fit associated to the control group dataset is 0. 00311 (Table  6 ). The best fits for group Cd1 are obtained considering Hypothesis 3. 1, i. e. , decreased response of β-cells to glucose in the slow phase of insulin production.
Results at PND26 Concerning the experiments at PND26, after weaning at PND21 the pups shifted from a milk-based, i. e. , lipid-dominated, diet to conventional rodent chow which is rich in carbohydrates. This change of diet induces important changes in the regulation mechanism: this translates into considerable changes of the parameter set fitting the control group dataset. In addition, the three groups of young animals were no longer differently exposed to cadmium after weaning, and the AUC as previously reported  [18]  did not show any statistically significant difference. The initial conditions at PND26 are given in Table  7. The goodness of fit for each hypothesis on groups Cd1 and Cd2 are given in Table  8. The associated best parameters sets are given in Table  9. The simulation associated to the best fits are shown in Figure  5.
The goodness of fit associated to the control group dataset is 0. 00315 (see Table  6 ). We note that, unlike at PND21, there is no clear hypothesis that appears more likely than the others. Indeed, for group Cd1, although Hypothesis 2 yielded the best results, there is only a small difference with Hypotheses 1. 1-1. 3: thus, the present data cannot discriminate between changes of the insulin sensitivity of muscle and adipose tissue or of the insulin degradation rate. For group Cd2, we first observe that the higher variance on the experimental results leads to a lower error associated to the control parameter set (denoted no Hyp. in Table  8 ). In addition, even though Hypothesis 3. 2 (reduction in the RRP release rate) yields the best goodness of fit, there is no clear distinction with the other hypotheses because of the increased variance.
Results at PND60 Finally, considering the initial conditions from Table  10 , the parameter set fitted to the datasets at PND60 are given in Table  11. The goodness of fit of the control group dataset is 0. 00251 (see Table  6 ). It can be observed in Figure  6  that group Cd1 from mothers with medium cadmium burden and group Cd2 from the most intoxicated mothers show opposite behaviors with respect to the control curve. Whereas group Cd1 behaved in a similar fashion to the previous results at PND26 with a higher glucose peak than the control glucose response, the results of group Cd2 are characterized by a lower glucose peak. In Table  12 , we notice for group Cd1 that except Hypotheses 3. 1 and 3. 3, none of the other hypotheses have improved significantly the goodness of fit. However, Hypotheses 3.
Discussion As shown in the previous section, we tested through simulations various hypotheses that may explain the cadmium influence on the glucose regulation mechanism by insulin. To this aim, we started from a model reproducing correctly the control experiment with pups from mothers that were not intentionally exposed to cadmium. Then, we tested each hypothesis by varying a single parameter until it best fitted the dataset associated with the studied group of cadmium exposed pups. For this reason, we can only provide insight on the effect of one mechanism at a time. An apparent weakness of our approach might be the risk of overfitting a single curve with at least 10 parameters for four variables. However, once the initial conditions have been set, the procedure aims at testing a series of hypotheses by varying a single parameter at a time to optimize fitting of a complex curve.